Study of Factors Influencing Bacterial Identification by ...€¦ · Study of Factors Influencing...

Study of Factors Influencing Bacterial

Identification

by Raman Spectroscopy

Mya Myintzu Hlaing

A Thesis submitted for

Degree of Doctor of Philosophy

2015

Faculty of Science, Engineering and Technology

Swinburne University of Technology

FSET PhD Thesis/i

ABSTRACT

The formation of microbial biofilms causes serious problems in natural

environments, industrial systems and in medical situations. Therefore, for timely,

appropriate treatment and control measures, there is a need to develop an analytical

technique that can facilitate rapid, in situ microbiological identification directly from

biofilms. Raman spectroscopy has been promoted as a non-invasive optical technique

for differential bacterial identification at species and strain level as well as the study

of bacterial growth phases. To the best of our knowledge, the bacterial cells in these

Raman spectroscopic studies were mostly from planktonic suspension, cells

recovered from biofilm, cells within pseudo-mixed biofilms and cells from single-

species biofilms under controlled laboratory conditions. This thesis explores the

implications for extending Raman spectroscopy identification techniques to more

practical real-world settings. In particular, real-world biofilm samples will include

bacteria from different points in their life cycle, responding to the presence of other

organisms in the consortium and exposed to different physicochemical and

environmental conditions.

In view of the fact that individual cellular differences in macromolecular

composition contribute metabolic heterogeneity within a bacterial population, it is

necessary to attain specific bacterial identification and classification from different

time points of the growth cycle as well as throughout biofilm development. In this

study, Raman spectroscopy in combination with chemometric analysis was applied

for the identification of (and discrimination between) diverse bacterial species at

various growth time points. The results showed that bacterial cells from a particular

growth time point (as well as from random growth phase) can be well-discriminated

among the four species using principal component analysis (PCA). The results also

showed that the bacteria from different growth phases can be classified with the help

of a prediction model, based on principal component and linear discriminant analysis

(PC-LDA). These findings demonstrated that Raman spectroscopy with the

application of PC-LDA model rooted in chemotaxonomic analysis may provide

valuable applications in rapid sensing of microbial cells in environmental and clinical

studies. However, it is not yet representative of real-world situation for biofilm study.

Mya Myintzu Hlaing PhD Thesis/ii

Therefore, Raman spectroscopy experiments were performed on intact bacterial

colony and biofilm for moving towards realistic settings by examining the cellular

changes of surface-attached bacterial cells. The results showed that the content of

carbohydrates, proteins and nucleic acids in the biofilm matrix increased

significantly along with the biofilm growth of the four bacterial species. The findings

strongly suggested the Raman spectroscopy has significant potential for studying

chemical variations during biofilm formation. However, poor classification results

were obtained for surface-attached biofilm cells using PC-LDA planktonic model. It

is generally believed that cells within biofilm experience a unique mode of growth

and behave differently from their planktonic counterparts. Thus, it was not surprising

that the planktonic cell model was ineffective in classifying results of cells from

biofilm, highlighting that a new model was required for surface-attached cells. A PC-

LDA biofilm model was calibrated using single spectra from biofilm cells of each

species and validated using pure E. coli biofilms grown on quartz substrate,

achieving a high accuracy in potential classification. The application of this biofilm

model provided 75% sensitivity in detecting the presence of E. coli and V. vulnificus

species in dual-species biofilms. This prediction accuracy is useful not only for

understanding species interactions but also for analysing biofilm formation with

species of interest in a more complex community.

Finally, the effects of different surface chemistries on Raman identifiable

macromolecular changes in surface-attached bacterial cells were examined. The

interaction of E. coli cells with plasma polymer thin films containing hydrocarbon,

amine and carboxyl groups provided differences in cell attachment phenotypes, cell

viability and subsequent biofilm formations. The identification of surface-attached

cells from polymer surfaces was challenged by weak spectral features resulted from

polymer background involvement. Correct identification outcomes were achieved

when the surface attached cells were removed from the polymer coated substrate and

smeared onto clear CaF2 slides. While these results were encouraging, the outcome

may be dependent on intra-species variability versus inter-species variability and the

identification may become more difficult as more species are added to the

identification database. Nevertheless, this study opens the pathway to extend further

FSET PhD Thesis/iii

bacterial identification techniques in real-world setting by considering other

influencing factors such as environmental conditions.

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FSET PhD Thesis/v

ACKNOWLEDGEMENTS

Foremost, I would like to express my deepest gratitude to Prof. Sally McArthur, my

supervisor, for all her valuable guidance, advice, patience and support throughout my

three and a half years of study. Thanks for her creation of encouraging and energetic

working atmosphere in the lab. I am grateful for having discussion times which were

source of my new inspirations. Her guidance helped me in all the time of research

and writing of this thesis. I could not have imagined having a better supervisor for

my PhD study.

I would like to express my appreciation to all the members of the advisory

committee, especially warm thanks are due to A/Prof Paul Stoddard for his valuable

supportive encouragements, enthusiasm, and immense knowledge. I really appreciate

his detailed insights into the theory and experiment processes during my study.

I also owe special thanks to Prof. Peter Cadusch for his guidance, knowledge and

support of data analysis. My special thanks also go to Dr. Michelle Dunn for her

kindness, advice and contribution for multivariate statistical analysis.

I am grateful to all of my colleagues from the McArthur group: Dr. Scott Wade, Dr.

Adoracion Pegalajar Jurado (Dori), Dr. Chiara Paviolo, Dr. Mirren Charnley, Ms.

Jennifer Hartley, Ms Martina Abrigo, Ms Hannah Askew, Mr. Nainesh Godhani and

Ms. Nilusha Perera for providing me with a friendly and cheerful environment in the

biomedical lab. We had such a good time together, and their friendship has been a

source of real joy for me in the last years. I would like to thank Dr. Thomas

Ameringer and Dr. De Ming Zhu for their technical support.

I would like to acknowledge Faculty of Science, Engineering and Technology

(FSET) for scholarship support and DMTC funding for scholarship top-up.

Finally, I am really grateful to my friend, Dr. Shigeaki Kinoshita, for enlightening

me the first glance of research area at Swinburne University of Technology.

Last but not the least, I would like to thank my family, particularly my parents and

my husband, for supporting me spiritually throughout my life and encouraging me to

Mya Myintzu Hlaing PhD Thesis/vi

fight for my dreams. Without their support, my dream for this PhD study would not

have been come true.

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DECLARATION

This thesis contains no material that has been previously submitted or accepted for

the award of any other degree or diploma in any university or college of advanced

education. To the best of my knowledge and belief, the thesis contains no material

previously published or written by another person except where due reference is

made.

Mya Myintzu Hlaing

Mya Myintzu Hlaing PhD Thesis/viii

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CONTENTS

ABSTRACT .................................................................................................................. i

ACKNOWLEDGEMENTS ......................................................................................... v

DECLARATION ....................................................................................................... vii

CONTENTS ................................................................................................................ ix

LIST OF FIGURES ................................................................................................... xv

LIST OF TABLE .................................................................................................... xxiii

LIST OF ABBREVIATIONS .................................................................................. xxv

................................................................................................................ 1

LITERATURE REVIEW............................................................................................. 1

1.1 Introduction......................................................................................................... 1

1.2 Physiology of bacterial cells and biofilm ........................................................... 4

1.2.1 Prokaryotic bacterial cell ............................................................................... 4

1.2.2 Bacterial biofilm ............................................................................................ 9

1.2.2.1 Biofilm structure and composition ..................................................... 11

1.3 Bacterial identification...................................................................................... 14

1.3.1 Traditional culture-based methods ............................................................... 14

1.3.2 Molecular methods....................................................................................... 15

1.3.3 Spectroscopic methods................................................................................. 16

1.4 Raman spectroscopy for bacteria identification ............................................... 18

1.4.1 Theory of Raman spectroscopy ................................................................... 18

1.4.2 Application of Raman spectroscopy for bacterial identification ................. 20

1.4.2.1 Raman spectroscopy on bacterial biofilm .......................................... 23

1.4.3 Raman spectral data analysis ....................................................................... 24

1.4.3.1 Pre-processing of Raman spectra ....................................................... 25

1.4.3.1.1 Noise removal and Smoothing of Raman spectra ..................... 25

1.4.3.1.2 Fluorescence Background Subtraction from Raman Spectra .... 26

1.4.3.1.3 Normalisation and mean-centring of Raman spectra ................ 28

1.4.3.2 Chemometric methods for Raman spectrum data analysis................. 29

1.4.3.2.1 Principal component analysis (PCA) ......................................... 30

1.4.3.2.2 Linear Discriminant analysis (LDA) ......................................... 31

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1.4.3.2.3 Principal component logistic regression (PCLR) ...................... 31

1.5 Factors influencing bacterial chemistry ............................................................ 32

1.5.1 Bacterial characteristics ............................................................................... 33

1.5.2 Surface (substratum) characteristics in biofilm formation ........................... 35

1.5.2.1 Influence of surface hydrophobicity and roughness ........................... 35

1.5.2.2 Influence of surface charge ................................................................. 36

1.5.2.3 Influence of surface chemistry ........................................................... 37

1.5.3 Cell-cell interactions in biofilm formation ................................................... 39

1.6 Research motivation and thesis scope .............................................................. 41

............................................................................................................... 45

MATERIALS AND METHODS ............................................................................... 45

2.1 Materials ........................................................................................................... 45

2.1.1 Bacterial species and strains ........................................................................ 45

2.1.2 Bacterial culture media ................................................................................ 45

2.1.3 Substrates used for Raman experiments ...................................................... 46

2.1.4 Chemicals and reagents ................................................................................ 46

2.2 Methods ............................................................................................................ 47

2.2.1 Bacterial culture and growth conditions ...................................................... 47

2.2.2 Bacterial growth curve and phase measurement .......................................... 47

2.2.3 Sample preparation for Raman spectroscopy experiments .......................... 48

2.2.3.1 Planktonic sample preparation ............................................................ 48

2.2.3.2 Bacterial micro colony isolation ......................................................... 48

2.2.3.3 Biofilm cultivation .............................................................................. 49

2.2.4 Bacterial visualisation .................................................................................. 50

2.2.4.1 Bacterial viability test ......................................................................... 50

2.2.4.2 Two-dimensional cell counting and colour segmentation .................. 51

2.2.4.3 Fluorescence in situ hybridisation (FISH) .......................................... 52

2.2.4.3.1 Preparation of probe ................................................................... 53

2.2.4.3.2 Sample preparation for FISH ..................................................... 53

2.2.4.3.3 Pre-hybridization and hybridization .......................................... 54

2.2.4.4 Extracellular polymeric substance (EPS) staining .............................. 54

2.2.4.5 Visualisation of the hybridized E. coli cells and ConA stained EPS . 55

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2.2.4.6 Probe efficiency test ........................................................................... 55

2.2.5 Raman spectroscopy experimental set up .................................................... 58

2.2.5.1 Instrument set up, calibration and spectrum acquisition .................... 58

2.2.5.2 Raman signal pre-processing for statistical data analysis .................. 59

2.2.5.2.1 Cosmic ray removal ................................................................... 59

2.2.5.2.2 Background removal .................................................................. 60

2.2.5.2.3 Smoothing and intensity normalisation ..................................... 60

2.2.5.2.4 Mean-centring the data .............................................................. 60

2.2.6 Statistical data analysis ................................................................................ 61

2.2.6.1 Principal component analysis (PCA).................................................. 61

2.2.6.2 Principal component linear discriminant analysis (PC-LDA) ............ 61

2.2.6.3 Specific peak analysis (univariate analysis) ....................................... 62

.............................................................................................................. 63

OPTIMISATION OF RAMAN SPECTROSCOPY FOR BACTERIAL CELLS .... 63

3.1 Introduction....................................................................................................... 63

3.2 Experimental set up and spectrum acquisition ................................................. 63

3.2.1 Selection of substrate for Raman spectroscopy experiment ........................ 63

3.2.2 Raman spectra from reference samples ....................................................... 64

3.2.3 Raman spectra from bacterial cells .............................................................. 69

3.3 Attempts to achieve consistent fluorescence background subtraction ............. 71

3.3.1 Application of Raman software ................................................................... 71

3.3.2 Application of polynomial curve fitting ...................................................... 73

3.3.3 Weighted penalized least squares method in “R” language ......................... 75

3.4 Improved methods for fluorescence background subtraction from Raman

spectra ........................................................................................................................ 76

3.4.1 Experimental data ........................................................................................ 77

3.5 Raman signal pre-processing for statistical data analysis................................. 80

3.5.1 Intensity normalisation................................................................................. 80

3.5.2 Mean centring the data ................................................................................. 85

3.6 Sample preparation and storage for Raman spectroscopy ................................ 87

3.6.1 Materials and methods ................................................................................. 88

3.6.1.1 Bacterial strain, growth conditions and sample preparation .............. 88

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3.6.1.2 Raman spectroscopy measurements ................................................... 89

3.6.1.3 Raman data acquisition and processing .............................................. 89

3.6.2 Results and discussion ................................................................................. 90

3.6.2.1 Raman analysis of planktonic E. coli cells from fresh and stored

samples ............................................................................................... 90

3.6.2.2 Principal component analysis for Raman spectra of planktonic E. coli

cells from fresh and stored samples .................................................... 91

3.6.2.3 Raman spectroscopic analysis of planktonic E. coli cells from fresh

and frozen samples at different phases of the growth cycle ............... 95

3.6.3 Proposed protocol of sample preparation for bacteria identification ........... 98

3.7 Conclusions ....................................................................................................... 98

............................................................................................................. 101

RAMAN ANALYSIS OF PLANKTONIC BACTERIAL CELLS ......................... 101

4.1 Introduction ..................................................................................................... 101

4.2 Materials and methods .................................................................................... 103

4.3 Results and discussion .................................................................................... 103

4.3.1 Raman classification of planktonic cells at species level........................... 103

4.3.2 Raman classification of planktonic cells at the metabolic phase level ...... 112

4.3.3 Effect of growth phase on the differentiation of four bacterial species ..... 127

4.3.4 PC-LDA Classification model ................................................................... 133

4.3.5 PC-LDA Classification model for classification of metabolic phases in

individual species ................................................................................................... 140

4.4 Conclusion ...................................................................................................... 143

............................................................................................................. 144

RAMAN ANALYSIS OF BACTERIAL (MICRO) COLONIES AND BIOFILMS

ISOLATED ON SUBSTRATES ............................................................................. 145

5.1 Introduction ..................................................................................................... 145



5.3.1 Raman analysis of agar-grown bacterial (micro) colonies ......................... 147

5.3.2 Raman analysis of intact membrane-grown bacterial micro-colonies ....... 152

5.3.3 Raman analysis of bacterial cells in developing biofilms .......................... 160

5.3.3.1 Single-species surface-attached cells ................................................ 163

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5.3.4 PC-LDA models for classification of biofilm cells ................................... 178

5.3.4.1 Single-species surface-attached cells ............................................... 178

5.3.4.2 Raman- Fluorescence in situ hybridisation (FISH) analysis of bacterial

cells from dual-species biofilm ........................................................ 181

5.4 Conclusion ...................................................................................................... 188

............................................................................................................ 191

RAMAN ANALYSIS OF BACTERIA ON DIFFERENT SURFACE

CHEMISTRIES ....................................................................................................... 191

6.1 Introduction..................................................................................................... 191



6.3.1 Characterisation of the plasma polymer thin films .................................... 194

6.3.1.1 Surface wettability ............................................................................ 194

6.3.1.2 X-ray photoelectron spectroscopy .................................................... 195

6.3.1.3 Raman spectroscopy measurement................................................... 197

6.3.2 Bacterial adhesion to plasma-polymerised surfaces .................................. 198

6.3.3 Two-dimensional cell counting and quantifying cell viability .................. 202

6.3.4 Raman analysis of bacterial cells grown on polymer surfaces .................. 206

6.3.5 Raman analysis of bacterial cells from different polymer surfaces ........... 208

6.4 Conclusion ...................................................................................................... 220

............................................................................................................ 222

CONCLUSIONS ...................................................................................................... 223

REFERENCES ......................................................................................................... 229

APPENDIX .............................................................................................................. 259

LISTS OF PUBLICATIONS ................................................................................... 271

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LIST OF FIGURES

Figure 1.1 A schematic representation of a prokaryotic bacterial cell. ....................... 5

Figure 1.2 Overall structures of Gram-positive and Gram-negative bacteria cell

walls. ........................................................................................................... 7

Figure 1.3 Model of biofilm formation process. ....................................................... 11

Figure 1.4 Schematic of an energy diagram for Rayleigh and Raman scattering. .... 19

Figure 1.5 Model of a typical confocal Raman spectrometer system using a visible

laser, notch filter, spectrometer and the charge-coupled device (CCD)

detector. ..................................................................................................... 20

Figure 1.6 Summarised illustrations of the factors that can influence bacterial

adhesion in the initial stages of biofilm formation. .................................. 41

Figure 2.1 Application of the colour segmentation plugin implemented in ImageJ

software ..................................................................................................... 51

Figure 2.2 Specificity test of FISH rRNA probe efficiency with fixed planktonic

cells of E. coli and V. vulnificus species ................................................... 56

Figure 2.3 Two-dimensional confocal laser scanning microscope images of single-

species biofilms of E. coli ......................................................................... 57


species biofilms of E. coli during biofilm growth .................................... 58

Figure 3.1 Raman spectra of different substrates: ..................................................... 64

Figure 3.2 Original Raman spectra from reference samples: .................................... 66

Figure 3.3 Typical Raman spectra of polysaccharide (dextran), bulk protein

(fibrinogen), a mixture of dextran and fibrinogen in 1:8 molar ratios and

D-tyrosine. ................................................................................................ 68

Figure 3.4 Typical averaged Raman spectrum from planktonic E. coli cells with

characteristics peak assignments. ............................................................. 69

Figure 3.5 Background corrections using the baseline subtraction tool from the

WiRE 3.4 Raman software ....................................................................... 73

Figure 3.6 Background corrections using the polynomial curve fitting tool from

MATLAB. ................................................................................................. 74

Mya Myintzu Hlaing PhD Thesis/xvi

Figure 3.7 Background baseline corrections using the weighted penalized least

squares algorithm, implemented in “R” language. ................................... 75

Figure 3.8 Simulated data set fitted with adaptive-weight penalised least squares .. 77

Figure 3.9 Experimental data fitted by five different methods ................................. 79

Figure 3.10 Typical original and background corrected results for the Raman

spectrum of single planktonic E. coli cells using the APLS method ........ 80

Figure 3.11 Raman spectra of before and after signal processing. ............................ 82

Figure 3.12 Application of different normalisation methods .................................... 84

Figure 3.13 Application of different normalisation methods together with mean-

centring. .................................................................................................... 86

Figure 3.14 Flow chart summarising the different sample preparation procedures for

planktonic E. coli cells .............................................................................. 88

Figure 3.15 Background subtracted and normalised average Raman spectra from

planktonic E. coli cells taken from (i) fresh sample; (ii) refrigerated

sample before cell washing steps and (iii) frozen sample. ........................ 91

Figure 3.16 Principal component analysis of Raman spectra for planktonic E. coli

cells taken from (i) fresh sample; (ii) refrigerated sample before cell

washing steps; (iii) frozen sample. ............................................................ 93

Figure 3.17 Intensity changes of DNA/RNA and protein/lipid structure-specific

peaks in the E. coli Raman spectra ........................................................... 94

Figure 3.18 Score plots for the first and second principal components of Raman

spectra of planktonic E. coli cells from (A) fresh samples and (B) frozen

samples. ..................................................................................................... 95

Figure 3.19 Average value plots for the first principal component of the Raman

spectra of planktonic E. coli cells from (A) fresh samples and (B) frozen

samples. ..................................................................................................... 96

Figure 3.20 Loading value plots for the first principal component of Raman spectra

for planktonic E. coli cells taken from (A) fresh samples and (B) frozen

samples. ..................................................................................................... 97

Figure 4.1 Averaged, intensity-normalised and background subtracted Raman

spectra from planktonic cells of the four bacterial species. .................... 104

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Figure 4.2 Scatter plot from principal component analysis (PCA) of four different

bacterial species. ..................................................................................... 105

Figure 4.3 Principal component analysis of four different bacterial species: ......... 106

Figure 4.4 Scatter plot of the first and second principal components (PC1 and PC2)

................................................................................................................ 108

Figure 4.5 Loading plots from the principal component analysis (PCA) ............... 109

Figure 4.6 Intensity changes of DNA/RNA and protein/lipid structure-specific peaks

................................................................................................................ 110

Figure 4.7 Representative growth curves and viable cell counts of four bacterial

species ..................................................................................................... 113

Figure 4.8 Background-subtracted and intensity normalised Raman spectra of E. coli

cells at different phases of the growth cycle ........................................... 115

Figure 4.9 Principal component analysis of E. coli cells at different phases of the

growth cycle ............................................................................................ 116

Figure 4.10 Background-subtracted and intensity normalised Raman spectra of V.

vulnificus cells at different phases of the growth cycle. ......................... 118

Figure 4.11 Principal component analysis of V. vulnificus cells at different phases of

the growth cycle: ..................................................................................... 119

Figure 4.12 Background-subtracted and intensity normalised Raman spectra of P.

aeruginosa cells at different phases of the growth cycle ........................ 121

Figure 4.13 Principal component analysis of P. aeruginosa cells at different phases

of the growth cycle ................................................................................. 122

Figure 4.14 Background-subtracted and intensity normalised Raman spectra of S.

aureus cells at different phases of the growth cycle ............................... 124

Figure 4.15 Principal component analysis of S. aureus cells at different phases of the

growth cycle: ........................................................................................... 126

Figure 4.16 Scatter plots of principal component analysis (PCA) comparing the

Raman spectra of four planktonic bacterial species ................................ 127

Figure 4.17 PCA of the effect of physiological differences due to growth phase on

the clustering of four different bacterial species ..................................... 128

Figure 4.18 Normalisation against selected spectral features ................................. 130

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Figure 4.19 Comparison of the ratio of DNA/RNA to protein in four bacterial

species ..................................................................................................... 131

Figure 4.20 Linear discriminant analysis (LDA) based on the retained principal

components (PCs) for bacterial species differentiation .......................... 133

Figure 4.21 Calibration of PC-LDA model using a leave-one-out cross-validation

(LOOCV) with training and testing data................................................. 134

Figure 4.22 Validation of the PC-LDA model on 10 new spectra from individual

species and from mixed culture .............................................................. 137

Figure 4.23 Confocal laser scanning microscopy images of mixture of E. coli and V.

vulnificus planktonic samples (x–y sections) .......................................... 139


spectra from planktonic and colony cells of E. coli species ................... 147

Figure 5.2 Principal component analysis of Raman spectra collected from E. coli

planktonic and colony cells. .................................................................... 149

Figure 5.3 Analysis of specific peaks from the Raman spectra of E. coli planktonic

and colony cells. ...................................................................................... 150

Figure 5.4 Classification and identification of spectra from colony cells of E. coli

grown on nutrient agar using the PC-LDA planktonic model. ............... 151

Figure 5.5 Bacterial micro-colonies isolated on a nitrocellulose membrane .......... 153

Figure 5.6 Recovery of Raman spectra from intact colony grown on membrane: . 154

Figure 5.7 (A) Classification and identification of spectra from colony cells of four

bacterial species isolated on nitrocellulose membrane, based on the

planktonic PC-LDA model. (B) Test Raman spectra from the four

bacterial species. ..................................................................................... 155

Figure 5.8 Classification and identification of spectra from cells in different regions

of micro-colonies of four bacterial species isolated on nitrocellulose

membranes with the application of the PC-LDA planktonic model. ...... 157

Figure 5.9 Investigation of population behaviours of E. coli cells from spectra of

different regions of colony cells.............................................................. 159

Figure 5.10 (A) Optical micrographs of E. coli ATCC 25922 biofilms at different

time points. .............................................................................................. 162

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spectra from biofilm cells of the four bacterial species .......................... 164


spectra of E. coli surface-attached cells during biofilm development. ... 165


surface-attached cells during biofilm development ................................ 166

Figure 5.14 Intensity changes of (A) DNA/RNA and (B) protein/lipid structure-

specific peaks in the Raman spectra of E. coli surface-attached cells .... 167


spectra of V. vulnificus surface-attached cells during biofilm development

................................................................................................................ 168

Figure 5.16 Principal component analysis of Raman spectra collected from V.

vulnificus surface-attached cells during biofilm development ............... 169


specific peaks in the Raman spectra of V. vulnificus surface-attached cells

................................................................................................................ 171


spectra of P. aeruginosa surface-attached cells during biofilm

development ............................................................................................ 172

Figure 5.19 Principal component analysis of Raman spectra collected from P.

aeruginosa surface-attached cells ........................................................... 173


specific peaks in the Raman spectra of P. aeruginosa surface-attached

cells ......................................................................................................... 174


spectra of S. aureus surface-attached cells during biofilm development 175

Figure 5.22 Principal component analysis of Raman spectra collected from S. aureus

surface-attached cells during biofilm development ................................ 176


specific peaks in the Raman spectra of S. aureus surface-attached cells 177

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Figure 5.24 Classification and identification of spectra from surface-attached cells of

E. coli grown on a quartz substrate with the planktonic PC-LDA model.

................................................................................................................. 178


components (PCs) for bacterial species differentiation during biofilm

growth ..................................................................................................... 180

Figure 5.26 Validation of PC-LDA biofilm model on 9 new spectra of E. coli cells

from a single-species biofilm .................................................................. 181

Figure 5.27 Application of the PC-LDA biofilm model to 12 spectra from dual-

species (E. coli and V. vulnificus) biofilm culture .................................. 183

Figure 5.28 Two-dimensional confocal laser scanning microscope images of dual-

species biofilms of E. coli and V. vulnificus ........................................... 185

Figure 5.29 Spatial organisation of 79 h old dual-species biofilms ........................ 186

Figure 6.1 XPS survey spectra of plasma polymerised ........................................... 196


spectra collected from the plasma polymerised thin films and the control

quartz slide. ............................................................................................. 197

Figure 6.3 Two-dimensional CSLM images of E. coli attached to the surfaces at

initial attachment. .................................................................................... 199

Figure 6.4 Two-dimensional CSLM images of E. coli attached to the surfaces after

24 h of incubation. .................................................................................. 200


120 h of incubation. ................................................................................ 201

Figure 6.6 E. coli adhesion to different plasma-polymerised surfaces and quartz

substrate at 1 hour incubation time ......................................................... 202

Figure 6.7 Viability of E. coli cells from 120 h old biofilm grown on plasma

polymerised surfaces and quartz substrate .............................................. 204


spectra from ............................................................................................ 207

Figure 6.9 (A) Averaged intensity-normalised and background subtracted Raman

spectra of 24 h-old transferred cells from the surfaces (a: ppOD, b;

ppAAm, c; ppAAc and d; quartz) and (e) planktonic cells smeared on

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CaF2 substrate and (B) classification of surface-attached cells which were

transferred from the surfaces. ................................................................. 210

Figure 6.10 principal component analyses of Raman spectra from E. coli planktonic

cells and those from transferred E. coli surface-attached cells after 24 hour

incubation. ............................................................................................... 211

Figure 6.11 Principal component analyses of Raman spectra from E. coli planktonic

cells and those from E. coli surface-attached cells transferred to CaF2 after

24 hour incubation .................................................................................. 213

Figure 6.12 Scatter plots of the first and second principal components (PC1 and PC2)

comparing the Raman spectra of E. coli cells from the control quartz slide

with those from polymer surfaces ........................................................... 214

Figure 6.13 Intensity changes of DNA/RNA specific peaks in the Raman spectra of

E. coli surface-attached cells transferred from different surfaces, measured

relative to planktonic cells ...................................................................... 217

Figure 6.14 Intensity changes of protein/lipid specific peaks in the Raman spectra of

E. coli surface-attached cells transferred from different surfaces, relative

to planktonic cells ................................................................................... 219

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LIST OF TABLE

Table 1.1 Chemical composition of a prokaryotic bacterial cell ................................. 9

Table 1.2 Summary for bacterial identification methods mentioned in Section 1.4. 17

Table 2.1 Bacterial species and strains used in this study. ........................................ 45

Table 2.2 Oligonucleotides probe used in this study ................................................. 53

Table 3.1 Selected Raman frequencies and their peak assignments for the spectra.. 70

Table 4.1 Calibration of PC-LDA model based on the first 10, 16, 20 and 30

principal components (PCs) for a total of 144 spectra of four bacterial

species. .................................................................................................... 135

Table 4.2 Calibration accuracy results of PC-LDA model with the first 16 PCs on a

total of 144 spectra of four bacterial species. ......................................... 136

Table 4.3 Error rates for the calibration of PC-LDA model with the first 16 PCs on a

total of 144 spectra of four bacterial species. ......................................... 136

Table 4.4 Validation of PC-LDA model on new spectra from individual species and

from mixed culture. ................................................................................. 138

Table 4.5 Calibration of the PC-LDA model on a total of 36 spectra of individual

species in different growth phases. ......................................................... 141

Table 4.6 Classification accuracy results of PC-LDA model at metabolic phase level.

................................................................................................................ 141

Table 4.7 Classification results of 10 new spectra from individual species (spectra

from Table 4.4) using the PC-LDA model at metabolic phase level. ..... 142

Table 5.1 Classification of colony cells from four bacterial species ....................... 158

Table 5.2 Analysis of population behaviour of E. coli colony cells........................ 160

Table 5.3 Calibration accuracy results of the PC-LDA model with the first 16 PCs on

a total of 54 spectra of three bacterial species from their different biofilm

growth points. ......................................................................................... 180

Table 5.4 Application of PC-LDA model to 12 spectra from a dual-species biofilm.

................................................................................................................ 183

Table 6.1 Plasma polymerisation conditions for 1, 7-octadiene, allylamine and

acrylic acid .............................................................................................. 193

Mya Myintzu Hlaing PhD Thesis/xxiv

Table 6.2 XPS Atomic composition and atomic ratios of plasma polymerised thin

films deposited on quartz substrates. ...................................................... 196

Table 6.3 Identification of E.coli biofilm cells from different polymer surfaces using

dual-species biofilm model. .................................................................... 208

FSET PhD Thesis/xxv

LIST OF ABBREVIATIONS

A600 Absorbance at 600 nm

AFM Atomic force microscopy

APLS Adaptive-weight penalized least squares

APoly Adaptively weighted polynomial

CCD charge-coupled device

CFU Colony forming unit

CLSM Confocal laser scanning microscopy

ConA Concanavalin A, Tetramethylrhodamine conjugate

DNA Deoxyribonucleic acid

DPA Diaminopimelic acid

E. coli Escherichia coli

EM Electron microscopy

EPS Extracellular polymeric substance

ESEM Environmental scanning electron microscopy

FISH Fluorescence in situ hybridisation

FT-IR Fourier transform infrared spectroscopy

h Hour

IModPoly Improved modified polynomial

LDA Linear discriminant analysis

LOOCV Leave-one-out cross-validation

Mya Myintzu Hlaing PhD Thesis/xxvi

LPS Lipopolysaccharide

MALDI-TOF Matrix-assisted laser desorption ionization time of flight

MIC Microbiologically influenced corrosion

ModPoly Modified polynomial

mM Millimolarity

mW Milliwatt

NA Numerical aperture

OD Optical density

P. aeruginosa Pseudomonas aeruginosa

PBS Phosphate buffered saline

PC-LDA Principal component linear discriminant analysis

PCA Principal component analysis

PCR Polymerase chain reaction

ppAAc Plasma polymerised acrylic acid

ppAAm Plasma polymerised allylamine

ppOD Plasma polymerised 1,7-octadiene

rpm Revolutions per minute

RNA Ribonucleic acid

S. aureus Staphylococcus aureus

SEM Scanning electron microscopy

TEM Transmission electron microscopy

FSET PhD Thesis/xxvii

V. vulnificus Vibrio vulnificus

v/v Volume per volume

w/v Weight per volume

WPLS Weighted penalized least squares

XPS X-ray photoelectron spectroscopy

Mya Myintzu Hlaing Literature Review/1

LITERATURE REVIEW

1.1 Introduction

The incidence of microbial biofilms in natural environments and in medical

situations has received considerable interest regarding the importance of microbial

aggregations, the understanding of the function of biofilm forming microbes and the

interpretation of cellular behaviour changes that occur within a biofilm (1, 2).

Biofilms are accountable for most chronic soft tissue and wound infections and are

the main cause of endocarditis, medical implant and cystic fibrosis-associated

infections (3). In industry, biofilms are also associated with food and drinking water

contamination (4), metal surface corrosion (5) and pollution of the environment (1).

In addition, highly complex processes including microbiologically influenced

corrosion (MIC) can occur from the presence of biofilms in aquatic environments.

The development of bacterial biofilm is one of the major progressive processes for

bacteria from the unicellular state to a multicellular community (6). Bacteria can be

grown in vitro as planktonic cultures, colonies on agar plates and biofilms in systems

(7). The term “biofilm” refers to an aggregation of microbial cells such as bacteria,

algae, fungi and protozoa enclosed in a matrix that is attached to a surface. The

biofilm matrix consists of a mixture of polymeric compounds, primarily

polysaccharides known as extracellular polymeric substance (EPS) (8, 9). EPS plays

a crucial role in initial bacterial adhesion and the development of complex

architectures in the later stages of bacterial biofilm formation (10). Once embedded

in the EPS architecture, pathogenic bacteria can be protected from antibiotics and

host immune responses (11). This can in turn lead to chronic and recurring infection.

In biofilm development, the bacteria undergo a transition from an individual,

planktonic way of life to a community-based existence in which they must interact

with various species within the enclosed EPS matrix environment. Cells within the

biofilm thus experience a unique mode of growth that allows the cell to survive in

hostile environments and behave differently from their planktonic counterparts (1,

12). The characteristics of different bacterial species found within a complex biofilm

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and the interactions between each of the cells can also influence the development of

a biofilm community (13). Apart from these bacterial cell characteristics, many

studies have suggested that surface properties and environmental response/signals

can also play a role as factors influencing biofilm development (14).

Early, rapid and reliable detection of pathogenic bacteria can be extremely beneficial

for the treatment of patients with severe infection (15). The ability to identify

different bacterial species in biofilm consortia can also improve the efficacy of

management and control measures (16). If we are to understand or correlate the

structure of the biofilm with infection or corrosion processes, we need methods to

characterize and identify bacterial communities within the intact biofilm. Many

methods (such as traditional culture-based methods, molecular methods and

spectroscopic methods) have been established for bacterial identification.

Conventional culture-based microbiological identification techniques are relatively

slow and time consuming as they are derived from analysis of the bacterial growth

characteristics using specific media and growth conditions (17). Moreover, many

bacteria from the natural environment are difficult to grow using standard isolation

media (18). Because of these challenges, analysis methods that enable a fast and

reliable identification, such as those based on molecular and chemotaxonomic

techniques, have become popular (19). However, molecular methods such as

polymerase chain reaction (PCR), sequencing, micro-arrays, southern blot and

nucleic acid in situ hybridisation, require more steps (such as DNA/RNA isolation)

and the limited availability of specific probes makes the process slow and costly

(20).

Conversely, chemotaxonomic methods based on differences and similarities in

chemical markers associated with macromolecules in bacterial cells have recently

been shown to enable rapid bacterial identification (21). Specifically, vibrational

spectroscopic techniques such as Fourier transform infrared spectroscopy (FT-IR)

and Raman spectroscopy can be used for the rapid identification of bacteria (22).

These methods are non-invasive, reagent-less and rapid, operating at single bacterial

cell level with minimal time and effort (23, 24). Applications of Raman spectroscopy

have many advantages in terms of requiring small sample volumes and involving


minimal peak overlap from water molecules (25). Raman spectroscopy has thus

attracted interest among spectroscopic techniques for differential bacterial

identification at species and strain level, as well as for the study of bacterial growth

phases (26-29).

To the best of our knowledge, the bacterial cells in most previous Raman

spectroscopic studies were taken from planktonic suspension, cells recovered from

biofilm (30) and cells within pseudo-mixed biofilms (31). In fact, bacterial cells

developing as a biofilm exhibit a number of properties that are dissimilar from cells

grown in suspension, including changes in protein production and in gene expression

levels (32, 33). Many studies based on proteomic approaches have been reported to

investigate the interrelationships among planktonic cells, colonies and biofilms (7,

32). However, few, if any, studies based on a spectroscopic approach have examined

intact biofilm cells in comparison with their counterparts and/or in differential

identification. Therefore, it is of interest to investigate whether the Raman

spectroscopic approach can be used for characterisation of bacterial cells as they

appear in intact biofilms.

To test this hypothesis, Raman spectroscopy was used in combination with

chemometric analysis to identify different bacterial species at different phases of

metabolic growth. From these Raman spectra of planktonic cells, a model was

constructed for each bacterial species. The prediction model was calibrated and

validated on new batch of planktonic cells and biofilms cells. The next step in this

study was to obtain specific bacterial identification and understand their spatial

distribution within a mixed biofilm community. Thus, the constructed fingerprinting

system for single bacterial species was tested on a dual-species biofilm model

consisting of Escherichia coli and Vibrio vulnificus. Raman spectral profiles and

spectral changes related to the cellular response during biofilm growth on different

plasma polymer films were also examined to get a better understanding of the effects

of cell-surface interactions.

This literature Chapter will begin with the structure and biochemical composition of

prokaryotic bacteria and a review of biochemical changes occurring during biofilm

FSET PhD Thesis/4

formation, followed by an overview of different bacterial identification techniques.

The fundamental theory of Raman spectroscopy and its application to bacteria will

then be covered. Finally, Raman spectroscopy studies of the bacterial growth curve

and heterogeneity of bacterial micro-colonies and biofilm cells will be reviewed, in

the context of demonstrating the feasibility of rapid bacterial identification in

environmental and clinical samples.

1.2 Physiology of bacterial cells and biofilm

Bacterial physiology is the study of the structures and functions that allow bacteria to

survive in natural environments. This includes a range of topics from the

composition of bacterial cells to the biomolecules involved in chemical or physical

functions. The study of bacterial functional activity and growth within a population

can be considered as a major approach for understanding the life style of biofilm

communities. A brief description of bacterial cell physiology and biofilm formation

is summarised in this section. This review summarises the current status of the field

and provides the background for this research.

1.2.1 Prokaryotic bacterial cell

All living organisms are composed of the cell which is the most basic structural,

functional and biological unit. The set of organisms whose cells lack a membrane-

bound nucleus are called prokaryotes, while those with a nucleus are called

eukaryotes. Bacteria are prokaryotic cells which are mostly unicellular. Prokaryotic

bacterial cells have a simple intracellular structure and are smaller in size (~1-5 µm)

compared to eukaryotic cells. As shown in Figure 1.1, a prokaryotic bacterial cell has

three main architectural regions, namely the extracellular structures (fimbriae, pili, S-

layers, Glycocalyx, flagella), cell envelope (capsule, cell wall, plasma membrane)

and intracellular structures (cytoplasm containing DNA, plasmids, ribosomes and

other inclusions).


Figure 1.1 A schematic representation of a prokaryotic bacterial cell.

(Adapted from http://www.clker.com/clipart-29949.html)

The most recognizable extracellular structures of bacterial cell are flagella. Different

species of bacteria have different numbers and arrangements of flagella. Flagella are

whip-like structures protruding from the bacterial cell wall and are responsible for

bacterial motility (i.e. movement). Flagella also seem to facilitate the attachment of a

bacterium to a surface (e.g. biofilm formation) or to other cells (2). Depending on

environmental conditions and bacterial metabolic growth phases, bacteria can exhibit

very different patterns of flagellum expression, motility and cellular morphology (34-

36).

Many bacterial cells have an outermost defined layer named the capsule which is

composed of polysaccharides (37). The capsule not only provides an extra source of

nutrients for bacteria but also protects them from environmental stress (such as pH,

temperature, osmotic pressure) and the host immune system. Moreover, the bacterial

capsule facilitates cell aggregation and attachment in biofilm formation. The inner

layer of the bacterial cell membrane is surrounded by a rigid cell wall which also

protects the cell from cell lysis due to mechanical damage. The cell wall is made up

of a substance called peptidoglycan or murein. The peptidoglycan layer is a crystal

lattice structure that is formed by linear chains of two alternating amino sugars (such

as N-acetylglucosamine and N-acetylmuramic acids). These alternating sugars are

Capsule

Cell wallPlasma membrane

Cytoplasm

Ribosome

Plasmid

FlagellumNucleotide (DNA)

Pili

FSET PhD Thesis/6

connected by peptide cross-links of L-alanine, D-alanine, D-glutamic acid and L-

lysine or diaminopimelic acid (DPA). The degree of cross-linking determines the

firmness of the cell wall and varies between different bacteria. The peptidoglycan

layer is thus responsible for the rigidity of the bacterial cell wall and determines the

cell shape (37).

Based on the cell wall structure as differentiated by Gram staining, bacteria can

generally be divided into two major groups, called Gram-positive and Gram-negative

bacteria (38). Structural differences in the cell wall of Gram-positive and Gram-

negative bacteria are shown in Fig. 1.2. The main differences between Gram-positive

and Gram-negative bacteria are the outer membrane and thickness of the cell wall.

The Gram-positive cell wall is simple and consists of a single thicker layer (20–

80 nm) of peptidoglycan with no outer membrane. Conversely, the Gram-negative

cell wall is a relatively complex multilayered structure. The Gram-negative bacteria

cell wall has only a thin layer of peptidoglycan (2–3 nm) surrounded by the outer cell

membrane. The outer membrane of Gram-negative bacteria is made up of

lipopolysaccharide (LPS), which contains polysaccharides and proteins (39). In

addition, the structure of the peptidoglycan layer is different between Gram-positive

and Gram-negative bacteria. As mentioned above, peptidoglycan is a polymer of

disaccharides cross-linked by short chains of amino acid (L-alanine, D-alanine, D-

glutamic acid and L-lysine or DPA). In Gram-negative bacteria, D-alanine of one

unit is directly linked to DPA of the next. However, in some gram-positive bacteria,

D-alanine of one unit is linked to lysine molecules of another unit via a peptide

consisting of 5 glycine molecules (pentapeptide) (38, 39). Another structural

difference in the bacterial cell wall is the presence of teichoic acid covalently linked

to the peptidoglycan layer in the Gram-positive cell wall. Teichoic acid is a

phosphodiester polymer of glycerol or ribitol joined by phosphate groups with side

chains of amino acids and sugars. Although the function of teichoic acid is not well

understood, it appears to stabilize the cell wall and make it stronger. These bacterial

structural characteristics are believed to play a role in biofilm formation (39).


Figure 1.2 Overall structures of Gram-positive and Gram-negative bacteria cell

walls.

(Adapted from http://medimoon.com/wp-content/uploads/2013/04/gramstructure)

The inner layer after the cell wall is the cytoplasmic membrane (cell membrane)

which encloses the intracellular components (i.e. proteins, genetic material and other

metabolites) of prokaryotic cells. The bacterial cell membrane is composed of a

phospholipid bilayer with proteins (~40%) and glycoproteins (~60%). Inside the

bacterial cytoplasmic membrane, the chromosomal DNA, which aggregate to form

the nucleoid, can be found (38).

A short review of bacterial cell structure and the predominant chemical composition

is shown in Table 1.1 (39, 40). All bacterial cells are composed of water (as the

major constituent), macromolecules (proteins, nucleic acids, polysaccharides and

lipids), small molecules (amino acids, nucleotides, fatty acids, carbohydrate and

coenzymes, etc.) and inorganic ions (39). The polar properties of water enhance the

stability of large molecules and plays a crucial role in the formation of biological cell

structures (38). Among the macromolecules, proteins and nucleic acids such as

deoxy ribonucleic acid (DNA) and ribonucleic acid (RNA) are known as

informational macromolecules of the cell (38). The sequence of monomers in nucleic

acid carries genetic information whereas those in proteins carry structural and

functional information (39). Bacterial classifications are generally based on the

LipoteichoicTeichoic acid

Membrane protein

Cytoplasmic

membrane

Periplasmic space

Peptidoglycan

Outer

membrane layer

PolysaccharidesPorins

Protein

Lipids

Gram-positive Gram-negative

FSET PhD Thesis/8

chemical and molecular composition of their cell wall/membrane structure (such as

polysaccharides, phospholipid, lipid A), nucleic acids, proteins, amino acids and

peptide bonds. Moreover, the synthesis of these macromolecules (i.e. DNA/RNA and

protein) can vary throughout the metabolic growth cycle of bacterial cells (39).

Bacterial growth is the asexual reproduction (division) of one copy of the bacterial

cell into two daughter cells in a process called binary fission. The normal bacterial

growth cycle (curve) has four stages referred to lag phase, log (exponential) phase,

stationary phase and decline (death) phase in batch culture (41). Lag, log and

stationary phases are characterized by distinct biochemical reactions for the synthesis

of cellular components necessary for cell growth and division. In the lag phase,

although there is no growth, bacteria begin to prepare for reproduction. An increase

in overall bacterial enzyme production can be seen in this phase (42). During the log

phase, the number of bacterial cells becomes double with every unit of time by

binary fission reproduction. Bacterial metabolic activities such as protein and

DNA/RNA synthesis also increase and secretions of EPS begin (43). Because of

nutrient exhaustion and waste accumulation in continuous incubation, the bacteria

growth rate becomes the same as the death rate and cellular metabolic activity

decreases in the stationary phase (43). In the stationary phase, the biochemical

composition of cells is different from that in the log phase and a heterogenous cell

population can be seen (41). Finally, the decline phase is reached due to nutrient

depletion, more waste accumulation, depletion of cellular energy and pH changes

(42). Moreover, secretion of EPS also varies throughout the growth phases. Some

bacterial species have maximum EPS production in the exponential phase (44, 45),

while for others, EPS production is maximized in the stationary phase (46-48). In the

sense that bacterial species are defined by their unique DNA sequence, structural and

metabolic characteristics, differences in chemical composition should provide

reliable species identification. Moreover, studying the changes in biochemical

composition during bacterial growth could improve our understanding of microbial

physiology as well as the population behaviour of different bacterial species and

strains.


For more than a century, bacteria have been identified by isolation in culture

followed by enzymatic reactions and morphological analyses. In recent years,

molecular and chemotaxonomic techniques have become popular for bacterial

identification (20). The challenges of traditional bacterial identification methods and

the advantages of molecular and chemotaxonomic techniques for bacterial

identification are discussed in the following sections (Section 1.4). The applications

of Raman spectroscopy on bacterial cells are further broadly reviewed (Section 1.5).

Table 1.1 Chemical composition of a prokaryotic bacterial cell (39, 40).

Location in the cell Macromolecule Primary subunit

Cell wall / membrane, pili,

flagella, ribosomes, as enzymes

Proteins amino acids

Membranes, storage depots Lipids fatty acids

Cell wall, capsule, inclusions Polysaccharides carbohydrates

Membranes Lipopolysaccharides Sugars and fatty

acids

Ribosomes RNA Nucleotides

nucleoid, plasmid DNA Nucleotides

Abbreviations: DNA, deoxyribonucleic acid; RNA, ribonucleic acid.

1.2.2 Bacterial biofilm

A biofilm is an aggregation of matrix-enclosed microorganisms irreversibly attached

to a surface (8, 9). Biofilms can consist of different types of microorganisms, such as

bacteria, fungi, algae and protozoa which are enclosed by a matrix of EPS. Inside this

self-produced EPS matrix, bacterial cells can be protected from the host immune

system, environmental stress and antimicrobial agents (49, 50). Biofilms may form

FSET PhD Thesis/10

on a wide range of surfaces, including living tissues, implanted medical devices,

industrial or potable water system piping, or natural aquatic systems (14).

Typical bacterial biofilm formation involves a series of distinct stages consisting of

reversible attachment, irreversible attachment, maturation and detachment. A

schematic representation of the biofilm formation process is shown in Fig 1.3. First,

biofilm formation starts with weak, reversible adhesion of planktonic (free-floating)

bacterial cells to a surface. This initial attachment is basically influenced by

electrostatic forces (e.g., repulsion, attraction) and other interactions between bacterial

cells and the surface. Extracellular organelles (such as fimbriae, flagella and pili) and

adhesion proteins help the bacteria to overcome the interfacial repulsive forces and

achieve stable attachment (14).

If the bacterial cells become irreversibly attached to a surface, they will then begin to

grow and form micro colonies. Once bacterial colonization has begun, the biofilm

matrix develops through self-production of EPS (49). These substances mediate

bacterial adhesion to surfaces by providing a cohesive, three-dimensional polymer

network where biofilm cells are immobilised. This biofilm matrix provides a

favourable living environment for the resident bacteria and protects them from the

host immune system, environmental stresses (such as pH, temperature, osmotic

pressure) and antimicrobial agents (49, 50).

Within the biofilm matrix, bacterial cells are able to communicate and interact with

each other through quorum sensing and signalling molecules which are required for

biofilm maturation (51, 52). In this step, the biofilm architecture becomes more

complex by additional recruitment and colonisation of planktonic bacteria. Increased

synthesis of EPS and the development of antibiotic resistance associated with

surface-attached bacteria can be seen in this maturation step (52). These biofilm

bacteria may also develop other properties such as increased resistance to UV light,

increased rates of genetic exchange, altered biodegradability and enhanced

production of secondary metabolites. All of these situations appear to create a

protective environment for biofilm bacteria and cause biofilms to be a persistent

clinical problem (2).


The final stage of biofilm formation is known as the detachment or dispersal process.

In this process, bacteria have evolved ways to recognize environmental changes and

measure whether it is still beneficial to reside within biofilm or whether it is time to

resume a planktonic lifestyle (50). Throughout the biofilm process, microorganisms

thus undergo profound changes during their transition from planktonic organisms to

cells that are part of a complex, surface-attached community.

Figure 1.3 Model of biofilm formation process.

1.2.2.1 Biofilm structure and composition

Biofilms are highly hydrated (98% water) and tenaciously bound to the underlying

surface. The biofilm structure is heterogeneous with water channels that allow

diffusion of essential nutrients and oxygen to the microbial cells growing within the

biofilm (53). The biofilm is primarily composed of micro-colonies of microbial cells

and EPS matrix. The micro-colonies that make up the biofilm can contain single-

species populations or consortium communities of bacteria. The proportion of EPS in

biofilms is approximately 50-90% of the total organic matter and can be considered

as the primary matrix material for biofilm architecture (54).

EPS from different bacterial species may vary in chemical and physical properties,

but it is mainly composed of polysaccharides and extracellular DNA (eDNA. Some

polysaccharides of EPS matrix from Gram-negative bacteria are either neutral or

polyanionic whereas eDNAs are polyanionic components of EPS. The presence of

uronic acids (D-glucuronic, D-galactouronic and mannuronic) or ketal-linked

I. Reversible

adsorption of

bacteria

II. Irreversible

attachment of

bacteria

III. Growth

and division

of bacteria

IV. EPS

production and

biofilm

formation

V. Detachment

or dispersal of

bacterial cells

Biotic/ Abiotic surface

FSET PhD Thesis/12

pyruvates provide the anionic properties (53). This property allows an association

with divalent cations (such as calcium and magnesium) which subsequently crosslink

the polymer strands and strengthen the biofilm structure (54). In contrast, the

chemical composition of EPS from Gram-positive bacteria can be slightly different

due to their primarily cationic nature (55). EPS may associate with metal ions and

macro molecules of bacterial cells (such as proteins, nucleic acids and lipids). Apart

from EPS and microbial cells, the biofilm matrix can contain blood components,

other non-cellular materials such as mineral crystals, corrosion particles and silt

particles, depending on the environment where they form (56).

A range of methods have been proposed for the study of biofilm. For instance, some

methods are for quantification of biofilm matrix while others allows the evaluation of

live and dead cells in biofilm. Specifically, colorimetric methods to evaluate biofilm

matrix (i.e. crystal violet, dimethyl methylene blue) (57-59) or viable cells (i.e.

fluorescein-di-acetate and LIVE/DEAD BacLight) (60, 61) and molecular methods

to estimate the bacterial population (i.e. polymerase chain reaction, PCR and

fluorescence in situ hybridisation, FISH) (62-64) have been reported. Advanced

microscopic techniques such as electron microscopy (EM), confocal laser scanning

microscopy (CLSM) and atomic force microscopy (AFM) are employed to visualise

microbial biofilms.

Before the use of CLSM, electron microscopy (EM) was the method of choice for

biofilm characterization (65). In particular, transmission electron microscopy (TEM),

scanning electron microscopy (SEM) and environmental scanning electron

microscopy (ESEM) are used for qualitative assessment of the biofilm’s contribution

to surface deterioration (66). TEM has the capability to image the interior of biofilms

and intracellular features from cross-sections of ultra-thin sliced samples, while SEM

reveals the surface topography and composition of biofilms at a high magnification

(67). Although these EM techniques take advantage of the higher resolution allowed

by the use of an electron beam to resolve nanometre-scale details, the drawback of

using these methods is that sample preparation introduces artefacts and samples need

to be dehydrated for vacuum operations (68). The use of ESEM may however allow

direct visualisation of intact hydrated and non-conductive biofilm samples (68).


Confocal laser scanning microscopy (CSLM) using a wide range of specific

fluorescent probes and nonspecific fluorescent compounds has been effectively

applied for the visualisation of biofilm structure and EPS components for the last

decade (69-71). CLSM is performed on an optical microscope equipped with a laser

beam. It is mainly used in biology and life sciences to scan thick biological samples

(e.g. a microbial biofilm) by acquiring images in the x, y and z axes. Biological

samples must be stained with a specific fluorescent dye so that the fluorescent light

emitted from the illuminated spot is collected into the objective and transformed by a

photodiode into an electrical signal that can be processed by a computer (72).

Basically, CLSM scans a sample sequentially point by point, line by line or multiple

points at once and assembles the pixel information in a high contrast and high

resolution three-dimensional image (73). Although CLSM is a powerful optical

microscopic technique, the main limitations are that only a few fluorescent stains can

be applied simultaneously, showing just a few components in the same image and a

restricted number of excitation wavelengths are available with common lasers

(referred to as laser lines) (74).

Atomic force microscopy (AFM) is a scanning probe microscopy technique which

uses a sharp probe or tip to scan the sample in close vicinity to its surface. AFM is

widely used for the characterization of bacterial cells and biofilms because it

provides high resolution down to the nanometre scale, allows non-destructive

analysis in air and in water and requires no special sample preparation (75, 76). The

main limitation of AFM techniques is that only the sample surface and the inner

portion close to the surface can be analysed. Moreover, the maximum scan size and

average time taken to obtain an AFM image are typically 70 µm and 10 min (77).

Thus, another limitation of this technique is the inability to obtain large area survey

scans before increasing the magnification.

The analytical techniques to study microbial biofilm described above are mostly used

to visualise the biofilm structure and its components. The information obtained from

application of these techniques is mainly morphological in nature with little

information on the identity or chemistry of the bacterial cells within the biofilm.

Since bacterial biofilms consist of a very complex environment of single or mixed

FSET PhD Thesis/14

bacterial species enclosed within EPS, there is a need to better identify the bacterial

species within biofilm so as to gain more understanding of the biofilm formation.

1.3 Bacterial identification

Bacterial taxonomy includes the classification (on the basic of similarities),

nomenclature (naming of these groups) and identification (verification that a

bacterium belongs to one of these groups) (78). Bacterial identification is of great

importance in clinical medicine, public health, environmental, food and drinking

water contamination studies. Early, rapid and reliable detection of pathogenic

bacteria can be extremely beneficial for the treatment of patients with severe

infection (15). The ability to identify different bacterial species in biofilm consortia

can also improve the efficacy of management and control measures (16). In addition,

it has been proven that rapid bacterial identification results in clinical and financial

benefits (79). Many methods have been established for bacterial identification and a

brief overview of some of them (such as traditional culture-based methods,

molecular methods and spectroscopic methods) are discussed below.

1.3.1 Traditional culture-based methods

Traditional bacterial identification methods rely on bacterial phenotypes such as their

morphology and ability to grow in selective media under a variety of conditions (80).

These phenotypic identification techniques include differential staining techniques

(Gram stain, acid-fast stain and capsule stain etc.), growth characterisation,

biochemical screening and serological confirmation (81). Traditional methods are

often slow as organisms take time to grow and up to 72 hours are required to obtain

confirmed results. Indeed, a complex series of tests need to be performed before the

identification is confirmed. The results of these tests are sometimes hard to interpret

for highly related species due to limitations in corresponding databases. Moreover,

many bacteria from the natural environment are difficult to grow using standard

isolation media (18). Although culture-based approaches are useful for understanding

the physiology of isolated organisms, they cannot offer comprehensive information

on the microbial communities in biofilm matrix.


1.3.2 Molecular methods

For microorganisms that cannot be identified using standard culture-based methods,

the primary source of information for their identification is based on their

biomolecules such as nucleic acids, lipids and proteins. Molecular methods such as

polymerase chain reaction (PCR), sequencing, micro-arrays, southern blot and

nucleic acid in situ hybridisation have been introduced for identification since the

mid-1980s (82, 83).

Most of these methods are targeted at whole genomes or selected genes that allow

species-specific identification or demonstrate the presence of antibiotic resistance

genes (84). The major advantages of molecular methods are high sensitivity,

specificity and being faster than culture-based methods. Since only one copy of

bacterial DNA is generally required for PCR based methods, it becomes possible to

detect non-cultivable organisms or identify fastidious organisms at an earlier time

(85). However, PCR techniques often require DNA extraction steps and provide only

the presence of bacterial cells but not information on their spatial localisation in the

biofilm community (86). Fluorescence in situ hybridisation (FISH) methods with

rRNA-targeted oligonucleotide (probe) have become a powerful tool for studying the

presence and spatial distribution of bacterial cells. The limitation of these FISH

methods is that they are dependent on the availability of specific and suitably

discriminating probes (87). New technologies such as DNA and protein microarray

methods have been proposed and use high numbers of molecular probes to

discriminate between different strains and species in one chip (88, 89). The

microarray technology can be applied for rapid and high-throughput screening in

gene expression and gene identification. The limitations of this technology are

associated with conflicts in interpretation of the results depending on the efficiency

of nucleic acid labelling with fluorescent dyes, instability of isolated RNA and

challenges in building protein array chips (17).

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1.3.3 Spectroscopic methods

Spectroscopic techniques based on differences and similarities in chemical markers

associated with macromolecules in bacterial cells have recently been shown to enable

rapid bacterial identification. Matrix-assisted laser desorption ionization time of

flight (MALDI-TOF) mass spectrometry has become a prominent technique in

microbiological analysis due to its high speed (90). The limitations of the MALDI-

TOF mass spectrometry technique include the destructive nature of analysis and the

requirement for mixing with an ionising matrix, thus increasing sample preparation

and leaving the sample unfit for further analyses (91, 92).

Over the last few years, vibrational spectroscopic techniques such as Fourier

transform infrared spectroscopy (FT-IR) and Raman spectroscopy have demonstrated

a great potential for rapid identification of bacteria (21, 22). In fact, the applications

of FTIR and Raman spectroscopy have been reported for the study of prokaryotic

and eukaryotic cells (93-97). These vibrational spectroscopic techniques provide

significant benefits as non-invasive, reagent-less and rapid diagnostic tools at the

single cell level (24). In biomedical studies, vibrational spectroscopy shows the

advantage of providing information on both chemical composition and the structure

of biological molecules for proteins, nucleic acids, lipids and carbohydrates (98, 99).

In microbial studies, this approach is also mentioned as a 'whole-organism' finger

print, because different microorganisms have unique spectral characteristics (26, 100,

101). Moreover, these methods can provide information on bacteria at the strain level

with minimal time and effort (23).

Compared to FT-IR, Raman spectroscopy requires only small sample volumes

(comparable to the size of a single cell) with minimal peak overlap from water

molecules (25). The different bacterial identification methods discussed in this

section are summarised in Table 1.2. The work presented in this thesis (see details

mentioned in later Chapters) investigates the ability of Raman spectroscopy to

identify bacteria at any stage of their life cycle and in any environment. Therefore, an

overview of the applications of Raman spectroscopy in bacterial identification will

be discussed in the next section.


Table 1.2 Summary for bacterial identification methods mentioned in Section 1.4.

Traditional methods

(culture-based)

Molecular methods Spectroscopic methods

-Differential staining

(Gram, acid-fast, capsule

stain etc.)

-Growth characteristics

(colony morphology,

selective media)

-Biochemical methods

-Serological methods

(agglutination test, ELISA,

Western blots etc.)

-Identification based on

biomolecules

(nucleic acids, lipids, proteins)

-Unique genome analysis of

individual species and strains

-PCR, sequencing, micro-

arrays, Southern blot, nucleic

acid in situ hybridization

-Based on differences and

similarities in chemical

markers associated with

macromolecules

- MALDI-TOF MS, FTIR,

RM,

Advantages:

-Pure isolates,

-Unique characteristics

-Accurate identification

Limitations:

-Take time to grow and

confirm results

-Highly related species

difficult to separate

-Limited corresponding

databases

-Many bacterial species are

difficult to grow

Advantages:

-Highly sensitive and specific

-Faster than culture-based

methods

-Detection of fastidious

bacteria and those which are

difficult to grow

-For presence and spatial

distribution of bacterial cells

(i.e. FISH)

-Discrimination of bacteria at

species/stains level (i.e. DNA,

protein microarray)

-High throughput screening in

gene expression

Limitations:

-DNA extraction steps

-Availability of probe

-Efficiency of nucleic acid

labelling

-Instability of isolated RNA

Advantages:

-simple, non-invasive, reagent-

less, rapid and reproducible

method

-‘whole-organism’ fingerprint

identification

-non-destructive study to

sample (i.e. FTIR, RM)

-Requirement of small sample

volume (i.e. RM)

-Minimal peak overlap from

water molecules (i.e. RM)

Limitations:

-Destructive nature due to

requirement for mixing with an

ionising matrix

(i.e.MALDI-TOF)

-Water peak interference in

FTIR

Abbreviations: ELISA, Enzyme-linked immunosorbent assay; PCR, Polymerase

Chain Reaction; FISH, Fluorescence in situ hybridisation; MALDI-TOF MS, Matrix

assisted laser desorption ionisation time-of flight mass spectrometry; FTIR, Fourier

transform infrared spectroscopy; RM, Raman spectroscopy.

FSET PhD Thesis/18

1.4 Raman spectroscopy for bacteria identification

1.4.1 Theory of Raman spectroscopy

Raman spectroscopy (named after Sir C. V. Raman) is a spectroscopic technique

based on inelastic scattering (Raman scattering) of monochromatic light, usually

from a laser in the visible, near infrared, or near ultraviolet range. When

monochromatic light interacts with a molecule, the photons which make up the light

can excite the molecule from the ground state to a virtual energy state. In most cases,

the molecule will relax back to its original state, emitting a photon of the same

energy and this is called Rayleigh scattering (Fig 1.4). However, in the spontaneous

Raman scattering process, the excited molecule from a virtual energy state returns to

a different rotational or vibrational state when it relaxes. In this process, the energy

difference between the original state and this new state results a shift in the emitted

photon's frequency from the excitation wavelength (Fig 1.4). In general, a small

fraction of the scattered photons (approximately 1 in 10 million) have a frequency

different from the incident photons. The shift in energy gives information about the

vibrational modes of the molecule (102, 103).

There are two potential ways of shifting energy in Raman scattering, known as

Stokes and anti-Stokes processes. In the Stokes shift, a molecule is excited to a final

vibrational state which is more energetic than the initial ground state. Then, the

scattered photon will be shifted to a lower frequency or energy (longer wavelength)

because of the energy loss between the two states. Conversely, when the emitted

photon has more energy and thus higher frequency, the energy difference is called an

anti-Stokes shift (Fig 1.4). Anti-Stokes signal is usually an order of magnitude

weaker than Stokes signal (at room temperature), hence in Raman spectroscopy, only

the more intense Stokes line is typically measured. Raman shifts are normally

described in wavenumbers, which are proportional to photon energy or frequency

and have units of inverse wavelength (cm-1) (103).


Figure 1.4 Schematic of an energy diagram for Rayleigh and Raman scattering.

Raman spectra provide detailed information about the chemical composition,

bonding situation, symmetry, structures and physical parameters of materials and

compounds (104). The combination of Raman spectroscopy and confocal

microscopy provides a better spatial resolution to allow measurements from small

sample volumes, as well as from single cells (100, 105, 106). With the application of

the confocal technique, the excitation laser is focussed on a small area of the sample

through a microscope objective and the scattered Raman signal is limited to the focal

region by a confocal pinhole. The smaller the pinhole, the better is the axial (depth)

resolution, but on the other hand, so too is the signal intensity decreased (102).

As shown in Fig 1.5, a typical confocal Raman spectroscope system consists of the

following components: (1) a monochromatic light source (laser), (2) filtering steps to

remove weak emissions other than the main exciting line of the laser, (3) microscope

unit for directing the laser beam onto the sample and (4) spectrometer unit. The back

scattered radiation collected from the microscope is incident on the filter

(holographic ‘notch’ or dielectric ‘edge’ filter) and the light is then passed through a

diffraction grating for splitting the Raman scattered light into component

wavelengths, i.e. a spectrum. Finally, the Raman spectra are recorded with the

charge-coupled device (CCD) detector (103).

Ground state

Vibrational state

Virtual energy state

Rayleigh

scattering

Stokes anti-Stokes

Raman scattering

En

erg

y l

eve

l

FSET PhD Thesis/20

Figure 1.5 Model of a typical confocal Raman spectrometer system using a visible

laser, notch filter, spectrometer and the charge-coupled device (CCD) detector.

The application of confocal Raman microscopy, in combination with appropriate

chemometric processing, has been proposed for the characterization, discrimination

and identification of microbes at species level (26, 107). In addition, by

understanding the chemical and structural variations between cells, this approach

could be used to monitor phenotypic changes from environmental stress and cell

heterogeneity during the growth cycle (29). A detailed review of the application of

Raman spectroscopy on bacterial cells is discussed in the next section.

1.4.2 Application of Raman spectroscopy for bacterial identification

Raman spectroscopy was being applied and was co-occurring with laser

developments in biological studies since the early 1970s. The potential of Raman

spectroscopy for identification purposes in microbiology was initially introduced by

Dalterio et al. in late 1986 through the study of chromobacteria species from water

with a resonance Raman microprobe (108, 109). Since then, Raman spectroscopy has

become a method of great interest to scientists. Many research articles for Raman

spectroscopy applications on single bacterial cells, yeast cells and cell components of

single bacteria or spores have subsequently been reported. Some of those reports

which are mostly related to this study are discussed here.

The application of Raman spectroscopy was expanded for bacterial identification

after the study by Pupples et al. on single living cells, chromosomes and human

microscope

camera

sample

notch

filter

filters

pinholegrating

CCD

detector

Laser

source


granulocytes by confocal Raman microspectroscopy (105, 110). Choo-Smith et al.

first reported a novel method to detect the Raman signal from micro-organisms

grown on solid growth media using confocal Raman micro spectroscopy (111, 112).

Applications of Raman spectroscopy have seen a steady increase in bacterial

identification and classification by introducing the different types of experimental

apparatus and analysis methods permitting microbial investigations at the single-cell

level (26, 104, 113-115).

Among a range of these reports, an interesting study of Huang et al. demonstrated

the combination of the Raman confocal microscope with multivariate methods that

could discriminate seven different bacterial species (26). In their experiment, they

generated a Raman spectral profile from a single microbial cell of each different

species, thereby using this profile to detect differences in the profiles according to

the physiology of the individual species. In their analysis method, the baseline

corrected and normalised Raman spectra were first analysed by the multivariate

technique of principal component analysis (PCA) to get the greatest differentiation

between dimensions of multivariate data. Then, discriminant functional analysis

(DFA) was used to discriminate between groups based on these retained principal

components. The data showed that the clustering of the three species, based on

species phenotypic differences, was robust despite differences in cellular physiology

during the growth. From their analysis of specific peaks selected from the first PC

loading plot, a decrease in DNA/RNA- related peak intensities and an increase in

protein-specific peak intensities was seen over time (i.e. from exponential to

stationary phase). This is most likely due to growth-phase dependent variations in

proteins and nucleic acids synthesis as a response to environmental stress induced by

the depletion of nutrients. This report opened the door for further work to establish

spectral databases to determine whether these findings hold true for a larger range of

organisms. Moreover, it is interesting to investigate the spectral changes associated

with cellular responses throughout the bacterial growth curve, including exponential,

stationary and decline phases, in order to confirm that the bacteria can still be

identified regardless of their growth phase.

FSET PhD Thesis/22

With the benefit of the high spatial resolution provided by confocal Raman

spectroscopy, phenotypic heterogeneity within microbial populations based on

culture conditions can also be investigated (104, 112, 116). In the study of Choo-

Smith et.al, they conducted a broad Raman spectroscopy study to compare

compositional heterogeneities of single bacterial cells in (micro) colonies cultured in

time series (112). They found that RNA and glycogen content were growth stage

dependent as higher contents were seen in the deeper layer of colonies (older cells).

In addition, they reported that cells from longer incubation times appeared to be

more heterogeneous in their biochemical composition. Their experiment also

demonstrated that non-destructive Raman spectroscopic techniques can be useful

tools for examining the nature of colony development and biofilm formation.

Very recently in 2014, species-level identification of clinically relevant

microorganisms directly from an agar culture by Raman spectroscopy was reported

by Espagnon et.al (117). Raman spectra were recorded directly from colonies

(macro-colony and micro-colony) of different bacterial species grown on TSA. Then,

they performed a classification analysis at the species level using linear discriminant

analysis for the Raman spectra collected from macro-colonies and micro-colonies.

The authors reported that correct identification rates were obtained from both the

macro-colony and micro-colony data (94.1% and 91.5% respectively). Moreover,

they mentioned that the spectral differences observed between micro-colonies and

macro-colonies were due to biological differences as a result of different growth

stages. The authors suggest that the micro-colony average spectrum shows some

characteristics of metabolic activity markers (i.e. higher nucleic acid content

observed in the exponential phase compared to the stationary phase). However, there

is uncertainty about the sample preparation for macro-colony and micro-colony. The

macro-colony in their experiment was 24 hour (h) old colony grown on TSA

inoculated from a stock culture. The micro-colony was 6 h old culture of

intermediate overnight culture primarily inoculated from a stock culture. It would be

more interesting to see the spectral changes of colony development based on time

series and the comparison of these changes with the planktonic growth curve data.


The research of Espagnon et al. provides a useful parallel study with the research

presented in this thesis in investigating the phenotypic changes within microbial

populations in colonies. A Raman spectroscopy study of the chemical properties of

bacteria over their lifetime in a biofilm matrix provides a further extension of this

study. Therefore, the literature for Raman spectroscopy applications on biofilm cells

is reviewed in the following section.

1.4.2.1 Raman spectroscopy on bacterial biofilm

As discussed in Section 1.2.2, bacteria can form structured communities called

biofilms and spend most of their life in the biofilms. In a biofilm, bacterial cells are

encased in self-produced EPS matrix adherent on a living or non-living surface.

Bacterial cells from biofilms are clearly distinguished from the planktonic cells by

their unique characteristics. A number of biological studies have been devoted to

understanding the functions and structures of biofilms to assist in the development of

control measures. Rapid differential identification of bacterial species and strains is

also important to any field where biofilm forms. A literature review of Raman

spectroscopy studies on bacterial biofilm is briefly discussed in this section.

In 2004, Marcotte et al. first reported that Raman micro spectroscopy could be used

to investigate in situ spatial distribution of the biomass and chemical diffusion in

hydrated bacterial biofilms (118). Their research provided a further approach for

Raman spectroscopy applications to determine the diffusion of molecules, including

antibiotics, in bacterial biofilms (119). Furthermore, a comprehensive study on the

applicability of Raman microscopy for non-destructive chemical analysis of biofilm

and EPS matrix has been performed by Ivleva et al in 2009 (120). From their

experiment, characteristic frequency regions and specific marker bands for different

biofilm constituents were revealed based on Raman reference spectra of biofilm-

specific polysaccharides and proteins. Their experiment provides further motivation

to study the structure and function of biofilms by understanding the chemical

information of different substances within the biofilm matrix.

For the differential identification of bacteria in biofilm, Huang et al. published a

comparative single-cell analysis showing that bacterial cells recovered from biofilm

FSET PhD Thesis/24

have an identifiable Raman spectral profile in comparison with planktonic cells (30).

In 2010, Beier et al. reported that confocal Raman spectroscopy analysis, in

combination with a training (prediction) model based on chemometric methods,

enables discrimination between different Gram-positive bacteria grown in pseudo-

mixed biofilms (31). In their study, they first created a training model for two

bacterial species from dehydrated biofilms. The prediction model constructed from

principal component analysis and logistic regression was calibrated and validated

using pure biofilms of each species achieving 96% overall accuracy. Finally, they

applied this model to pseudo-mixed biofilms (stained/unstained cells of known

species) and 97% were correctly identified.

A few years later, the same research group reported the successful application of this

model to identify the species within hydrated biofilms and further application to

species identification in two-species grown biofilm (121). With the ability to detect

the presence of these two bacterial species in the mixed biofilm, the author

mentioned that their study is the first report for creating spatial maps within biofilm.

In their experiment, however, there was no reference method mentioned to provide a

confirmation for the accuracy of correct species identification in the mixed biofilm.

Therefore it would be interesting to apply Raman microscopy together with a

molecular technique, in particular, fluorescence in situ hybridization (FISH) for

simultaneous independent confirmation of differential identification within biofilm.

The literature review mentioned above is highly encouraging to extend confocal

Raman spectroscopy in combination with appropriate chemometric methods for

bacterial identification to more practical real-world settings.

1.4.3 Raman spectral data analysis

In general, Raman spectral data analysis has two main steps: (1) pre-processing of

Raman spectra and (2) chemometric methods for extracting and interpreting the

qualitative and quantitative information from the spectra.


1.4.3.1 Pre-processing of Raman spectra

Many approaches using different algorithms and chemometric methods have been

reported for signalling processing of Raman spectra (28, 100). In spite of these

established approaches, the application of Raman spectroscopy in a routine clinical

laboratory is still hampered by the challenges in the analysis of Raman spectra (122).

This is because of a fact that Raman spectra contain Raman fingerprint information

together with other features like cosmic ray spikes, Gaussian noise, fluorescence

background and other effects dependent on experimental parameters (123). These

features have to be removed and calibrated before the analysis, in order to ensure that

the analysis is based on the Raman measurements and not on other effects. The pre-

processing procedures are categorised in three steps (smoothing for noise reduction,

background subtraction, normalisation and mean-centring) and they are briefly

reviewed below.

1.4.3.1.1 Noise removal and Smoothing of Raman spectra

Since Raman spectrometers are generally coupled with charge-coupled device (CCD)

detectors, cosmic rays produced by high energy particles hitting the CCD can often

be seen as visible spikes in Raman spectra. These spikes are normally narrow

bandwidth, positive unidirectional peaks and are in random positions. Cosmic spikes

can disturb or even destroy the meaningful chemical information from Raman

spectra. Several approaches using algorithms have been proposed in the literature for

the detection and removal of cosmic spikes. Some of these approaches can only

detect whether a spectrum contains spikes and cannot find their exact positions in the

spectrum and others can both detect and remove spikes (124). In this study, the

WiRE 3.4 Raman software integrated in the Renishaw inVia Raman spectroscopy

system has been applied for cosmic ray removal (see details in Chapter 2).

The low signal-to-noise ratio in Raman spectra can arise due to several reasons such

as: (1) low scattering intensities from a sample, (2) low signals in specific spectral

regions caused by detector falloff, (3) decline in the grating efficiency. Filtering or

smoothing methods are used to remove Gaussian distributed noise originating from

uncorrelated processes. In some cases, smoothing can help to remove enough noise

FSET PhD Thesis/26

and visualise the presence of peak features. However, over smoothing the spectra can

lose the peak information as well, so it must be applied with great care (125). Many

commercial packages are available for smoothing routines. Among them, the

Savitzky-Golay filter, which is a smoothing algorithm based in a least-squares

polynomial fitting, is probably the most versatile method and widely used in

analytical chemistry (123, 126, 127). In this study, the Savitzky-Golay filter (span =

7, polynomial degree = 2, curve fitting toolbox in MATLAB) was used to reduce the

noise of the spectra (see details in Chapter 2).

1.4.3.1.2 Fluorescence Background Subtraction from Raman Spectra

The most significant challenge for many applications of Raman spectroscopy is that

the spectra are often accompanied by noise superimposed on a broad background.

This background is generally dominated by intrinsic fluorescence from the sample

(128). Consequently, the fluorescence background has to be removed in order to

perform further quantitative analysis on the Raman spectra, including multivariate

analysis. A brief overview of methods applied in fluorescence background removal

from Raman spectra and in preparation for chemometric analysis of Raman data are

discussed in this section.

In order to remove fluorescence background from measured Raman signals,

approaches based on instrumental, experimental and computational methods have

been widely applied (129). Instrumental approaches to minimise the fluorescence

background, such as excitation wavelength shifting, time-gating and photo-

bleaching, require hardware modifications in the spectroscopic system (130-132).

The excitation wavelength shifting technique requires two closely spaced excitation

wavelengths to achieve two spectra and further processing to fit the Raman spectrum.

Although it requires some system modification, it has been reported to eliminate both

the fluorescence background and systematic noise from the spectra (133). There are a

number of reported attempts to develop a time-gating system to solve the problem of

low signal-to-noise ratio spectra, but there are difficulties in system modification to

achieve low peak power pulses with high gating efficiency at a safe threshold for

biological samples (134, 135). Photo-bleaching of samples has been proposed to

reduce the broad fluorescence background, but the relative heights of Raman peaks


obtained from the sample are progressively altered as a consequence of the

irradiation and the removal of fluorescence background from the samples may be

inadequate (136, 137). Experimental approaches such as selection of substrates

(calcium fluoride or zinc selenide) and improved sample preparation have been

proposed in order to increase the data quality by minimizing the fluorescence

background (138). However, substantial background remains in the data due to the

interaction between the light source and the intrinsic fluorescence in many samples.

As a result of these challenges, computational methods have become the standard

way to correct for contributions from fluorescence in the background. These require

no system modifications and impose no limitations on sample preparation. Among

these mathematical techniques, first- and second-order derivatives, frequency-

domain filtering, polynomial fitting and wavelet transformation methods have been

proposed as useful tools for background removal in certain situations (129). The

accuracy of the first- and second-order derivative methods is reliant on peak

selection. Due to the difficulty of peak picking, particularly in the presence of

multiple overlapping peaks such as occur in complex biological samples, missing

some peaks could result in aspects of the Raman spectrum being placed in the

baseline, resulting in a poor baseline estimate. The first- and second-order derivative

methods can severely diminish and distort Raman spectral features unless there are

complex mathematical fitting algorithms to reproduce a traditional spectral form

(139, 140). Fast-Fourier transform filtering (FFT) is one of the frequency-domain

filtering techniques and also requires the separation of the frequency components of

the Raman spectrum from those of the background and noise (141). Polynomial

fitting has become the most popular fluorescence removal technique for a wide range

of applications. However, manual polynomial fitting relies on user intervention for

selection of locations where the curves are to be fitted in the data. Although

automatic polynomial fitting methods have been proposed to remove the need for

manual curve-fitting, their use can be limited in high noise circumstances (142-144).

Wavelet transform methods can also be used to automate the curve fitting, but

difficulties in the selection of suitable wavelet thresholds and the proper level of

resolution to represent the baseline may affect the background removal results (145).

FSET PhD Thesis/28

Recently, a background-correction algorithm for Raman spectra has been developed

using wavelet peak detection, wavelet derivative calculation for peak width

estimation and penalized least squares background fitting (146). This approach

adaptively separates the measured data samples into peak and non-peak

(background) values by setting the least-squares weights to one for background and

zero for peak regions. The application of these binary valued weights may cause

some sudden changes in gradient that appear questionable in the context of a Raman

background subtraction.

In this study, an enhanced automated algorithm for fluorescence removal based on a

combination of adaptive weighting factors with penalized least squares estimation

has been applied (147). A detailed discussion on the application of the algorithm has

been included in Chapter 3).

1.4.3.1.3 Normalisation and mean-centring of Raman spectra

Another challenge is that Raman spectra acquired in sequence or intermittently even

from the same sample can exhibit variations in intensity. These variations can affect

the classification and quantitative comparison of Raman spectra. This effect can be

eliminated by a spectral normalization step, which is an adjustment to the data set

that equalizes the magnitude of each sample (148). Normalization is basically

performed by dividing each intensity value of a Raman spectrum by a constant value.

Different normalization techniques are based on the choice of this constant value.

The most common techniques used in normalization are using the highest peak (149)

and vector normalization (150, 151). Peak normalization can be performed by using

the height of a selected peak as the normalization constant. If the highest peak is

chosen, this effectively can set the value of the highest peak to 1.0 and all the other

peaks are scaled accordingly. Peak normalization is most appropriate in situations

where there is a spectral component that is relatively constant across the data set

(internal standard). This situation does not apply for most bacteria and therefore was

not used in this work. Instead vector normalization was done by calculating the sum

of the squared intensity values of the spectrum and using the squared root of this sum

as the normalization constant. Vector normalization may thus be considered as a


method for total intensity normalization and has been widely applied in previous

work (152).

1.4.3.2 Chemometric methods for Raman spectrum data analysis

Raman spectra taken from bacterial samples are generally complex with many

overlapping peaks. Due to this complexity and the subtle changes between the

spectra obtained from different samples, extracting the qualitative and quantitative

information from the spectra is not always straight forward. The application of

chemometric methods has allowed for discrimination of Raman spectra taken from

different samples. Chemometric has been defined as a chemical discipline that uses

mathematical, multivariate statistical and computational methods to extract and

interpret the chemical information in data from analytical instrumentation (153).

Despite the broad definition of chemometric, the most important feature is the

application of multivariate statistical methods to analyse chemistry-relevant data.

The word “multivariate” not only means many variables, but also means that these

variables might be correlated. Statistical methods refer to a range of techniques and

procedures for analysing data, interpreting data, displaying data and making

decisions based on data. Therefore, multivariate statistical methods are collections of

methods and procedures that analyse, interpret, display and make decisions based on

multivariate data. The multivariate statistical methods were gradually developed,

starting from the beginning of the twentieth century (154). Analysis of variance

(ANOVA) is a general technique that can be used to test the hypothesis that the

means among two or more groups are equal, assuming that the sampled populations

are normally distributed. If there is a set of multiple random variables to compare,

the univariate analysis of variance (ANOVA) will become a multivariate analysis of

variance (MNOVA). In MNOVA, the variation in the response measurements is

partitioned into components that correspond to different sources of variation.

Chemometrics using multivariate statistical methods are usually classified as

unsupervised and supervised approaches. The unsupervised or objective

classification method does not require any prior knowledge of the sample and can

provide patterns, grouping and detection of outliers. Principal component analysis

FSET PhD Thesis/30

(PCA) and hierarchical cluster analysis (HCA) are examples of unsupervised

methodologies. On the other hand, the supervised methods require prior knowledge

of the sample for pattern recognition purposes. A model consisting of a set of well

characterised samples can be trained so that it can predict the identity of unknown

samples. Multiple linear regression (MLR), principal component analysis (PCA),

partial least squares regression (PLS) and linear discriminant analysis (LDA) are

examples of supervised methods (155). In this thesis, PCA and LDA were used in

discriminating the Raman spectra taken from different bacterial species, different

metabolic states and biofilm growth (details mentioned in Chapter 3 and results and

discussion sections from Chapters 4 and 5). Another chemometric approach,

principal component logistic regression (PCLR), which is a combination of

unsupervised and supervised methods, was also applied in this study (see details in

Chapter 3). In particular, the leading principal components of the training set are first

established using PCA, followed by constructing a logistic regression model for

classification. This approach was applied to discriminate two bacterial species taken

from dual-species biofilm, as outlined in Section 5.3.4. There are several multivariate

statistical analyses used for Raman data analysis and selections of those that are more

relevant to this study are reviewed in the following Sections.

1.4.3.2.1 Principal component analysis (PCA)

Principal component analysis (PCA) is a statistical procedure that uses orthogonal

transformation to convert the original set of variables into a smaller set of linear

combinations that account for most of the variance in the original data. The purpose

of PCA is thus to determine the data patterns and underlying factors (i.e., principal

components, PCs) that cause the similarities and differences in the original data

without any prior knowledge (154). PCA starts with an eigenvector decomposition of

the original data matrix into eigenvectors and eigenvalues. In particular, the original

data matrix with objects (spectra) and variables (intensity) is decomposed into two

matrices, the scores matrix related to the objects and loadings matrix related to the

variables. The PCs are the eigenvectors of the score matrix and the eigenvalues

represent the data variance captured by the PCs. The first PC is related to the

eigenvector of the highest eigenvalue, so it has the largest variance and the following


PCs follow the same order (156, 157). PCA with the use of eigenvector-based

methods is the most common approach to identify clusters in Raman spectra (102).

Many reports show that PCA has been widely applied to discriminate between the

different growth phases of a single species and differential identification between

diverse bacterial species (26, 100, 158).

1.4.3.2.2 Linear Discriminant analysis (LDA)

Linear discriminant analysis (LDA) is a statistical method used to predict a

dependent grouping variable based on one or more predictor variables. In brief,

linear combinations of variables are computed to determine directions in the spectral

space in which discriminant functions maximize the variance between groups and

minimize the variance within groups according to Fisher’s criterion (157, 159). As

mentioned previously, LDA is a supervised method and requires a set of well-

characterised known samples which are used for pattern recognition and

classification of the unknown predicted sample. There are several methods to

validate the LDA model and the most common is the leave-one-out cross-validation

(LOOCV). In this method, all spectra except one are used to build a LDA model,

which is then used to classify the left out spectrum. LDA models have been widely

used in Raman spectroscopic analysis for identification and classification of bacterial

species (114, 155, 160).

1.4.3.2.3 Principal component logistic regression (PCLR)

Principal component logistic regression (PCLR) is a logistic regression analysis

technique that is based on principal component analysis (PCA). In general,

regression analysis is similar to discriminant analysis. The main difference is that

regression analysis deals with a continuous dependent variable, while discriminant

analysis acts on a discrete dependent variable. As in many other regression methods,

logistic regression has a very high number of predictor variables so that a dimension

reduction method is required. In PCLR, instead of using the dependent variable on

the explanatory variables as regressors, the principal components of the explanatory

variables are used as regressors. In selecting principal components as regressors, the

principal components with higher variances (the ones based on eigenvectors) are

FSET PhD Thesis/32

often selected. However, for the purpose of predicting the outcome, the principal

components with low variances may also be important, in some cases even more

important (161). A prediction model based on principal component analysis and

logistic regression has been reported for differential identification of bacterial species

from pseudo-mixed biofilm (31). A detailed explanation and discussion of the

multivariate statistical analysis used in this study are discussed in Chapter 2.

1.5 Factors influencing bacterial chemistry

Although prokaryotic bacterial cells are generally believed to be strictly unicellular

as discussed above (Section 1.2.1), most are capable of forming stable aggregate

communities in a polymer matrix, known as biofilm (40, 162). Each biofilm

bacterium lives in a customized micro niche in a complex microbial community that

has primitive homeostasis and metabolic cooperation (163, 164). Each of these

sessile cells in the biofilm matrix reacts to its special environment so that it differs

fundamentally from a planktonic cell of the same species (164). Therefore, biofilm

cells generally have distinct patterns of gene expression (phenotypic differentiation)

compared to their planktonic counterparts. These changes in expression are thought

to result from a cell-to-cell signalling phenomenon known as quorum sensing (162).

It is thus believed that the transition from planktonic state to biofilm is a complex

and highly regulated process. Apart from intercellular and intracellular signalling,

there are many factors that can influence biofilm formation. These factors include the

characteristics of the different species within the microbial community, surface

characteristics, nutrient availability and environmental sensing. Since this literature

review is focusing on bacterial identification in real-world biofilm samples, the

factors influencing biofilm formation can, in turn, influence the identification of

biofilm-forming bacteria. This is a particular concern for identification techniques

based on chemical composition, such as Raman spectroscopy. A brief overview of

some of the main factors influencing biofilm formation is reviewed in this section

based on the followings:

Diversity of bacterial characteristics in communities;


Physical and chemical properties of surfaces which can influence cell

adhesion to surfaces and their development into biofilms;

Chemical communication between bacterial cells which can affect the

organization of biofilm communities.

1.5.1 Bacterial characteristics

The diverse physicochemical characteristics of bacteria within species and strains

can affect the bacterial adhesion properties to different extents during the biofilm

formation process. These characteristics involve the bacterial cell surface

hydrophobicity and charge, the presence of fimbriae and flagella and production of

EPS.

As mentioned in Section 1.2.1, the bacterial cell wall is mainly composed of a

peptidoglycan layer, teichoic and teichuronic acids, lipopolysaccharide, a variety of

polysaccharides and proteins. Some of these molecules are exposed at the cell

surface or extend from the outer membrane of the cell. These cell wall/membrane-

associated proteins are responsible for the hydrophobicity of the bacterial cell surface

which can render it either hydrophobic or hydrophilic. In addition, most bacterial

fimbriae also play a role in cell surface hydrophobicity due to the presence of

hydrophobic amino acid residues in relatively high concentrations (165). This

hydrophobic property of fimbriae and increased bacterial cell wall hydrophobicity

may enhance the bacterial adhesion by overcoming the initial electrostatic repulsion

force between the cell and surface (166, 167). In general, hydrophobic bacteria prefer

to attach to hydrophobic surfaces and the same phenomenon could be seen for

hydrophilic bacteria on hydrophilic surfaces (168, 169). The majority of the reports

regarding bacterial characteristics in biofilm formations are based on the behaviour

of single bacterial species.

In real-world environments, bacteria embedded in an EPS matrix with a multispecies

community tend to adopt a biofilm-specific phenotype which can be radically

different from those expressed in the corresponding planktonic cells. Therefore,

biofilm-growing bacteria undergo a number of complex physiological, metabolic and

phenotypic differentiations. With phenotypic switching, the biofilm bacteria can

FSET PhD Thesis/34

successfully colonise new environments and have a better chance of surviving in

hostile environments. It has been reported that the phenotypic changes that occur in

biofilm can alter the bacterial morphology, bacterial virulence and antimicrobial

resistance profile, all of which would be expected to modify the overall chemical

composition of the cells.

As an example of phenotypic changes in cell morphology, Mangwani et al. reported

that biofilm-forming marine bacterium Paenibacillus lautus within biofilm changed

their morphology from non-motile cocci to motile rods due to competition for space

and survival (170). Kim et al. reported the correlation between bacterial virulence

and spontaneous phenotypic variation, as revealed by a transition from translucent to

opaque colonial morphology (171). Their results showed that the opaque colony

variants of Streptococcus pneumonia exhibited higher virulence than translucent

variants. This phenotypic variation in opacity is associated with differences in

capsular polysaccharide secretion and teichoic acid synthesis on the bacterial cell

wall. The study of Drenkard et al. proposed the impact of phenotypic variation on

other biological process of antibiotic resistance (172).

Apart from the consequences mentioned above, phenotypic variation can affect the

expression of lipopolysaccharides, pili and flagella of the bacterial cell, resulting in

antigenic variations (173, 174). Moreover, Hanlon et al. reported changes in bacterial

cell surface hydrophobicity during biofilm growth (175). To date, it has been

reported that bacterial phenotype heterogeneity is not a genetic variation, but an

alteration in chemical reactions for DNA and protein synthesis, although the

mechanism involved in these changes is still not clear (176, 177). Any variation in

chemical information from bacterial cells may influence bacterial identification

outcomes using traditional phenotypic techniques and emerging spectroscopic

techniques for the detection and characterisation of bacteria. The majority of

experimental reports regarding phenotypic variation in biofilms have been done

under controlled laboratory conditions. However, in real situations, changes within

biofilms occur throughout the biofilm development and are unlikely to remain

constant over time. Therefore, it is of interest to study how phenotype changes affect


bacterial identification by Raman spectroscopy under the real-world setting of

biofilms.

Since biofilm formation is a complex process involving interaction between bacterial

cells and a surface, the factors influencing this process are not only dependent on the

bacterial characteristics, but also on the surface characteristics.

1.5.2 Surface (substratum) characteristics in biofilm formation

Bacterial adhesion to surfaces begins with the initial attraction of the cells to the

surface followed by adsorption and attachment (178). Generally bacteria prefer to

grow on available surfaces rather than in the surrounding aqueous phase. Although

bacterial movement to a surface is believed to be influenced by several physical

forces (e.g. Brownian motion, Lifshitz-van der Waals), other factors (such as

gravitational forces, electrostatic interactions and diffusible or surface bound

chemical factors) also contribute to this process (179, 180). Therefore, the surface

properties that influence biofilm formation can be characterised in terms of their

physicochemical properties such as surface hydrophobicity, roughness, charge and

surface chemistry.

1.5.2.1 Influence of surface hydrophobicity and roughness

Surface hydrophobicity has been reported as one of the important properties involved

in the cell adhesion phenomenon (181, 182). Van Oss et al. stated that, in biological

systems, the hydrophobic interaction is believed to be the strongest of the long-range

non-covalent interactions (183). In general, hydrophobic, non-polar surfaces are

more favourable for bacterial attachment than hydrophilic surfaces (184). It has been

postulated that the preferential association between hydrophobic, low-energy

surfaces and hydrophobic moieties on bacterial cells (i.e. cell wall and extracellular

organelles) result in more stable interactions (185). Sousa et al. reported that

hydrophilic bacteria could also adhere to a higher extent on a silicon surface which

was more hydrophobic than a less hydrophobic material (acrylic) (186). This result

shows the importance of the hydrophobicity of the surface in the bacterial adhesion

process. Moreover, the authors also claimed that surface roughness seemed to exert

an effect on the bacterial adhesion, since the silicon used in their experiment had a

FSET PhD Thesis/36

higher surface roughness than the acrylic material. It is generally accepted that

surface roughness can influence bacterial adhesion. This is because increased surface

area and reduced shear forces on rougher surfaces might enhance the bacterial

colonisation (14, 187).

Although it is known that rougher surfaces promote bacterial adhesion (188), the

degree of surface roughness has a considerable influence on the amount of microbial

adhesion (189). Taylor et al. showed that as roughness increased up to about 1.24

µm, so too was the bacterial adhesion and biofilm formation enhanced, whereas

further increases inhibited adhesion and reduced biofilm formation. The authors

suggested that surfaces with larger pits and gullies corresponding to increased

roughness values offered bacterial cells less protection from shear forces. Therefore,

a high degree of hydrophobicity, as well as a certain extent of surface roughness can

promote bacterial adhesion for biofilm formation. The reports discussed here are

mainly to illustrate the effect of surface characteristics on bacterial adhesion patterns.

The effect of surface hydrophobicity and roughness on identification-related bacterial

phenotype changes, particularly in terms of bacterial chemistry, is still far from being

well understood.

1.5.2.2 Influence of surface charge

In consideration of surface charge involvement in the biofilm formation process, a

positively charged surface favours rapid and tight attachment of negatively charged

bacterial cells (190). On the other hand, electrostatic repulsion between negatively

charged surfaces and bacterial cells can destabilize the cell adhesion. However, this

destabilizing interaction during the initial stages of attachment is often overcome by

extracellular organelles and can be diminished in high ionic strength liquids, as

discussed in Section 1.2.2.

The surface charge is involved not only in initial cell attachment, but also in long

term bio-fouling processes. Terada et al. showed that a positively charged surface

resulted in higher cell adhesion and uniform biofilm formation, while the opposite

effect was found on a negatively charged surface. However, a high bactericidal effect

could be seen in the initial adhesion stage on the positively charged surface, due to


the electrostatic attraction compromising bacterial cell membrane integrity. The

damaged cells can in turn act as a scaffold to initiate and promote a dense and

homogenous biofilm upon later incubation (191).

The correlation between surface charge interaction and cell adhesion is not always

simple. The degree of hydrophobicity and charge of the cell need to be considered as

well. A study by Dai et al. suggests that the effect of surface charge is more

important for adhesion of weak hydrophobic and more negatively charged cells

compared to cells with the opposite character. Their experiment showed a higher

adhesion of cells (stronger hydrophobic and less negatively charged nature) to a

negatively charged, hydrophobic surface compared with a positively charged,

partially-wetted surface. This demonstrated that non-charge based forces, such as a

hydrophobic effect, may overcome the influence of a weaker electrostatic force and

become dominant in cell adhesion (192). Interestingly, a study conducted by Rozhok

and Holz showed that negatively charged E. coli cells managed to attach on

negatively charged surface by experiencing cell wall destruction (193). The authors

claimed that the observed perturbations in the shape of bacteria attached to the

negatively charged surface were likely due to damage in lipopolysaccharide

molecules of the cell wall induced by the surface. Thus, it appears that the surface

charge of the substratum is one of the factors influencing biofilm formation and

altering bacterial chemistry, although it might not be considered a dominant factor.

1.5.2.3 Influence of surface chemistry

Many reports on the effect of defined surface chemistries on bacterial attachment

have been published over the past decades. In particular, Cunliffe et al. tried to assess

bacterial adhesion on modified glass surfaces with different functional groups, such

as an amine and amides of different chain lengths. These different functional groups

on the surfaces provided a range of surface energy, thereby resulting in varying

degrees of hydrophobicity of the surfaces. They found that bacterial adhesion on the

surface with amine groups was very high, whereas less adhesion was seen with the

reduction in chain length of the amide functional group. The higher adhesion can be

explained due to the fact that the surface with amine groups in their experiment has

FSET PhD Thesis/38

lower surface energy, resulting in it being slightly more hydrophobic than the other

modified surfaces (194).

Moreover, Speranza et al. reported that bacterial adhesion is also influenced by

Lewis acid–base interactions. They investigated the role of bacterial adhesion on

polymers having different chemical properties (acid/basic character) and showed that

higher cell adhesion could be seen on the acid moiety of the polymer surface due to a

preferred interaction with the basic nature of the negatively charged bacterial cell

(195). This demonstrates the importance of chemical interactions between the surface

and the bacterial cell. It also raises a question whether the strength of this interaction

during the initial attachment of bacteria to the surface influences the chemical

composition of the bacterial cells for subsequent colonization and biofilm formation.

Different surface chemistries can influence not only the bacterial adhesion, but also

the morphology and viability of attached bacteria (196). In the study of Parreira et

al., the effect of surface chemistry on bacterial adhesion was evaluated using self-

assembled monolayers of alkanethiols on gold surfaces. The alkanethiols exposed

different functional groups such as OH-, ethylene glycol and CH3. The functional

groups on these surfaces provided different wettabilities, ranging from more

hydrophilic surfaces that presented OH- groups, to more hydrophobic surfaces that

presented CH3 groups. They reported that bacteria adhered preferentially to the CH3

exposed surface compared to the OH- exposed surface. A partially-wetted ethylene

glycol coated surface, which was used as a typical non-fouling and protein resistant

surface, showed a significant loss of viability in the few adherent bacterial cells. The

enhanced bacterial adhesion seen on the CH3 coated gold surface can be explained

due to the increased surface hydrophobicity of the CH3 functional group, since the

differences in surface charge and roughness of the surfaces was very low. Although

the viability of the attached cells was not significantly affected by the surface

chemistry, cell morphologies were affected on all of the surfaces with the exception

of the CH3 coated surfaces.

Taken altogether, the effects of surface chemistry on bacterial adhesion are always

linked to the surface hydrophobicity, charge and roughness. The surface charge and


degree of hydrophobicity of the substratum vary depending on its surface chemistry.

Properties such as low electronegative surface charge, high surface hydrophobicity

and a certain extent of surface roughness have been shown to be correlated to high

bacterial adhesion, although this cannot be generalized because the physicochemical

properties of the bacteria and others factors (such as temperature, pH, salt

concentration and presence of signalling molecules) can also influence the adhesion.

1.5.3 Cell-cell interactions in biofilm formation

Since bacterial biofilm is an aggregation of multiple populations of bacterial cells,

cell-cell interactions are a key factor in biofilm formation. Mixed-species biofilms

are dominant in nature compared with single-species biofilm. In order to survive and

proliferate in such complex consortia, bacterial cells have developed cell-cell

communication pathways that govern how they cooperate or compete in their

metabolic activity. These interactions can have synergistic or antagonistic effects on

biofilm formation in terms of structure, development, nature and survival of the

biofilm community (197).

Consortia of bacterial species that influence each other in synergistic ways can

enhance biofilm formation by co-colonisation and metabolic cooperation where one

species utilizes a metabolite produced by another species (198, 199). This synergistic

effect in mixed-species biofilms increases biofilm resistance to the host immune

system, antimicrobial agents and environmental stress (200). Conversely, the

antagonistic effect can decrease biofilm formation due to competition for nutrients

and growth inhibition between the species (201-203). Understanding the bacterial

behaviour and mechanisms of species interactions in the biofilm environment is

important for controlling biofilm formation. Many studies exploring the mechanisms

involved in species interactions have been widely reported. Among them,

communication between bacterial cells via quorum sensing is the most studied

mechanism.

Quorum sensing (QS) is a system of stimulus and response correlated to population

density. Bacteria use the QS system to coordinate gene expression and cell-cell

communication through certain signalling molecules (autoinducers) and receptors

FSET PhD Thesis/40

(inducers). Bacterial cell-cell communication through quorum sensing has important

implications for microbial infections, especially in terms of the virulence and

pathogenic potential of bacteria. The ability of bacteria to communicate and behave

as a group for social interactions through QS has provided significant benefits to

them in colonisation, adaptation to environmental stress and biofilm formation.

Many reports have highlighted quorum sensing and its roles in bacterial social

activities, biofilm formation and infectious diseases over the last decades (65, 204-

206)

Quorum sensing systems in bacteria have been divided into at least three classes: (1)

LuxI/LuxR–type quorum sensing in Gram-negative bacteria, which uses acyl-

homoserine lactones (AHL) as signal molecules; (2) oligopeptide-two-component-

type quorum sensing in Gram-positive bacteria, which uses small peptides as signal

molecules; and (3) luxS-encoded autoinducer 2 (AI-2) quorum sensing in both Gram-

negative and Gram-positive bacteria (207-210). Different signalling molecules are

required for different QS systems in cell-cell communications. During inter-species

communication, several species of Gram-negative and Gram-positive bacteria use

AI-2 signaling molecules (211). Conversely, for intra-species communication, Gram-

negative bacteria usually use AHL signalling molecules while Gram-positive bacteria

use small autoinducer peptides (212). Apart from AI-2 and AHL signals, it has been

reported that a QS system mediated by diffusible signal factor molecules (cis-

unsaturated fatty acids) also plays a role in biofilm formation and influences the

behaviour of bacterial species within a mixed biofilm (213).

The brief literature review presented above has considered factors influencing

biofilm formation and has been summarised in Fig 1.6. Among these factors, targets

for bacterial behaviour and activity in multi-species communities could be

considered as one of the key issues in biofilm management. In order to prevent or

control biofilm formation, effective approaches need to take into account the

complex and obstinate nature of biofilms. Therefore, it is important to obtain specific

identification of organisms and understand their spatial distribution within a mixed

biofilm community. The differential identification and estimation of the relative

proportions of bacteria within a biofilm may also facilitate biofilm management.


However, the overall chemical composition of bacteria can change when the bacteria

form part of a biofilm and this may potentially interfere with efforts to identify the

bacteria by means of Raman spectroscopy.

Figure 1.6 Summarised illustrations of the factors that can influence bacterial

adhesion in the initial stages of biofilm formation. (A) Surface characteristics which

are relevant to the initial interaction between bacterial cell and surface: surface

roughness, positively charged surface and high surface hydrophobicity enhance

adhesion. (B) Bacterial characteristics which can influence adhesion:

polysaccharides, lipopolysaccharides, cellular components on cell wall, EPS and

signaling molecules. (C) Environmental factors which can be involved in adhesion:

temperature, pH, salt concentration.

1.6 Research motivation and thesis scope

The study of microbial biofilms has rapidly risen in prominence recently due to

increased awareness of the occurrence and impact of biofilms in natural

environments, industrial systems and in medical situations. Biofilms cost billions of

dollars every year for equipment damage, product contamination and infections, so it

is desirable to prevent or moderate their growth. The specific identification of

organisms and understanding their spatial distribution within a mixed biofilm

community may facilitate biofilm management.

Because of some limitations in the application of traditional culture-based methods

and molecular methods, Raman spectroscopy has been proposed as a rapid, non-

destructive bacterial identification method. To date, few, if any, studies based on

FSET PhD Thesis/42

vibrational spectroscopic approaches have examined intact biofilm cells in

comparison with planktonic cells and/or in differential identification.

Therefore the overall aims of this study are as follows:

Investigate the ability of Raman spectroscopy to identify different

bacterial cells at different points in their life cycle;

Extend the Raman spectroscopy technique to the identification of

different bacterial cells over their lifetime in micro colony and biofilm

growth;

Test the ability of Raman spectroscopy to identify bacterial cells in the

presence of physiological changes due to cell-cell and cell-surface

interactions during biofilm formation.

To address these goals, the first requirement was to develop the Raman signal

processing model from a combination of enhanced automated algorithms for

fluorescence removal and appropriate multivariate data analysis. During this study, a

novel background subtraction method for improving the fluorescence background

removal using adaptive-weight penalised least squares fitting was applied and

contributed to a report in the Journal of Raman Spectroscopy (147) (see details in

Chapter 3). With this method, Raman spectra taken from bacterial cells and intact

biofilm samples had the background removed successfully, providing a significant

improvement for the performance of further data analysis of the Raman spectra.

Chapters 4-5 present a Raman study of gram-negative and gram-positive bacteria

from different metabolic states, micro colonies and biofilm matrix. A brief

introduction with a literature review and the objectives of each study are presented in

each of these Chapters. The application of Raman spectroscopy to characterize the

chemical composition of single bacterial species at different metabolic growth phases

is discussed in Chapter 4. A model from these Raman spectra of planktonic bacterial

cells at different points in the metabolic cycle is constructed. Once this fingerprinting

system for single bacterial species is established, the analysis moves towards

identifying multiple bacterial species. By applying this model, a complete analysis of


the changes in the individual Raman peaks according to the same type of assigned

biomolecules is conducted in order to gain further insight into the non-destructive

study of bacterial micro colonies and biofilm matrix (see Chapter 5). At the core of

this work, the effect of species interactions on biofilm formation is also examined

using a dual-species biofilm model consisting of Escherichia coli (E. coli) and Vibrio

vulnificus (V. vulnificus). E. coli is selected for this experiment because it is a

widespread bacterial species in clinical and environmental settings and there is a

broad knowledge of its biofilm formation. V. vulnificus is chosen because it is also a

common bacterial species highly abundant in aquatic environments, including

estuaries, marine coastal waters and freshwater environments. Moreover, V.

vulnificus has antibiofilm properties that tend to inhibit biofilm formation of other

bacteria and disrupt established biofilms (58, 203), although they are also believed to

be a serious human pathogenic microorganism (214). This dual-species model

contributes not only to an understanding of species interactions but also to biofilm

formation with species of interest in a more complex community with multiple

environmental species (see details in Chapter 5).

In order to understand the further application of the Raman technique, the study of

cell-surface interaction is discussed in Chapter 6. The interaction of bacterial cells

with allylamine, carboxyl and hydrocarbon rich plasma polymer coatings are studied

in this Chapter. The surface chemistry, namely specific functional groups on the

material, which can alter the adhesion and viability of attached bacteria, is discussed

in this Chapter. The Raman spectral profiles and spectral changes from the cellular

response to the plasma polymer surfaces during biofilm growth provides a better

understanding of factors influencing both biofilm formation as well as secondary

corrosion processes enabled by the biofilm. Finally, the overall conclusions from this

research and future directions are presented in Chapter 7.

In the longer term, following on from the work described here, there is a need to

develop a method which can identify and map the spatial distribution of multiple

species in real-world biofilm samples. If Raman spectroscopy is successful in this

regard, it would use the differences in intrinsic chemical composition of bacterial

cells to create multidimensional maps of microbial structures without extensive

FSET PhD Thesis/44

knowledge of the genetic information of the cell and without requiring any invasive

sample preparation.

Mya Myintzu Hlaing Materials and Methods/ 45

MATERIALS AND METHODS

2.1 Materials

2.1.1 Bacterial species and strains

The ATCC reference strains of Escherichia coli (E. coli), Vibrio vulnificus (V.

vulnificus), Pseudomonas aeruginosa (P. aeruginosa) and Staphylococcus aureus (S.

aureus) used in this study are shown in Table 2.1.

Table 2.1 Bacterial species and strains used in this study.

Strain Description References (Source)

E. coli ATCC 25922 Professor Enzo Palombo’s

laboratory, FSET, Swinburne

University of Technology.

V. vulnificus ATCC 27562 BioNovus Life Sciences, NSW,

Australia.

P. aeruginosa ATCC 10145 Professor Enzo Palombo’s



S. aureus ATCC 25923 Professor Enzo Palombo’s



2.1.2 Bacterial culture media

The following bacterial culture media were used in this study. The nutrient broth

contains casein peptone, 4.3 g/L, meat peptone, 4.3 g/L and sodium chloride, 6.4 g/L,

adjusted to pH 7.5. The nutrient agar is similar to nutrient broth but with 15 g/L

Bacto agar. Nutrient broth containing 15% (v/v) glycerol was used as a freezing

medium for the preservation of all bacteria used in this study at -80 °C. The nutrient

FSET PhD Thesis/46

media (broth and agar) were purchased from Oxoid Ltd., Basingstoke, Hampshire,

England.

2.1.3 Substrates used for Raman experiments

Fused quartz microscope slides (ProSciTech, Cat. No. G381, Kirwan, Australia) and

calcium fluoride (CaF2) polished slides (Crystran, Raman grade, Poole, Dorset

United Kingdom) were used as substrates for the Raman spectroscopy experiments.

2.1.4 Chemicals and reagents

All chemicals used in this study were analytical grade. Most of them were purchased

from Sigma-Aldrich, Australia unless otherwise specified. Sodium chloride

(molecular formula NaCl, 99.8 % purity, molecular weight (MW) 58.44) was

purchased from Riedel-de Haen. Di-sodium hydrogen phosphate (molecular formula

Na2HPO4, 99 % purity, MW 141.96) was obtained from Chem-supply and potassium

chloride (molecular formula KCl, 99.5 % purity, MW 74.55) and potassium

phosphate monobasic, anhydrous (molecular formula KH2PO4, 99 % purity, MW

136.09) were purchased from Astral Scientific. LIVE/DEAD BacLight Bacterial

Viability Kits were purchased from Life Technologies Australia Pty Ltd. The

fluorescence labelled oligonucleotide probe for 16S rRNA targets of E. coli ATCC

25922 (details in Section 2.2.4.3) and Concanavalin A, Tetramethylrhodamine

conjugate (ConA) probe were purchased from Life Technologies Australia Pty Ltd.

Analytical grade NaCl, KCl, Na2HPO4 and KH2PO4 were used to prepare phosphate

buffered saline (PBS, pH 7.4, 137 mM NaCl, 2.7 mM KCl, 10 mM Na2HPO4, 1.8

mM KH2PO4). Milli-Q purified water (18.2 MΩ.cm at 25 °C, Millipore, Australia)

was used as ultrapure laboratory grade water for all reagent preparation and sample

washing steps.

Fixative solution for fluorescence in situ hybridisation, FISH, (4 %

paraformaldehyde) was prepared by adding 4 g of paraformaldehyde (P6148, Sigma)

in 100 mL of PBS. The permeabilisation solution for FISH was prepared to get a

final concentration of 70 U/ µL Lysozyme (L-6876, Sigma), 100 mM Tris-HCl

(molecular formula NH2C(CH2OH)3 · HCl, MW 157.60) and 5 mM


Ethylenediaminetetraacetic acid, EDTA, (molecular formula

(HO2CCH2)2NCH2CH2N(CH2CO2H)2, MW 292.24) in Milli-Q water. Hybridisation

buffer was prepared for a final concentration of 0.9M NaCl, 20 mM Tris-HCl, 0.01

% sodium dodecyl sulphate (SDS) and 30 % formamide (molecular formula

HCONH2, MW 45.04) in Milli-Q water. Washing buffer was prepared in a final

concentration of 20 mM Tris-HCl, 5 mM EDTA, 0.01 % SDS and 159 mM NaCl in

Milli-Q water.

The permeabilisation solution, hybridisation buffer and washing buffer were adjusted

to a pH of 7.5 with HCl. All of the solutions and buffers were sterilised by filtration

using gamma-sterilized Millex-HV Syringe Filter Unit (Cat # SLHV033RS, 0.45

µm, PVDF, 33 mm) purchased from Merck Millipore, Bayswater, Australia. Tris-

EDTA (TE) buffer (pH 7.5 - 8.0, 10 mM Tris-HCl, 1 mM EDTA) was used for

preparation of the 7 µg/ µL stock solution of fluorescence labelled oligonucleotide

probe used for the FISH technique.

2.2 Methods

2.2.1 Bacterial culture and growth conditions

Bacteria from the -80 C stock were isolated on a nutrient agar plate for

approximately 12 hour (h) prior to the experiments. A single bacterial colony was

then inoculated from the plate into 20 mL of nutrient broth medium and then

incubated at 37 °C, with orbital shaking at 200 revolutions per minute (rpm).

2.2.2 Bacterial growth curve and phase measurement

A bacterial growth curve and phase measurement experiment was performed by

detecting the total biomass of the bacteria culture using optical density measurements

at λ= 600 nm with a spectrophotometer. In brief, the overnight culture of bacteria

was diluted to approximately 1×107 cells/mL with fresh sterile nutrient broth in batch

culture. The bacterial growth phases were monitored by verifying the bacterial

culture every 1-2 h time spectrophotometrically using Varian’s Cary 50 Bio, UV-

visible spectrophotometer (Agilent Technologies, Australia). The absorbance values

were plotted against the different functional growth times. A total of nine broth

FSET PhD Thesis/48

cultures evaluating three different growth phases (i.e. early, middle and late of the

exponential phase, stationary phase and decline phase respectively) were

independently harvested for Raman spectral analysis.

For the viable bacteria count, each sample suspension collected at the individual

growth phases was diluted in 0.9% (w/v) sodium chloride (NaCl) in a series up to 10-

6 to determine the number of viable bacteria. A 100 μL aliquot of the diluted sample

was inoculated on nutrient agar and incubated overnight at 37 °C. Each viable unit

(cell) grown as a colony was then counted as a colony forming unit (CFU). The

number of CFU per mL of the sample is related to the viable number of bacteria in

the sample.

2.2.3 Sample preparation for Raman spectroscopy experiments

2.2.3.1 Planktonic sample preparation

After overnight incubation in broth media, the bacterial cells were collected by

centrifugation and washing processes to remove the traces of broth media. A 1 mL

sample of bacterial cells in a microcentrifuge tube was collected by centrifugation for

2 min at 15,294 g (12,000 rpm) (Centrifuge 5804 R, Eppendorf, Australia). The

supernatant was decanted after centrifugation and the cell pellet was washed three

times with sterilised Milli-Q water (ultra-purified water, Millipore) using

centrifugation at the same speed for 2 min. The pellet was then resuspended in 30 µL

sterilised Milli-Q water by repeated gentle pipetting. For the dried-droplet sample

preparation, a 10 µL volume of washed bacterial cell suspension was dropped onto

the CaF2 microscope slide, allowed to air-dry for 3-5 min and finally analysed by

Raman spectroscopy.

2.2.3.2 Bacterial micro colony isolation

The overnight broth cultures were diluted 1:10 in fresh nutrient broth and then

incubated until the optical density at 600 nm (OD at A600 nm) reached 0.3. This

measurement of optical density at λ= 600 nm was performed with a

spectrophotometer and related to the total biomass of the bacteria culture. After that,

the bacterial culture was diluted in a series up to 10-6 to get isolated single bacterial


(micro) colonies. An aliquot of 100 μL of the diluted sample was then inoculated

onto a pre-warmed nutrient agar and incubated at 37 °C overnight to allow formation

of bacterial colonies. Similarly, another 100 μL aliquot of the diluted sample was

then inoculated onto a sterile nitrocellulose transfer membrane (Prostran, BA 85),

placed on a pre-warmed nutrient agar plate and incubated at 37 °C overnight. After

overnight incubation, colony E. coli cells from the nutrient agar were mixed with 10

µL of Milli-Q water on a CaF2 slide, smeared and air-dried. Raman spectra were

collected from microbial smears on the substrate surface and this method has been

reported on several research publications (94, 215) . For the study of intact single-

species bacterial colony, the colony with the membrane was transferred to the CaF2

microscope slide for Raman spectral analysis.

2.2.3.3 Biofilm cultivation

A static biofilm formation assay was carried out on a sterile quartz microscope slide

in a sterilised bacteria culture petri dish, as previously described (216, 217). In brief,

the optical density of the overnight bacterial broth culture was determined

spectrophotometrically at 600 nm (OD at A600 nm). The bacteria cells were collected

by centrifugation for 5 min at 12,000 rpm and washed three times in sterilised 10

mM PBS to remove the residual nutrient medium. The washed bacterial cells were

resuspended in PBS to a concentration equivalent to an OD at A600 nm of about 0.3.

The suspension was then immediately used for initial attachment and biofilm

cultivation.

A 200 µL volume of prepared cell suspension was loaded onto the surface of 12

sterile quartz microscope slides and incubated at room temperature for 1 h. A

negative control for this biofilm cultivation experiment was prepared by loading 200

µL of PBS on a sterile substrate. After that, the substrates were carefully washed

three times by PBS solution to remove unbound bacterial cells. Then, two of the

substrates were kept at 4 C in PBS for further tests such as Raman spectroscopy

measurements and cell viability tests for evaluation of the initial bacterial

attachment. After that, each petri dish containing the remaining 10 washed substrates

and negative control substrate was filled with 15 mL aliquots of the sterilised nutrient

broth respectively. The plates were incubated at 37 °C without shaking at room

FSET PhD Thesis/50

temperature for 4, 8, 24, 79 and 120 h. Old culture media were replaced with fresh

nutrient media after every 24 h of biofilm cultivation. After each cultivation period,

two substrates were gently washed three times with PBS to remove suspended cells

and residual medium and then kept at refrigerated temperature (~ 4-8 C) in PBS

until required for further tests (for 24 h). A previous study by Adetunji et al. for

assessment of biofilm development in different cultural conditions showed that the

biofilm development on the substrates were higher at room temperature compared to

refrigeration temperature (218). Therefore, the subsequent storage for biofilm

samples at the refrigeration temperature in this study was aimed for minimising

further bacterial growth (219) and biofilm development. Finally, the attached cells

and intact biofilm surfaces were rinsed with sterile Milli-Q water to remove the

traces of PBS and were air-dried prior to Raman spectral analysis.

2.2.4 Bacterial visualisation

2.2.4.1 Bacterial viability test

The viability of bacterial cells from each washed biofilm sample was visualised by

using LIVE/DEAD BacLight Bacterial Viability Kits (Life Technologies, Australia).

A mixture of the propidium iodide, PI and SYTO 9 dye components provided with

the kits was first prepared in PBS to get the final concentrations of 30 µM and 5 µM,

respectively. A 200 µL volume of the dye mixture was applied to cover each biofilm

sample and then incubated at room temperature in the dark for 15 minutes. After that,

the samples were gently rinsed twice with PBS to remove the excess stain and

followed by rinsing with ice-cold sterile Milli-Q water to remove any trace of salt

from PBS. Each stained sample was then covered with a coverslip avoiding air

bubbles and stored at room temperature in the dark until microscopic examination.

The SYTO9 stained live cells and PI stained dead cells were viewed and imaged

using a 100× oil immersion lens with a confocal laser scanning microscope (CSLM)

(FV1000 Olympus with IX81). Excitation wavelengths of 488 nm (for SYTO 9) and

543 nm (for PI) were used. Low-speed image acquisition (40 or 100 µs/pixel),

640640 pixel resolution, and three frames filter was used for each image.


2.2.4.2 Two-dimensional cell counting and colour segmentation

Manual cell counting analysis was performed from the 2-D CSLM images of the

surface-attached E. coli cells using the cell counter plugin installed in ImageJ

software (220). With the application of this cell counter plugin, live and dead cells

were manually marked up as two different groups of cells, and each group was

counted separately via the software.

Figure 2.1 Application of the colour segmentation plugin implemented in ImageJ

software: (A) two-dimensional confocal laser scanning microscope (CSLM) image of

120 hr biofilm with SYTO 9 stained E. coli live cells in green and propidium iodide

stained dead cells in red; (B) green and red pseudochannels segmented from the

original colour image and (C) specification of pseudochannel codes and proportional

ratios which represented area percentages covered by green and red labelled cells

over the total observed area.

FSET PhD Thesis/52

To evaluate the live and dead cell population within the mature biofilms grown on

the surfaces, an analysis based on colour segmentation of 2-D CSLM colour images

were performed using the colour segmentation plugin installed in ImageJ software

(221). This ImageJ plugin allowed performing segmentation of the original colour

image by pixel clustering. Figure 2.1 represents general steps which were performed

to segment the colour images using this plugin for analysing the live and dead

populations of E. coli cells attached to the surface.

First, the colour image (RGB) of a biofilm stained with SYTO 9 (green) and PI (red)

was opened to process (Fig 2.1A). The colour clusters were chosen and defined

manually through the interface and identified by red (R), green (G) and blue (B)

codes. The RGB values of the pixel under the cursor automatically displayed in the

cluster identification box (Fig 2.1C) providing the information used to set the colour

tolerance on a scale which defined the range in foreground pixels. The colour

appearances and mean values on the RGB channels were evaluated and adjusted by

examining the standard deviation values (). The colour segmentation algorithm then

automatically analysed the image, pixel-by-pixel, based on these assigned threshold

values. After the pixel classification was completed, the plugin system created and

displayed a pseudo-coloured segmentation output image with the percentage of the

area covered by the cells (Fig 2.1B and C).

2.2.4.3 Fluorescence in situ hybridisation (FISH)

Fluorescence in situ hybridization (FISH) is a molecular-cytogenetic investigation

method to detect and localize the presence or absence of specific RNA or DNA

sequences on chromosomes. This method uses fluorescent probes that bind to only

those parts of the chromosome with which they show a high degree of sequence

complementarity. FISH was used in this study for species identification of E. coli

ATCC 25922 to detect 16S rRNA targets (Accession: X80724, GI: 1240023, 1452

base pairs, genomic DNA). (Detailed information of the nucleotide sequence are

shown in Appendix A)


2.2.4.3.1 Preparation of probe

The oligonucleotide for the probe used in this FISH technique is shown in Table 2.2.

As seen in Table 2.2, the probe was designed from E. coli ATCC 25922 16S rRNA

(Accession: X80724. GI: 1240023. 1,452 basepairs, genomic DNA). The

oligonucleotide probe sequence was designed and blasted using the primer tool

software (222). Selection of the oligonucleotide probe sequence was done based on

detailed primer reports such that the probe length is 22 bases where the base

composition is 45% G-C and there is no self-complementarity within the probe

(223). For rapid specificity and coverage evaluations of the probe, the web server,

probeCheck, was applied to check the probe against selected databases of

phylogenetic and functional marker genes (224). The probe labelled with Alexa

Fluor 647 was synthesised and purchased from Life Technologies Australia Pty Ltd.

The 7 µg/µL stock solution of fluorescence labelled oligonucleotide probe (MW;

7641.6 g/mol) was calculated and prepared by dissolving the synthesised probe in 1

mL TE buffer.

Table 2.2 Oligonucleotides probe used in this study

Oligonucleotide

number

Sequence Location/ Description

EC1_485 5 GTATCTAATCCTGTTTGCTCCC -3

E. coli ATCC 25922 16S rRNA

(Accession: X80724. GI:

1240023. 1,452 basepairs,

genomic DNA)

Complementary nucleotide 765-

786

2.2.4.3.2 Sample preparation for FISH

Sample fixation was performed to maintain the cell structure before the

permeabilisation step, which was required to ensure sufficient binding of the probe to

the target. For fixation of the biofilm samples (i.e. substrates with biofilms), the

sample was fixed in 4% paraformaldehyde (3 volumes) in phosphate-buffered saline

FSET PhD Thesis/54

(PBS) (1 volume) for 3 h at 4 C immediately after removal from biofilm cultivation.

For the planktonic cells on substrates, Gram-positive bacteria were fixed with 50%

EtOH and Gram-negative bacteria were fixed with 4% paraformaldehyde. The

samples were subsequently washed with sterile PBS. When the hybridisation steps

could not be performed immediately after the fixation step, biofilms were stored at 4

C in PBS for a maximum of three days. For the permeabilisation step, the samples

were treated with 25 µL of lysozyme (Sigma) [70 U/µL in 100 mM Tris-HCl, 5 mM

EDTA, pH 7.5] for 7-10 min at 37 C in a humid chamber. The samples were rinsed

with sterile Milli-Q water (ultrafiltered water) and dried for 10 min in a vertical

position.

2.2.4.3.3 Pre-hybridization and hybridization

Before the hybridization step, the samples were first pre-hybridized in hybridization

buffer (0.9 M NaCl, 20 mM Tris-HCl, 0.01% sodium dodecyl sulfate, 25%

formamide, pH 7.5) for 15 min at 46 C. After that, the samples were hybridized

with 2 mL of hybridization buffer containing the designated oligonucleotide probe

(5–20 µg/mL) at the annealing temperature of the probe (46 C) for 90 min

(maximum 180 min) in a humid atmosphere in the dark. After hybridization, the

samples were washed in preheated washing buffer (20 mM Tris-HCl, 5 mM EDTA,

0.01% sodium dodecyl sulfate, and 159 mM NaCl, pH 7.5) for 15 min at 48 C.

Finally the samples were rinsed briefly in ice-cold sterile Milli-Q water.

2.2.4.4 Extracellular polymeric substance (EPS) staining

Subsequent to the hybridization steps, to stain the α-mannopyranosyl and α-

glucopyranosyl sugar residues of EPS, Concanavalin A (ConA, Molecular Probes,

Invitrogen) conjugated with tetramethylrhodamine (0.2 g/L) was applied just to cover

the samples and the samples were incubated for another 30 min. Since ConA, a

lectin, is a carbohydrate-binding protein, it will bind exopolysaccharides containing

sugar moieties. ConA can also bind with proteins and glycoconjugate groups

associated with cell walls. After each of these staining stages, the samples were

washed twice with PBS to remove excess stain. Finally, the samples were covered


with cover slips carefully avoiding air bubbles and could be stored at room

temperature in the dark for up to 6 h prior to microscopic examination.

2.2.4.5 Visualisation of the hybridized E. coli cells and ConA stained EPS

The samples were visualised under confocal laser scanning microscopy (CLSM)

[Olympus FV1000 confocal microscope with IX71 base] with ×100 oil-immersion

lens at an excitation wavelength of 633 nm and emission wavelength of 668 nm for

detection of E. coli cells hybridised with the probe labelled with Alexa Fluor 647.

An excitation wavelength of 543 nm and emission wavelength of 618 nm was used

to visualise the EPS stained with ConA. The images were taken using both “XY

image acquisition” mode for x-y plane and “Z-stack image acquisition” mode for x-

y-z plane. The confocal image processing for 2D images captured with “XY image

acquisition” was performed using FV10-ASW 4.1 viewer software (225). CLSM

generated Z-stack images by a series of 2D images of parallel planes in a sample. To

generate 3D visualization of such scans for the structure of the biofilm sample, Z-

stack image processing was performed with the application of ImageJ software.

2.2.4.6 Probe efficiency test

To determine the optimal hybridisation conditions, the hybridisation efficiency of the

synthesised probe to specific and nonspecific target nucleic acid was optimised over

a range of parameters (64). Specificity tests under stringent FISH conditions using

planktonic cells of E. coli and V. vulnificus species showed that the probe displayed

the anticipated specificity to the E. coli species (Fig 2.2).

Two-dimensional confocal laser scanning microscope (CSLM) images of single-

species biofilm shows successful probe penetration through biofilm grown cells. The

efficiency was evaluated with both the specific FISH rRNA probe and the EPS stain

for the target E. coli strain (Fig 2.3). The cell permeabilisation process of

paraformaldehyde-fixed biofilms by exposure with lysozyme for 9 mins provided

effective fluorescence intensity from FISH hybridized E. coli cells and EPS (-D-

glucopyranose polysaccharide) stained with ConA. Although ConA can also bind

with proteins and glycoconjugate groups associated with cell walls, Alexa Fluor 647

FSET PhD Thesis/56

dye which is a bright, far-red–fluorescent dye from FISH rRNA probe was able to

differentiate FISH hybridized E. coli cells from stained EPS (Fig 2.3).

(A) (B)

Figure 2.2 Specificity test of FISH rRNA probe efficiency with fixed planktonic

cells of E. coli and V. vulnificus species. Panel (A) represents the differential

interference contrast (DIC) confocal images and panel (B) represents fluorescence

confocal micrographs of FISH hybridized cells with an excitation wavelength

633 nm. Scale bar = 10 µm applies to all images.

Taking advantage of the specificity of the FISH rRNA probe and ConA stain, the

next investigations were performed to verify whether the probe and stain could be

used to visualise the labelled E. coli cells and EPS matrix during biofilm

development at different growth time points. As mentioned in the literature Chapter,

biofilm architecture in mature biofilm is believed to become more complex by

additional recruitment and colonisation by planktonic bacteria and increased

synthesis of EPS. Denser groupings of E. coli cells with more EPS content were seen

in the older biofilms (Fig 2.4). Despite an increased thickness of EPS matrix in

mature biofilm, the biofilm sample preparation processes which were applied in this

study (i.e. cell fixation and permeabilisation) provided effective probe penetration

through 120 h old biofilm.



species biofilms of E. coli. (A) DIC confocal image; (B) FISH hybridized E. coli

cells labelled with Alexa Fluor 647; (C) EPS (-D-glucopyranose polysaccharide)

stained with Concanavalin A; (D) visualisation of E. coli cells in red with EPS matrix

in blue. Scale bar = 10 µm applies to all images. (White arrow shows labelled E. coli

and red arrow shows stained EPS)

FSET PhD Thesis/58


species biofilms of E. coli during biofilm growth. FISH hybridized E. coli cells

labelled with Alexa Fluor 647 in red and EPS matrix (-D-glucopyranose

polysaccharide) stained with Concanavalin A in blue can be visualised. Scale bar =

10 µm applies to all images.

2.2.5 Raman spectroscopy experimental set up

2.2.5.1 Instrument set up, calibration and spectrum acquisition

A Renishaw InVia Raman spectrometer, equipped with a Leica microscope plus a

deep-depletion charge-coupled device detector (CCD) and a computer motorised x-y-

z stage was applied. The Raman spectroscopy system was controlled and configured

by the Renishaw WiRE 3.4 software. For acquiring spectra from each sample, the

operational parameters and instrumental specifications such as 2400 lines per mm

grating, a holographic notch filter and ~ 1.5 mW (50 % of laser power) of 514 nm

radiation from an argon-ion laser was used.

One of the benefits of the InVia Raman spectrometer used in this study is the

automation that facilitates automatic alignment and health checks. Once the system

8 h

10 µm

24 h

79 h 120 h


was commissioned, the health check function was always applied to verify the laser

and spectrometer alignment for optimal performance. If any parameters are starting

to drift then the appropriate auto align or calibration routine was performed

according to the suggestion of the health check program. The system was then

calibrated and monitored using a silicon reference (520.5 cm-1) as an external control

before the measurements. As an internal control, the system was then calibrated and

manually health checked using an internal silicon reference (520.5 cm-1) for

calibration of peak intensity.

For each measurement, a single bacterial cell/biofilm sample was brought into focus

using a 100× microscope objective (NA = 0.85 in air). The accumulation time for

each acquisition was 80 s and three accumulations were collected for a single

measurement on each sample area. The spectra were always collected in the 500 to

2000 cm-1 range that covers the fingerprint region of most biological materials (226).

For calibration and normalisation purposes, the spectra were first collected in the 500

to 3200 cm-1 range with 10 s acquisition and one accumulation to check the

prominent C–H stretching band (associated with polysaccharides and proteins) which

can be observed between 2800–3000 cm-1 of the spectrum (227). This C–H

stretching peak provided confirmation of the bacterial spectral bands as well as the

intensity of the Raman response which correlates with correct positioning of the

focal plane and the number of cells present in the sample (228).

2.2.5.2 Raman signal pre-processing for statistical data analysis

2.2.5.2.1 Cosmic ray removal

For cosmic ray removal, the WiRE 3.4 Raman software integrated in the Renishaw

inVia Raman spectroscopy system was applied. Cosmic ray features (CRFs) are

generally more intense and sharper than Raman bands. With the application of the

WiRE 3.4 Raman software, CRFs were automatically detected and removed during

data collection by using a ‘running median average’ method. This is a highly

effective method, but involves an additional scan (acquisition) resulting in

significantly more time expenditure on the data collection. Therefore, in this study,

methods which can remove CRFs following data collection process were applied.

FSET PhD Thesis/60

These methods are much faster than the ‘running median average’ data collection

method. Two different methods such as ‘nearest neighbour’ and ‘width of feature’

can be utilised within WiRE software to achieve datasets containing no or limited

CRFs. In this study, the ‘width of feature’ method was applied by manually selecting

the minimum bandwidth attributable to a real CRF first and then the selected CRF

was ‘zapped’. The width of feature method was used in every case throughout the

study as CRFs are random and sample-independent events.

2.2.5.2.2 Background removal

In this thesis, enhanced automated algorithms for fluorescence removal based on a

combination of adaptive weighting with penalized least squares (APLS) estimation

were applied (147). A detailed discussion on this algorithm is presented in Chapter 3.

Custom written MATLAB code for the APLS tests was applied. The second-order,

single-weighted background removal scheme (O2W1) was chosen for all fluorescence

background subtraction processes applied throughout the study.

2.2.5.2.3 Smoothing and intensity normalisation

The Savitzky-Golay filter (span = 7, polynomial degree = 2, curve fitting toolbox in

MATLAB) was used to reduce the noise of the spectra. In order to perform

multivariate analysis, the intensities of the background-subtracted spectra were

normalised using total intensity normalisation to account for variations in intensity.

For this total intensity calculation, the data is divided by the sum of the intensities in

the data set. For each single spectrum, the absolute intensity values of each wave

number were further normalized to that of the Raman peak which corresponds to

DNA backbone (O-P-O stretching) at the wave number of 1095 cm-1. The total

intensity normalisation and internal peak feature normalisation was custom written in

MATLAB together with multivariate statistical data analysis.

2.2.5.2.4 Mean-centring the data

The background-subtracted, smoothed and normalized Raman spectra were then

mean-centred to reposition the centroid of the data to the origin. The mean-centred

data is calculated by subtracting the mean of the data from the original data. The


calculation of mean-centring is set in the MATLAB code together with multivariate

statistical data analysis.

2.2.6 Statistical data analysis

To analyse the Raman spectra obtained from bacterial cells, the multivariate

statistical methods of principal component analysis (PCA) and linear discriminant

analysis (LDA) were applied. Specific peak analysis (univariate analysis) was

performed for the peaks identified from multivariate analysis.

2.2.6.1 Principal component analysis (PCA)

PCA was first performed for data reduction of the 1407 included pixels from each

spectrum of the bacterial cells. In brief, the mean-centred data were analysed by

calculating the principal components (PCs) and then creating scores plots for the first

and second PCs. Mean score values of the first and second principal components,

standard deviations were calculated in MATLAB with custom-written codes. Two-

tailed p-values of each sample group compared to others were calculated in

Microsoft Excel using the TTEST function. A p-value is a measure of evidence

against the null hypothesis which refers to a hypothesis of "no difference" between

two measured phenomena. A small p-value is evidence against the null hypothesis

while a large p-value means little or no evidence against the null hypothesis. The

corresponding loadings plots that relate the scores to specific regions in the original

Raman data were plotted. MATLAB code for the PCA was custom written in this

study.

2.2.6.2 Principal component linear discriminant analysis (PC-LDA)

LDA which can discriminate between groups was further performed based on the

retained principal components (PCs) and the a priori knowledge of which spectra

belonged to each bacterial species. This combined method of principal component

and linear discriminant analysis (PC-LDA) was then performed to maximize between

group variance and minimize within-group variance. After preliminary studies

described in Section 4.3.3, a prediction model based on the first 16 principal

components (PCs) of the four different species of planktonic cells which account for

FSET PhD Thesis/62

approximately 92% of variance in the data set was used for analysis. To validate the

discrimination performed by the PC-LDA model, leave-one-out cross-validation

(LOOCV) was employed. In particular, a single spectrum was removed from the

database and a training data set was created using the remaining spectra. The

classification label of the left out spectrum was determined and the process was

repeated for every single spectrum of the data set. The linear discriminant classifier

in the dimensional space using [obj = ClassificationDiscriminant.fit] was applied

from MATLAB and the custom written code was applied throughout the study.

2.2.6.3 Specific peak analysis (univariate analysis)

For specific peak analysis, the intensity values of Raman peaks were first normalized

by the total intensity values. Normalised Raman spectra were then curve-fitted using

CasaXPS software (229) (version 2.3.15). Mixed Gaussian (Y %) – Lorentzian (X

%) spectral profiles, which are identified as GL(X) in CasaXPS, were used for each

component of the spectra. The best profile of Gaussian–Lorentzian components was

applied to all samples (an example of quantification parameters for the components

and the fitted spectrum are shown in Appendix B). The intensity values of fitted

Raman peaks identified from multivariate analysis were then averaged by adding the

maximum intensity and the intensity values of the two neighbouring channels for

each fitted component. Statistical comparison of the relative mean intensity changes

(log2 fold change) was performed for the selected peaks to compare between the

samples.

Mya Myintzu Hlaing Chapter 3/ 63

OPTIMISATION OF RAMAN SPECTROSCOPY FOR

BACTERIAL CELLS

3.1 Introduction

This chapter investigates the optimisation of experimental and data analysis methods

for Raman analysis of bacterial cells. The initial optimisation step involved the

choice of substrate to minimise spectral interference and optimise the signal-to-noise

ratio. Concurrently, reference spectra were collected from isolated bacterial cell

components (polysaccharides and proteins) to gain some insights into the spectral

features and differences between cells and extracellular matrix signals. Given that all

Raman spectra from biological samples have varying levels of fluorescence and

thermal background, a range of background removal methods and spectral pre-

processing methods for chemometric analysis are explored within this chapter. Due

to the fact that biochemical composition of bacterial macromolecules could be

affected by the sample preparation process and thus create challenges in spectral

analysis, different processing and preparation methods for the bacterial samples were

also explored. This helped to understand the optimal method for obtaining the signal

from individual bacteria and ensuring the repeatability of the sample analysis

methods, both within a sample and across multiple repeat sample sets.

3.2 Experimental set up and spectrum acquisition

3.2.1 Selection of substrate for Raman spectroscopy experiment

The choice of quartz slides and calcium fluoride (CaF2) as the substrates to collect

the Raman spectra from bacterial samples was decided by reviewing the common

practice in the literature (230) and then examining the background scattering signals

from each of the slides. An ideal substrate for bacterial samples would be one with

negligible background scattering, causing little interference with the Raman signal

from single bacterial cells. The most commonly used substrates, such as glass, quartz

and CaF2 were evaluated in this study. The Raman spectra of the glass, quartz and

CaF2 slides are shown in Figures 3.1 (A-C) for comparison. The quartz slide has

FSET PhD Thesis/64

broad features in the range 700-900 cm-1, but they are much less intense than the

peaks from the glass substrate, such as the one at 1100 cm-1. The broad background

signal associated with the quartz can easily be normalized and subtracted from the

Raman signal, as described later in this chapter. The CaF2 slide gave no significant

signal in the spectral range analysed. As such, CaF2 and quartz were selected as the

preferred substrates for the project

(A) (B) (C)

Figure 3.1 Raman spectra of different substrates: (A) glass slide, (B) quartz slide and

(C) calcium fluoride (CaF2) slide.

3.2.2 Raman spectra from reference samples

As mentioned in the literature Chapter, all bacterial cells are composed of water,

macromolecules (proteins, nucleic acids, polysaccharides and lipids), small

molecules (amino acids, nucleotides, fatty acids, carbohydrate and coenzymes, etc.)

and inorganic ions (Section 1.2.1 and Table 1.1). Therefore, before trying to get

typical Raman spectra from bacterial cells, Raman spectra from a range of reference

samples (polysaccharide, proteins and amino acid) that relate to the bacterial cells

were first collected and analysed. Dextran (#31424, Fluka, Mw 410,000), fibrinogen

fraction I from bovine plasma (#F 8630, Sigma, Mw 340,000) and D-tyrosine

(#855456, Sigma, Mw 181.19) were chosen as a reference for polysaccharide and

proteins respectively. 29.4 µM of fibrinogen and 24 µM of dextran were dissolved in

Milli-Q water and used for collection of Raman spectra from individual samples as

well as for optimisation of different molar ratios in mixtures of the various

biomolecules. 3 mM of D-tyrosine in MilliQ was used for collection of Raman


spectra from an aromatic amino acid. Droplets of 10 µL of each sample were air

dried on a quartz slide prior to Raman analysis.

A Renishaw InVia Raman spectrometer equipped with a Leica microscope, deep

depletion charge-coupled device detector, 2400 lines per mm grating, holographic

notch filter and ~ 7 mW of 514 nm radiation from an argon-ion laser was used for

acquiring spectra from the sample. The system was calibrated and monitored

according to the protocol mentioned in Section 2.2.5.1. For each measurement, the

sample was brought into focus using a 50× microscope objective (NA = 0.75 in air).

The accumulation time for one spectrum was 10 s and three accumulations were

collected for a single measurement on each sample area. The spectra were then

averaged over three different sample areas.

Raman spectroscopy on proteins and polysaccharide was hampered by the

intrinsically low scattering cross Section of these molecules. Therefore, mono- or

multi-layers of proteins and polysaccharide adsorbed on the substrate were prepared

to improve signal-to-noise ratios and enable peak assignment. As shown in Fig 3.2,

in mono-layer preparation, the characteristics peak assignments of the dextran were

not seen clearly against the quartz background, while those of fibrinogen and

mixtures were clearly visible. The appearance of the dextran peaks was improved

when multi-layers (in particular, 4 layers) of dextran suspension were applied to the

substrate whereas only small changes were seen in the spectra of fibrinogen and

mixtures of protein/dextran. The multi-layered preparation of dextran, fibrinogen and

dextran-fibrinogen mixture (molar ratio of 1:8) were chosen for further analysis. The

Raman spectrum from a mono-layer preparation of the amino acid (i.e. D-tyrosine) is

shown in Fig 3.2C.

FSET PhD Thesis/66

(A) (B)

(C)

Figure 3.2 Original Raman spectra from reference samples: (A) mono-layer, (B)

multi-layer preparation of dextran, bulk protein fibrinogen fraction I from bovine

plasma and mixed aqueous solutions of dextran and fibrinogen at different weight

ratios (1:6 and 1:8 ratios) and (C) mono layer preparation of D-tyrosine.

500 1000 1500 2000

460

690

920

1150

1200

2400

3600

4800

580

870

1160

14500

410

820

1230

Wavenumber / cm-1

D&F (1:8)

D&F (1:6)

Fibrinogen

Ra

ma

n I

nte

nsity / A

rbitr.

Un

its

Dextran

500 1000 1500 2000

1590

2120

2650

3180

1220

1830

2440

3050340

680

1020

1360

250

500

750

1000

Wavenumber / cm-1

D&F (1:8)

D&F (1:6)

Ram

an Inte

nsity / A

rbitr.

Units

Fibrinogen

Dextran

quartz signal


The characteristic peak assignments of the reference samples are shown in Fig 3.3.

Peak assignments typically associated with polysaccharides (dextran) included

glucose-saccharide peaks at wave numbers 530–540 cm-1 (231) and peaks that are

associated with the glycosidic ring deformation at 1090-1125 cm-1 (120). The

symmetric stretch bands of the carboxyl ion (COO-) appearing at 1460 cm-1 could

also be seen in the Raman spectra of dextran. Similarly, protein-related peaks could

be clearly seen in the bulk protein sample of fibrinogen. The vibrations (deformation)

of amine groups were evident at 838 cm-1. The sharp band at 1001 cm-1 seen in the

fibrinogen spectra is related to the phenylalanine (ring breathing mode). The amide

III peak could be seen at 1240 cm-1 (1295-1230 cm-1). The peak at 1337 cm-1 is

related to the CH vibrations of the protein backbone, while the 1424 cm-1 peak

represents the COO- stretching mode. The peaks at 1448 cm-1 as arise from the

deformation modes of both CH3 and CH2 vibrations. The amide I band, which is

sensitive to the secondary structure in protein, can be observed at 1667 (1620-80)

cm-1 in the fibrinogen sample. As shown in the D-tyrosine Raman spectra, the

strongest band arising from the ring breathing vibration forms a Fermi doublet with

bands located at 828 and 845 cm-1. The ring-O stretching vibration located at 1263

cm-1 could also be seen. These peak assignments for D-tyrosine are based on the

analysis of Raman peaks of amino acids published by Culka et al. (232).

FSET PhD Thesis/68

Figure 3.3 Typical Raman spectra of polysaccharide (dextran), bulk protein

(fibrinogen), a mixture of dextran and fibrinogen in 1:8 molar ratios and D-tyrosine.

Raman peak assignments are based on studies in the references mentioned in the text

and in Table 3.1. Abbreviations: def, deformation; Phe, phenylalanine; symm,

symmetric; str, stretching.

500 1000 1500 2000

1000

2000

3000

2000

3000

600

1200

1800

600

1200

1800500 1000 1500 2000

Wavenumber / cm-1

D-tyrosine

D&F (1:8)

Ra

man

In

ten

sity / A

rbitr.

Un

its

Fibrinogen

Dextran

sid

e g

rou

p d

ef

(CO

H;C

CH

;OC

H)

gly

co

sid

icri

ng

(C-C

;C-O

-C;

C-C

-O)

CH

/CH

2d

ef

Sym

mC

OO

-s

tr

Am

ide I

Am

ide I

I

Am

ide I

II

ph

e

Am

ine


3.2.3 Raman spectra from bacterial cells

The overnight broth culture of E. coli planktonic cells was prepared for Raman

spectroscopic analysis. In brief, the washed bacterial cells were smeared and air-

dried on a quartz substrate. Raman spectra were collected from E. coli single cells

following the protocols mentioned in Section 2.2.3.1. A typical Raman spectrum

obtained from E. coli single cells and the dominant peak assignments are shown in

Fig. 3.4 and Table 3.1. The spectrum shows the characteristic Raman bands found in

the literature and associated with the abundant cellular components such as

carbohydrates, lipids, proteins and nucleic acids (94, 215, 233, 234). There was a

relatively high fluorescence background in the data for this planktonic cell sample.

Therefore, attempts were performed for fluorescence background removal using

different approaches and the results are mentioned in the next sub-section.

Figure 3.4 Typical averaged Raman spectrum from planktonic E. coli cells with

characteristics peak assignments. Abbreviations: Phe, phenylalanine; Tyr, tyrosine;

str, stretching; def, deformation. Assignments are based on studies in the references

shown in Table 3.1.

FSET PhD Thesis/70

Table 3.1 Selected Raman frequencies and their peak assignments for the spectra.

Wave number (cm-1) Peak assignment Reference

DNA/RNA

668 T, G (ring breathing) (98, 235)

726 A (ring breathing) (236-238)

746 T (ring breathing) (236)

781 C, U (ring breathing) (94, 239)

785 U, T, C (ring breathing), backbone

O-P-O str.

(98, 235, 236)

811 (808) O–P–O str. RNA (240)

1095 Phosphodioxy group (O-P-O in

nucleic acids)

(240)

1288 Phosphodiester groups in nucleic

acids

(241)

1325-1330 CH3CH2 wagging mode in purine

bases of nucleic acids

(242)

1373 T, A, G (ring breathing), DNA/RNA

bases)

(236)

1485 A, G (ring breathing) (237, 243)

1506-1510 C (244)

1575 A,G (ring breathing) (236)

Proteins/Lipids

622 Phe (C-C twisting) (239, 245)

640 Tyr (C-C twisting), C-S str (105, 236, 239)

838 Amine groups deformation

vibrations

(246)

852 Tyr (ring breathing), Pro (C-C str) (239, 245)

1001 Phe (ring breathing, sym) (120, 239)

1125 C–C, C–N str (94, 120, 236)

1155 C–C, C–N str (236, 239)

1240 (1295-1230) Amide III (22, 94, 237,

247)

1337 CH def (237, 238)

1360 Trp (248)

1440 CH, CH2 and CH3 deformation

vibrations

(231, 249)

1447 CH2 def (94, 236)

1452 CH2 def (94, 236)

1550 (1580-1480) N-H def and C-N str, amide II (243, 247)


Wave number (cm-1) Peak assignment Reference

1602 C=C def, phenylalanine (protein

assignment)

(250, 251)

1615 Tyr, Trp, C=C (236)

1662 (1680-1620) C=O str, amide I (26, 94, 243,

247)

1734-1738 C=O str, lipid (ester) (240, 252)

Abbreviations: A, adenine; G, guanine; T, thymine; C, cytosine; U, uracil; str,

stretching; sym, symmetric; def, deformation; Phe, phenylalanine; Tyr, tyrosine, Pro,

proline; Trp, Tryptophan. Assignments are based on studies in the references.

3.3 Attempts to achieve consistent fluorescence background subtraction

As discussed in Chapter 1, the intrinsic fluorescence background can be orders of

magnitude larger than the Raman scattering and so background removal is one of the

foremost challenges for quantitative analysis of Raman spectra in many samples. A

range of methods anchored in instrumental and computational programming

approaches have been proposed for removing fluorescence background signals.

Because of the challenges associated with instrumental approaches (see Section

1.5.3.1.2), computational methods have become the standard way to correct for

contributions from fluorescence in the background. In this study, several approaches

were initially applied in an attempt to get successful background subtraction from

Raman spectra.

3.3.1 Application of Raman software

The first attempt for background subtraction from Raman spectra was the application

of the baseline subtraction tool from the WiRE 3.4 Raman software integrated in the

Renishaw inVia Raman spectroscopy system. The baseline subtraction was

performed by manually defining the background to be removed from the spectrum

and was initialised by selecting the “Subtract baseline” tool. As shown in Fig 3.5, the

window was split into two panes where the upper pane displays the original spectrum

and baseline. The lower shows a preview of the baseline-subtracted spectrum. For a

linear baseline fit, the baseline could be initialised first to a straight line between the

ends of the spectrum. These two baseline-definition points may be dragged

(vertically) to alternative positions (Fig 3.5A). Additional baseline-definition points

FSET PhD Thesis/72

can be added by clicking at the place where the new point is required and non-linear

baselines based on a polynomial fit can be performed using the “Cubic Spline

Interpolation” function in the software (Fig 3.5B). These additional points can be

dragged (in any direction) to alter their position. The defined points can be selected

based on the subtraction mode and the baseline type. As more points are added, the

baseline subtraction appears to become more consistent with an “expected” result.

Although apparently better results could be obtained by carefully adding more points

and by using the “Cubic Spline Interpolation” function in the software, the manual

selection of the points relies on user intervention and judgement as to where the

curves should be fitted in the data. Clearly different users may have different views

on how the background should be fitted, making this approach subjective and

potentially difficult to reproduce. Therefore, an automatic background remove

method was evaluated to avoid the need for manual curve-fitting.


(A)

(B)

Figure 3.5 Background corrections using the baseline subtraction tool from the

WiRE 3.4 Raman software; (A) linear baseline fit and (B) non-linear baseline based

on a polynomial fit.

3.3.2 Application of polynomial curve fitting

The second attempt for background subtraction from Raman spectra was the

application of the curve fitting tool from MATLAB. The baseline data tips (points)

were first selected and baseline data fitting was then performed using a quadratic

polynomial. A baseline correction algorithm using polynomial fitting was applied to

each spectrum. In brief, the syntax “cftool” (X, Y) creates a curve fit to x input and y

output. These X and Y must be numeric, have two or more elements and have the

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same number of elements. A second-degree quadratic polynomial curve was then

fitted. The curve-fitting coefficients (with 95% confidence bounds) and equation

were applied to calculate the curve-fitting value from each raw spectrum. Finally, the

curve-fitting values were subtracted from the raw spectrum to get the fitted spectrum.

As shown in Fig 3.6, the accuracy of background baseline corrections using the

polynomial curve fitting tool from MATLAB is highly dependent on the baseline

data point selection. Due to inconsistencies between the complex shape of the

fluorescence background and the simplified form of a second order polynomial,

some characteristics of the Raman spectrum were placed below the baseline,

resulting in a poor baseline estimate and severely distorted Raman spectral features.

While the selected baseline data points can be more accurately fitted by means of a

higher order polynomial, this still results in distortions in spectral regions between

the selected points.

Figure 3.6 Background corrections using the polynomial curve fitting tool from

MATLAB. The solid line is the fitted spectrum and the dotted black line is the raw

spectrum. Baseline data points (X, Y) selected from the raw data are also shown.


3.3.3 Weighted penalized least squares method in “R” language

The poor results derived from polynomial fitting, meant that a more complex

algorithm was required. Zhang et al. have developed an algorithm that combines

wavelet peak detection, wavelet derivative calculation for peak width estimation and

penalized least squares background fitting (146). The background corrections using

this weighted penalized least squares algorithm on bacterial data are shown in Fig

3.7. The result shows that this automated background subtraction method provides

better outcomes than the simpler polynomial methods discussed above. However,

some aspects of this method are unsatisfactory. In particular, the background is

pulled up into some of the peaks and the tails of some peaks appear to be truncated

abruptly at the baseline, rather than smoothly merging into the baseline.

Figure 3.7 Background baseline corrections using the weighted penalized least

squares algorithm, implemented in “R” language.

Wavenumber/cm-1

Raw spectrum

Fitted spectrum

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3.4 Improved methods for fluorescence background subtraction from Raman

spectra

In an effort to address the limitations of the existing background subtraction

methods, an enhanced adaptive weighting scheme for automated fluorescence

removal was developed, applicable to both polynomial fitting and penalized least

squares approaches. This work was performed in collaboration with Professor Peter

Cadusch from the Centre for Quantum and Optical Science, Faculty of Science,

Engineering and Technology, Swinburne University of Technology. MATLAB

codes for improved methods of background subtraction were kindly generated by

Prof. Cadusch. Analysis of the background fitting results from application of this

method and other methods were performed in this study. The efficiency and accuracy

of this enhanced automated algorithm for fluorescence removal was evaluated for

both simulated and experimental data and was published in the Journal of Raman

Spectroscopy (147).

The initial motivation for developing the enhanced algorithm arose from application

of the previously published background-correction algorithm for Raman spectra

using wavelet peak detection, wavelet derivative calculation for peak width

estimation and penalized least squares (PLS) background fitting approach (146). That

approach adaptively separates the measured data samples into peak and nonpeak

(background) values by setting the least squares weights to one for background and

zero for peak regions. The application of these binary-valued weights may cause the

sudden changes in gradient that appear questionable in the context of a Raman

background subtraction. The enhanced automated algorithm for fluorescence

removal was based on a statistically adapted weighting together with either PLS

estimation or polynomial estimation (147). The proposed method significantly

improved the background fit over the range of signal, shot noise and background

parameters tested, while significantly reducing the subjective nature of the process.

This enhanced background subtraction method was applied for all subsequent Raman

analyses in this study.

In order to assess the efficiency and accuracy of the proposed algorithm, it was tested

with simulated Raman spectra. The simulated spectra consisted of a number of


randomly positioned Gaussian peaks (typically up to 20 peaks) and a known variable

background with randomly generated Lorentzian (shot) noise. The results from these

simulated spectra suggest that the method is robust and reliable and can significantly

improve the background fit over the range of signal, shot noise and background

parameters tested, while reducing the subjective nature of the process (Fig 3.8). For a

more detailed explanation of the method and discussion of the simulated spectra, the

reader is referred to (147).

Figure 3.8 Simulated data set fitted with adaptive-weight penalised least squares.

The solid line (red online) is the known background and the dashed black line is the

recovered background.

3.4.1 Experimental data

To demonstrate the application of the fluorescence background correction

algorithms, Raman spectra obtained from a 1:8 mixture of dextran and fibrinogen

were tested. The intention of this mixture was to model a combination of

biomolecules (i.e. polysaccharides and proteins) found in typical biological samples.

As mentioned in Section 3.2.2, the suspension of dextran (Fluka, 24 µM) and

fibrinogen fraction I from bovine plasma (Sigma, 29.4 µM) dissolved in Milli-Q

water were mixed together to get a final molar ratio of 1:8. Droplets of 10 µL of the

sample mixture were air dried on a quartz slide for Raman analysis.

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A comparative study of background removal was performed using five different

methods with similar fitting parameter values. The methods included:

1. The Modified Polyfit method of Lieber and Mahadevan-Jansen (253) which

uses least squares fitting of a polynomial background with (in effect) adaptive

elimination of peak regions from the fit (ModPoly).

2. The Improved Modified Polyfit method of Zhao et al (143) which is similar

to ModPoly but improves the peak removal scheme to allow for statistical variations

in measured quantities and includes an automated iteration cut-off (IModPoly).

3. The probability-based adaptively weighted polynomial fit method proposed

by Cadusch et al. (147) (APoly).

4. The method of Zhang et al (146): a penalised least squares method which

differs from APLS in the way in which the adaptive weights are set (wavelet peak

detection with hard peak / background segmentation) (WPLS).

5. The probability-based adaptive weight penalised least-squares method

proposed by Cadusch et al. (147) (APLS).

The main difference between the methods proposed by Cadusch et al. (APoly and

APLS) and the other similar methods (ModPoly, IModPoly and WPLS) is that the

weighting schemes in the existing methods, which consist of hard background /

foreground segmentation schemes of increasing complexity, are replaced by a single,

simple weighting scheme based on the statistical properties of the signal. MATLAB

code for the ModPoly, IModPoly, APoly and APLS tests was custom written by

Cadusch et al. (147) and the R code of Zhang et al. for Baseline Wavelet version

4.0.1 (254) was used for the WPLS method.

The results of the comparative study are shown in Fig 3.9. Experimental results show

that the proposed methods (APoly and APLS) can automatically identify background

regions and that the results are comparable with or superior to previously reported

methods for fluorescence background subtraction. The APLS approach generally

improves on the method of Zhang et al. (146) while avoiding the questionable

features associated with rapid changes in slope of the fitting curve. With application


of the adaptively weighted algorithms, consistent Raman spectra with significantly

improved background subtraction can be obtained with minimal user input.

Figure 3.9 Experimental data fitted by five different methods: (a) modified

polynomial fit (ModPoly) (b) improved ModPoly, (c) adaptively weighted

polynomial fit (APoly), (d) weighted penalized least squares (WPLS) and (e)

adaptive-weight penalized least squares (APLS). APoly and APLS are probability-

based methods proposed for this study. The extracted background is the dashed line

in red.

The efficiency and accuracy of the proposed background correction algorithms

(APoly and APLS) was further tested on Raman spectra collected from E. coli

bacterial cells. A typical Raman spectrum obtained from a single E. coli cell is

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shown in Fig 3.10, together with the background-corrected result using the APLS

algorithm for fluorescence removal. The adaptive weighting, combined with the

penalised least squares estimation developed for this study, automatically identified

likely background regions and subtracted the background region from the spectra to

leave the Raman scattering component. These background corrected spectra show the

characteristic E. coli Raman bands found in the literature and discussed in Fig. 3.4. It

should be noted that the background correction procedure requires an optimal

“stiffness” parameter which can be kept fixed for consistent analysis of a series of

related spectra, thereby assuring a greater level of repeatability.

Figure 3.10 Typical original and background corrected results for the Raman

spectrum of single planktonic E. coli cells using the APLS method. Abbreviations:

Phe, phenylalanine; Tyr, tyrosine; str, stretching; def, deformation. Assignments are

based on studies in the references shown in Table 3.1.

3.5 Raman signal pre-processing for statistical data analysis

3.5.1 Intensity normalisation

As mentioned earlier, in Raman spectral analysis, the recorded data always contains

some noise. In addition to variance between replicates, measurements can have a

degree of variance due to fluctuations in instrumental parameters. Normalisation can

not only reduce the effect of these variations, but can also improve the quality and

interpretability of the data for further statistical analysis. Ideally, normalisation

Am

ide

I

Am

ide I

I

Lip

id

C-H

de

f

Am

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III

Ca

rbo

hyd

rate

Ph

e

Tyr

(C-C

str

)

DN

A/R

NA

Ph

e,

Tyr


would result in identical spectra for replicate samples. From the literature, it is

unclear which normalisation method produces the most consistent results. Therefore,

three different normalisation methods, namely standardisation, root mean square

(RMS) normalisation and total intensity normalisation, were performed as an

optimisation step in this study.

After background subtraction with the APLS algorithm mentioned in Section 3.4 and

prior to normalisation, smoothing was performed with a Savitzky-Golay filter

(span=7, polynomial degree =2, curve fitting toolbox in MATLAB)Section. For

optimisation of the Raman intensity normalisation, four Raman spectra were taken

from different E. coli planktonic cells. The original Raman spectra and background-

subtracted, smoothed spectra are shown in Fig 3.11A. The standard deviations of

these four Raman spectra (before and after background subtraction) and the

corresponding mean spectra are shown in Fig 3.12B. Standard deviation (SD)

normally expresses a dispersion of individual observations about the mean. In other

words, SD characterizes a typical distance of an observation from distribution center

or middle value. If observations are more disperse, then there will be more

variability. Thus, a low SD signifies less variability while high SD indicates more

spread out of data. The results revealed a high SD among pre-processed spectra

indicating that the data points were spread out over a large range of values compared

to those of background-subtracted smoothed spectra Fig 3.11B.

In order to quantify and compare the SDs of the spectra, the average SD was

calculated by the following equation:

𝑚𝑒𝑎𝑛 = √∑ (𝑖)𝑛𝑖=1

2

𝑛−1 (1)

where, 𝑚𝑒𝑎𝑛 is the mean SD, 𝑖 is the SD of each data point i and ∑ (𝑖)𝑛

𝑖=12

𝑛−1 is the

average variance of all data points.

The average SD value of the original Raman spectra was ~25225 while that of

background-subtracted, smoothed spectra was ~1773. The pre- processed spectra (i.e.

after background removal and then smoothing) still showed intensity differences

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among the spectra which were likely due to variation in laser power, differences in

focusing depth and sample volume. Therefore, different normalisation methods

mentioned above were applied and optimised to reduce systematic differences among

measurements.

(A) (B)

Figure 3.11 Raman spectra of before and after signal processing. (i) Original Raman

spectra and (ii) smoothed Raman spectra after background subtraction. The spectra

were taken from four E. coli single cells. Panel A shows individual Raman spectra

and panel B shows the mean spectra. Standard deviations are highlighted in grey

colour.

For optimisation of spectral normalisation step, firstly, a standardisation method

known as “Z-score scaling” was tested on the smoothed and background-subtracted

Raman spectra. This method was performed by subtracting the mean intensity of

each data point from the original data. Then the result was divided by the standard

deviation of the data set (described in equation 2). This standardisation transformed

all variables in the data set to have equal means and standard deviation value to be 1.

This method is a standard way of normalising data but produced poor results in this

case as it reduced the effects of the individual peaks (Fig 3.12(i)).

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𝑋𝑖,1𝜎 = 𝑋𝑖−��𝑠𝜎𝑋,𝑆

(2)

where, 𝑋𝑖,1𝜎 is the normalised intensity of the data point i standardised to 1 (also

known as Z-score scaling), 𝑋𝑖 is the intensity of each data point i, ��𝑠 is the average

intensity of all sample data points and 𝜎𝑋,𝑆 is the standard deviation of all data points.

Another approach is RMS (the root mean square) normalisation, where the original

data is divided by the root mean square of the data set (described in equation 3). This

is a common way of normalising data, especially for data with both negative and

positive values. The average SD values of the spectra using the standardisation

method and RMS were ~1.15 and ~0.24 respectively. The RMS normalization

produced a better result for the normalised data compared to the standardisation

method since it sustained the actual features of the individual peaks and provided the

lower average SD value (Fig 3.12(ii)).

𝑋𝑖,RMS=𝑋𝑖

√∑ (𝑋𝑖)𝑛𝑖=1

2

𝑛−1

(3)

where, 𝑋𝑖,1𝜎 is the normalised intensity of the data point i which was normalised to

the RMS of the intensity of the data set, 𝑋𝑖 is the intensity of each data point i,

∑ (𝑋𝑖)𝑛𝑖=1

2

𝑛−1 is the mean square of the intensity all data points.

Finally, total intensity normalisation was performed by dividing the data with the

sum of the intensities in the data set (described in equation 4). As shown in Figs

3.12(iii), the total intensity normalisation method produced comparable outcomes to

those of RMS. Both RMS and the total intensity normalisation methods provided low

average standard deviations (~0.24 and ~0.00025 respectively). These results

indicate that the data points tend to be very close to the mean and there were less

sample to sample variations after normalisation.

𝑋𝑖,TI=𝑋𝑖

∑ 𝑋𝑖𝑛𝑖=1

(4)

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where, 𝑋𝑖,TI is the normalised intensity of the data point i which was normalised to

the total the intensity of the data set, 𝑋𝑖 is the intensity of each data point i, ∑ 𝑋𝑖𝑛𝑖=1 is

the total intensity of all data points.

(A) (B)

Figure 3.12 Application of different normalisation methods (i) standardisation, (ii)

root mean square (RMS) normalisation and (iii) total intensity normalisation. Panel

A shows individual Raman spectra and panel B shows the mean spectra, with

standard deviations indicated by the grey colour.

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3.5.2 Mean centring the data

Mean centring was performed after normalisation to adjust and reposition the

centroid of the data to the origin. Mean-centred data is calculated by subtracting the

mean of the data from the original data. In brief, a set of normalised spectral

intensities was mean-centred on a wavenumber by wavenumber basis. The mean

intensity over all of the samples at that wavelength was calculated and then this mean

was subtracted from the intensity value at this wavelength measured in each

spectrum of the sample. Thus, the process of mean centring is to calculate the

average spectrum of the data set and subtract that average from each spectrum.

Mathematically, it can be described by the following equation:

𝑋𝑖,c = 𝑋𝑖 − �� (5)

where, 𝑋𝑖,c is the mean-centred intensity of the data point i, 𝑋𝑖 is the intensity of

each data point i, �� is the average intensity of each data point i of all the samples.

After mean-centring, the mean intensity value over all samples of the new data

matrix will become zero and the variances are spread around zero. As can be

observed in Fig 3.13, mean-centring process could not adjust the standardised data to

a centroid whereas this process provided a centroid of both RMS and total intensity

normalised data (Fig 3.13). Therefore, total intensity normalisation and mean-

centring processes were performed throughout this study to provide consistency in

Raman signal pre-processing for statistical data analysis.

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(A) (B)

Figure 3.13 Application of different normalisation methods together with mean-

centring. The spectra were normalised by different normalisation methods (i)

standardisation, (ii) root mean square (RMS) normalisation and (iii) total intensity

normalisation and then the normalised spectra were mean centred. Panel A shows

individual Raman spectra after normalization and mean centring, while panel B

shows the mean spectra.

(i)

(ii)

(iii)

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3.6 Sample preparation and storage for Raman spectroscopy

As mentioned in Chapter 1, the bacterial structural components consist of

macromolecules such as DNA, RNA, proteins, polysaccharides and phospholipids.

The primary structures of these macromolecules are amino acids, carbohydrates,

fatty acids and nucleotides and differences in the relative abundance of these

components can reveal functional aspects of the cells (39). The biomolecules that

make up the cells are responsible for complex biochemical information present in the

Raman spectra taken from bacterial cells (255). During sample preparation, unless

care is taken, these macromolecules, structures or metabolites could be removed

from the cell or altered in other ways. This complicated biochemical information and

the dependence on sample processing presents some challenges when analysing the

Raman spectra from bacterial cells. Furthermore, the study of cellular responses to

antimicrobial agents and the analysis of cell growth behaviour typically require the

bacteria to be cultured over an extended period of days to weeks. Therefore, a

defined and effective sample preparation and sample storage method is essential if

spectral acquisition is to be undertaken throughout the sample test period.

There remains a lack of information regarding appropriate sample preparation and

storage protocols in previous Raman studies, even for a commonly studied bacterium

such as Escherichia coli (E. coli). In this study, the effects of different sample

preparation procedures on E. coli Raman spectra were investigated. Typically, cells

are washed to remove excess media prior to analysis. Two protocols for storage of

the cells, based on freezing at -80 C in glycerol and refrigeration at 4 C, were

investigated and compared to data from freshly prepared cell samples. In a

subsequent study, cells were grown to different stages in the growth cycle and then

frozen at -80 C. After thawing, the cells were analysed by Raman microscopy and

the result of frozen storage was compared to fresh samples at different stages

throughout the metabolic cycle. The detailed analysis of the possible effects of

different sample preparation procedures on bacterial Raman spectra was published in

the International Journal of Integrative Biology (256).

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3.6.1 Materials and methods

3.6.1.1 Bacterial strain, growth conditions and sample preparation

The reference strain Escherichia coli (E. coli ATCC 25922) was used to study the

effect of different sample preparation procedures on the Raman spectra of bacterial

cells. Bacteria from -80 C glycerol stock were grown following the growth

conditions mentioned in Section 2.2.1. The overnight culture of E. coli planktonic

cells was prepared according to the planktonic sample preparation procedure

mentioned in Section 2.2.3.1.

In order to investigate possible changes in the Raman spectra resulting from

metabolic activity or cell damage because of the washing process, the overnight

cultures of E. coli planktonic cells were first kept at 4 C before they were washed

and prepared for Raman measurement. For the study of storage effects at frozen

temperature, the overnight cultures were kept at -80 C in glycerol solution before

the E. coli cells were recovered for Raman analysis. The Raman spectra taken from

these samples were compared with those from fresh samples. An outline of the

preparation of the three different samples is shown as a flow chart in Fig 3.14.

Figure 3.14 Flow chart summarising the different sample preparation procedures for

planktonic E. coli cells: (i) fresh sample, (ii) refrigerated sample before cell washing

steps and (iii) frozen sample.


A bacterial growth curve and phase measurement experiment was also performed in

order to understand the effects of storage-dependent changes in molecular

composition on the Raman spectra of the bacterial cells during the growth cycle. The

overnight culture of E. coli was diluted to approximately 1×107 cells/mL with fresh

sterile nutrient broth in batch culture. The bacterial growth phases were monitored by

detecting the total biomass of bacteria culture and cells from a total of nine different

growth phases were collected as mentioned in the Section 2.2.2. The collected cells

were stored at -80 C in glycerol solution for more than 96 h for frozen sample

preparation. E. coli cells recovered from the glycerol stock and the fresh cells were

processed for the Raman growth/phase experiment described in Section 2.2.2.

3.6.1.2 Raman spectroscopy measurements

Raman spectra from each sample were collected with a Renishaw InVia Raman

spectrometer, equipped with a Leica microscope as mentioned in Section 2.2.5.1. As

described in Section 2.2.5.1, the system was first calibrated and monitored using a

silicon reference (520.5 cm-1) before the measurements. The accumulation time for

each acquisition was 80 s and three accumulations were collected for a single

measurement on each sample area. The spectra were then averaged over six different

cells for each preparation method and growth phase.

3.6.1.3 Raman data acquisition and processing

Raman signal pre-processing for statistical data analysis was performed as described

in Section 2.2.5.2. Commercially available software (MATLAB) was used for all

data processing. Spectra were collected in the 500 to 2000 cm-1 range that covers the

fingerprint region of most biological materials (226). For fluorescence background

removal, an enhanced automated algorithm based on a combination of adaptive

weighting factors with penalised least squares estimation described in Section 3.4

was applied to each spectrum (147).

For multivariate analysis, the intensities of the spectra were normalised using the

total intensity normalisation as mentioned in Section 2.2.5.2.3. The background-

subtracted and normalised Raman spectra were then mean centred as mentioned in

Section 2.2.5.2.4. Finally, the mean-centred data were analysed by calculating the

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principal components and creating scores plots for the first principal component and

loadings plots of the first and second principal components that relate the scores to

specific regions in the original Raman data (Section 2.2.6).

For specific peak analysis, total intensity normalised Raman spectra were curve-

fitted using CasaXPS software (229) (version 2.3.15) as mentioned in Section

2.2.6.3. The intensity values of fitted Raman peaks identified from multivariate

analysis were then averaged by adding the maximum intensity and the intensity

values of the two neighbouring channels for each fitted component. Statistical

comparison of the relative changes in mean intensity (log2 fold change) as mentioned

in Section 2.2.6.3 was performed for the selected peaks to compare the sample

preparation methods.

3.6.2 Results and discussion

3.6.2.1 Raman analysis of planktonic E. coli cells from fresh and stored

samples

Typical background-corrected Raman spectra obtained from fresh and stored samples

and their peak assignments are shown in Fig. 3.15 and Table 3.1. These spectra show

the characteristic Raman bands found in the literature and associated with the

abundant cellular components such as carbohydrates, lipids, proteins and nucleic

acids (94, 215, 233, 234). Changes in Raman peak intensities can be seen between

the cells from fresh samples and stored samples, especially in the 600 to 800 cm-1

region (see shaded regions in Fig 3.15), which relates to DNA/RNA synthesis.

Furthermore, spectral fluctuations attributed to macromolecules containing amide

groups in the protein backbone (1200-1680 cm-1), amino acid containing phenyl

groups (617-640 cm-1) and the ester group of lipids (1734-1738 cm-1) are also

noticeable when comparing both the refrigerated and frozen samples (ii and iii) to the

fresh samples (i).


Figure 3.15 Background subtracted and normalised average Raman spectra from

planktonic E. coli cells taken from (i) fresh sample; (ii) refrigerated sample before

cell washing steps and (iii) frozen sample. The dominant peaks are shown with the

wave number (cm-1). Shaded regions indicate the main spectral changes in different

samples.

3.6.2.2 Principal component analysis for Raman spectra of planktonic E.

coli cells from fresh and stored samples

Principal component analysis of the spectra taken from planktonic cells of fresh and

stored samples is shown in Fig. 3.16. This data shows the variation between the

spectral data sets, which are related to changes in the primary structure and/or

composition of bacterial macromolecules due to the storage process. The average

value plots for the first principal component showed that there is some overlap

between the refrigerated and fresh samples, whereas the frozen glycerol stock

samples vary significantly from the other sample groups (Fig 3.16A). The results

illustrate that keeping the cells at 4 C before the washing step maintains bacterial

EPS and may well induce the encapsulation of microorganisms by encouraging the

production of more EPS, (120, 257) which in turn protects the cells from damage.

Inversely, washing the cells prior to refrigeration can remove the majority of EPS

61

7-7

40

66

8

72

67

46 78

1-7

85

85

28

11

10

01

11

25

11

55 1

23

0-1

295

13

37

14

47

-14

58

14

85

15

75

16

80

-16

20

17

34

-17

38

(i)

(ii)

(iii)

FSET PhD Thesis/92

and significantly affected the resulting Raman spectra (data shown in Appendix C).

The bacterial EPS is believed to protect the cells from environmental stresses such as

temperature, desiccation and pH variation (258-260). On the other hand, the result

seen in the frozen samples creates some potential concerns about the application of

glycerol stock samples for Raman analysis. Ice crystals can damage cells by

dehydration, leading to denaturation of proteins in long-term storage. Although

glycerol is believed to reduce the harmful effects of ice crystals on bacteria, the

loading plots for the first principal component of these data demonstrate significant

changes in specific molecular species between the cells (Fig 3.16B). The dominant

spectral changes can be seen in the range of 1620-1680 cm-1 and 1480-1580 cm-1,

which are attributed to the amide I and amide II bands which are common in the

protein backbone (234, 261). Other significant spectral changes around 1335-1373

cm-1 and 1440-1460 cm-1 suggest changes corresponding to CH2 deformation and C-

H bending modes of structural proteins (94, 261). This may indicate the detrimental

effect of frozen storage on the bacterial cell wall, since the cell wall consists of many

polymers and macromolecules which possess amide, carboxyl, hydroxyl and

phosphate functional groups. The positive and negative values of the loading plots at

1001 cm-1 indicates a frequency shift in the amino acid containing phenyl groups

seen amongst the cells. This frequency shift suggests that there was a modification in

the structural environment of the phenylalanine containing components when the

cells were stored at 4 C and -80 C.


(A) (B)

Figure 3.16 Principal component analysis of Raman spectra for planktonic E. coli

cells taken from (i) fresh sample; (ii) refrigerated sample before cell washing steps;

(iii) frozen sample. (A) Average value plots and (B) loading value plots for the first

principal component (*** p value < 0.005).

Further detailed analysis of the intensity values for specific fitted peaks related to

DNA/RNA and protein synthesis, which were selected from the loading plots (Fig

3.16B), are shown in Fig 3.17. The larger intensity changes of the frozen samples

versus the fresh samples in DNA/RNA-specific peaks, in comparison to the changes

of the refrigerated sample, indicates the cellular response of bacteria to

environmental stress at frozen temperatures (39). The overlap in intensity values (i.e.,

log2 fold change ~ 0) of the DNA/RNA and protein peaks between the fresh sample

and the refrigerated sample before the washing step suggests that there was no

change in the metabolism of bacterial cells when they were kept at 4C in culture

media without washing and the cells remained viable for this time frame (262). The

change in the dominant protein/lipid structure-specific peaks can also be seen to be

most severe in the frozen sample. The dominant spectral variations corresponding to

the C-H vibrational mode of protein (1337 cm-1) and the C-H3 symmetric

deformation of lipids (1379 cm-1) in the frozen sample indicates that some

lipid/protein denaturation may be induced by this sample preparation method (263).

Interestingly, a significant spectral change was seen in the amide II band (1550 cm-1)

for the refrigerated sample, whereas there was no significant variation in the amide I

(i) (ii) (iii)

***

16

20-1

64

0

1447

1663

14851335-1

373

1520

-1550

1001

66

8-8

11

1738

FSET PhD Thesis/94

band (1663 cm-1) between the three different samples. The dominant peaks

corresponding to the amide I and II vibrations are sensitive to subtle changes in the

secondary structure of proteins. Thus, this type of data for DNA/RNA and protein

markers could be used to study biomolecular changes associated with storage and

different methods of sample preparation. Therefore this principal component analysis

allows a well-defined and effective sample preparation methodology to be

established to facilitate subsequent bacterial analysis and identification by Raman

spectroscopy.

Figure 3.17 Intensity changes of DNA/RNA and protein/lipid structure-specific

peaks in the E. coli Raman spectra taken from a refrigerated sample before cell

washing steps and a frozen sample, relative to spectra from a fresh sample. Each

group consisted of five replicates, where each replicate represents the average of six

individual bacterial spectra. The Raman frequencies and their peak assignments are

shown in Table 3.1.

-1.5

-1

-0.5

0

0.5

1

Re

lati

ve

In

ten

sit

y c

ha

ng

e (

log

2fo

ld)

Specific peak (Curve)

Refrigerated sample before washing Frozen sample

DNA/RNA Protein/Lipid


3.6.2.3 Raman spectroscopic analysis of planktonic E. coli cells from fresh

and frozen samples at different phases of the growth cycle

Six different stages in the growth cycle of fresh planktonic E. coli were sampled and

the frozen samples were prepared from the cells after they were frozen at –80 C and

thawed prior to analysis. The Raman spectra of these cells were recorded and scores

plots of the first and second principal components were generated (Fig 3.18). For the

fresh sample, the first principal component is sufficient to separate the cells from the

exponential and early stationary phases from the other phases (Fig 3.18A). However,

the poor group clustering for frozen samples (Fig 3.18B) indicates that there was a

heterogeneous population of cells in each growth stage, especially at the early

exponential and early stationary phases. This may be due to the fact that some cells

were still actively continuing metabolic changes whereas others were approaching

the deterioration stage from cell damage during freezing.

(A) (B)

Figure 3.18 Score plots for the first and second principal components of Raman

spectra of planktonic E. coli cells from (A) fresh samples and (B) frozen samples.

Cells were taken at different phases of the growth cycle: (a) early exponential; (b)

late exponential; (c) early stationary; (d) late stationary and (e) decline.

FSET PhD Thesis/96

The results from the average values plots for the first principal component in Fig

3.19 clearly show that fresh planktonic E. coli cells at different stages of the growth

cycle were significantly differentiated from each other, whereas for the frozen

samples, it was difficult to identify the corresponding stages in the growth cycle. The

variation in the spectra was highest for the early exponential phase of the frozen

samples.

(A) (B)

Figure 3.19 Average value plots for the first principal component of the Raman

spectra of planktonic E. coli cells from (A) fresh samples and (B) frozen samples.

Cells were measured at different phases of the growth cycle; (a) early exponential;

(b) late exponential; (c) early stationary; (d) late stationary and (e) decline.

The loading value plots for the first principal component of the Raman spectra for

planktonic E. coli cells taken from fresh and frozen samples at different stages of the

growth cycle are shown in Fig 3.20. From the loading plots for the fresh samples, the

dominant spectral variation in specific molecular species is seen in the range of 688-

811, 1001, 1447, 1485, 1575, 1620-1680 and 1716-1741 cm-1 which mostly covers

DNA/RNA, protein and lipid synthesis. These bands display the highest absolute

variation during the bacterial growth and account for most of the separation observed

in the score plots. This also suggests that these peaks can be used as Raman markers

to classify the metabolic state of an unknown E. coli bacterial cell. Interestingly,

several of these spectral changes (such as 668-726, 1485, 1575 and 1716-1741 cm-1)

(c) (b) (c) (d) (e) (a) (b) (c) (d) (e)


were not identified to differentiate the corresponding growth stages in the loading

plots for the frozen samples, although some other features remain present.

(A) (B)

Figure 3.20 Loading value plots for the first principal component of Raman spectra

for planktonic E. coli cells taken from (A) fresh samples and (B) frozen samples.

Combining this observation with the results mentioned previously from Figs. 3.16

and 3.17 suggests that the differences between fresh and frozen samples are mainly

due to subtle changes in the structure of proteins during the early exponential phase

of the frozen sample. This can be explained by there being less phenotypic tolerance

to environmental stress in the cells during the exponential phase. This may be due to

bacterial EPS secretion only starting in the exponential phase and maximum EPS

yield only being observed at the beginning of the stationary phase (264). The

presence of a mature EPS matrix at the later growth stages is thought to be

responsible for the cells having enhanced resistance mechanisms (265). As such, the

unexpected spectral fluctuations are thought to be caused by the macromolecules of

the early exponential phase cells being subjected to the harmful effects of ice crystals

at freezing temperatures. Consequently, the direct application of glycerol stock from

long term storage may not be suitable for identifying the growth phase of the bacteria

or for the examination of time-dependent behaviour over the lifetime of the growth

cycle. Changes due to freezing may also interfere with the identification of species

present in unknown samples. A restriction on frozen storage also has implications for

the identification of bacteria that cannot be cultured.

668 7

26

781

811

1001

1335-1

373

1485

1575

1716-1

741

1001

602

-622

781

811

1447

16

20-1

68

0

FSET PhD Thesis/98

3.6.3 Proposed protocol of sample preparation for bacteria identification

Based on these results, a simple protocol for sample preparation for bacterial

identification by Raman spectroscopy has been developed as follows. In brief, an

overnight culture of bacteria is prepared by inoculating a single isolated bacterial

colony from the plate into 20 mL of nutrient broth medium (or selective media for

fastidious bacteria) and then incubated at 37 °C, 200 rpm (unless there is a special

requirement for incubation). After overnight incubation, the cells are collected by

centrifugation and washing processes to remove the traces of the nutrient medium. At

this stage, the overnight bacterial culture can be kept at 4 C in the medium if sample

analysis has to be delayed. Then 1 mL of bacterial cells in a microcentrifuge tube are

collected by centrifugation for 2 min at 12,000 rpm. The supernatant is decanted after

centrifugation and the cell pellet is washed three times with sterilised Milli-Q water

by centrifugation at the same speed for 2 min. The pellet is then resuspended in 30

µL sterilised Milli-Q water by repeated gentle pipetting. For the dried-droplet sample

preparation, a 10 µL volume of washed bacterial cell suspension is dropped onto a

quartz microscope slide, allowed to air-dry for 3-5 min and finally analysed by

Raman spectroscopy. The application of this protocol is very simple, fast and easy to

perform. This protocol is appropriate for reagentless, rapid bacterial identification by

Raman spectroscopy because of its excellent repeatability and reproducibility (Fig

3.18 & 3.19).

3.7 Conclusions

The application of Raman spectroscopy presents challenges in dealing with random

and systematic variations in the spectra. These variations are related to the high

fluorescent background from biological samples, signal intensity variations linked to

changes in optical throughput, cosmic ray spikes and low signal to noise ratio.

Moreover, for applications in the field of microbiology, poor sample preparation

could affect and alter bacterial macromolecules, structures or metabolites in other

ways. Complicated variations in biochemical information might confound the

analysis of Raman spectra from bacterial cells. This chapter explored a wide range of

techniques for optimisation of both the signal pre-processing before multivariate

statistical analysis and the bacterial sample preparation process.


Enhanced methods for fluorescence background removal were investigated using

probability based adaptive-weights. A single, relatively simple parameter is used to

determine the stiffness of the curve used in penalised least squares and polynomial

estimations of the background. The data used to fit the background is adaptively

weighted based on the probability that a point is part of the background given the

Poisson statistics of the signal. Compared to related methods that use a simple binary

classification of peaks and background, this continuously variable weighting helps to

improve the background estimation. The results from experimental spectra provided

a significant improvement on the proposed methods (APoly and APLS) for the

application of the fluorescence background correction algorithms

For pre-processing of the Raman signal before statistical data analysis, the results

proved that total intensity normalisation and mean-centring processes removed

unwanted degrees of freedom from the data, thus allowing further statistical analysis

to focus on the differences between the data points and providing the best outcomes

from principal component analysis.

From the optimisation of bacterial sample preparation and storage, it can be

concluded that washing the bacterial cells collected from culture media with Milli-Q

water eliminated the culture media and provided the typical Raman spectra of E. coli.

The results from comparative PCA for spectra taken from fresh and stored samples

demonstrate that storage of the cells at -80 C can cause spectral alterations

associated with changes in biochemical composition. Storage of bacterial cells at 4

C before the washing step resulted in more typical Raman spectra with no

significant differences from the fresh sample according to Student’s t-test. This

indicates that the bacterial cells remained viable and maintained EPS in the culture

medium, thus providing phenotypic tolerance of the cells to temperature stress.

Monitoring spectral changes during the lifetime of the bacterial growth cycle

indicated that large variations in the biomolecular components of the bacterial cells

can be observed in frozen cells from the early exponential phase. It is plausible to

suggest that these changes may arise from fluctuations in metabolic activity of the

bacterial cells and modification of macromolecules on the cell surface at freezing

temperatures.

FSET PhD Thesis/100

This study of detailed spectral changes owing to different storage methods provides

useful methodological background for Raman applications in microbiology. By

understanding the most effective sample storage as well as preparation methodology,

the techniques presented here can also be beneficial for studying the metabolic status

of bacteria and their growth-rate dependent cellular responses, thereby investigating

whether these responses can affect bacterial identification by Raman spectroscopy.

On the other hand, for bacterial identification, these results suggest that

environmental samples should be analysed promptly, as long term storage may affect

the accuracy of the Raman analysis.


RAMAN ANALYSIS OF PLANKTONIC BACTERIAL CELLS

4.1 Introduction

This chapter investigates the Raman analysis of bacterial cells and construction of

models for differential bacterial identification. The Raman analysis started with a

study of four bacterial species after a specific growth time point. For this initial

study, E. coli, Pseudomonas aeruginosa (P. aeruginosa) and Vibrio vulnificus (V.

vulnificus) were chosen as widespread Gram-negative bacilli of clinical and

environmental importance in their biofilm-forming properties. Staphylococcus

aureus (S. aureus), which is biofilm-forming Gram-positive bacterium, was also

chosen as a potentially life threatening source of nosocomial infection and

community-acquired infection.

E. coli and P. aeruginosa are the two most common bacteria associated with biofilm

in hospital-acquired infections and are mainly observed in patients with indwelling

bladder catheters and surgical implants. One of the reasons for including V.

vulnificus in this study was that Vibrio species are gram-negative bacteria highly

abundant in aquatic environments, including estuaries, marine coastal waters and

freshwater environments. Among Vibrio species, V. cholerae, V. parahaemolyticus

and V. vulnificus are serious human pathogenic microorganisms (214). Vibrio

vulnificus is a severe food-borne opportunistic pathogen which can often cause fatal

infections in susceptible persons (266). This bacterium occurs naturally in warm salt

water environments and can cause disease mostly associated with consumption of

raw oysters (267). Another source of V. vulnificus infection is through open wounds

or skin abrasion exposed to warm seawater harboring these bacteria. It has been

reported that people with underlying liver disease, hemochromatosis (iron overload),

diabetes and immune-compromised patients are associated with an increased risk of

V. vulnificus infection (268).

The diagnosis of V. vulnificus infection and isolation of bacteria from seafood require

conventional bacterial identification methods such as plating on selective-differential

FSET PhD Thesis/102

media, followed by confirmation tests using biochemical or molecular techniques

(269). Despite the availability of refined and high-tech instrumental detection

methods, the definitive identification of V. vulnificus involves more than 2 days of

sample processing. Therefore, the development of rapid diagnostic measures that can

identify V. vulnificus within hours is important for effective control and prevention of

food-borne illness. In this study, Raman spectroscopy was applied for rapid

identification of V. vulnificus as well as to distinguish the different phases of the

metabolic cycle (i.e. exponential, stationary and decline phases) from planktonic

bacterial cells. Moreover, it has recently been reported that V. vulnificus have

antibiofilm properties that inhibit biofilm formation by other bacteria as well as

disrupt established biofilm (58, 203), although they are believed to be a serious

human pathogenic microorganisms (214). Therefore, the Raman spectra of V.

vulnificus planktonic cells discussed in this chapter were used as a reference for

Raman spectroscopic analysis of biofilm cells which will be mentioned in the next

chapter.

S. aureus is a major pathogen of increasing importance due to the rise in antibiotic

resistance and biofilm-associated infection. S. aureus has an ability to attach to

indwelling medical devices through direct interaction with polymer surfaces of the

device or by establishing connections to human matrix proteins that have covered the

device (270). For the purpose of strategies to interfere with biofilm formation in the

environment and diagnose human disease by S. aureus, this study investigated the

Raman spectra profiles from planktonic cells throughout growth cycle for rapid

differential identification.

In view of the fact that individual cellular differences in macromolecular

composition contribute metabolic heterogeneity within a bacterial population,

bacterial identification and classification from different time points of the growth

cycle for these four bacterial species was examined in this chapter. A prediction

model based on chemotaxonomic analysis of these Raman spectral profiles was

constructed with the purpose of investigating applications for rapid microbial sensing

in environmental and clinical studies.


4.2 Materials and methods

Each bacterial species (see Table 2.1) was prepared according to the protocols for

planktonic sample preparation discussed in Section 2.2.3.1. To characterise

planktonic bacterial cells at species level, an overnight culture (~ 18 h) was prepared

and the Raman spectra from four individual cells from each species was collected

using the methods described in the Section 2.2.5.

To determine whether the Raman spectra obtained were dependent on the metabolic

growth phase, the four bacterial species were grown and samples were analysed at

different time points through OD measurement (Section 2.2.2). The metabolic

growth phases of the collected cells from different time points were further measured

and confirmed by counting viable units (cells) grown as colony forming units (CFUs)

as described in Section 2.2.2. A total of nine cultures from different growth phases

(i.e. early, middle and late of the exponential phase, stationary phase and decline

phase, respectively) were independently prepared from each of the four species.

Raman spectra were collected randomly from four individual cells of each culture.

Raman signal pre-processing and statistical multivariate data analyses were

performed as mentioned in Section 2.2.6.

4.3 Results and discussion

4.3.1 Raman classification of planktonic cells at species level

The averaged and intensity-normalised Raman spectra for the four bacterial species

(i.e. E. coli, V. vulnificus, P. aeruginosa and S. aureus) are shown in Fig 4.1.

Characteristic peaks determined from the literature (Table 3.1) for abundant cellular

components, such as carbohydrate, lipid, protein and nucleic, were clearly visible in

the spectra of all species. Typical Raman spectra obtained from single cells of E. coli

and S. aureus and their dominant peak assignments were examined based on the

previous published spectra (26, 215). Raman spectra and dominant peaks obtained

from single cells of P. aeruginosa were observed and checked with the previous

published spectra of pseudomonas species (i.e. Pseudomonas putida and

Pseudomonas fluorescens) (26, 30). The tentative Raman peak assignments of V.

vulnificus planktonic bacteria cells were determined based on Raman bands found in

FSET PhD Thesis/104

the literature which are associated with the more abundant cellular components (120,

257-259). To the best of our knowledge, this is the first reported Raman spectrum for

V. vulnificus.

Although the Raman spectral profiles for the different species appeared generally

similar, certain differences in peak intensity could be observed visually (highlighted

in grey box). In particular, the spectral differences could be seen in the regions of

600 to 800 cm-1 and 1055-1135 cm-1 which relates to DNA/RNA synthesis and

carbohydrate peaks respectively. Furthermore, spectral differences attributed to

macromolecules containing amide groups in the protein backbone (1620-1680 cm-1),

amino acid containing phenyl groups (617-640 and 1001 cm-1), tryptophan groups

(1360 cm-1) and the ester group of lipids (1734-38 cm-1) were also noticeable when

comparing the samples between four different species. To analyse these subtle

changes of the Raman spectra obtained from the bacterial cells, multivariate analysis,

in particular, principal component analysis (PCA) was further performed.


spectra from planktonic cells of the four bacterial species. Abbreviations: A, adenine;

G, guanine; def, deformation; Phe, phenylalanine; Trp, tryptophan; Tyr, tyrosine. The

dominant peaks for DNA/RNA and proteins are shown with the peak assignments

mentioned in Table 3.1. Shaded regions indicate the main spectral changes in

different samples.

CH

2 d

ef

A,

G

Ph

e, Tyr

Tyr A

mid

e I

II

Am

ide I

I

Am

ide I

C-H

def

Carb

oh

yd

rate

Ph

e

E. coli

V. vulnificus

S. aureus

P. aeruginosa

DN

A/R

NA

Trp


To determine whether Raman spectroscopy could reproducibly discriminate between

the bacterial species, all 16 collected Raman spectra (4 species × 4 individual cells)

were analysed using principal component analysis (PCA). As seen in Figure 4.2, the

first two principal components (PC1 and PC2) accounted for 69% of the variation in

the data set and were sufficient to differentiate the four different bacterial species.

The PCA scores plot showed distinct clustering of the different species. PC1

differentiated V. vulnificus and E. coli from P. aeruginosa and S. aureus while PC2

differentiated P. aeruginosa and V. vulnificus from S. aureus and E. coli.

Figure 4.2 Scatter plot from principal component analysis (PCA) of four different

bacterial species. The first and second principal components are plotted as a scatter

plot. (Circular regions overlaid on the data points shown in the scatter plot are

included as a visual guide.)

To examine the significance in the separation between species within the data set,

mean score values of the first and second principal components, standard deviations

and p-values of each sample group compared to others were calculated and are

shown in Figs. 4.3A and C. The average value plots of PC1 and PC2 show a

significant separation between the data from each bacterial species with p value <

0.005. The PC1 loadings plot (Fig 4.3B) demonstrates the peaks at 1002, 1447, 1663

cm-1 (associated with proteins/lipids), 781 and 1485 cm-1 (associated with

DNA/RNA) which contributed most to the separation observed in the scatter plot.

P. aeruginosa V. vulnificus

E. coli

S. aureus

FSET PhD Thesis/106

The peaks seen in the positive loading values (i.e. 1002, 1447 and 1663 cm-1) could

be considered as the finger print regions where E. coli and V. vulnificus can be

separated from P. aeruginosa and S. aureus. Similarly, the peaks seen in the negative

loading values (i.e. 781 and 1485 cm-1) are related to the separation of P. aeruginosa

and S. aureus.

(A) (B)

(C) (D)

Figure 4.3 Principal component analysis of four different bacterial species: (A-B)

average value and loading values plots of the first principal component and (C-D)

average value and loadings plots of the second principal component (***p value <

0.005). (The peak assignments are shown in Table 3.1).

Fir

st

pri

nc

ipa

l c

om

po

ne

nt

(a.u

.) (

51

.9%

)

E. coli V. vulnificus P. aeruginosa S. aureus

0

-4

-8

4

8

Different bacterial species

******

***

******

***

1447

1002

781

1680

-1620

1093

1485

X10-3

15

821

48

513

60

12

99

-13

13

78

1

85

2

14

47

16

20

-80

X10-3

Seco

nd

pri

ncip

al co

mp

on

en

t (a

.u.)

(19.2

%)

0

-4

-6

2

4

Different bacterial species

6

******


-2


Likewise, the PC2 loadings plot (Fig 4.3D) demonstrates the protein/lipid associated

peaks (at 852, 1299-1313, 1360, 1447, 1582, 1663 cm-1) and the DNA/RNA

associated peaks (at 781 and 1485 cm-1) which contributed most to the separation

observed in the scatter plot. The peaks seen in the positive loading values (i.e. 1299-

1313, 1360, 1485 and 1582 cm-1) indicate the regions where V. vulnificus and P.

aeruginosa were separated from E. coli and S. aureus. Similarly, the peaks seen in

the negative loading values (i.e. 781, 852, 1447 and 1620-80 cm-1) were related with

the separation of E. coli and S. aureus. The analysis was further performed between

species in order to understand the key finger print region of individual species.

In routine biochemical tests for bacterial identification, one key difference between

the Vibrio group and enteric bacteria is the oxidase test, which is a test used to

determine whether a bacterium produces a particular cytochrome c oxidase enzyme.

The V. vulnificus are oxidase-positive while E. coli (enteric bacteria) are oxidase-

negative. Bacterial cells are able to generate energy (ATP) from nutrients through

respiration or through fermentation. Different bacteria can ferment a wide variety of

carbon sources (usually sugars) and other compounds. Another important test in the

bacterial identification process is therefore to determine the fermentation pattern for

a series of different energy/carbon sources by an unknown bacterial species.

Although Pseudomonas species are oxidase-positive, their fermentation pattern is

different from lactose fermenter Vibrio and other enteric bacteria as Pseudomonas

species are non-lactose fermenter (39). The clustering results of the PCA (Fig 4.4),

suggest that the specificity of the Raman spectra enabled the differentiation of

oxidase-positive bacteria from oxidase-negative bacteria, lactose fermenter from

non-lactose fermenter and Gram-positive bacteria from Gram-negative bacteria.

FSET PhD Thesis/108

Figure 4.4 Scatter plot of the first and second principal components (PC1 and PC2):

(a-c) Scatter plots comparing the Raman spectra of E. coli with (a) V. vulnificus (b)

P. aeruginosa and (c) S. aureus, then V. vulnificus cells compared with (d) P.

aeruginosa and (e) S. aureus and (f) P. aeruginosa cells versus S. aureus.

The corresponding loadings plots for PC1 reflect the spectral changes between

species (Fig 4.5). The loadings have a spectral dimension, where positive and

negative peaks can be observed. Comparing between E. coli cells and the other three

species (Fig 4.5 a-c), positive peaks in the loadings indicate the corresponding peaks

in the E. coli spectra that contribute to the separation of E. coli cells from the rest. In

contrast, negative loading peaks refer to a contribution of the respective signals in the

Raman spectra of the other species. In particular, an increase in the peaks at 1680-

(a)

(b)

(c)

(d)

(e)

(f)


1620 cm-1, 1447 cm-1, 1001 cm-1, 852 cm-1 (associated with proteins/lipids), 1093

cm-1 (associated with DNA/RNA) could be seen in E. coli cells. A decrease in the

peaks at 1360 cm-1 (associated with amino acid, tryptophan) and 785 cm-1 (associated

with DNA/RNA) were seen in E. coli cells compared with other species. In

comparing Gram-positive and Gram-negative bacteria, it can be seen that the

DNA/RNA-related peak at 785 cm-1 contributed most in separating S. aureus from

others (Fig 4.5 c, e and f). Further detailed univariate analysis of the intensity values

for specific peaks related to DNA/RNA and protein synthesis, which were selected

from the loading plots (Fig 4.5), are shown in Fig 4.6.

Figure 4.5 Loading plots from the principal component analysis (PCA). (a-c):

Loadings exhibit the spectral differences of E. coli cells compared to (a) V.

vulnificus cells, (b) P. aeruginosa cells and (c) S. aureus cells; (d-e): loadings depict

peak fluctuations of V. vulnificus cells versus spectra of: (d) P. aeruginosa (e) S.

aureus; and (f) P. aeruginosa cells versus spectra of S. aureus.

17

38

16

80

-16

20

1447

14

85

1360

10

98

10

01

85

2

78

1

(a)

(b)

(c)

(d)

(e)

(f)

FSET PhD Thesis/110

As shown in Fig 4.6, E. coli has higher intensity values for the protein/lipid-specific

peaks (i.e. c, 1002 cm-1; f, 1447 cm-1; g, 1663 cm-1) in comparison to the other cells.

The increased intensities of these protein related peaks in E. coli cells indicates that

these cells undergo a change in their intracellular protein concentration at that

particular culture time point (18 h) compared to the other species. This high protein

expression may be related to the secretion level of EPS of E. coli suggesting that EPS

production might be higher than other three bacteria at that time frame. Some

bacterial species have maximum EPS production in the exponential phase (44, 45),

while for others, EPS production is maximized in the stationary phase (46-48).

Figure 4.6 Intensity changes of DNA/RNA and protein/lipid structure-specific peaks

in the Raman spectra of E. coli, V. vulnificus, P. aeruginosa and S. aureus. Raman

peaks (a, 781 cm-1; b, 852 cm-1; c, 1002 cm-1; d,1093 cm-1; e, 1360cm-1; f, 1447 cm-1;

g, 1663 cm-1; h, 1738 cm-1) were selected from the loading plots. Each point was

calculated from four replicates. The Raman frequencies and their peak assignments

are shown in Table 3.1.

800 1000 1200 1400 1600 1800

0.0

1.0E-3

2.0E-3

3.0E-3

4.0E-3

5.0E-3

6.0E-3

Norm

alised R

am

an I

nte

nsity (

a.u

)

Wavenumber (cm-1)

E.coli

V.vulnificus

P.aeruginosa

S.aureus

a

b

c

d

e

f

g

h


The overlap in the intensity values of the peaks at 852 cm-1 (associated with

tyrosine) and 1360 cm-1 (associated with tryptophan) between the E. coli and S.

aureus cells suggests that a similar expression pattern of amino acid containing

tryptophan and tyrosine groups could be seen in these two bacterial species, both of

which have oxidase-negative properties. In contrast, consistent spectral overlap at

852 cm-1 (associated with tyrosine) was seen in the two oxidase-positive bacteria (i.e.

V. vulnificus and P. aeruginosa). Moreover, the higher intensity value of the peak at

1360 cm-1 (associated with tryptophan) in these two bacterial species further

indicated that amino acid containing tryptophan group expression was more

predominant in the oxidase-positive bacteria compared to the oxidase-negative

bacteria. Interesting, the highest peak intensity at 785 cm-1, which is associated with

ring breathing modes in the DNA/RNA bases (C, cytosine and U, uracil) and DNA

backbone (O-P-O) vibrations, was seen in the S. aureus bacteria compared to the

other Gram-negative bacteria. This high peak intensity at 785 cm-1 was consistent

with increasing peak intensity at 1095 cm-1, which is associated with the DNA

backbone (O-P-O). The increasing intensity of the peaks related with DNA/RNA in

the spectra from S. aureus cells suggests that during the growth phase their DNA

content is higher than other Gram-negative bacteria. These results suggested two

interesting pathways to explore further within the study:

1) Can Raman spectroscopy be used as a method for exploring the changes in

biochemical composition that occur during the growth phases of individual

species?

2) If bacterial Raman signatures vary significantly over the growth cycle, does

this have a deleterious effect on species level identification in mixed growth

phase samples i.e. biofilms?

FSET PhD Thesis/112

4.3.2 Raman classification of planktonic cells at the metabolic phase level

To investigate the potential of Raman microscopy to detect differences in the

physiological state of various bacterial species, Raman spectroscopy analysis was

performed on the cells of four bacterial species. The cells were collected at

appropriate time points within the growth cycle of each species.

Growth curves and phase measurements for planktonic E. coli, V. vulnificus, P.

aeruginosa and S. aureus bacterial populations are shown in Fig. 4.7. The total

biomass of the four bacterial species was measured by optical density at 600 nm

(OD600) and shows the typical bacterial growth phases, which are the lag phase,

exponential phase (2-10 h), stationary phase (14-30 h) and decline phase (after 32 h)

(Fig 4.7). Viable bacterial count experiments confirmed the correct time point of

sample collection from every growth phase. The plotted values, which represent log10

transformed viable bacterial counts (CFU/mL), show that a typical exponential trend

of the bacterial growth curve reached its peak at the stationary phase and declined

after the late stationary phase (Fig. 4.7).

The viable bacterial counts of all of these bacterial species clearly revealed that the

bacterial population reached its maximum level at the stationary phase. The

stationary phase of V. vulnificus was reached after 10 h incubation time, whereas that

of other bacterial species was reached only after 15 h incubation time. Interestingly,

S. aureus took longer to reach to the stationary phase (i.e. 20 h).

During the stationary phase, the viable count of bacteria was at the maximum level

and remained almost constant. After the late exponential phase, the bacterial

population growth rate was counterbalanced by the death rate due to depletion of

essential nutrients and the accumulation of toxic acidic or alkaline waste products in

the medium. As a consequence of comparable growth and death rate, constant viable

count was seen for a few hours. Because of the ongoing depletion of nutrients and

accumulation of metabolic waste, the later stage of the stationary phase approached a

decline phase. The prominent decline phases were seen among E. coli, V. vulnificus

and P. aeruginosa species after 20 h incubation time while S. aureus growth curve

revealed the decline phase only after 30 h incubation time. Because of the somewhat


variable pattern of the growth curves among these four bacterial species, the cells

were prepared for Raman analysis based on growth phases but not on time points.

The collected planktonic cells from different stages of the growth cycle (i.e. early,

middle and late of exponential, stationary and decline phase) were used for Raman

analysis.

(A) (B)

(C) (D)

Figure 4.7 Representative growth curves and viable cell counts of four bacterial

species: (A) E. coli, (B) V. vulnificus, (C) P. aeruginosa and (D) S. aureus. Growth

(total biomass) monitored spectrophotometrically by measuring the optical density

(OD) at 600 nm are shown in blue and viable counts of collected bacteria at different

time points of growth are shown in red. Plotted values for bacterial OD and viable

count are log10 transformed.

FSET PhD Thesis/114

E. coli

The background-subtracted and total intensity normalised Raman spectra of

planktonic E. coli cells at different phases in the metabolic cycle are shown in Fig

4.8. The typical Raman peaks of E. coli planktonic bacterial cells determined from

the literature could be seen in the spectra of the cells from all metabolic growth

phases. Raman peak intensity changes, especially from the 600 to 800 cm-1 region,

which mostly covers DNA/RNA and protein synthesis, were seen between the

exponential phase and other later phases. Furthermore, some spectral fluctuations in

macromolecules (such as lipids and proteins) were also more visible in the

exponential phase than the later phases. In particular, spectral intensity differences

could be seen in the region of amino acid containing phenyl groups (1001 cm-1).

Spectral fluctuations attributed to macromolecules containing amide groups in the

protein backbone, such as the amide II band at 1550 cm-1 and amide I band at 1620-

1680 cm-1, were also identified. Moreover, intensity changes were clearly visible in

regions which are associated with the C-H and C-H2 vibrational modes of proteins

(1337 cm-1 and 1447 cm-1) when comparing the samples between different growth

phases. To analyse these subtle changes of the Raman spectra obtained from the

bacterial cells at different stages of the growth cycle, multivariate analysis, in

particular, principal component analysis (PCA) was further performed.


Figure 4.8 Background-subtracted and intensity normalised Raman spectra of E. coli

cells at different phases of the growth cycle. Cells were collected at early, middle and

late stages of the exponential, stationary and decline phases. Assignments are based

on studies in the references shown in Table 3.1. Abbreviations: Phe, phenylalanine;

Carb, carbohydrate; def, deformation.

From the PCA, the cells from each individual phase of the growth cycle of E. coli

species were reasonably well separated from each other in the early and late phases

of growth, with some overlap between the transitional phases (i.e. late exponential to

early stationary phases and mid-stationary to late decline phases) (Fig 4.9A).

Significant separation (p value < 0.005) of groups at the early metabolic phases (i.e.

early and mid-exponential) was probably due to rather fast metabolic changes in the

cells, whereas there was poor separation in the relatively stable metabolic phases (Fig

4.9B). These differences may be associated with changes in the secretion of EPS,

which varies throughout the growth phases. Some bacterial species have maximum

EPS production in the exponential phase (44, 45), while for others, EPS production is

maximized in the stationary phase (46-48). Moreover, Eboigbodin et.al reported that

the protein content in bound and free EPS extracted from E. coli cells varied

significantly as the cells grew from the exponential to the stationary growth phase

(271).

(i)

(ii)

(iii)

(iv)

(v)

(vi)

(vii)

(viii)

(ix)

Ph

e

DN

A/R

NA

syn

thes

isExponential phase

Stationary phase

Decline phase

CH

2d

ef

Am

ide I

I

Am

ide I

Am

ide I

II

CH

de

f

Ca

rb

FSET PhD Thesis/116

(A)

(B) (C)

Figure 4.9 Principal component analysis of E. coli cells at different phases of the

growth cycle: (A) scatter plot of the first and second principal components, (B)

average value plot and (C) loading values plot of the first principal component. (***p

< 0.005 and **p < 0.05). (Circular regions overlaid on the data points shown in the

scattered plot are included as a visual guide). Abbreviations: E, exponential; S,

stationary; D, decline; C, cytosine; U, uracil; A, adenine; G, guanine; Phe,

phenylalanine; def, deformation.

Early E

Mid E

Late E

Early S

Mid SLate S

Early D

Late D

Mid D

A,

G

Am

ide

I

CH

2d

ef

A,

G

Ph

e

O-P

-O,

RN

AC

, U

Exponential Stationary Decline

Different metabolic phases

Early Mid Late Early Mid Late Early Mid Late

***

**

*****

***** ***


The loading values plot of PC1 indicates the Raman peaks which contributed most of

the separation in the scatter plot (Fig 4.9C). The results show that the peaks related to

DNA/RNA represented the highest absolute variance of the exponential phase from

the later phases, whereas the protein-specific peaks were related to the variance of

the later phases. These variations of DNA/RNA and protein-specific peaks over

growth time indicate biochemical or metabolic heterogeneity due to cellular

differences in macromolecular composition or activity during the growth cycle. The

reduced variability between the Raman spectra after the stationary phase, which was

reflected by the overlap in PC1 loading of the later phases (Fig 4.9B) suggests that

the DNA and protein composition stabilised as the metabolism of the bacterial cells

becomes inactive during the stationary phase. These observations, together with the

results mentioned previously from Fig 4.8 and Fig 4.9A, highlight information about

the Raman spectral changes of E. coli species in DNA/RNA synthesis and protein

synthesis all the way through the growth cycle.

V. vulnificus


planktonic V. vulnificus cells at different phases in the metabolic cycle are shown in

Fig 4.10. The tentative Raman peak assignments of V. vulnificus planktonic bacterial

cells determined from the literature were seen in the spectra of the cells at all

metabolic growth phases. Similar to the Raman spectral changes seen for the E. coli

growth phases, Raman peak intensity changes, especially in the region associated

with DNA/RNA and protein synthesis (600 to 800 cm-1) were more obvious between

the exponential phase and other later phases. Moreover, growth phase-dependent

spectral fluctuations in macromolecules (such as lipids and proteins) were also

visible in the exponential phase compared to the later phases. The spectral intensity

differences seen in the region of amino acid containing phenyl groups (1001 cm-1),

amide II band at 1550 cm-1 and amide I band at 1620-1680 cm-1 were consistent with

the intensity changes seen in the Raman spectra of the E. coli growth phases.

Interestingly, a similar pattern of intensity changes compared with those of the E.

coli samples were also clearly noticeable in the regions which are associated with C-

H2 vibrational modes of protein (1447 cm-1).

FSET PhD Thesis/118

Figure 4.10 Background-subtracted and intensity normalised Raman spectra of V.

vulnificus cells at different phases of the growth cycle. Cells were collected at early,

middle and late stages of the exponential, stationary and decline phases. Assignments

are based on studies in the references shown in Table 3.1. Abbreviations: Phe,

phenylalanine; Carb, carbohydrate; def, deformation.

From PCA, the first principal component was sufficient to separate the cells in the

exponential phase and stationary phase from those in the decline phase (Fig 4.11A).

In fact, bacterial cells in the early exponential phase (i.e. 2 h) and decline phases (i.e.

58 h and 74 h) could be identified and visualized more clearly whereas the other

groups of cells were pooled together. The poor group clustering for the mid and late

exponential phase (i.e. 10 h and 18 h), stationary phases and early decline phase (i.e.

48 h) indicates that bacterial cells in these phases might have similar population

behaviour and nature of development. The results from the average values plots for

the first principal component (Fig 4.11B) clearly show that planktonic V. vulnificus

cells at early and mid-exponential phases (i.e. 2 h and 10h), mid and late stationary

phases (i.e. 30 h and 38 h) and early and mid-decline phases (i.e. 48 h and 58 h) of

incubation time were significantly differentiated from each other. However, for the

samples at 30-38 h and those at 48-58 h, it was difficult to identify the corresponding

stages according to the growth curve from OD measurement (shown in Fig 4.7B).

However, it appears that Raman spectroscopy is able to detect subtle changes in

macromolecules of bacterial cells at some points in the metabolic growth cycle.

(i)

(ii)

(iii)

(iv)

(v)

(vi)

(vii)

(viii)

(ix)

Ph

e

DN

A/R

NA

syn

thes

isExponential phase

Stationary phase

Decline phase

CH

2d

ef

Am

ide I

I

Am

ide I

Am

ide I

II

CH

de

f

Ca

rb


(A)

(B) (C)

Figure 4.11 Principal component analysis of V. vulnificus cells at different phases of

the growth cycle: (A) scatter plot of the first and second principal components, (B)

average value plot and (C) loading value plot of the first principal component. (***p

< 0.005 and **p < 0.05). (Circular regions overlaid on the data points shown in the

scattered plot are included as a visual guide). Abbreviations: E, exponential; S,

stationary; D, decline; C, cytosine; U, uracil; A, adenine; G, guanine; Phe,


Mid E

Stationary

Early D

Late D

Mid D

Early ELate E

A,

G

Am

ide

I

CH

2d

ef

A,

G

Ph

e

O-P

-O,

RN

AC

, U




**

**

** ***

FSET PhD Thesis/120

The significant separation of bacterial cells at 2 h from the rest of the growth stages

was probably due to the cells preparing for DNA replication and an increased amount

of DNA associated with cell division, which occurred in the early stage of the growth

cycle. The clustering of bacterial cells at incubation times from 10 h to 30 h may be

related to more consistent cellular population behaviour from maximum cell

reproduction in the later stages of the exponential phase and stationary phase.

Interestingly, the separation of bacterial cells at 36 h from those at 30 h could be

seen, even though there were no significant changes in the optical density

measurement (OD) for the growth curve (Fig 4.7B). The poor separation and high

spectral variations between the later stages of cell growth (i.e. 36 h, 48 h and 58 h)

indicated that the population of cells gradually became more heterogeneous in the

later stages of the growth cycle, with some cells still actively continuing metabolic

changes, whereas others were approaching the deterioration stage. The improved

group separation after the 48 h time point suggests that there was more deterioration

of cell physiology due to depletion of nutrients.

The loading values plot for the first principal component of the Raman spectra for

planktonic V. vulnificus cells sampled at different stages of the growth cycle are

shown in Fig 4.11C. From the loadings plot, the dominant spectral variance in

specific molecular species was seen at spectral positions of 1741-1716, 1663, 1575,

1480, 1447, 1001, 811 and 781 cm-1, which mostly covers lipid, protein and

DNA/RNA. This demonstrated the Raman peaks with the highest absolute variance

during the bacterial growth and most of the separation observed in the score plots.

Combining this observation with the results mentioned previously from Fig 4.10 and

Fig 4.11A provides information about Raman spectral changes in DNA/RNA

synthesis and in protein synthesis during normal cell growth of V. vulnificus.

P. aeruginosa


planktonic P. aeruginosa cells at different phases in the metabolic cycle are shown in

Fig 4.12. The Raman peak assignments of P. aeruginosa planktonic bacteria cells

determined from the literature could be seen in the spectra of the cells from all


metabolic growth phases. Similar to E. coli and V. vulnificus species, the main

spectral fluctuations were seen in the regions which are associated with DNA/RNA

(600-800 cm-1), C-H2 vibrational modes of protein (1447 cm-1), amide II band at

1550 cm-1 and amide I band at 1620-1680 cm-1 throughout the growth phases.

Interestingly, intensity changes in the region of amino acids containing tryptophan

groups (1360 cm-1) were noticed clearly in the samples throughout the growth cycle.

In fact, compared to the previous two species (i.e. E. coli and V. vulnificus), Raman

spectra taken from the different growth phases of P. aeruginosa were much more

variable, based on visual inspection. These results suggest that there might be greater

cellular heterogeneity in the samples of P. aeruginosa.

Figure 4.12 Background-subtracted and intensity normalised Raman spectra of P.

aeruginosa cells at different phases of the growth cycle. Cells were collected at early,



phenylalanine; Carb, carbohydrate; def, deformation; Trp, tryptophan.

(i)

(ii)

(iii)

(iv)

(v)

(vi)

(vii)

(viii)

(ix)

Ph

e

DN

A/R

NA

syn

the

sis

Exponential phase

Stationary phase

Decline phase

CH

2d

ef

Am

ide

II

Am

ide

I

Am

ide

III

CH

de

f

Ca

rb

Trp

FSET PhD Thesis/122

(A)

(B) (C)

Figure 4.13 Principal component analysis of P. aeruginosa cells at different phases

of the growth cycle: (A) scatter plot of the first and second principal components, (B)

average value plot and (C) loading values plot of the first principal component.

Abbreviations: E, exponential; S, stationary; D, decline; C, cytosine; U, uracil; Phe,

phenylalanine; def, deformation; Trp, tryptophan.

Am

ide

I

Am

ide

IITrp

CH

2d

ef

Ph

eC,U





PCA of the Raman spectra of the growth cycle of P. aeruginosa species is shown in

Fig 4.13. From the scatter plot of the first and second principal components, it can be

seen that there is relatively poor clustering within each growth phase and little

separation between growth phases. From the average PC score plot, the overlap

between the samples is visible and individual growth phases cannot be well separated

from each other. Moreover, the relatively large error bars (compared to E. coli and V.

Vulnificus) seen in the samples from later phases of the growth cycle indicated that

there was higher cellular heterogeneity in these phases compared to other earlier

growth phases (i.e. early and mid-exponential phases).

Interestingly, the peak intensity changes in the region of amino acid containing

tryptophan groups (1360 cm-1) were responsible for the separation of decline phases

from the other phases. Tryptophan is known to be a precursor of several important

signalling molecules for cell-cell communication (quorum sensing) and a tryptophan-

dependent pathway is indeed observed in Pseudomonas species (272). It was

reported that tryptophan catabolites accumulated in culture supernatant act as a

quorum sensing signal and are involved in the virulence gene expression during the

transition from a low- to a high-cell-density state of P. aeruginosa (273). In fact,

bacterial cells are believed to exhibit a variety of physiological and morphological

changes upon entering the stationary phase in order to compete and survive in their

living environment. Therefore, the spectral changes discussed above may justify

future investigations to study the relationship between growth phase dependent

physiological differences and the clustering of bacterial identification.

S. aureus

This Section presents PCA of the Raman spectra collected at different metabolic

growth phases of S. aureus, which is a Gram-positive bacteria and one of the model

organisms for biofilm studies. The background-subtracted and total intensity

normalised Raman spectra of planktonic S. aureus cells at different phases in the

metabolic cycle are shown in Fig 4.14. The Raman peak assignments of S. aureus

planktonic bacteria cells determined from the literature were seen in the spectra of

the cells from all metabolic growth phases. Similar to Raman data collected

FSET PhD Thesis/124

throughout the growth phases of E. coli, V. vulnificus and P. aeruginosa species, the

main spectral fluctuation occurred in the regions which are associated with

DNA/RNA (600-800 cm-1), C-H2 vibrational modes of protein (1447 cm-1) and the

amide II band at 1550 cm-1 throughout the growth phases. Interestingly, the Raman

intensity in the region of amino acid containing phenylalanine groups (1001 cm-1) is

at constant level and doesn’t vary significantly at any stage in the growth cycle.

From the PCA shown in Fig 4.15A, it can clearly be seen that the first principal

component separated the exponential phases and early stationary phase from the later

metabolic period. It is also observed that samples taken in the decline phases showed

more heterogeneity than samples taken at the exponential and stationary phases.

Raman spectra of the samples in early exponential phase tended to cluster together.

The samples from mid and late stationary phases also showed sub-clustering

although these phases are reasonably close to those from the decline phase (Fig

4.15A). However, no clear separation was seen between each of the data groups.

Figure 4.14 Background-subtracted and intensity normalised Raman spectra of S.

aureus cells at different phases of the growth cycle. Cells were collected at early,



phenylalanine; Carb, carbohydrate; def, deformation.

(i)

(ii)

(iii)

(iv)

(v)

(vi)

(vii)

(viii)

(ix)

Ph

e

DN

A/R

NA

syn

thes

is

Exponential phase

Stationary phase

Decline phase

CH

2d

ef

Am

ide I

I

Am

ide I

Am

ide I

II

CH

de

f

Carb


The average values plot of the first principal component shows a general trend for

separation of the earlier growth phases (i.e. exponential phase and early stationary

phase) from the later phases of the growth cycle but it was not significant in terms of

p-value (Fig. 4.15B). The significant separation was seen only between the samples

from early and mid-stationary phase (p value < 0.05). Similar to the growth cycle

data of other species, the results show that peaks related to DNA/RNA represented

the highest absolute variance of the exponential phase from the later phases, whereas

the protein-specific peaks, in particular the amide II band at 1550 cm-1, were related

to the variance of the later phases. The dominant peak regions which are associated

with U, T, C (ring breathing) and DNA/RNA phosphate backbone (O-P-O stretching)

were mainly responsible for the separation of the exponential phases and early

stationary phase from the other phases. As mentioned in Section 4.3.1.1, the S.

aureus spectrum displays a relatively intense peak at 780 cm-1 when compared with

other Gram-negative bacteria used in this study (Fig 4.1). The results from univariate

analysis (Section 4.3.1.2) also show that the highest peak intensity at 780 cm-1 was

seen in the spectra of S. aureus in comparison with other bacterial species (Fig 4.6).

FSET PhD Thesis/126

(A)

(B) (C)

Figure 4.15 Principal component analysis of S. aureus cells at different phases of the

growth cycle: (A) scatter plot of the first and second principal components, (B)

average values plot and (C) loading values plot of the first principal component. (**p

< 0.05). (Circular regions overlaid on the data points in the scatter plot are included

as a visual guide). Abbreviations: E, exponential; S, stationary; D, decline; C,

cytosine; U, uracil; A, adenine; G, guanine; Trp, tryptophan; def, deformation.

Early E

Mid S

A,

G

Am

ide

II

CH

3,

CH

2d

ef

Trp

C,

U




**


4.3.3 Effect of growth phase on the differentiation of four bacterial species

To determine whether the differences in cellular physiology during growth will affect

the clustering of the four bacterial species (E. coli, V. vulnificus, P. aeruginosa and S.

aureus), PCA was first performed on Raman spectra from the same growth phase

(i.e. at early, middle and late of exponential, stationary and decline phase) among the

species. The results are shown in Fig 4.16.

Figure 4.16 Scatter plots of principal component analysis (PCA) comparing the

Raman spectra of four planktonic bacterial species: E. coli, V. vulnificus, P.

aeruginosa and S. aureus at (a-c) early, middle and late exponential phases, (d–f)

early, middle and late stationary phases and (g–i) early, middle and late decline

phases, respectively.

(d) (e) (f)

(g) (h) (i)

(a) (b) (c)

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The scatter plots show that spectra were clustered closer within groups (i.e. replicate

samples) and generally well separated between groups (i.e. different species) based

on similar growth phases. In particular, the clustering of cells in early exponential

phase, stationary phases, early and late decline phases was robust despite temporal

differences in cellular physiology for each species during the phases of growth, as

discussed in the earlier Sections.

However, when PCA was performed on Raman spectra from all growth phases

together, the poor separation between species as well as imperfect clustering of the

same species was seen (Fig 4.17). Since PCA considers all variables and the total

data structure, this method is counting within-group variance as well as between-

group variance. Therefore, these results revealed that PCA clustering of the four

different bacterial species was affected when taking into account physiological

differences of these species all the way through the growth cycles.

Figure 4.17 PCA of the effect of physiological differences due to growth phase on

the clustering of four different bacterial species: E. coli, V. vulnificus, P. aeruginosa

and S. aureus.

Exponential

Exponential

Exponential

Exponential

Stationary

Stationary

Stationary

Stationary

Decline

Decline

Decline

Decline

E.coli

V.vulnificus

P.aeruginosa

S.aureus


In fact, Raman spectral variations were observed for all four species and have been

attributed to growth-phase variations in bacterial membrane compounds,

polysaccharides, proteins, lipids and nucleic acids (see Sections 4.3.2 and 4.3.3). For

these reasons, attempts were made to identify alternative methods of normalising the

spectra in order to accentuate the differences between the species and achieve more

reliable classification. The peak intensities of the spectra were first normalized with

the total intensity. As a normalisation of some internal spectral features for four

bacterial species, the total intensity normalised peaks were further normalised against

the intensities of the peaks associated with amino acid containing phenylalanine

group (1001 cm-1), nucleic acid phosphate backbone (O-P-O stretching) (1095 and

780 cm-1) and the ratio of DNA/RNA to protein (ratio of 780 and 1001 cm-1). These

peaks were chosen to normalise the spectra in order to test whether they may serve as

an “internal standard”, thereby accentuating more relevant changes in biochemical

composition. The normalisation results shown in Fig 4.18 revealed no improvement

in the clustering amongst and separation between the spectra and the outcome

appears to be inferior to the result using the total intensity normalisation process

(shown in Fig 4.17).

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Figure 4.18 Normalisation against selected spectral features: (A) phenylalanine peak

at 1001 cm-1, (B-C) nucleic acid phosphate backbone peaks at 780 and 1095 cm-1 and

(D) the ratio of DNA/RNA to protein (ratio of 780 and 1001 cm-1).

The ratios of DNA/RNA related peaks (726, 746, 781, 785, 808, 811, 1095, 1485,

1575 cm-1) to phenylalanine peak (1001 cm-1) were calculated to see whether there

were any growth phase dependent variations between the four different species. The

results from comparative analysis are shown in Fig 4.19.

For all four species, in the exponential phase, the ratios of DNA/RNA to protein

were generally higher than those in the stationary phase. However, in S. aureus, the

ratio of the A, G (ring breathing) region (1575 cm-1) to protein in the stationary phase

was higher than the exponential phase. On average amongst the four species, S.

aureus showed the highest value for the ratios of DNA/RNA to protein in both

phases. Given these relatively high values for the ratios and the increasing intensity

of the peaks related to DNA/RNA (from univariate analysis shown in Fig 4.6), the S.

aureus cells appear to express higher DNA content than the other Gram-negative

bacteria in both the exponential and stationary phases. Conversely, P. aeruginosa

(A) (B)

(D)(C)


showed the highest value for the ratio of the A, G (ring breathing) region (1575 cm-1)

to protein amongst the four species in the decline phases. Based on these results, it

can be concluded that growth phase dependent spectral variations among the four

species have an influence on the PCA clustering of four different bacterial species

seen in Fig 4.17.

Figure 4.19 Comparison of the ratio of DNA/RNA to protein in four bacterial

species: (A-I) ratio of DNA/RNA related peaks (726, 746, 781, 785, 808, 811, 1095,

1485, 1575 cm-1) to phenylalanine peak (1001 cm-1), respectively. Comparative

analysis was performed on the average intensity ratios for all cells from early, middle

and late stages of the exponential, stationary and decline phases. Peak assignments

are based on studies in the references shown in Table 3.1.

With the purpose of maximizing the variance between species and minimizing

variance within species, discriminant analysis (DA) was performed. In particular, the

procedure for DA classification was done by four main steps. The first step was data

pre-processing, which attempted to remove fluorescence background, smooth and

normalise the Raman spectra. PCA was then carried out using MATLAB for data

0.0

0.4

0.8

1.2

Early Exponential

Mid Exponential

Late Exponential

Early Stationary

Mid Stationary

Late Stationary

Early Decline

Mid Decline

Late Decline

Ra

tio

of

nu

cle

ic a

cid

to

pro

tein 0.0

0.4

0.8

1.2

0.0

0.4

0.8

1.2

A B C D E F G H I

0.0

0.4

0.8

1.2

DNA/RNA peak

0.0

0.4

0.8

1.2

A B C D E F G H I

0.0

0.4

0.8

1.2

DNA/RNA peak

0.0

0.4

0.8

1.2

E. coli

V. vulnificus

P. aeruginosa

S. aureus

0.0

0.4

0.8

1.2

1.6

2.0

2.4

A B C D E F G H I

0.0

0.4

0.8

1.2

DNA/RNA peak

FSET PhD Thesis/132

reduction and feature creation from the 1407 included pixels from each spectrum of

the cells from different growth phases. The third step involved canonical linear

discriminant analysis (LDA), which is the classical form of DA on the created

features. This classification algorithm was performed using the discriminant

command tool in OriginPro software (version 9.0.0) and provided the predicted class

of the sample. The final step was the evaluation and validation of the classification

accuracy.

LDA classification was performed based on the first 10, 16, 20 and 30 principal

components (PCs) generated from MATLAB which accounted for approximately 92

%, 93 %, 95 % and 96 % of the variance in the data set. The canonical score was

plotted for the first two canonical discriminant functions, as they reflect the most

variance in the discriminant model. The canonical score plot shows how the first two

canonical functions classify observations between groups. As shown in Fig 4.20,

LDA generally discriminated between different species based on the retained PCs

and the a priori knowledge of which spectra were replicates. With the application of

PCs, LDA effectively discriminated and classified the bacterial taxa into four groups

despite physiological variation within the species.



components (PCs) for bacterial species differentiation: LDA was performed based on

(A) 10 PCs, (B) 16 PCs, (C) 20 PCs and (D) 30 PCs.

4.3.4 PC-LDA Classification model

To validate the discrimination performed by LDA, projection analysis was also

employed to project test data into the discriminant function analysis (DFA) space

generated by the training set. A principal component linear discriminant (PC-LDA)

prediction model, based on the first 10, 16, 20 and 30 principal components (PCs) of

the four different species which account for approximately up to 96% of variance in

the data set was constructed. For evaluation and calibration of this model, a single

spectrum was removed from the database and a training data set was created using

the remaining spectra. In each of the test cases shown in Figure 4.21, comprising one

cell spectrum from a sub-growth phase of each bacterial species, the “left out

(A) (B)

(C) (D)

-9 -6 -3 0 3 6-8

-4

0

4

8

Dis

crim

inant

function 2

Discriminant function 1

E. coli

V. vulnificus

P. aeruginosa

S. aureus

-9 -6 -3 0 3 6-8

-4

0

4

8

Dis

crim

inant

function 2


E. coli

V. vulnificus

P. aeruginosa

S. aureus

-9 -6 -3 0 3 6 9

-8

-4

0

4

8

Dis

crim

inant fu

nction 2


E. coli'

V. vulnificus

P. aeruginosa

S. aureus

-9 -6 -3 0 3 6-8

-4

0

4

8D

iscrim

inant fu

nction 1


E. coli

V. vulnificuus

P. aeruginosa

S. aureus

FSET PhD Thesis/134

spectrum” clustered among the data within its respective training set. The example in

Fig 4.21 was calculated with the application of 16 PCs and is shown as an example

for visualisation. The classification label of the test set (left out spectrum) was

determined and the process was repeated for each of the four cells in the nine growth

phases for the four species, evaluated against the training set (108 spectra).

Figure 4.21 Calibration of PC-LDA model using a leave-one-out cross-validation

(LOOCV) with training and testing data. Bacterial species differentiation was

validated by projection analysis. Numbers 1-4 represent test data and the rest of the

points indicate training data used to validate the discrimination. The test samples (1-

4) are the cells from early exponential phases of E. coli, V. vulnificus, P. aeruginosa

and S. aureus respectively.

The cross-validation results of PC-LDA based on the first 10, 16, 20 and 30 principal

components (PCs) of the four different species are shown in Table 4.1. The results

indicate that there were unidentifiable cells from P. aeruginosa and S. aureus with

the application of the PC-LDA model based on the first 20 and 30 PCs, while there

was some misidentification of the species in the other two models. In particular, one

cell sample in S. aureus species could not be identified as any of bacterial species

with the model based on the first 20 PCs. Similarly, in the model of 30 PCs, one cell

1

23

4

-9 -6 -3 0 3 6

-4

0

4

8

Dis

crim

inant

function 2


E. coli

V. vulnificus

P. aeruginosa

S. aureus

Test sample


each from P. aeruginosa and S. aureus species were not be able to identified, while

two other cells from S. aureus species were misidentified.

The calibration of the selected PC-LDA model with the first 16 PCs for classification

of four species provides >94% classification sensitivity and >98% classification

specificity in the leave-one-cell-out cross-validation (LOCOCV) (Table 4.2). The

error rate for sensitivity and specificity for four bacterial species are shown in Table

4.3. Given the marginal improvement in performance for the tests with more than 16

PCs, the PC-LDA model using the first 16 PCs was chosen as the training model for

further validation tests.

Table 4.1 Calibration of PC-LDA model based on the first 10, 16, 20 and 30

principal components (PCs) for a total of 144 spectra of four bacterial species.

No.

of

PCs

Predicted Group

E. coli V. vulnificus P. aeruginosa S. aureus Unidentified Total

10 E. coli 36 0 0 0

36

V. vulnificus 0 35 1 0

36

P. aeruginosa 1 5 30 0

36

S. aureus 0 0 1 35

36

16 E. coli 36 0 0 0

36


36


36

S. aureus 0 0 2 34

36

20 E. coli 36 0 0 0

36


36


36

S. aureus 0 0 1 34 1 36

30 E. coli 36 0 0 0

36


36

P. aeruginosa 0 0 35 0 1 36

S. aureus 1 0 1 33 1 36

FSET PhD Thesis/136

Table 4.2 Calibration accuracy results of PC-LDA model with the first 16 PCs on a

total of 144 spectra of four bacterial species.

Predicted Group Sensitivity Specificity

E. coli V. vulnificus P. aeruginosa S. aureus (%) (%)

E. coli 36 0 0 0 100 99.1

V. vulnificus 0 35 1 0 97.2 99.1

P. aeruginosa 1 1 34 0 94.4 98.1

S. aureus 0 0 2 34 94.4 100

Table 4.3 Error rates for the calibration of PC-LDA model with the first 16 PCs on a

total of 144 spectra of four bacterial species.

Error rate

Sensitivity Specificity

E. coli 0.00% 0.92%

V. vulnificus 2.78% 0.93%

P. aeruginosa 5.56% 1.85%

S. aureus 5.56% 0%

MATLAB code for the PC-LDA model along with the corresponding species labels

was custom written and applied for species identification of cells from a separate

culture batch (see Section 2.2.6.2). The calibrated PC-LDA model using the spectra

from planktonic cells of each species was validated on 10 spectra each from

individual (pure) planktonic cells of E. coli and V. vulnificus achieving 100% and

80% accuracy in prospective classification (Table 4.4). This calibrated PC-LDA

model was then applied to detect the presence of two species in mixed inoculums of

E. coli and V. vulnificus. When a two-species sample from the mixed culture were

examined, the presence of both E. coli and V. vulnificus were detected in 15 and 4

out of 20 sample regions respectively using the PC-LDA model based on the first

16 PCs of four bacterial species (Table 4.4). A visualisation of the classification of

the test samples projected into the DFA space generated by the training set is shown

in Fig 4.22.


Figure 4.22 Validation of the PC-LDA model on 10 new spectra from individual

species and from mixed culture: (A) E. coli cells, (B) V. vulnificus cells and (C)

mixture of E.coli and V. vulnificus. Numbers 1-15 represent the test data and the rest

indicate training data used to validate the discrimination. The test samples which are

misidentified are labelled with (×) symbol in red.

123

45

67 89 10

11 121314151617181920

-9 -6 -3 0 3 6-8

-4

0

4

8

Dis

cri

min

an

t fu

nctio

n 2


E. coli

V. vulnificus

P. aeruginosa

S. aureus

Test sample

1

2 3

4

5678910

-9 -6 -3 0 3 6

-8

-4

0

4

8

Dis

crim

ina

nt

fun

ction 2


E. coli

V. vulnificus

P. aeruginosa

S. aureus

Test sample

(A) (B)

(C)

X

X

X

1234

5 67

8

910

-9 -6 -3 0 3 6

-4

0

4

8

Dis

crim

ina

nt

fun

ctio

n 2


E. coli

V. vulnificus

P. aeruginosa

S. aureus

Test sample

FSET PhD Thesis/138

Table 4.4 Validation of PC-LDA model on new spectra from individual species and

from mixed culture.

Reference Accuracy PC-LDA model

E. coli V. vulnificus Others species (%)

Cla

ssif

ica

tio

n r

esu

lt

E. coli 10 - - 100 4 species’ model

V. vulnificus - 8 2 80 4 species’ model

Cells from

mixed

culture

15 4 1 95* 4 species’ model

* Calculated as the detection accuracy for the presence of two species in the sample.

The next step of the process was to biochemically validate the bacterial identity of

each of the cells that were analysed using Raman to confirm that the PC-LDA model

has identified the bacteria correctly. To confirm the presence and spatial distribution

of the bacterial cells, a fluorescence in situ hybridisation (FISH) technique with

rRNA-targeted oligonucleotide (probe) for E. coli (ATCC 25922) was performed.

The probe efficiency test and FISH techniques have already been optimised in

preliminary work (details in Section 2.2.4.2). The nucleic acid probe (SYTO 9,

Invitrogen) was used to stain the nucleic acid of all bacterial cells in the sample

following the protocols mentioned in Section 2.2.4.1. The results of FISH using

rRNA-targeted probe and nucleic acid probe (SYTO 9) are shown in Fig 4.23. E. coli

cells labelled with rRNA-targeted probe and SYTO 9 appear in yellow and V.

vulnificus labelled with only SYTO 9 is seen as green. As shown in Fig 4.23C,

Raman spectra were collected across the white dotted line (from left to right) with 2

µm steps along the x axis. The first three cells in green colour were likely to be V.

vulnificus species and were identified as V. vulnificus species using the PC-LDA

model. Similarly, the subsequent cells in yellow colour were identified as E. coli

species with the model. Therefore the FISH results support the validity of the

identification provided by the PC-LDA model (data shown in Fig 4.22C and Table

4.4).


Figure 4.23 Confocal laser scanning microscopy images of mixture of E. coli and V.

vulnificus planktonic samples (x–y sections). Bacteria were stained with the rRNA-

targeted oligonucleotide probe for E. coli (ATCC 25922) and the nucleic acid probe

(SYTO 9). Panel A shows bacteria cells with one channel for rRNA-targeted probe

and panel B shows bacteria cells with two channels for both rRNA-targeted and

nucleic acid probes. Panel C shows the enlarged area where Raman spectra were

collected. The sample area used for Raman spectroscopy was relocated with the help

of microscope grid (grid size 300 mesh × 83 μm of pitch, Sigma). Raman spectra

were collected along the horizontal dotted arrow line (in white) with 2 µm steps (x

axis) from left to right. The yellow colour represents E. coli cells labelled with both

rRNA targeted and nucleic acid probes and green represents V. vulnificus cells

labelled with only the nucleic acid probe. The 20 µm scale bar shown in panel (A)

applies to panel (B) as well.

20 µm

20 µm

(A)

(B)

(C)

FSET PhD Thesis/140

4.3.5 PC-LDA Classification model for classification of metabolic phases in

individual species

The capability to investigate the growth phase-dependent differences in physiology

within a single species has important potential applications for control measures in

food and medical microbiology, where single-species communities are common.

Therefore, a PC-LDA prediction model was constructed based on the first 16 PCs of

individual species at 9 different growth points for pattern recognition of the

metabolic phases (i.e. early, mid and late of exponential, stationary and decline

phases respectively). The classification label of the test set (leaving out one spectrum

from each growth point) was determined against the training set (27 spectra) and the

process was repeated for all 36 cells of each individual species. The model provided

>90% classification accuracy in a LOOCV, except for the P. aeruginosa species

(Tables 4.5 and 4.6), which were less accurate.

From the PC-LDA results, the P. aeruginosa cells from different phases of the

growth cycle were poorly clustered and not well separated, with data overlapping

across each growth phase (see Section 4.3.2.2.3). The exponential phase of P.

aeruginosa provides 83.3% sensitivity and 88% specificity. However, this drops to

58% sensitivity and approximately 80% specificity in the stationary and decline

phases. The poor group separation for the later growth phases of P. aeruginosa cells

indicates that there is an increasingly heterogeneous population of cells at different

stages, with some cells actively continuing metabolic changes whereas others are

approaching the deterioration stage. This can be explained by the fact that most

bacteria express changes in gene transcription, cell behaviour and physio-chemistry

between the exponential phase and the stationary phase (274, 275).


Table 4.5 Calibration of the PC-LDA model on a total of 36 spectra of individual

species in different growth phases.

Classification result

E.coli V. vulnificus P. aeruginosa S. aureus

Exp Stat Decl Exp Stat Decl Exp Stat Decl Exp Stat Decl

Exp 11 1 0

E.coli Stat 1 9 2

Decl 0 2 10

Exp

12 0 0

V. vulnificus Stat

0 12 0

Decl

0 0 12

Exp

10 1 1

P. aeruginosa Stat

2 7 3

Decl

1 3 7

Exp

12 0 0

S. aureus Stat

2 10 0

Decl

0 1 11

Abbreviations: Exp, exponential phase; Stat, stationary phase; Decl, decline phase.

Table 4.6 Classification accuracy results of PC-LDA model at metabolic phase level.

Growth

phase

Sensitivity

(%)

Specificity

(%)

E. coli Exponential 91.7 95.8

Stationary 75 95.5

Decline 83.3 91.7

V. vulnificus Exponential 100 100

Stationary 100 100

Decline 100 100

P. aeruginosa Exponential 83.3 88

Stationary 58.3 79.2

Decline 58.3 82.6

S. aureus Exponential 100 92.3

Stationary 83.3 95.7

Decline 91.7 100

FSET PhD Thesis/142

The ten E. coli cells and eight V. vulnificus cells which were identified from the PC-

LDA model at species level (see Table 4.4) were further examined for their

corresponding metabolic phases using the metabolic level PC-LDA model. As shown

in Table 4.7, the results revealed that heterogeneity of cellular metabolic population

was present in both species. In particular, 60% of E. coli cells were classified in the

stationary phase population, whereas 86% of V. vulnificus cells were in the stationary

phase. In fact, the bacterial samples tested in this classification experiment were

collected from overnight incubation and were thus expected to be in the stationary

phase. Although it could not be explicitly confirmed whether the PC-LDA model

was able to detect the correct metabolic behaviour of the cells, the classification

results showed the possible trends of the population behaviour of the tested cells.

Table 4.7 Classification results of 10 new spectra from individual species (spectra

from Table 4.4) using the PC-LDA model at metabolic phase level.

Growth

phase Classification results

E. coli

(10 cells)

V. vulnificus

(7 cells)

Ref

eren

ce (

fro

m m

od

el)

E. coli

Exponential 3

Stationary 6

Decline 1

V. vulnificus

Exponential

1

Stationary

6

Decline

-


4.4 Conclusion

In this chapter, confocal Raman spectroscopy was applied to the identification of

single bacterial cells from different metabolic growth phases. The results have

demonstrated the effectiveness of principal component analysis of Raman spectra for

the discrimination of four species of microorganisms (i.e. E. coli, V. vulnificus, P.

aeruginosa and S. aureus) either in culture at a particular growth time point, or in

culture at a random growth point. This analysis indicates that Raman spectroscopy

can provide reasonably accurate identification of a range of planktonic bacteria,

despite the presence of spectral variations associated with different growth phases.

Bacterial cells from different growth phases can also be classified to varying degrees

of success with the help of principal component linear discriminant analysis (PC-

LDA), although the single-cell spectra are relatively variable between individual

cells. PC-LDA of single cell Raman spectra shows that individual cells can be

discriminated from batch cultures of E. coli and V. vulnificus species at the stationary

growth phase with reasonably high confidence. These results showed that confocal

Raman spectroscopy may be used for rapid environmental sensing of microbial cells

recovered at arbitrary growth time points. Since the PC-LDA model appears to be

able to discriminate between the two species in a mixed culture, it was decided to

investigate further applications of this model on species identification for two-

species grown biofilms.

FSET PhD Thesis/144

Mya Myintzu Hlaing Chapter 5/145

RAMAN ANALYSIS OF BACTERIAL (MICRO) COLONIES

AND BIOFILMS ISOLATED ON SUBSTRATES

5.1 Introduction

While the previous chapters have focused on cells grown in culture, this chapter

investigates the behaviour of bacterial cells grown on two substrates, nitrocellulose

membranes and quartz glass.

Bacterial colonies play an important role in the isolation and identification of

bacterial species and the culture-based method is regarded as the gold standard

screening method in clinical and environmental studies. A bacterial colony, also

known as a colony biofilm, consists of millions of densely packed individual bacteria

along with extracellular materials (such as EPS) (276). During bacterial micro-

colony development, the process of continuous division of bacteria, interactions

among bacterial offspring and between bacteria and their surroundings results in

biochemical or metabolic heterogeneity in a population.

To observe bacterial population behaviour, both membrane grown micro colonies

and the development of biofilms grown on quartz were monitored by collecting

Raman spectra from the four bacterial species (E. coli, V. vulnificus, P. aeruginosa

and S. aureus) over a range of time points. From these Raman spectra collected from

cells in different areas of the colonies, differential identification of bacterial cells

grown on surfaces was performed with the application of the principal component

and linear discriminant (PC-LDA) planktonic model, as described in the previous

chapter.

A prediction model, based on PC-LDA, was subsequently constructed from biofilm

cells of each species. The constructed fingerprinting system for single bacterial

species (PC-LDA biofilm model) was tested on a dual-species biofilm to obtain

specific bacterial identification. A fluorescence in situ hybridisation (FISH)

technique was then used to confirm the identification results using the PC-LDA

biofilm model and to understand their spatial distribution within a mixed biofilm

community.

FSET PhD Thesis/146


Colonies of E. coli ATCC 25922 were isolated on a nutrient agar plate following the

protocols mentioned in Section 2.2.3.2. Colonies of all four bacterial species were

grown on nitrocellulose membranes according to the protocols detailed in the same

Section.

To investigate surface-attached and biofilm bacterial cells, a static biofilm cultivation

process on quartz glass samples was prepared for all four bacterial species following

the methods detailed in section 2.2.3.3. For dual-species biofilm cultivation,

planktonic bacterial cultures were collected at the stationary growth phases of E. coli

and V. vulnificus according to the growth curve results discussed in Section 4.3.2.1.

The collected bacterial cultures were then washed and resuspended in PBS to a

concentration equivalent to an OD at 600 nm of about 0.3 as mentioned in Section

2.2.3.3. Each of the diluted samples was then gently mixed by pipette before they

were used for initial attachment and biofilm cultivation. The dual-species biofilms

were grown until 79 hours as stated in Section 2.2.3.3. The bacterial cells from

colonies and biofilm were analysed under Raman spectroscopy with the parameters

mentioned in Section 2.2.5.

The morphology of the E. coli biofilm cells was observed using scanning electron

microscopy (SEM). The images were obtained using a field-emission scanning

electron microscopy (Fe-SEM) instrument (SUPRA 40VP, Carl Zeiss SMT,

Germany) with 3 kV acceleration voltage and ×5000 magnification. Prior to imaging,

biofilm samples were coated with 10-15 nm of gold using a Dynavac CS300 thermal

deposition chamber. For visualisation of E. coli biofilm cells and the extracellular

polymeric substance (EPS) of dual-species biofilm, a fluorescence in situ

hybridisation (FISH) technique was performed using 16S rRNA targeted probe and

EPS staining as detailed in sections 2.2.4.2 and 2.2.4.3. The hybridized E. coli cells

and ConA stained EPS were visualised under confocal laser scanning microscopy

(CLSM) using the protocols detailed in section 2.2.4.4.



5.3.1 Raman analysis of agar-grown bacterial (micro) colonies

As discussed in the literature review (Section 1.5), during biofilm formation, bacteria

have to adapt to their changing environmental conditions by expressing different

phenotypes which are distinct from planktonic growth. These bacterial phenotype

heterogeneities are associated with random alterations in chemical reactions for DNA

and protein synthesis. These chemical changes may alter the Raman spectrum of the

cell to an extent that it interferes with bacterial identification. Therefore, in order to

investigate how significant an effect the chemical changes have on the Raman

spectrum, E. coli cells from micro-colonies were analysed by Raman spectroscopy.

The E. coli colony cells were first isolated on the nutrient agar and then smeared on a

quartz substrate for analysis (Section 2.2.3.2), with the results shown in Fig 5.1.


spectra from planktonic and colony cells of E. coli species. Abbreviations: A,

adenine; G, guanine; def, deformation; Phe, phenylalanine; Trp, tryptophan; Tyr,

tyrosine. The dominant peaks for spectra of DNA/RNA and proteins are shown with

the peak assignments from Table 3.1. Shaded regions indicate the main spectral

changes between the two samples.

Ph

e, Tyr

Tyr

Ph

e

Ca

rbo

hyd

rate

Am

ide

III

CH

2 d

ef A

mid

e I

Am

ide

II

C-H

de

f

DN

A/R

NA

A,

G

Planktonic

Colony

FSET PhD Thesis/148

The Raman spectra of the colony cells were compared with the spectra collected

from the planktonic cells which were cultured under similar conditions. The

characteristic Raman peaks of E. coli cells (determined from the literature and shown

in Fig 4.1), which are associated with carbohydrate, lipid, protein and nucleic acids,

were clearly visible in the spectra of both planktonic and colony cells. The Raman

spectral profiles for the planktonic and colony cells appeared generally similar, but

certain differences in peak intensity could be observed visually (highlighted in the

grey boxes). These spectral fluctuations could be seen in the region of 600 to 800 cm-

1 which relates to DNA/RNA synthesis, the peaks associated with the CH, CH2

deformation mode and the macromolecules containing amide groups in the protein

backbone (1337, 1447-1452, 1620-1680 cm-1). These subtle changes of the Raman

spectra were further investigated by performing principal component analysis (PCA)

and the results are shown in Fig 5.2.

The scores plot from the first two principal components (PC1 and PC2) of PCA

shows a clear separation between the two sample groups accounting for 65% of the

variation in the data set. The average value plot of PC1 shows a significant

separation of the two sample groups with p value < 0.005. The loading plot of PC1

further demonstrates the dominant peaks which contributed to the data separation

seen in the score plots. The results show that the peaks related to DNA/RNA

synthesis represented the main variance of the planktonic cells from the colony cells,

whereas the protein-specific peaks were related to the variance of the colony cells.

The specific peaks selected from the loadings plot of PC1 were further analysed

using univariate statistical analysis, as mentioned in Section 2.2.6.3. As shown in Fig

5.3, the intensity of the DNA/RNA-specific peaks was higher in the planktonic cells

in comparison to the colony cells. It has been reported that the DNA/protein ratio

usually increases during the transition from exponential growth to the stationary

phase because of continuous cell division. Thus, the increased intensities of these

DNA/RNA related peaks in the planktonic cells indicate that these cells may have

been in the stationary phase when they were collected for Raman measurement.


(A) (B)

(C)


planktonic and colony cells. (A) Scatter plot of the first and second principal

components, (B) average value plot and (C) loading values plot of the first principal

component (***p < 0.005). Abbreviations: A, adenine; G, guanine; Phe,


The increased intensity of protein-related peaks in the colony cells might be

associated with more secretion of extracellular polymeric substance (EPS) and higher

protein expression in the colony compared to the planktonic cells. As discussed in

the literature Chapter (Section 1.2.2), it is well established that cells in colonies

secrete more EPS (49, 52). These results suggest that the colony cells at the agar-air

interface were metabolically different from planktonic cells. Therefore, the variations

of DNA/RNA and protein-specific peaks seen here indicate biochemical or metabolic

***

Ph

e

AO

-P-O

, R

NA

DNA/RNA CH

2d

ef

A,G

A,G

Am

ide I

FSET PhD Thesis/150

heterogeneity between planktonic cells and agar-grown colony cells. In this context,

the next step was to see whether this heterogeneity can affect the identification of

colony cells using the PC-LDA planktonic model.

(A)

(B)

Figure 5.3 Analysis of specific peaks from the Raman spectra of E. coli planktonic

and colony cells. Univariate analysis was performed on the normalised intensity of

(A) DNA/RNA and (B) protein/lipid structure-specific peaks in the E. coli Raman

spectra taken from planktonic and colony samples. Each group consisted of seven

replicates. (***p < 0.005, **p < 0.05, *p < 0.1). Abbreviations: A, adenine; G,

guanine; T, thymine; C, cytosine; U, uracil; Phe, phenylalanine; def, deformation.

T, G A C, U U, T, C A,G A,G0.0000

0.0005

0.0010

0.0015

0.0020

Me

an N

orm

alis

ed In

tesn

ity / A

rbitr.

Units

Specific peak (Wavenumber)

planktonic

colony

***

***

*

***

***

***

Phe CH2 def Amide I0.000

0.001

0.002

0.003

0.004

0.005

0.006

Specific peak (Wavenumber)Me

an

No

rma

lise

d I

nte

sn

ity /

Arb

itr.

Un

its

planktonic

colony

*****

**


The 16 principal components (PCs) of the Raman spectra from E. coli colony cells

were further analysed using the PC-LDA planktonic model. Each test spectrum was

clustered and overlapped with the corresponding species (i.e. E. coli species) within

the training model, thus showing 100% classification accuracy (Fig 5.4). The results

suggested that the subtle changes in macromolecules related with DNA/RNA and

protein synthesis (between planktonic and colony cells) are not related to the key

components identified in the PCA and thus are not critical for the identification of

the samples. Therefore, agar-grown colony cells could be used for identification

purpose despite these subtle changes.

Given the encouraging classification outcomes for agar-grown cells, the next stage of

the study investigated whether surface-attached cells (i.e. intact colony/biofilm cells)

can achieve similar classification accuracy.

Figure 5.4 Classification and identification of spectra from colony cells of E. coli

grown on nutrient agar using the PC-LDA planktonic model. The model

discriminated and identified all test spectra correctly as “E. coli” species. The mean

of each group in the training model is shown with () symbol in yellow.

-9 -6 -3 0 3 6-8

-4

0

4

8

12

Dis

crim

inant fu

nction 2


E. coli

V. vulnificus

P. aeruginosa

S. aureus

Test sample

Group Means

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5.3.2 Raman analysis of intact membrane-grown bacterial micro-colonies

Micro-colonies of each of the bacterial species were inoculated onto a sterile

nitrocellulose transfer membrane which was placed on a pre-warmed nutrient agar

plate as shown in Fig 5.5A. A micrograph of an E. coli bacterial colony from

overnight culture was captured in the x-y and x-z planes with 3D optical profiler

microscopy system (Fig 5.5B). It has recently been reported that the diameter of the

pie-like monolayer of the bacterial colony increases and a two-cell layer appears at

the centre of during colony development. As the growth of bacteria and division

inside the colony continue, the diameter and layers of the bacterial micro-colony are

believed to be expanding outwards on the agar plane and upward in the third

dimension (277). As shown in Fig 5.5B, typical bacterial micro-colony structures,

which are associated with a more dense appearance at the centre core than in the

outer ring, could be seen under the 3D optical profiler microscopy. Raman spectra

were collected from the centre core region (C), the outer ring region (R) and middle

region between the centre and outer ring (M) as shown in Fig 5.5B. This pattern was

seen in colonies of all four bacterial species that were tested in this study.

Raman spectra were recorded from a single cell in the outermost ring (monolayer

region) of an E. coli colony on the membrane, as well as from a region of the

nitrocellulose membrane (shown in Fig 5.5 B and C). Raman spectra were acquired

from upper layers of different regions of the colony. Maquelin et al. detected

different RNA in Raman spectra taken from cells located in different depths within

the colony structure. They speculated that the cells in the higher layers of the colony

were more actively dividing than cells in the deeper layers. Therefore, in order to

minimise biochemical heterogeneity of cells in bacterial colonies, Raman spectra

were consistently taken from upper layers of the colony for all regions in this study.


(A) (B)

(C)

Figure 5.5 Bacterial micro-colonies isolated on a nitrocellulose membrane placed on

nutrient agar. Micro-colony was observed: (A) with the naked eye (marked with red

circle) (B) on the x-y and x-z planes with 3D optical profiler microscopy system and

(C) a schematic representation of a growing bacterial micro-colony through

overnight incubation. Images were taken after overnight cultivation. A light outer

ring and a dense centre core seen in (B) are labelled with “R” and “C”, respectively;

middle area between the centre core and the outer ring of the whole micro-colony is

labelled ‘‘M’’, in (B and C). Red arrows in (C) indicate cells growing outward and

upward in the micro-colony.

5 mm100 m

Centre core

Outer ring

RMC

(x)

(y)

(x-z plane)

FSET PhD Thesis/154

Figure 5.6 Recovery of Raman spectra from intact colony grown on membrane: (a)

from single cell of intact E. coli colony on membrane, (b) from nitrocellulose

membrane, (c) recovered spectrum of an E. coli single cell by subtracting membrane

spectrum from (a) after normalisation with the nitrocellulose membrane peak at 1282

cm-1, (d) from single cell of planktonic E. coli. The arrows indicate the Raman signal

(846 cm-1 and 1282 cm-1) from the nitrocellulose membrane.

The collected Raman spectra from different regions of the colony and the membrane

spectra were shown in Fig 5.6. The nitrocellulose provided a consistent background

signal at 846 cm-1 and 1282 cm-1 in the spectrum of the membrane (Fig 5.6b). These

peaks did not significantly overlap or interfere with the Raman peak assignments

from macromolecules of the bacterial cells. To recover the spectra from the bacterial

cells, the peak intensities of the bacterial spectra collected from an intact colony

isolated on the membrane were normalized by dividing with the intensity of the

nitrocellulose membrane signal at 1282 cm-1 after background subtraction. This

normalisation process was also performed on the background subtracted membrane

spectra. The spectrum from a single cell of intact bacterial colony was then recovered

by subtracting the normalised nitrocellulose membrane spectrum from the

normalised spectrum of bacterial cell together with membrane. As shown in Fig 5.6,

the peak assignment of the recovered E. coli single cell spectrum is consistent with

that of the E. coli spectrum collected from a planktonic cell analysed on CaF2 (see

Fig 5.6d). The recovered spectrum of bacteria using this normalisation method is


comparable or better than vector normalisation method mentioned in previous study

(215) (result shown in Appendix D).

The effectiveness of this normalisation and peak recovery process was confirmed by

classification of the spectra using the previously constructed PC-LDA planktonic cell

model (Fig 5.3). The classification results provided the correct identification of the

four bacterial species, showing that the membrane peak correction process was able

to allow for Raman efficiency variations that may have been induced by irregularities

in the scattering from the sample, variations in laser exposure or focal point shifts

due to the introduction of the bacterial layer over the membrane substrate.

(A) (B)

Figure 5.7 (A) Classification and identification of spectra from colony cells of four

bacterial species isolated on nitrocellulose membrane, based on the planktonic PC-

LDA model. (B) Test Raman spectra from the four bacterial species. Numbers 1-4

represent the test data and the rest indicate training data used to validate the

discrimination. The model validated the discrimination and identification of test

spectra (1-4) correctly as “E. coli”, “V. vulnificus”, “P. aeruginosa” and “S. aureus”.

Raman spectra collected from the centre core region (C), the outer ring region (R)

and middle region between the centre and outer ring (M) of micro-colonies of each

bacterial species were also added into the classification analysis. In brief, the

normalisation and peak recovery steps were performed from background corrected

Raman spectra as mentioned above. PCA was then performed on the recovered

spectra. The 16 principal components (PCs) of each species were used for

FSET PhD Thesis/156

classification analysis with PC-LDA planktonic model. The E. coli and S. aureus

appeared to cluster most closely to their respective species within the training set, but

there was some ambiguity in the overlap for V. vulnificus and P. aeruginosa (Fig.

5.8). The reliability of classification was examined by calculating the posterior (post)

probabilities, which indicate the probability of the observations matching the

different groups. The observations of the test samples were then located to the group

with the highest post probability. Moreover, the observed test sample was also

classified to the nearest group (i.e. the smallest Mahalanobis distance value from

each of the group means to the observation). Based on this analysis, all of the

samples were correctly classified, except for 33% of V. vulnificus and 11% of S.

aureus which were incorrectly classified (marked with crosses in Fig. 5.8).

Given that surface-attached bacterial cells are believed to be different in gene

expression pattern from those of planktonic cells (such as from proteomic and

transcriptomic analysis) (32, 33, 278), it was not surprising to find some incorrect

classification results. In fact, the classification generally relied on the smallest

Mahalanobis distance due to relatively poor clustering of the data with the training

set. Since there were only four bacterial species (groups) in the constructed PC-LDA

model, it is possible that the test data sample may be classified as another more

closely related group if more groups (bacterial species) were added to the training

set. Another possibility is that the membrane peak removal method for membrane-

grown colonies cells was not perfect, thus leaving some residual of the membrane

signal in the test spectra. Thus, the spectra of membrane-grown colony cells were in

the right general area for the corresponding species, but they were not overlapping

with the training data set.

Nevertheless, the results reported here provide preliminary data for using a PC-LDA

planktonic training model for classification of surface-attached bacterial cells. These

results have highlighted that more bacteria species need to be included in the training

data set in future to evaluate the reliability of the outcomes. Furthermore, the results

suggested that a more reliable computational method may be required to

automatically and completely remove the membrane peak. The results from

calculation of classification accuracy are shown in Table 5.1. The identification-


accuracy achieved in the present analysis shows that Raman spectroscopic

techniques can be applied in rapid bacterial identification of micro-colonies on

nitrocellulose membrane, despite some loss in classification accuracy due to changes

in the colony spectra in comparison with planktonic cells.

(A) (B)

(C) (D)

Figure 5.8 Classification and identification of spectra from cells in different regions

of micro-colonies of four bacterial species isolated on nitrocellulose membranes with

the application of the PC-LDA planktonic model. Raman spectra were collected from

the outer ring, centre core and middle area between the core and outer ring of whole

micro-colony cells of (A) E. coli (B) V. vulnificus (C) P. aeruginosa (D) S. aureus.

Cyan-labelled points represent the test data and the rest indicate the training data of

the PC-LDA planktonic model used for classification. The mean of each group in the

training data set is shown with () symbol in yellow. The test samples which were

misidentified are labelled with (×) symbol in red.

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Table 5.1 Classification of colony cells from four bacterial species isolated on

nitrocellulose membrane with the application of the PC-LDA planktonic model.

Reference Accuracy


Miss

classifi

cation

(%)

Cla

ssif

ica

tio

n r

esu

lt E. coli 9

- 100

V. vulnificus

6 3 66.7

P. aeruginosa 9 - 100

S. aureus 8 1 88.9

In an attempt to investigate the metabolic growth phases within colony development,

the retained 16 PCs of Raman spectra collected from different regions of whole

micro-colony cells were further analysed for every species. The constructed PC-LDA

planktonic model was used for this analysis and the results of E. coli colony cells are

shown in Figure 5.9 and Table 5.2.

Based on the first discriminant function, the results suggest that the outer ring region

of the E. coli colony might have contained cells that were in the early exponential

phase. This finding can be explained by a fact that the outer ring of the colony is a

monolayer of newly divided cells, that the cells in this region are exposed to an

enriched nutrient environment from the culture agar and that the cells undergo

exponential cycles of cell growth and division. The exponential growth of bacterial

cells could also be seen in the middle region of E. coli colony, whereas some cells in

the middle regions of other species might have ceased their exponential increase in

biomass, thus entering a stationary culture phase (data shown in Appendix D). The

population of cells in the middle regions of P. aeruginosa and S. aureus colonies

might face starvation conditions due to very limited access to nutrients and they were

thus in stationary phase.

Interestingly, the centre core region of the E. coli colony showed heterogeneous

population behaviours with observations of some cells being in the exponential phase


while some in the decline phase. It can be postulated that bacterial growth in the

central core of the bacterial colony may be entering the stationary phase and decline

phase because of inadequate nutrient levels. From these results, it can be assumed

that some of the cells in the centre core region might be in the starved condition.

Eventually, when a required nutrient became exhausted (or the concentration of toxic

waste products becomes too high), the cessation of reproduction and growth will

occur. During this condition, cell population growth may become unbalanced and

more heterogeneous (i.e., some cells are still growing and dividing while others are

deteriorating). These findings are consistent with previous studies for colony

development of in the literature (279, 280). Meunier et al. reported that

Saccharomyces cerevisiae cells in the centre of a colony gradually enter stationary

phase and later the bacterial growth occur predominantly at the periphery.

Figure 5.9 Investigation of population behaviours of E. coli cells from spectra of

different regions of colony cells isolated on nitrocellulose membrane with the

application PC-LDA planktonic model. Numbers 1-9 represent 3 Raman spectra

collected from each of the outer ring (1-3), centre core (4-6) and middle area between

the core and outer ring of whole micro-colony cells (7-9) respectively.

Abbreviations: EE, early exponential; ME, mid exponential; LE, late exponential; ES,

early stationary; MS, mid stationary; LS, late stationary; ED, early decline; MD, mid

decline; LD, late decline.

1

2

3

4

5

6

789

-60 -30 0 30 60 90

0

25

50

Dis

crim

inant

function 2


EE

ME

LE

ES

MS

LS

ED

MD

LD

Test sample

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Table 5.2 Analysis of population behaviour of E. coli colony cells.

Reference

(PC-LDA

planktonic

model)

Met

ab

oli

c

ph

ase

Classification results

E. coli

Growth region

Outer ring Middle Centre core

E. coli

(9 cells)

EE 3

ME

3 1

LE

ES

MS

LS

ED

2

MD

LD

Abbreviations: EE; early exponential, ME; mid exponential, LE; late exponential, ES;

early stationary, MS; mid stationary, LE; late stationary, ED; early decline, MD; mid

decline, LD; late decline.

Moreover, a previous study from Naumann group on cell growth in microbial

colonies using FTIR spectroscopy showed that cell population heterogeneity could

be seen in even in relatively young colonies (279). Therefore, the results seen in this

study using PC-LDA model of Raman spectra can further provide the detailed

information of population behaviour of colony cells. To confirm these findings in

future work, the morphological changes and DNA contents of bacterial cells in the

outer ring and other regions of the colony could be investigated by microscopic

techniques (i.e. fluorescence microscopy for nucleic acid labelling and scanning

electron microscopy). These techniques would provide evidence for the presence of

larger cells in exponential phase compared to stationary-phase cells and for different

DNA contents among the cells from different growth phases (281). However, these

detailed analyses of the heterogeneity of bacterial cells from micro-colonies

(particularly in terms of their physiology) were beyond the scope of this study.

5.3.3 Raman analysis of bacterial cells in developing biofilms

Biofilms grown on sterile quartz microscope slides under static conditions were

monitored for up to 5 days for biofilm matrix structures, which were then analysed

with Raman spectroscopy. Optical micrographs of E. coli ATCC 25922 biofilms at


different growth time points are shown as examples of the biofilm formation and

morphology in Fig 5.10A). The initial attachment and cell adherence of E. coli could

be seen at 1 h incubation. Bacterial cell division and growth (increase in cell biomass

and number) was visible after 4 h cultivation with nutrient media. After continuous

cultivation, the adhered bacteria cells aggregated and developed a micro-colony at 8

h growth time. More cells aggregated and early biofilm structures were formed after

8 h incubation time. In this phase, E. coli bacterial cells might generate more self-

produced EPS matrix since the morphology of single cells was hard to differentiate

inside micro-colonies. It is believed that the amount of EPS synthesis within the

biofilm may depend greatly on the availability of nutrient status of the growth

medium (such as excess available carbon and limitation of nitrogen, potassium, or

phosphate promote EPS synthesis) (53). The slow bacterial growth observed in most

biofilms promote the synthesis of EPS (53).

Given that fresh nutrient media were replaced every 24 h to induce continuous

growth leading to biofilm formation and minimise nutrient starvation, more cells

were produced and more complex architectures of mature biofilm were formed at the

79 h growth time. After 120 h of culture, a mature biofilm developed on the quartz

surface and a large amount of EPS could be observed for all the four bacterial species

in this study. Field-emission scanning electron microscopy (Fe-SEM) micrographs

further demonstrated the initial attachment of E. coli to the surface (shown in Fig

5.10B) and the aggregation of E. coli cells enclosed in an EPS matrix.

FSET PhD Thesis/162

(A)

(B)

Figure 5.10 (A) Optical micrographs of E. coli ATCC 25922 biofilms at different

time points. Observation of initial attachment to surfaces after 1 h and 4 h incubation;

cell aggregates (early biofilm forming) after 8 h and 24 h incubation; and mature

biofilm after 79 h and 120 h incubation, were detected with ordinary light

microscopy. (B) Field-emission scanning electron microscopy (Fe-SEM)

micrographs of E. coli attached to surfaces and E. coli biofilm with 5000 times

magnification. The arrows indicate the individual cells that are typically selected for

Raman analysis. The scale bars for 10 µm and 1 µm represent all of the images in

series (A) and (B), respectively.


5.3.3.1 Single-species surface-attached cells

A tentative assignment of the peaks that appeared in the average Raman spectra of E.

coli, V. vulnificus, P. aeruginosa and S. aureus surface-attached bacteria is shown in

Figure 5.11. Raman spectra were collected from single cells of each biofilm grown at

cultivation periods of 1, 4, 8, 24, 72 and 120 hours on quartz substrates. The Raman

spectra of biofilm cells from the four bacterial species contained the peaks which are

associated with cellular components, such as carbohydrate, lipid, protein and nucleic

acids, as determined from the literature (94, 215, 233, 234) (Table 3.1). However, the

peak features were not as prominent compared as those of planktonic cells and

colony cells.

This can be explained by two possible reasons. Firstly, the spectral features of

bacterial cells are overlaid with the background signal of the quartz slides. The effect

of the quartz signal on bacterial spectral quality was explained in Chapter 3.

Secondly, it may be due to the EPS production during biofilm development. As

discussed in Chapter 1, in the mature biofilm, the majority of biofilm matrix (70-

95%) is occupied by EPS secretions enclosing the surface-attached cells inside.

Moreover, the EPS matrix from the mature biofilm recruits more bacterial cells to

attach to the biofilm surface. The amount and thickness of EPS may influence the

intensity of the background signal of the quartz substrate, thereby complicating the

removal of the quartz background. In order to minimise chemical information from a

complex EPS which can interrupt bacterial identification within the biofilm matrix,

Raman experiments could be designed to analyse on recovered (collected and

washed) cells from biofilm (30). However, the motivation of this study is to

investigate the behaviour of surface-attached bacterial cells and to facilitate the

bacterial identification from biofilm samples. Because of the heterogeneity of

bacterial cells from different locations of the biofilm even within a single species, it

was difficult to analyse the chemical variation during biofilm formation of the four

species as a function of time. Therefore, Raman spectral changes were analysed and

compared amongst the biofilm cells of individual species.

FSET PhD Thesis/164


spectra from biofilm cells of the four bacterial species. Abbreviations: Phe,

phenylalanine; Carb, carbohydrate; def, deformation. The dominant peaks for

scattering from DNA/RNA and proteins are shown with the peak assignments

mentioned in Table 3.1.

E. coli

The average Raman spectra of E.coli biofilm cells at different biofilm phases (e.g., 1

h and 4 h for initial attachment; 8 h and 24 h for bacterial colony and early biofilm;

79 h and 120 h for mature biofilm) are shown in Fig. 5.12. The results indicate that

the features of the Raman spectra mainly change in the DNA/RNA related region at

685–800 cm-1, phenylalanine region at 1001 cm-1 and proteins/lipids associated peaks

at 1002, 1239, 1447, 1663 cm-1. These spectral variations are more distinctive in the

comparison between 120 h old biofilm cells and the cells from the earlier biofilm

phases.

From the scatter plot of PCA shown Fig 5.13A, a good separation between these 120

h biofilm cells and the other cells can clearly be seen. The average value plot of the

first principal component revealed a significant separation (p value < 0.05) of biofilm

cells at the later mature biofilm phases (i.e. at 79 h and 120 h) (Fig 5.13B). The

significant separation of the 120 h data set from the rest was probably due to


distinctive spectral intensity changes resulting from the increased density of

microorganisms and more EPS secretion during biofilm formation.

The loading values plot of the first principal component displays the Raman peaks

which contributed most of the separation in the scatter plot (Fig 5.13C). The results

show that the peaks related to DNA/RNA corresponded to the variance of the earlier

phases of biofilm from the later phase, while the protein-specific peaks were related

to the variance of 120 h old biofilm from other phases. These variations of

DNA/RNA and protein-specific peaks indicate the biochemical and/or metabolic

heterogeneity of bacterial cells and self-secreted EPS throughout biofilm

development.


spectra of E. coli surface-attached cells during biofilm development. Abbreviations:

Phe, phenylalanine; Carb, carbohydrate; def, deformation.

As mentioned in Section 1.2.2, EPS are composed of polysaccharides, proteins,

nucleic acids, lipids and humic-like substances. It is believed that the level of

polysaccharides in biofilm-associated EPS was much higher than that in planktonic

cells (282). Moreover, because of increased EPS synthesis and more complex

architecture in mature biofilm, it has been reported that the protein content of mature

biofilm was also higher than that in younger stages of multispecies biofilms (120).

1 hour

4 hour

8 hour

24 hour

79 hour

120 hour

DN

A/R

NA

syn

thes

is

Ph

e

Am

ide

I

Am

ide

III

CH

2d

ef

Ca

rb

CH

de

f

FSET PhD Thesis/166

(A) (B)

(C)


surface-attached cells during biofilm development: (A) scatter plot of the first and

second principal component, (B) average values plot and (C) loading values plot of

the first principal component (**p < 0.05). Abbreviations: Phe, phenylalanine; def,

deformation.

In order to investigate the DNA/RNA and protein content of the E. coli biofilm,

further detailed univariate analysis of the intensity values for specific peaks which

were selected from the loadings plot were performed (Fig 5.14). As shown in Fig

5.14, the intensity values of protein/lipid-specific peaks (i.e. phenylalanine peak at

1002 cm-1, amide III peak at 1239 cm-1, CH2 deformation peak from protein

backbone at 1447 cm-1, amide I peak at 1663 cm-1) were higher in the E. coli cells

from the mature biofilm than in the other biofilm phases. Conversely, the intensity of

**

**

Am

ide IP

he

CH

2d

ef

DNA/RNA


DNA/RNA-specific peaks decreased in the E. coli biofilm cells from the mature

biofilm when compared with the other biofilm phases.

(A)

(B)


specific peaks in the Raman spectra of E. coli surface-attached cells during biofilm

development. Raman peaks were selected from the loadings plot (Fig 5.13(C)). Each

group of biofilm cultivation was an average of four replicates. The Raman

frequencies and their peak assignments are shown in Table 3.1. Abbreviations: T,

thymine; G, guanine; C, cytosine; U, uracil.

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V. vulnificus

The average Raman spectra of V. vulnificus biofilm cells at different biofilm phases

are shown in Fig. 5.15. The results indicate that the main changes in the Raman

spectra occur in the DNA/RNA related region at 685–800 cm-1, phenylalanine region

at 1001 cm-1 and protein/lipid associated peaks at 1002, 1242, 1452, 1663 cm-1.

Unlike E. coli biofilm cells, these spectral variations are more distinctive when

comparing 1 h old biofilm cells with cells from later biofilm phases.


spectra of V. vulnificus surface-attached cells during biofilm development.

Abbreviations: Phe, phenylalanine; Carb, carbohydrate; def, deformation.

The scores plot from PC1vs PC2 also showed the separation of these 1 h substrate

grown cells from later time points (Fig 5.16A). A significant separation (p value <

0.05) of biofilm cells between 1 h old biofilm phase and other older phases was seen

from the average value plot of the first principal component (Fig 5.16B). The loading

values plot of the first principal component shown in Fig 5.16C indicates the Raman

peaks which contributed most of the separation in the scatter plot. Interestingly, the

results show that the peak fluctuations related to DNA/RNA synthesis corresponded

to the later phases of biofilm growth, while the protein-specific peaks were related to

the difference between initially attached cells at 1 h and the later phases of biofilm

development. The variation in the DNA/RNA related peaks in the later phases could

be explained by the release or accumulation of extracellular DNA from bacterial

1 hour

4 hour

8 hour

24 hour

79 hour

120 hour

DN

A/R

NA

syn

the

sis

Ph

e

Am

ide

I

Am

ide

III

CH

2d

ef

Ca

rb

CH

de

f


cells into the biofilm matrix in mature biofilm development (see further discussion

below).

(A) (B)

(C)

Figure 5.16 Principal component analysis of Raman spectra collected from V.

vulnificus surface-attached cells during biofilm development: (A) scatter plot of the

first and second principal component, (B) average values plot and (C) loading values

plot of the first principal component (**p < 0.05, *p < 0.1). Abbreviations: Phe,

phenylalanine; carb, carbohydrate; def, deformation.

The results seen in Fig 5.16 were further confirmed by detailed analysis of specific

peaks related to DNA/RNA synthesis (Fig 5.17A). A higher intensity of DNA related

peaks was seen in the later biofilm phases. In particular, higher intensity values

started to appear from 4 h old biofilm and the highest intensity was seen in the 79 h

old biofilm. Extracellular DNA has recently been reported as a major structural

component in the biofilm matrix and found to play various roles in biofilm

*

**

DNA/RNA

Ph

e

Am

ide I

II

CH

2d

ef

Am

ide I

FSET PhD Thesis/170

development, including enhancement of adhesion and cohesion of biofilm, as well as

exchange of genetic information (283-285). Moreover, it has been further reported

that the release of extracellular DNA was mediated by certain genes (such as lytic

transglycosylase and cytoplasmic N-acetylmuramyl-L-alanine amidase genes in

Neisseria meningitides) to facilitate initial biofilm formation (286). Therefore, it can

be concluded that higher concentrations of extracellular DNA might exist in the

mature biofilm matrix than in earlier phases of V. vulnificus biofilm (i.e., initial

attached cells and bacterial colonies) due to DNA accumulation or release as

observed in this study.

Judging from the appearance probabilities of the spectra (Fig 5.15) and the univariate

analysis of intensity changes for the selected peaks (Fig 5.17B), it can be seen that

the protein content was significantly higher in early biofilm cells compared with

mature biofilm (at 4 h cultivation and onwards). This increased level of protein

synthesis in early biofilm cells differs from the opposite trend observed in the E. coli

biofilm cells (mentioned in Section 5.3.2.1.1). Although it cannot be explained

exclusively, this raised level in V. vulnificus might be related to the high expression

level of lipopolysaccharides, capsular polysaccharide and adhesion proteins on the

outer membrane of the bacteria during initial attachment to the surface.


(A)

(B)


specific peaks in the Raman spectra of V. vulnificus surface-attached cells during

biofilm development. Raman peaks were selected from the loadings plot. Each group

of biofilm cultivation was an average of four replicates. The Raman frequencies and

their peak assignments are shown in Table 3.1. Abbreviations: A, adenine; C,

cytosine; U, uracil; T, thymine.

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P. aeruginosa

The average Raman spectra of P. aeruginosa cells from biofilm growth are shown in

Fig. 5.18. As was seen in the E. coli and V. vulnificus data, key features in the Raman

spectra are associated with the DNA/RNA related region at 685–800 cm-1,

proteins/lipids and phenylalanine associated peaks at 1337 and 1602 cm-1. Similar to

E. coli biofilm cells, these spectral features are more distinctive in the 120 h old

biofilm cells compared to the cells from earlier biofilm phases.


spectra of P. aeruginosa surface-attached cells during biofilm development.


Univariate analyses were performed to investigate the trends in DNA and protein

synthesis during biofilm growth. The intensities value of DNA-related peaks selected

from the loading plots showed a decreasing trend in DNA content during biofilm

growth (Fig 5.20A). Although the overall spectral variation were insignificant (p >

0.05) as shown in the average values plot, the detailed analysis of specific peaks

revealed a significant increasing trend in the protein-related peaks, which were again

similar to the trend of E. coli cells during biofilm development (Fig 5.20B).

Moreover, the large error bars seen in the samples from mature biofilm indicated that

there was higher cellular heterogeneity in this phase compared to other earlier

biofilm growth phases. This phenomenon was also seen in the planktonic cells of P.

aeruginosa, but it was even more striking in the biofilm cells. This suggests that P.

1 hour

4 hour

8 hour

24 hour

79 hour

120 hour

DN

A/R

NA

syn

thes

is

Ph

e

Am

ide

I

Am

ide

III

CH

2d

ef

Ca

rb

CH

de

f


aeruginosa might exhibit a variety of physiological and morphological changes from

a low- to a high-cell-density state in mature biofilm. As discussed in the literature

Chapter, biofilm cells are likely to encounter nutrient and oxygen limitations, as well

as higher levels of waste products and secondary metabolites. Moreover, it is

believed that the production of EPS by surface-attached cells and the mechanisms of

the biofilm development process are quite different from species to species (287).

Because of this, it is perhaps not surprising that there was more intrinsic cellular

heterogeneity in P. aeruginosa biofilm cells compared to other species tested in this

study.

(A) (B)

(C)

Figure 5.19 Principal component analysis of Raman spectra collected from P.

aeruginosa surface-attached cells during biofilm development: (A) scatter plot of the

first and second principal component, (B) average values plot and (C) loading values

plot of the first principal component (***p < 0.005). Abbreviations: Phe,


***C

=C

,de

f,

ph

e

CH

de

f

DNA/RNA

FSET PhD Thesis/174

(A)

(B)


specific peaks in the Raman spectra of P. aeruginosa surface-attached cells during



their peak assignments are shown in Table 3.1. Abbreviations: T, thymine; G, guanine

C, cytosine; U, uracil.


S. aureus

The average Raman spectra from biofilm cells of the Gram-positive bacterium, S.

aureus, are shown in Fig 5.21. The main changes in the Raman spectra throughout

biofilm growth were seen in the DNA/RNA related region at 685–800 cm-1,

phenylalanine and protein/lipid associated peaks at 1002, 1452 and 1663 cm-1.

Raman spectral features in these regions are actually more distinctive in the initial

attached biofilm cells (i.e. 1 h and 4 h old biofilm) compared to the cells from

bacterial colonies or mature biofilm.


spectra of S. aureus surface-attached cells during biofilm development.


The good separation between biofilm cells and cells at initial attachment can be seen

in the scatter plot of the first and second principal components (Fig 5.22A). The

average values plot of the first principal component revealed significant spectral

variations between earlier biofilm cells and mature biofilm cells (Fig 5.22B). The

loadings plot of the first principal component provides the peak regions which

contributed to the separation of cells during biofilm growth, as seen in the scatter plot

(Fig 5.22C). The results indicate that the peaks which are associated with

lipid/protein synthesis represented the main spectral variation of surface attached

bacterial cells compared to the biofilm cells. From the analysis of specific peaks, it

can be seen that the DNA peak intensities were relatively stable during biofilm

growth. As mentioned in the earlier sections, the intensity of the protein-related

1 hour

4 hour

8 hour

24 hour

79 hour

120 hour

DN

A/R

NA

syn

the

sis

Ph

e

Am

ide

I

Am

ide

III

CH

2d

ef

Ca

rb

CH

de

f

FSET PhD Thesis/176

peaks increased significantly for Gram-negative bacteria from initial bacterial

attachment to mature biofilm. However, for S. aureus, this increase was different to

that of E. coli and P. aeruginosa, as a decreasing trend was observed along with the

biofilm cultivation. This increased intensity of protein-related peaks during initial

attachment might be due to the higher expression of surface proteins in S. aureus

bacteria for cell adherence to the surface. As mentioned in Section 1.2.2, bacterial

cells that do not have extracellular organelles (such as fimbriae, flagella and pili)

normally produce adhesion proteins to overcome the interfacial repulsive forces and to

promote stable attachment to the surface. The role and expression of surface adhesion

proteins in S. aureus for adherence to surfaces has been recently reported (288).

(A) (B)

(C)

Figure 5.22 Principal component analysis of Raman spectra collected from S. aureus

surface-attached cells during biofilm development: (A) scatter plot of the first and

second principal components, (B) average values plot and (C) loading values plot of

the first principal component (***p < 0.005). Abbreviations: Phe, phenylalanine; def,

deformation.

*****

DNA/RNA

Ph

e

O-P

-O,

RN

A

Am

ide I

CH

2d

ef


(A)

(B)


specific peaks in the Raman spectra of S. aureus surface-attached cells during



their peak assignments are shown in Table 3.1. Abbreviations: T, thymine; G, guanine

C, cytosine; U, uracil.

FSET PhD Thesis/178

5.3.4 PC-LDA models for classification of biofilm cells

5.3.4.1 Single-species surface-attached cells

As discussed in the literature review, the identification of microbial species within an

intact biofilm sample is important for microbiological studies of biofilm formation in

clinical and environmental settings. Based on the limitations of standard methods for

species identification, a measurement method that could detect the presence of

bacteria and map the spatial distribution of multiple species in intact biofilm samples

in a label-free, reagentless fashion would be invaluable. Therefore, an attempt was

made to create a model which could be used in longitudinal studies of environmental

biofilm samples.

The results from the previous sections have shown significant variations in the

Raman spectra of cells at different growth points in single species biofilms. A first

attempt was made to classify the surface-attached cells (biofilm cells) using the

previously constructed PC-LDA planktonic model. The results revealed that 72%

classification sensitivity was achieved for the Raman spectra collected from surface-

attached cells of E. coli species during biofilm growth (Fig 5.24).

Figure 5.24 Classification and identification of spectra from surface-attached cells of

E. coli grown on a quartz substrate with the planktonic PC-LDA model. The mean of

each group in the training model is shown with () symbol in yellow. The test

samples which were misidentified are labelled with (×) symbol in red.

-9 -6 -3 0 3 6-8

-4

0

4

8

12 E. coli

V. vulnificus

P. aeruginosa

S. aureus

Test sample

Group Means

Dis

crim

inant fu

nction 2


XX

X

XX


Although 72% correct identification was achieved, it can be seen that the surface-

attached cells were clustered near the general area of the corresponding species

within the model without overlapping. As discussed in Section 5.3.2, the

classifications results of surface-attached cells using PC-LDA planktonic cells were

imperfect due to the differences between surface-attached and planktonic cells. In

fact, efforts to classify the other species (i.e. V. vulnificus, P. aeruginosa and S.

aureus) were a failure as a consequence of these variations between surface-attached

and planktonic cells and intrinsic cellular heterogeneity among biofilm cells.

The next step in the study investigated whether the differences between cells at

different biofilm growth points impacted on the classification and identification of

isolates from intact biofilm using a biofilm model. To explore this, Raman spectra

from the three bacterial species E. coli, V. vulnificus and S. aureus were analysed at

different points of biofilm growth using principal component and linear discriminant

analysis (PC-LDA) since biofilm data of P. aeruginosa showed more intrinsic

cellular heterogeneity compared to other species (shown in Fig 5.19). PCA was first

performed for data reduction of the 1407 included wavenumbers from each spectrum

of the cells from different phases of biofilm growth using MATLAB. With the

application of OriginPro software (version 9.0.0), LDA was further performed based

on the first 10 principal components (PCs) generated from MATLAB which

accounted for approximately 98 % of variance in the data set. As shown in the

canonical scores plot, which was plotted against the first two canonical discriminant

functions, the PC-LDA classification method effectively discriminated and classified

the bacterial taxa into three groups, despite physiological variations between the

same species cells during biofilm development (Fig 5.25).

For evaluation and calibration of the PC-LDA model, leave-one-out cross-validation

(LOOCV) was performed. To perform LOOCV, a single spectrum was removed as a

test spectrum from the database and a training data set was created using the

remaining spectra. The classification label of the test set (left out spectrum) was

determined and the process was repeated for all 18 cells against the training set (36

spectra). Clustering of each test set (one cell from every biofilm growth phase of

each bacterial species) was observed among the data within its respective training

FSET PhD Thesis/180

set, thus validating the model. The cross-validation results of PC-LDA based on the

first 16 PCs of the three different species are shown in Table 5.4. The results provide

100 % classification sensitivity and specificity in a leave-one-cell-out cross-

validation (LOCOCV) for all three species.


components (PCs) for bacterial species differentiation during biofilm growth: LDA

was performed based on 16 PCs of the Raman spectra collected from the surface-

attached cells of E. coli, V. vulnificus and S. aureus biofilms. The mean of each

group in the training model is shown with () symbol in yellow.

Table 5.3 Calibration accuracy results of the PC-LDA model with the first 16 PCs on

a total of 54 spectra of three bacterial species from their different biofilm growth

points.

Predicted Group Sensitivity Specificity Error rate

E. coli V. vulnificus S. aureus (%) (%) (%)

E. coli 18 0 0 100 100 0

V. vulnificus 0 18 0 100 100 0

S. aureus 0 0 18 100 100 0

-10 -5 0 5 10-8

-4

0

4

8

12

Dis

crim

ina

nt

fun

ctio

n 2


E. coli

V. vulnificus

S. aureus

Group Means


MATLAB code for the PC-LDA model along with the corresponding species labels

was custom written and applied for species identification of the E. coli biofilm cells

from separate batch culture. The calibrated PC-LDA model using spectra from

biofilm cells of each species was validated on 9 spectra of E. coli biofilm cells

achieving 100% accuracy in prospective classification. As discussed in Section 5.3.2,

the PC-LDA classification was based on the smallest distance value from each of the

group means to the sample. The classification of the test samples projected into the

DFA space generated by the training set is shown in Fig 5.26. Since this PC-LDA

biofilm model correctly identified the species of origin of single-species biofilms, the

next step was to apply this model for detection of two bacterial species in mixed

biofilms.

Figure 5.26 Validation of PC-LDA biofilm model on 9 new spectra of E. coli cells

from a single-species biofilm: the numbers 1-9 represent the test data and the rest of

the points indicate training data used to identify the bacterial cells.

5.3.4.2 Raman- Fluorescence in situ hybridisation (FISH) analysis of

bacterial cells from dual-species biofilm

Techniques based on FISH and confocal laser scanning microscopy have become

well-established for the analysis of the spatial organization of in vitro bacterial

biofilms (18, 64, 65, 69). There are many advantages of using rRNA as the main

target molecules for FISH, because ribosomes can be found in the cells of all living

1

2

3

45

67

89

-10 -5 0 5 10 15 20

-4

0

4

8

12

Dis

cri

min

an

t fu

nctio

n 2


E. coli

V. vulnificus

S. aureus

Test sample

FSET PhD Thesis/182

organisms for translation processes. They are relatively stable and occur in high copy

numbers. Each prokaryotic ribosome (i.e. ribosomes of bacteria and archaea)

contains 5S, 16S and 23S rRNA with lengths of approximately 120, 1500 and 3000

nucleotides, respectively (20, 87).

Experiments were set up to enable Raman and FISH analysis of individual bacterial

cells from dual-species biofilm. Raman spectra were collected to determine the

chemical fingerprints of the individual microbial cells within the biofilm. These

spectra were then projected into the PC-LDA biofilm model in an attempt to identify

the species. The FISH technique was then used to confirm the location of E. coli

within the biofilm. The FISH technique also allowed in situ visualization of the

complex biofilm structure and spatial distribution of the cells.

Raman spectra were collected from 79 h old dual-species biofilm and signal pre-

processing was performed as mentioned in Section 2.2.5.2. To track the location of

the biofilm cells, which were selected to collect the Raman spectra, an electron

microscopy grid (#G4901, grid size 300 mesh × 83 μm of pitch, Sigma) was used.

The microscope grid was mounted on the back of the quartz slide where the biofilms

were grown. A single bacterial cell from biofilm sample was brought into focus to

collect the Raman spectra. After each Raman acquisition, the grid was brought into

focus and microphotographs were taken for tracking the location of the cell.

The custom-written MATLAB code for the PC-LDA model, along with the

corresponding species labels of biofilm cells from each single-species biofilm, was

applied for species identification of cells from a dual-species biofilm. The model was

applied to detect the presence of two species in dual-species biofilms of E. coli and

V. vulnificus. When 12 spectra collected from dual-species biofilms were examined,

the presence of both E. coli and V. vulnificus was detected in 5 and 4 out of 12

sample regions respectively and providing 75 % for overall sensitivity using the PC-

LDA model based on the first 16 PCs of three bacterial species (Table 5.4). The

classifications of the test samples projected into the DFA space generated by the

training set are shown in Fig 5.27. As discussed in Section 5.3.2, the PC-LDA

classification was based on the smallest distance value from each of the group means


to the sample. Thus, the test samples labelled with (x) symbol in red (shown in Fig

5.27) were probably allocated to S. aureus species, which was the nearest group to

the test data point. To confirm the presence and identification of bacterial cells with

the PC-LDA biofilm model, fluorescence in situ hybridisation (FISH) technique with

rRNA-targeted oligonucleotide (probe) for E. coli (ATCC 25922) was performed.

The probe efficiency test and FISH techniques are discussed in Section 2.2.4.3.

Table 5.4 Application of PC-LDA model to 12 spectra from a dual-species biofilm.

Reference Sensitivity

E. coli V. vulnificus Misidentification (%)

Cla

ssif

ica

tio

n

resu

lt Cells from dual-

species biofilm

culture

5 4 3 75

Figure 5.27 Application of the PC-LDA biofilm model to 12 spectra from dual-

species (E. coli and V. vulnificus) biofilm culture: Numbers 1-12 represent the test

data and the rest of the points indicate training data used to identify the bacterial

cells. The test samples numbered 1, 4-6, 8 were identified as E. coli and the test

samples numbered 2, 10-12 were identified as V. vulnificus. The test samples which

were misidentified are labelled with (×) symbol in red.

1

2

3

4

5

6

7

89 101112

-10 -5 0 5 10 15

-4

0

4

8

12

Dis

crim

ina

nt

fun

ctio

n 2


E. coli

V. vulnificus

S. aureus

Test sample

X

XX

FSET PhD Thesis/184

In this study, FISH was undertaken with 16S rRNA targeted oligonucleotide (probe)

for E. coli. The rRNA targeted oligonucleotide probe and its hybridization conditions

were discussed in Section 2.2.4.3. The probe sequences used (EC1_485) were

designed to specifically target the 16S rRNA of E. coli ATCC 25922 (Accession:

X80724, GI: 1240023, 1452 base pairs, genomic DNA). The probe was

manufactured by Life Technologies Australia Pty Ltd and was labelled with Alexa

Fluor 647. Probe sequences were designed and pre-validated using the Primer-blast

tool from National Center for Biotechnology Information (NCBI) before synthesis.

The probe specificity, efficiency and EPS staining protocols with Concanavalin A

(ConA, Molecular Probes, Invitrogen) were optimised (details in Sections 2.2.4.3,

2.2.4.4 and 2.2.4.6).

The optimised FISH techniques were applied to the dual-species biofilms of E. coli

and V. vulnificus after Raman analysis. Two-dimensional (2-D) confocal laser

scanning microscope (CLSM) images of dual-species biofilms are shown in Fig 5.28.

The results show that the target E. coli cells were generally labelled with both FISH

rRNA probe and ConA, while the cells which were stained with ConA only were

considered to be V. vulnificus species. This strategy is based on the expectation that

the cells that were identified indirectly by ConA stain outnumber the cells labelled by

the FISH rRNA probe, which is the case here (Fig 5.28B and C).


Figure 5.28 Two-dimensional confocal laser scanning microscope images of dual-

species biofilms of E. coli and V. vulnificus. (A) DIC image, (B) FISH hybridized E.

coli cells labelled with Alexa Fluor 647, (C) Concanavalin A stained EPS (-D-

glucopyranose polysaccharide) and proteins/glycoconjugate groups associated with

bacterial cell walls and (D) visualisation of E. coli cells in red with EPS matrix and

bacterial cell wall in blue. (White arrow shows labelled E. coli and red arrow shows

stained EPS and bacterial cell wall)

In order to investigate the cellular densities, spatial distribution of bacterial species

and structural information of the biofilm matrix, CLSM images of 79 h old dual-

species biofilm were captured with the z-stack function tool from CLSM (Fig 5.29).

The three-dimensional (3-D) reconstruction of the confocal z-stack images was

performed with ImageJ software. Selected 2-D cross-sectional images of CLSM

showed the spatial distribution of bacterial species across the surfaces of dual-species

biofilms in the x-y, y-z and x-z planes. The two species were equally distributed

across the surfaces of biofilms in the x-y plane, whereas more than two defined layers

of cells could be seen in the vertical distribution of the z axis. The top layer was

mainly composed of labelled E. coli cells and another layer close to the substrate

FSET PhD Thesis/186

surface was mostly V. vulnificus cells/EPS matrix stained with ConA and. In terms of

species interactions in these dual-species biofilms, the cell density of E. coli was

sightly affected by the presence of V. vulnificus (Fig 5.29A). The higher cell density

of V. vulnificus can be seen more clearly in the 3-D CLSM image and the formation

of differentiated 3-D structures like ‘‘stacks’’ of micro-colonies could also be

observed (Fig 5.29B).

(A)

(B)

Figure 5.29 Spatial organisation of 79 h old dual-species biofilms. (A) 2-D confocal

laser scanning microscope (CLSM) images of dual-species biofilms showing spatial

distribution of E. coli and V. vulnificus species on x-y, y-z and x-z planes, (B) 3-D

CLSM images showing dual-species biofilms with FISH hybridized E. coli cells

labelled with Alexa Fluor 647 in purple (blue + red) and concanavalin A stained EPS

(-D-glucopyranose polysaccharide) and proteins/glycoconjugate groups associated

with bacterial cell walls of V. vulnificus species in blue.

x-y plane

10 µm

x-z plane

y-z plane


The identification results from the PC-LDA biofilm model suggested a homogeneous

population of E. coli and V. vulnificus cells (i.e. approximately 50% of each of the

identifiable cells). However, the data from FISH technique showed a smaller cell

density of E. coli in the dual-species biofilm. From the results of the planktonic

growth curve experiments which were mentioned in Section 4.3.2.1, the growth rates

of E. coli and V. vulnificus, were found to be similar (approximately 0.77/h and

0.65/h respectively). On account of the same number of initial loaded cells on the

substrate and the similar growth rate, the cell densities of the two bacterial species

were expected to be similar in the dual-species biofilm. The reduced number of E.

coli in the biofilm in the presence of V. vulnificus is probably due to competition for

nutrients during biofilm growth. Moreover, it has been reported that the capsular

polysaccharide of V. vulnificus inhibits attachment and biofilm formation (203).

FISH hybridized E. coli cells labelled with Alexa Fluor 647 probe were seen as red

cells clusters. EPS (α-D-glucopyranose polysaccharide) and proteins/glycoconjugate

groups associated with cell walls labelled with ConA stain were seen as in blue.

Thus, E. coli cells aggregation in the biofilm were seen as in purple coloured cells

cluster (blue plus red) because of labelling with both FISH probe and ConA. As a

consequence of having overlapped bacterial cells within biofilm matrix (as seen in

Fig 5.29), the cells cluster of the micro-colonies could not be identified clearly

whether they were E. coli or V. vulnificus. Since unstained cells were not excluded

from undetectable cells, the cell densities could not be accurately enumerated or

estimated from visualisation using only one species-specific fluorescence-labelled

probe in this experiment.

The results from confocal imaging underscored the involvement of extracellular

polysaccharide of V. vulnificus, which may relate to both the initial attachment and

biofilm development of E. coli cells when these two species were co-cultured. For

the reason that the two bacterial species were on the top of each other within the

multi-layered biofilms, the Raman spectra collected from the same focal plane may

have mixed spectral features from the two bacterial species. Therefore, the

identification results from dual-species biofilms may be unpredictable. Thus, the

classification and identification results of the cells from the mature dual-species

FSET PhD Thesis/188

biofilms were not successfully confirmed by FISH. If less mature biofilm samples

were used instead, the outcomes may be different as the Raman spectra of surface-

attached cells from the monolayer biofilms may be more reliable for PC-LDA

classification.

The first obstacle in performing the FISH technique was that some of the collected

Raman spectra from the multi-layered biofilm had mixed spectra features from the

two bacterial species. This issue may be minimised if Raman spectra were collected

from cells of mono-layered biofilm samples. Thus, this problem could be solved in

future studies, possibly by collecting “z” profile Raman spectra using a combination

of FISH with Raman spectroscopy instrumentation, providing that the fluorescence

doesn’t swamp the Raman spectral region.

Another challenge for the FISH technique applied here is that the presence of other

charged particles in the biofilm matrix, such as DNA and sugar acid residues could

impede the penetration of probes to the target cells. Therefore, it is very difficult to

rely on the labelling efficiency of a one species-specific probe in a mixed biofilm by

FISH. This difficulty could be revised in future, possibly by using species-specific

fluorescence-labelled probes for every species in the consortium biofilm. By

performing multiplex FISH analysis, it will assure that only CoA stained cells in the

biofilm were due to substantial losses of the probe penetration to the target cells

during the staining procedure. Then a sample preparation step for hybridisation (i.e.

permeabilisation using lysozyme) could be optimised to achieve the best outcomes.

Although we were not able to confirm that the model detected the correct species, to

the best of our knowledge, this is the first study in which two different bacterial

species have been detected and identified in dual-species biofilm using Raman

spectroscopy.

5.4 Conclusion

In conclusion, the application of Raman spectroscopy to bacterial identification in

intact bacterial colonies has been developed in this study. This approach allows for

simple sample preparation for direct investigation of bacterial micro-colonies

isolated on nitrocellulose membranes, which were laid on pre-warmed nutrient agar.


Moreover, the ability to detect and identify the bacteria that were loaded on the

membrane can be applied in food-processing environments and water analysis.

The nitrocellulose membrane provides a very narrow and sharp Raman peak at a

wavenumber of 1282 cm-1 and the Raman signal from this peak was used as an

internal standard for normalization of the spectra. Although the membrane peak

removal method for membrane-grown colonies cells was challenging and not so

perfect at this point, high accuracy in differential identification was achieved using

PC-LDA planktonic model except for V. vulnificus species. These results encouraged

that the nitrocellulose membrane could be used in routine Raman analysis as it is

cost-effective and commercially available. As a preliminary approach, the population

behaviours of bacterial cells isolated on the membrane were analysed with the PC-

LDA planktonic model and promising results were obtained. With the appropriate

reference method to provide confirmation of the population behaviour of bacterial

cells, this approach shows good potential for use in analysis of membrane attached

cells. Overall, this technique allows the simple creation of a Raman biosensor for

differential bacterial identification.

Raman spectroscopy has recently proven to be a promising technique for

characterizing the chemical composition of the biofilm matrix (119, 120). In the

present study, to fully understand the chemical variations during biofilm formation,

Raman spectroscopy was applied to evaluate the chemical components in the biofilm

matrix at different growth phases, including initial attached bacteria, colonies and

mature biofilm. Meanwhile, field-emission scanning electron microscopy (Fe-SEM)

was also applied to study the changes in biofilm morphology. Four model bacteria,

including E. coli, V. vulnificus, P. aeruginosa and S. aureus, were used for Raman

analysis of surface grown biofilm cells. Single-species biofilms of these species were

studied as a simplified model of biofilm forming bacteria in clinical and

environmental studies. The results showed that the content of carbohydrates, proteins

and nucleic acids in the biofilm matrix changed significantly during the biofilm

growth of the four bacteria, as demonstrated by the univariate analysis of related

marker peaks which were selected from PCA. The findings suggest that Raman

FSET PhD Thesis/190

spectroscopy has significant potential for studying chemical variations during biofilm

formation.

Despite the Raman spectral variations of cells from different growth points for a

single species, confocal Raman spectroscopy combined with chemometric statistical

analysis (PCA and LDA) provided a good discrimination between different species

of biofilm cells. Analyses were performed using three bacterial species which

included Gram-negative and Gram-positive bacteria (i.e. E. coli, V vulnificus and S.

aureus). A prediction model based on principal component and linear discriminant

analysis (PC-LDA model) was calibrated using single spectra from biofilm cells of

each species and validated on pure E. coli biofilms grown separately, achieving

100% accuracy in classification although PC-LDA planktonic model provided poor

classifications for biofilm cells.

When the PC-LDA biofilm model was applied to a dual-species biofilm, the presence

of E. coli or V. vulnificus was detected in nine out of twelve biofilm regions,

providing 75% sensitivity. Performing FISH was the next motivation to confirm the

species identification results from dual-species biofilm with the PC-LDA biofilm

model. However, many challenges were encountered in performing the FISH

technique. In future studies, collecting “z” profile Raman spectra using a

combination of FISH with Raman spectroscopy instrumentation could solve some

difficulties of having mixed spectra features from the two bacterial species.

Moreover, performing multiplex FISH analysis will be able to confirm whether the

PC-LDA model detect the correct species within dual or multi-species biofilm

samples.


RAMAN ANALYSIS OF BACTERIA ON DIFFERENT SURFACE

CHEMISTRIES

6.1 Introduction

This chapter presents a Raman spectroscopy study of E. coli grown on hydrocarbon

rich, amine-terminated and carboxyl-terminated plasma polymer surfaces (i.e. 1, 7-

octadiene, allylamine and acrylic acid). It is believed that surface-attached bacteria

normally sense and respond to a substratum surface, resulting in adaptive responses

(i.e. subtle changes in morphology and chemical composition) during their struggle

for survival (289). This reaction/response from bacterial cells is due to the adhesion

forces that make bacteria aware of their adhering state on a surface and drive the

change from a planktonic to a biofilm phenotype (290). As also discussed in Chapter

1, physical and chemical properties of surfaces can influence bacterial cell adhesion

to surfaces and their development into biofilms (181, 182, 194). In this Chapter, the

primary intention was to evaluate the effect of different surfaces on the ability to

identify the bacteria using planktonic and biofilm models. The initial attachment and

viability of attached bacteria and subsequent growth and biofilm formation on

different plasma-polymerized surfaces are also discussed. In order to observe cell-

surface interactions during biofilm development, multivariate and univariate analyses

of Raman spectra collected from bacterial cells attached to plasma polymer films

were performed.


Fused quartz slides (dimension 76 × 25mm; thickness 1 mm) purchased from

ProSciTech, Australia (detailed in Section 2.1.3) were used as a substrate for

deposition of the plasma polymerised thin films. 1, 7-octadiene (molecular formula

C8H14, 98.50% purity, MW 110.20, d = 0.740) and allylamine (molecular formula

C3H7N, 98+%, extra pure, MW 57.09, d = 0.763) were purchased from Acros

Organics, USA. Acrylic acid (molecular formula C3H4O2, 99% purity, MW 72.06, d

= 1.051) was purchased from Sigma-Aldrich, USA. Isopropyl alcohol (IPA,

FSET PhD Thesis/192

molecular formula C3H8O, 99.8 % purity) and ammonia 30% solution (molecular

formula NH4OH, MW 35.05, d = 0.89) were obtained from Chem- Supply Pty Ltd,

Australia. Hydrogen peroxide 30% (molecular formula H2O2, MW 34.01) was

purchased from Ajax Finechem Pty Ltd, Australia.

Plasma polymerisation experiments were performed in collaboration with Ms.

Hannah Askew who is a PhD candidate in the McArthur group. The optimal

deposition conditions for plasma polymerisation, i.e. radio frequency, input power,

monomer flow rate and deposition time were established from previous studies

undertaken within the McArthur group (291).

Prior to plasma polymerisation, quartz substrates were first sonicated in IPA for 10

minutes. The substrates were then cleaned by incubating in a solution containing

Milli-Q water, 30% ammonia and 30% hydrogen peroxide (ratio of 5: 1: 1) at 70 °C

for 10 min, followed by extensive rinsing with Milli-Q water. The cleaned substrates

were blown dry with N2 gas before treatment in a UV Ozone Cleaner (Bioforce

Nanosciences) for 10 min. Plasma polymerisation was carried out in a custom-built

stainless steel T-shaped reactor with stainless steel end plates that were sealed with

Viton O-rings, as previously described (291). The gas pressure was controlled using

a fine (CMV-VFM-2-P-KK) or medium (CMV-VFM-3-P-KK) flow needle valve

(Chell Instruments Ltd, UK) depending on the monomer being used and was

monitored with a Pirani gauge (Edwards, UK). The reactor was pumped down to a

base pressure of 1 × 10-3 mbar. Monomers were degassed using a minimum of three

freeze-thaw cycles. Stabilisation of the defined monomer flow rates was performed

according to the standard operating procedure established by the McArthur group

(291). Plasma was ignited via an aluminium internal disc electrode connected to a

radio frequency (13.56 MHz) power source (Coaxial Power Ltd.) for 20 mins once a

stable flow rate was reached. Plasma deposition conditions for the different

monomers are summarised in Table 6.1, which includes deposition power, monomer

flow rate and deposition time.


Table 6.1 Plasma polymerisation conditions for 1, 7-octadiene, allylamine and

acrylic acid.

Monomer Structure Power

(W)

Flow rate

(sccm)*

Time

(min)

1, 7-octadiene

20 1.5 20

Allylamine 20 1.5 20

Acrylic acid

20 1.5 20

* Standard cubic centimeters per minute (sccm) units for the monomer flow rate

The choice of polymer coated thin films for this study was based on previous studies

of the McArthur group revealing that surface chemistry plays a critical role in

bacterial attachment (292, 293). Therefore, this study was conducted to further

understand the interaction of E. coli cells to the hydrocarbon rich, amine-terminated

and carboxyl-terminated plasma polymer surfaces using Raman spectroscopy.

The surface chemistry of plasma polymerised thin films (i.e. ppOD (1, 7-octadiene),

ppAAm (allylamine) and ppAAc (acrylic acid)) was determined by X-ray

photoelectron spectroscopy (XPS) with the help of Dr. Deming Zhu. The XPS

measurements were carried out with an AXIS-NOVA XPS spectrometer (Kratos

Analytical Inc., Manchester, UK) using a monochromated Al Kα source with a

power of 150 W (15 kV × 10 mA) at a pass energy of 20 eV for high resolution scans

and 160 eV for wide scans. The total pressure in the sample analysis chamber during

analysis was on the order of 10-8 Torr (1.33 × 10-8 mbar). Three different positions on

each sample were analysed. CasaXPS software (Casa Software Ltd., Cheshire, UK)

was used to determine the elemental composition and the main components present

in the plasma polymers using sensitivity factors supplied with the instrument.

Surface wettability of plasma polymerised thin films was determined by static

contact angle measurements using a contact angle goniometer (Ramé-Hart, Inc.,

Mountain Lakes, NJ, USA). These contact angle measurements were performed in

FSET PhD Thesis/194

collaboration with Ms. Hannah Askew. Contact angles were measured statically on

each of the samples using a contact angle goniometer. A droplet (~ 2μL) of Milli-Q

water was lowered manually into the sample using a needle. The contact angle was

then measured on the right side of the droplet. The measurements were repeated in

three different positions on the sample.

To investigate cell attachment and growth on different polymer coated surfaces, a

static biofilm cultivation process was undertaken on the quartz glass slides using E.

coli and following the methods detailed in Section 2.2.3.3. The samples were

collected after 1 h, 24 h and 120 h incubation time points. The biofilm samples were

kept in PBS and rinsed with Milli-Q water just before Raman measurement. For

Raman spectroscopy measurements of the transferred cells, the bacterial cells from

the polymer coated surfaces and quartz substrate were removed from the surfaces by

scrubbing with inoculation loops. The harvested cells were then mixed with 10 µL of

MilliQ water on a CaF2 slide, smeared and air-dried.

The surface-attached bacterial cells/biofilm cells from the intact biofilm samples, as

well as the transferred cells on the CaF2 slides, were analysed by Raman

spectroscopy with the parameters mentioned in Section 2.2.5. For visualisation of E.

coli live and dead cells within the biofilm matrix, bacterial viability tests were

performed as mentioned in Section 2.2.4.1. The SYTO9 stained live cells and

propidium iodide (PI) stained dead cells were visualised under confocal laser

scanning microscopy (CLSM) using the protocols detailed in Section 2.2.4.


6.3.1 Characterisation of the plasma polymer thin films

6.3.1.1 Surface wettability

The contact angles (surface wettability) of the plasma polymer thin films (ppOD,

ppAAm and ppAAc) and the control quartz substrate were determined using a

goniometer. The results confirmed that the hydrocarbon rich, ppOD thin film was

hydrophobic with a contact angle of 90°±0.8 compared to the more hydrophilic

oxygen-rich ppAAc coated surface and quartz slide with contacts angles of 50°±2

and 20°±5 respectively. The ppAAm thin film displayed moderate surface wettability


with a contact angle of 60°±3. These results correlated well with the contact angle

data for both of these plasma polymers and clean quartz substrate found in the

literature (294-296).

6.3.1.2 X-ray photoelectron spectroscopy

The surface chemistry of each of the plasma polymers was determined by XPS. A

survey spectrum of each plasma polymer showing each of the elements (i.e. oxygen,

O 1s; carbon, C 1s; and nitrogen, N 1s) present on the sample surfaces is presented in

Fig 6.1. The atomic composition and O/C and N/C ratios for each polymer thin film

are shown in Table 6.2. The results indicated that as expected the 1, 7-octadiene thin

film (ppOD) was hydrocarbon rich with low levels of oxygen incorporation due to

oxidation of the film (297). The acrylic acid thin film (ppAAc) contained the

expected levels of both carbon and oxygen. The plasma polymerised allylamine thin

film (ppAAm), contained oxygen, nitrogen and carbon as expected with oxygen

again incorporated due to oxidation. The results obtained in this study were

correlated with previous studies reported by the McArthur group (293). Based on the

survey spectra which showed all elements present on the sample surfaces, subsequent

high-resolution XPS spectral acquisition was performed for O 1s, N 1s and C 1s to

analyse the functionality of the components of the peak. These XPS results were just

for validation of surface chemistry on polymer surfaces demonstrating that the

coatings were not contaminated and could be correlated with coatings produced

previously in the group.

FSET PhD Thesis/196

Figure 6.1 XPS survey spectra of plasma polymerised 1, 7-octadiene (ppOD),

allylamine (ppAAm) and acrylic acid (ppAAc) deposited on quartz slides.

Table 6.2 XPS Atomic composition and atomic ratios of plasma polymerised thin

films deposited on quartz substrates.

Atomic composition (%) Relative ratio to carbon

C 1s O 1s N 1s O/C N/C

ppOD 96.8 ± 0.1 3.2 ± 0.1 - 0.03 -

ppAAm 84.7 ± 0.4 2.1 ± 0.3 13.1 ± 0.1 0.02 0.15

ppAAc 77.0 ± 0.2 23.0 ± 0.2 - 0.29 -

* Listed are the mean values (± standard deviation) based on 3 analyses performed

on each sample.

wide

x 104

2

4

6

8

10

CP

S

1000 800 600 400 200 0

Bi ndi ng E nergy (eV)

Binding Energy (eV)

10

8

6

4

2

x104

Inte

nsity /

Arb

itr.

un

its

O1s C1sppAAc

wide

x 104

2

4

6

8

10

12

14

CP

S

1000 800 600 400 200 0


1000 800 600 400 200

10

8

6

4

2

12

ppOD

Binding Energy (eV)

Inte

nsity /

Arb

itr.

un

its

O1s C1s

x104wide

x 104

2

4

6

8

10

12

CP

S

1000 800 600 400 200 0


10

8

6

4

2

12

Binding Energy (eV)

Inte

nsity /

Arb

itr.

un

its

O1s C1sN1sppAAm

x104

1000 800 600 400 200

1000 800 600 400 200


6.3.1.3 Raman spectroscopy measurement

In order to examine the Raman spectra of the plasma polymer thin films deposited on

quartz substrates (ppOD, ppAAm and ppAAc), spectra were collected from the films

and the control quartz slide. A previous study from the McArthur group showed that

the thickness of the polymeric thin films was 40-50 nm (292). Since the same

standard operating procedure established by the McArthur group was applied, the

polymer films in this study were expected to have the same thickness. The Raman

spectrum of each plasma polymer film is shown in Fig 6.2.


spectra collected from the plasma polymerised thin films and the control quartz slide.

The dominant peaks regions covering 700-900 cm-1 and 1000-1100 cm-1 were

consistently seen in the spectra of all polymer films. These peak regions can be

assigned to the broad Raman features from the quartz slide and there is almost no

information associated specifically with the film evident in the spectra. Given that

the thickness of the polymer film is relatively thinner than the quartz substrate and

smaller than the height of the confocal sampling region, Raman spectra taken from

the polymer coating is expected to be dominated by background signal from quartz.

Thus, it can be seen that the Raman spectra of the plasma polymer films were quartz

spectra creating difficulties for the analysis of Raman spectra from polymer surface-

attached cells.

ppOD

ppAAm

ppAAc

Quartz

FSET PhD Thesis/198

6.3.2 Bacterial adhesion to plasma-polymerised surfaces

As discussed in the literature Chapter, the effect of surface chemistry on the cell-

surface interaction could be determined by investigating the bacterial adhesion and

proliferation on the surface. Live and dead cell staining was performed and the

viability of E. coli adhered to the polymer surface was examined by confocal laser

scanning microscopy (CLSM). Representative images for viability tests of the cells

adhering to the polymer surfaces in comparison with the control quartz slide (from

initial attachment throughout biofilm development) are shown in Figs 6.3-6.5.

From CLSM images of E. coli cells adhered to the surfaces after 1 h incubation time,

a relatively large number of adhered cells were observed on the ppAAm surface (Fig

6.3). In contrast, the smallest number of adhered cells was seen on the ppAAc

surface. The individual cell morphology of E. coli was noticed on the quartz,

whereas the cells attached to the polymer surfaces were seen as aggregated cells. As

discussed in Section 1.2.2, it is generally believed that stressful conditions induce an

enhanced bacterial EPS production. Due to cellular responses to the functional

groups of the polymer surfaces, the cells on the polymer surfaces were likely to

produce more EPS secretion in comparison with those on the quartz surface.

Consequently, cell aggregations with indistinct cell morphology were seen on the

polymer surfaces as the cells might be embedded in EPS.

Among the cells attached to the polymer surfaces, the cells from ppOD displayed

larger bacterial colonies on the surface. Deposition of plasma polymerised 1, 7-

octadiene (ppOD) onto fused quartz slides increased the hydrophobicity of the

surface due to the introduction of non-polar hydrocarbon functionalities (CH2) on the

surface. As discussed in the literature review Chapter, hydrophobic moieties on E.

coli cells (i.e. cell wall, fimbriae and extracellular organelles) are expected to result

in more stable interaction and stronger adhesion with the hydrophobic hydrocarbon

rich ppOD polymer surface. These attached bacteria then continued to grow into

micro colonies on the ppOD surface. The large number of cells seen on ppAAm after

initial attachment can be explained due to either an increase in the number of

attached cells on the surface or rapid cell growth within the attached cells.


Figure 6.3 Two-dimensional CSLM images of E. coli attached to the surfaces at

initial attachment. Cells from the polymer films (a-c): (a) ppOD, (b) ppAAm, (c)

ppAAc and (d) quartz slide were stained with BacLight Live/Dead staining kit.

SYTO 9 stained E. coli live cells are in green and propidium iodide, PI stained dead

cells are in red. Scale bar = 20 µm applies to all images.

Figure 6.4 shows CLSM images of E. coli colonies which were continuing grow on

the surfaces after 24 h of incubation. Similar increasing trends of bacterial

colonisation with respect to the initial attachment were observed. In particular, more

bacterial colonisation was seen on the ppAAm surface than any other surface.

Interestingly, similar patterns of biofilm development were seen on both ppAAm and

the quartz slide, whereas large bacterial clusters (colonies) were seen on the ppOD

surface. In the case of the surface-attached cells on ppAAm, a small number of PI

stained dead cells were seen mixed together with the live cells (yellow coloured). In

20 µm

(b)

(c) (d)

(a)

FSET PhD Thesis/200

contrast, no dead cells were seen at this growth time point among the cells attached

to the other polymer surfaces including the control.


24 h of incubation. Cells from the polymer films (a-c): (a) ppOD, (b) ppAAm, (c)



cells are in red. Scale bar = 20 µm applies to all images.

(a) (b)

(c) (d)

20 µm



120 h of incubation. Cells from the polymer films (a-c): (a) ppOD, (b) ppAAm, (c)



cells are in red. Combinations of live and dead cells appear yellow. Scale bar = 20

µm applies to all images.

The biofilm development at 120 h of incubation is shown in Fig 6.5. Distinctive

biofilm features and significantly higher numbers of bacterial colonies were observed

on all surfaces, except for the ppAAc surface where the number of initially attached

cells was the least. In terms of the live and dead cell populations, it can be visualised

that the ppOD and control quartz slide harboured nearly equal numbers of live and

dead E. coli cells, while higher dead cell counts were observed on the ppAAm

surfaces. Thus, based on these observations, it can be concluded that the ppAAm

surface supported the highest level of initial cell adhesion but also displayed the

highest dead cell population in subsequent biofilm formation. In order to study

(a) (b)

(c) (d)

20 µm

FSET PhD Thesis/202

quantitatively the effect of surface chemistry on bacterial adhesion, cell viability and

biofilm formation, the total number of live and dead cells attached on the surfaces

was counted. Bacterial viability, defined as the percentage of the area covered by live

attached bacterial cells versus the total area of adhered cells or total biofilm area,

were calculated.

6.3.3 Two-dimensional cell counting and quantifying cell viability

Cell counting was performed from the 2-D CSLM images of the attached E. coli cells

on different plasma-polymerised surfaces and control quartz slide after 1 h

incubation time. Manual cell counting analysis was performed using the cell counter

plugin installed in ImageJ software (220) and following the protocols mentioned in

Section 2.2.4.1. A comparison of the initial attachment of E. coli on the different

polymer surfaces after 1 h of incubation time is shown in Fig 6.6. The results show

that there was a significantly higher total number of cells attached on the ppAAm

surface compared to any of the other surfaces analysed (p value <0.05 for all

samples). It can be noticed that almost equal numbers of cells adhered on the ppOD

and the quartz slide and this number was significantly higher than the number of

cells attached to ppAAc.

Figure 6.6 E. coli adhesion to different plasma-polymerised surfaces and quartz

substrate at 1 hour incubation time. Abbreviations: ppOD, 1, 7-octadiene; ppAAm,

allylamine; ppAAc, acrylic acid. (*, ** significant differences between surfaces p<0.1

and p<0.05 respectively)

ppOD ppAAm ppAAc quartz0

1x1010

2x1010

3x1010

4x1010

5x1010

Bacte

ria c

ount /

m2 A

rea

Different surfaces

*

**

****

**


To evaluate the live and dead cell population within the mature biofilms grown on

each of the surfaces, an analysis based on colour segmentation of 2-D CSLM colour

images was performed using the colour segmentation plugin installed in ImageJ

software (221) (detailed protocols mentioned in Section 2.2.4.1). The percentages of

live and dead cell populations in the biofilm were calculated based on the percentage

of the total area covered by attached cells on the surfaces and plotted against those on

quartz surface for comparative study. The viability of E. coli cells in 120 h old

biofilm was evaluated from the ratio of the area covered by green and red labelled

cells.

The results of total biofilm area and cell viability of 120 h old biofilms which were

grown on the plasma polymerised surfaces and quartz slides are shown in Fig 6.7.

The maximum total area covered by biofilm cells was found on the quartz slide,

although the number of initial attached cells on ppAAm was found to be the highest

among the surfaces (Fig 6.7A). As expected, cell coverage on the ppAAc surface was

significantly lower than all other surfaces. When comparing between the biofilm

areas of the ppOD and ppAAm surfaces, it can be noticed that E. coli cells

preferentially adhered and developed biofilm on ppAAm surfaces.

In evaluating cell viability, it is interesting to see that the percentage of the area

covered by live cells on the ppOD surface was the highest with more than 75% of the

total area containing cells. In fact, less cell viability was seen on the ppAAm surface

compared to that on ppOD, although a higher number of adherent cells and more

biofilm covered area were seen on the former surface (Fig 6.7B). Moreover, the

lowest percentage of live cells was found on the ppAAc surface (Fig 6.7B). These

results reveal the trend of cell viability on surfaces which have different wettabilities,

ranging from more hydrophobic to hydrophilic. Based on these results, it can be

concluded that there was higher cell viability on the surface which had more

hydrophobicity (i.e. ppOD surface). These findings are correlated with the study of

Parreira et al. revealing that bacteria adhered preferentially to the more hydrophobic

surface compared to more hydrophilic OH- exposed surface (196). Moreover, as

discussed in Section 6.3.2, more cell viability may be due to stronger and stable

FSET PhD Thesis/204

adhesion of hydrophobic moieties on E. coli cells with the hydrophobic hydrocarbon

rich ppOD polymer surface.

(A)

(B)

Figure 6.7 Viability of E. coli cells from 120 h old biofilm grown on plasma

polymerised surfaces and quartz substrate. Cell viabilities are shown as a percentage

of the area covered by green labelled live cells and red labelled dead cells in the total

area of biofilm cells. (*, ** significant difference between surfaces p<0.1 and p<0.05

respectively)

0

20

40

60

80

100

ppOD ppAAm ppAAc quartz

Are

a c

ove

red

by b

iofi

lm c

ell

s (

%)

Different surfaces

Dead cell

Live cell


In conclusion, the effect of surface chemistry on bacterial adhesion, subsequent

biofilm formation and cell viability was evaluated in this study using plasma

polymerisation of hydrocarbon-rich (ppOD), amine (ppAAm) and carboxyl (ppAAc)

thin films on quartz surfaces. These different coatings expose different functional

groups such as CH2, NH2 and OH. The functional groups on these surfaces provide

different wettabilities, ranging from a more hydrophobic surface presenting CH2

groups to a more hydrophilic surface presenting OH-groups.

The total cell count results revealed that there was a significant increase in initial

bacterial adhesion to the ppAAm surface compared to the other polymer surfaces and

the quartz control. As discussed in Chapter 1, bacteria normally secrete a complex

variety of extracellular polymeric substances (EPS) including polysaccharides,

proteins and nucleic acids while they are in both planktonic and surface-attached

states. These EPS substances play an important role in bacterial colonisation of

surfaces by enhancing initial cell adhesion and aggregation with each other once they

attach to surfaces. The ppAAm surface, which has amine (NH2) functionality, might

interact favourably with both extracellular DNA from secreted EPS and attached

bacterial cells, providing an increase in the initial cell adhesion and subsequent

bacterial colonisation. These findings agree with the results reported by Hook et al.

for high DNA binding and adsorption efficiency to ppAAm surfaces (298). Another

possible reason for the higher cell adhesion seen on the moderately hydrophilic

ppAAm surface could be due to a favourable interaction between the basic behaviour

of the negatively-charged bacterial cell and the positively-charged surface, which

promotes the initial attachment and subsequent biofilm formation. However, the

amide functional group on ppAAm surface might be toxic to bacterial cells and thus

leads to a decrease in cell viability in later phases of biofilm growth compared to

those on ppOD and the control.

In contrast to the ppAAm surface, the lowest bacterial adhesion was seen on the

more hydrophilic ppAAc surface at initial attachment. The number of adherent cells

to the surface was relatively unchanged for at least 24 h. Finally, the lowest viability

of attached cells (i.e. highest proportion of dead cells) was found on ppAAc,

although the number of attached cells to remained almost stable even after 120 h of

FSET PhD Thesis/206

biofilm growth. Given that plasma polymers are known to conform to the substrate

where they are deposited (292, 293), the enhanced bacterial adhesion seen on the

ppOD surface compared to the ppAAc surface can be explained by an increased

surface hydrophobicity on ppOD due to the CH2 functional groups.

These investigations with model surfaces demonstrated that E. coli exhibits

differences in adhesion, biofilm properties and cell viability that depend on the

surface chemistry and specific functional groups exposed. Therefore this study raises

the question whether these changes due to cell-surface interactions have an influence

on bacterial identification by Raman spectroscopy.

6.3.4 Raman analysis of bacterial cells grown on polymer surfaces

In order to investigate whether Raman spectroscopy can still be used to identify

bacterial cells that have been affected by cell-surface interactions, Raman spectra

were collected from E. coli cells attached to the surfaces (i.e. polymer coated

surfaces and control quartz substrate) and from biofilms grown on these surfaces.

A tentative assignment of the peaks that appeared in the average Raman spectra of E.

coli cells attached to the surfaces after 24 h incubation time are shown in Fig 6.8. As

a reference spectrum, Raman spectra from planktonic E. coli samples after 24 h

incubation were also collected. Raman spectra of planktonic cells which were

smeared on the substrates (CaF2 and quartz) and the spectra of cells attached to the

control quartz substrate showed prominent peaks at 700-800, 1001, 1240 and 1447,

1663 cm-1, which could be characterized as nucleic acids, carbohydrates, proteins

and lipids, according to previous studies (26, 29, 100). However, the Raman spectra

of cells attached to the polymer surfaces displayed broad peaks and poor resolution

of the spectral features. These peaks were overlaid with background signals from the

quartz substrate (especially in the regions of 700-800 cm-1) and background signal

from the plasma polymer surfaces (shown in Fig 6.2). This phenomenon was most

severe in the spectra of the cells attached to ppAAm surface. The intense background

drowned out the signal from the cells, thereby making bacterial identification

impossible.



spectra from 24 h-old surface-attached cells and planktonic cells. The spectra were

collected from E. coli cells on different surfaces (a: ppOD, b: ppAAm, c: ppAAc and

d: quartz) and planktonic cells which were smeared on the substrates (e: CaF2 and f:

quartz). The dominant peaks for spectra of DNA/RNA and proteins are shown with

the peak assignments mentioned in Table 3.1. Abbreviations: Phe, phenylalanine;

Carb, carbohydrate; def, deformation.

Attempts were made to identify E. coli cells which were grown on the plasma-

polymerised surfaces using the PC-LDA biofilm model (details discussed in Chapter

5). The PC-LDA prediction model, which was constructed from biofilm cells of E.

coli and V. vulnificus species grown on quartz substrates, was tested for the direct

identification of E. coli grown on polymer surfaces. The preliminary intention was to

validate the constructed PC-LDA model for differential identification of surface-

grown bacteria. The classification and identification results were shown in Table 6.3.

The results provided a very low sensitivity (< 50%) in accurate identification of E.

coli cells for all tested cells from the polymer coated surfaces. As discussed above,

these poor classification results were probably due to interference from the polymer

and quartz background signals in the bacterial spectra.

(a)

(b)

(c)

(d)

Ph

e

Am

ide I

II

CH

def

CH

2d

ef

Am

ide I

DN

A/R

NA

syn

thesis

Carb

(e)

(f)

FSET PhD Thesis/208

Table 6.3 Identification of E.coli biofilm cells from different polymer surfaces using

dual-species biofilm model.

Test run True (+) False (-)

Sensitivity

(%)

E. coli cells from 1, 7-octadiene 9 3 6 33.3

E. coli cells from Allylamine 9 4 5 44.4

E. coli cells from Acrylic acid 9 3 6 33.3

E. coli cells from Quartz 9 5 4 55.5

Another possible explanation for these poor results is the nature of cellular

heterogeneity of biofilm cells. As discussed in the previous Chapters (Chapters 4 and

5), Raman spectra of individual bacterial cell within the population can be

fundamentally different, even though a population of bacteria may be genetically

identical. Compared to planktonic form, this phenomenon is more significant in the

bacteria’s struggle for survival, particularly when they are grown on a surface.

Therefore it was unsurprising for the poor classification results of polymer surface-

attached cells using PC-LDA biofilm model, which were constructed from the cells

grown on another substrate. The next step was to investigate whether biofilm cells

can behave differently from each other depending on surface where they attach and

continue cell growth thereby affecting their identification. In order to perform this

detailed spectral analysis, the background signal from polymer and quartz substrate

has to be avoided or minimised. Therefore, the results shown in Fig 6.8 and Table 6.3

suggest a need for an alternative way of transferring the surface-attached cells to a

substrate which can provide low background signal disturbance.

6.3.5 Raman analysis of bacterial cells from different polymer surfaces

In order to minimise or avoid the Raman background signal associated with the

plasma polymer surfaces interfering with the bacterial spectra (previously shown in

Fig 6.8), the 24 h-old bacterial cells from the polymer coated surfaces and quartz

substrate were transferred to CaF2 substrates with the help of sterilised inoculation

loops. Averaged intensity-normalised Raman spectra taken from the transferred cells


(n=4 cells for each polymer surface and quartz control) are shown in Fig 6.9A. The

Raman spectra of the transferred E. coli cells were comparable to those collected

from planktonic cells, enabling peak assignment of the standard features in the

spectra. Although the Raman spectral profiles for the cells from different surfaces

appeared generally similar to those of planktonic cells, certain differences in peak

intensity could be observed especially in the DNA/RNA related regions, the peaks

associated with macromolecule containing amide groups in the protein backbone

(1620-1680 cm-1) and the peaks attributed to the deformation mode of CH2

vibrations. Principal component combined with linear discriminant analysis (PC-

LDA) was performed to classify and identify the attached cells and the results are

shown in Fig 6.9B. All collected 16 Raman spectra (4 individual cells × 4 different

surfaces) were classified as E. coli species with the application of the PC-LDA

planktonic model (mentioned in Section 4.3.4). Although the surface-attached cells

were classified as E. coli, they group more closely together with each other without

completely overlaying the planktonic data from the model.

These classification results supported the potential application of the PC-LDA model

to identify surface-attached cells after they are transferred to a CaF2 substrate which

provides no significant signal disturbance in the spectral range analysed. However,

the visualisation of the classification results highlighted that the surface-attached

cells appeared to form their own cluster in PC space, which suggests some

biochemical changes in comparison with planktonic cells. The collected Raman

spectra from surface-attached cells and those from planktonic cells were further

analysed using principal component analysis (PCA) in order to investigate whether

Raman spectroscopy could reproducibly discriminate between the surface-attached

cells and planktonic cells.

FSET PhD Thesis/210

(A)

(B)

Figure 6.9 (A) Averaged intensity-normalised and background subtracted Raman

spectra of 24 h-old transferred cells from the surfaces (a: ppOD, b; ppAAm, c;

ppAAc and d; quartz) and (e) planktonic cells smeared on CaF2 substrate and (B)

classification of surface-attached cells which were transferred from the surfaces. The

dominant peaks for DNA/RNA and proteins are shown with the peak assignments

mentioned in Table 3.1. Abbreviations: Phe, phenylalanine; Carb, carbohydrate; def,

deformation.

Ph

e

Am

ide I

II

CH

def

CH

2d

ef

Am

ide I

DN

A/R

NA

syn

thesis

Carb

(a)

(b)

(c)

(d)

(e)


(A)

(B) (C)

Figure 6.10 principal component analyses of Raman spectra from E. coli planktonic

cells and those from transferred E. coli surface-attached cells after 24 hour

incubation. (A) Scatter plots of the first and second principal components (PC1 and

PC2), (B) average values plot and (C) loadings plot of PC1. (p<0.005 for all surface-

attached cells comparing with the planktonic cells in the average value plot)

The scatter plots of PCA analysis revealed that cells collected from the plasma

surfaces were clearly separated from the planktonic cells (Fig 6.10A). The first two

principal components (PC1 and PC2) accounted for more than 60 % of the separation

between the data sets. The average value plot of PC1 showed a significant separation

between planktonic cells and surface-attached cells (p<0.005 for all samples). The

loadings plot of PC1 revealed the dominant peaks which are associated with the

separation seen in the scatter plot (Fig 6.10B and C). The results show that the peaks

related to DNA/RNA synthesis represented the highest absolute variance of the

ppOD ppAAm ppAAc quartz planktonic

DN

A

RN

A

Am

ide

I

CH

2d

ef

Ph

e

A,G

FSET PhD Thesis/212

planktonic cells from the surface-attached cells, whereas the protein-specific peaks

were related to the variance of the surface-attached cells. In particular, the peaks at

1001 cm-1, 1447 cm-1, 1680-1620 cm-1 (associated with proteins/lipids) tended to be

higher in the E. coli cells grown on the quartz and plasma polymers substrates than in

the planktonic cells. The peaks region at 700-852 cm-1 (associated with DNA/RNA),

1093 cm-1 (associated with PO2 stretching vibration of the DNA backbone) and 1485,

1575 cm-1 (associated with DNA/RNA) were seen as those which contributed mostly

in separation of planktonic cells from surfaced-attached E. coli cells. These

variations in DNA/RNA and protein-specific peaks indicate the biochemical or

metabolic heterogeneity due to cellular differences in macromolecular composition

or activity during the transition from the planktonic phase to the surface-attached

phase. Therefore, the next step was to investigate the differences between planktonic

cells and cells attached to each polymer surface.

The PCA shown in Fig 6.11 revealed the differences between planktonic cells and

cells attached to the individual surfaces. The first principal component (PC1) of each

PCA accounted for more than 50 % of the separation between planktonic cells and

surface-attached cells (Fig 6.11). The highest PC1 value was seen in PCA between

cells from the more hydrophobic ppOD surface and planktonic cells, whereas lower

PC1 values were seen from the analysis of cells on the more hydrophilic surfaces

(i.e. ppAAm, ppAAc and quartz). Because adhesion to a surface is a survival

mechanism for bacteria, many previous studies implicated that bacterial cell surface

components (such as adhesins, polysaccharides, and proteins) play major roles in cell

modification to adhere to a surface (290, 299). The findings in this study thus raise

the question whether the cells might have modified their macromolecular

composition more intensely in order to attach to the hydrophobic surface.


(A) (B)

Figure 6.11 Principal component analyses of Raman spectra from E. coli planktonic

cells and those from E. coli surface-attached cells transferred to CaF2 after 24 hour

incubation. (A) Scatter plots of the first two principal components (PC1 and PC2)

and (B) loadings plots of PC1.

(i)

(ii)

(iii)

ppOD

ppAAm

ppAAc

quartz

planktonic

(iv)

FSET PhD Thesis/214

Interestingly, the loadings plots for the first principal components from PCA between

planktonic cells and the moderately hydrophilic surfaces (i.e. cells on ppAAm and

the quartz slide) revealed that the corresponding peaks for the separation seen in

PCA score plots were similar for both types of attached cells (Fig 6.11(ii) and (iv)).

These loadings plots suggest that the cell populations on ppAAm polymer surfaces

and quartz show similar differences to their planktonic counterparts. In order to

investigate similarities and differences among the bacterial cells which were grown

on polymer surfaces and the control quartz slide, PCA was further performed from

the collected Raman spectra.

Figure 6.12 Scatter plots of the first and second principal components (PC1 and PC2)

comparing the Raman spectra of E. coli cells from the control quartz slide with those

from polymer surfaces: (a) ppOD (b) ppAAm and (c) ppAAc.

ppOD

ppAAm

ppAAc

quartz

(a) (b)

(c)


PCA of Raman spectra from E. coli cells attached to different polymer surfaces

(ppOD, ppAAm and ppAAc) in comparison with those from the control quartz slide

after 24 hour incubation time points were performed. From the scatter plots shown in

Fig 6.12 (a-c), it can be seen that the first two principal components (PC1 and PC2)

described about 63% of the variation in the data set for comparison between cells

from ppOD and cells from the control quartz slide, whereas there was some overlap

between the data points from the other surface-attached cells. The significant

separation seen between the cells from hydrocarbon-rich ppOD surface and the

control quartz slide suggests that there were Raman identifiable changes between

these spectra (Fig 6.12a). The functional groups on the tested polymer surfaces

provided different wettabilities, ranging from the more hydrophobic CH2 exposed

surface (ppOD) to more hydrophilic surface (ppAAc) that presented OH- groups.

Overlapping and non-significant sample separations could be seen among the spectra

of the cells from the moderately hydrophilic ppAAm surface and more hydrophilic

ppAAc and quartz surfaces. Since PCA was applied to separate the data points, the

clustering of the data seen on the hydrophilic polymer surfaces can be explained due

to similarities in the spectra. These findings suggested that attached cells from more

hydrophilic surfaces might have similar cellular modifications.

Taken together with the results from Fig 6.11, Raman spectral changes of the

surface-attached cells might depend on the degree of surface hydrophobicity.

Therefore, these finding suggested that the surface-attached cells on more

hydrophilic surfaces could be used as the model to investigate Raman detectable

cellular changes in biofilm cells resulting from cell-surface interactions. The

dominant peaks from the loadings plots (Fig 6.11), which contributed to the data

separation seen in the score plots (Fig 6.11), were selected for univariate analysis to

investigate the relative intensity changes of surface-attached cells from the polymer-

coated surfaces to those of control planktonic samples.

The univariate statistical analyses of the selected specific peaks were performed

following the methods mentioned in Section 2.2.6.3. The intensity values of curve-

fitted Raman peaks identified from multivariate analysis were then normalised by the

total intensity values and averaged by adding the maximum intensity and the

FSET PhD Thesis/216

intensity values of the two neighbouring wavenumbers (Raman shifts). Statistical

comparison of the relative mean intensity changes (log2 fold change) for surface-

attached cells compared to planktonic cells were calculated and plotted. The results

of intensity values analysis for the specific peaks related to DNA/RNA synthesis are

shown in Fig 6.13. Lower intensity values of the DNA related peaks were seen in

surface-attached cells compared to planktonic cells (i.e. negative log2 fold change).

In response to changing environmental conditions, bacterial cells are able to adapt to

allow them to persist through time. One of the adaptive changes consists of

modifying its growth rate, which is accompanied by adjusting mechanisms to control

the timing of the cell-cycle (300). Under favourable conditions, bacteria often strive

towards cell growth and reproduction thereby initiating DNA replication.

Conversely, the cells shift from growth to survival functions under stressful

conditions. Thus, the lower intensity values of the DNA related peaks seen in the

surface-attached cells can be explained by the fact that these cells might experience

the stressful environment where the cells probably delayed DNA/RNA synthesis.

These results agree with a comprehensive study of transcriptomic analysis in biofilm

and planktonic cells by Lo et.al (301). Their study reported that the genes involved in

DNA replication were down-regulated in biofilm cells as opposed to planktonic cells.


Figure 6.13 Intensity changes of DNA/RNA specific peaks in the Raman spectra of

E. coli surface-attached cells transferred from different surfaces, measured relative to

planktonic cells. Abbreviation: ppOD, octadiene; ppAAm, allylamine; ppAAc, acrylic

acid; T, thymine; G, guanine; C, cytosine; U, uracil; A, adenine. (*, *** significantly

different to planktonic with p<0.1 and p<0.005, respectively)

The relative intensity changes in the surface-attached cells were not significantly

different among themselves for all of the tested DNA specific peaks, except for the

peak at 1575 cm-1. The overlapping in log2 fold change value of the DNA/RNA

peaks especially in the peaks related with ring breathing modes of cytosine, uracil,

-1

-0.5

0


A,G (1575 cm-1)

-2

-1.5

-1

-0.5

0


A,G (1485 cm-1)

-2.5

-2

-1.5

-1

-0.5

0


O-P-O backbone (811 cm-1)

-2

-1.5

-1

-0.5

0

ppOD ppAAm ppAAc quartzRela

tive In

ten

sit

y C

han

ge

(lo

g2 f

old

)

T, G (668 cm-1)

-1.5

-1

-0.5

0


C, U (781 cm-1)

*

*** *** *** ***

*** *** ***

****** *** *** ***

***

******

****** *** ***

***

***

*

*

-2.5

-2

-1.5

-1

-0.5

0


U, T, C (785 cm-1)

FSET PhD Thesis/218

thymine were noticed among the cells from polymer surfaces. These results

suggested that there were some similar differences between the cells from each

polymer surface and the planktonic cells. The cells from the ppAAm polymer surface

showed a significant change in the intensity value for the peak related to the ring

breathing mode of adenosine and guanine (1575 cm-1). This finding provides a

potential application of this DNA/RNA marker for identification of cells from this

polymer surface.

The results for analysis of intensity values of specific peaks related to protein

synthesis are shown in Fig 6.14. In contrast to DNA/RNA related peaks, higher

intensity values can be seen for the dominant protein/lipid structure-specific peaks in

all biofilm cells from surfaces, compared to planktonic cells. Interestingly, the

relative intensity changes for these protein-related peaks in the cells from the

ppAAm surface were the highest among the surface-attached cells. However, a

significant decrease in the relative intensity of the phenylalanine peak was seen in the

cells from the ppAAm surface compared to those of the cells from ppOD. The

increased intensity values of peaks associated with protein/lipid synthesis might be

related to EPS secretion due to the cellular response of bacteria to environmental

stresses during biofilm development. The log2 fold change values of the peaks

attributed to the deformation mode of CH2 vibrations and amide I were similar

among the cells from ppOD and ppAAc polymer surfaces.

The intensity fluctuation seen in the dominant peaks corresponding to the ring

breathing mode of phenylalanine (1001 cm-1), the CH2 deformation of protein (1447

cm-1) and amide I (1663 cm-1) in the biofilm samples from the polymer surfaces

indicated that some lipid/protein denaturation or up-regulation of protein synthesis

may be induced by the functional groups of the polymer-coated surfaces.

Interestingly, smaller relative intensity changes were seen at the phenylalanine peak,

CH2 deformation band and amide I band in all ppAAc samples. This can be

explained by two factors: either the protein synthesis in the cells attached to ppAAc

was not significantly different from the control planktonic samples or protein

synthesis in these samples was much lower than the other surface-attached cells. This

finding was consistent with the results observed in Fig 6.7, illustrating the smallest


biofilm area and lowest cell viability on the ppAAc surface. Given that the highest

protein content should be found in mature biofilm compared to younger biofilm, the

decreased intensity of the protein-related peaks in the cells from ppAAc surfaces

might be due to the unfavourable environment of the OH- exposed surface inhibiting

mature biofilm development. The results obtained within this work were comparable

with the results reported by Parreira et al., that bacteria adhered less favourably to

the OH- exposed surface than to the CH3 exposed surface.

Figure 6.14 Intensity changes of protein/lipid specific peaks in the Raman spectra of

E. coli surface-attached cells transferred from different surfaces, relative to

planktonic cells. Abbreviation: ppOD, octadiene; ppAAm, allylamine; ppAAc, acrylic

acid; def; deformation; phe, phenylalanine. (*, **, *** significantly different to

planktonic with p<0.1, p<0.05 and p<0.005, respectively)

The results shown in this Section using microscopic and spectroscopic techniques

have demonstrated that surface chemical properties (i.e. functional groups) and

surface wettability can alter the initial cell adhesion, viability of attached bacteria and

0

0.1

0.2

0.3

0.4


Amide I (1663 cm-1)

0

0.1

0.2

0.3

0.4

ppOD ppAAm ppAAc quartzRela

tive In

ten

sit

y C

han

ge

(lo

g2 f

old

)

Phe (1001 cm-1)

******

****

**

******

*

***

***

*** ***

0

0.1

0.2

0.3

0.4

0.5


CH2 def (1447 cm-1)

FSET PhD Thesis/220

subsequent biofilm formation. These variations in physiochemical properties of

bacteria due to their interactions with different surfaces could be detected by Raman

spectroscopy. The results showed that surface-attached cells were significantly

different from planktonic cells. However, the relative changes of attached cells (from

polymer surfaces) to planktonic cells did not significantly distinguish them from cells

grown on a quartz substrate except for the peak related to ring breathing mode of

adenosine and guanine (1575 cm-1) (Fig 6.13).

6.4 Conclusion

Given that bacteria can attach and form biofilm on any natural and synthetic surface,

scientific investigations of bacterial biofilm formation have become popular in

medical, industrial and environmental applications. Many studies on biofilms and

effects of surface modifications have been applied for better understanding of cell-

surface interaction and thereby finding means of controlling biofilm formation.

In this study, the effect of surface chemistry on bacterial adhesion and subsequent

biofilm formation was first evaluated using plasma polymerisation of hydrocarbon-,

amine- and carboxyl-rich precursors on quartz surfaces, thereby exposing different

functional groups such as CH2, NH2 and OH. These investigations with polymer

surfaces revealed that the E. coli strains exhibit differences in adhesion, biofilm

properties and cell viability that depend on the surface chemistry and specific

functional groups exposed. Detailed analysis of Raman spectra collected from E. coli

biofilm cells from different polymer surfaces revealed the DNA/RNA and protein

markers which were related to these subtle changes.

Despite subtle changes in macromolecular composition within the same species due

to cell-surface interactions, the classification results using the PC-LDA planktonic

model were highly accurate for the cells which were transferred from the surfaces.

These findings encourage the use of transferred cells for Raman spectroscopic

analysis of any surface-attached cells. On the other hand, Raman spectra taken

directly from the cells on the polymer surfaces, without transferring them to CaF2

substrate, were swamped by background signals and showed no characteristic peak

features of E. coli cells. For this reason, attempts at direct identification of biofilm


cells from polymer surfaces with the application of the PC-LDA biofilm model were

not successful.

While it may have been unsurprising to get poor classification results for these

spectra because of the nature of cellular heterogeneity in biofilm matrix, the success

of the transfer technique suggests that the background contributions from the quartz

substrates and fluorescence from the polymer surfaces were a major limitation in the

direct measurements. However, with the success of the transfer technique, the results

of this chapter support the potential for Raman spectroscopy to be used as a

substantial technique for identifying bacteria recovered from biofilm. In particular,

the results suggest that it might be possible to analyse environmental biofilm samples

from any surface by transferring cells to a substrate, with relatively high Raman

intensity and low fluorescence background, thereby providing an efficient and

reliable approach for bacterial identification. This application needs to be further

validated with a larger database of bacterial species. The results also demonstrate the

ability of Raman spectroscopy to evaluate phenotypic variability within species and

identifying the diversity of macromolecules that may play a role in initial cell

attachment and biofilm growth.

FSET PhD Thesis/222

Mya Myintzu Hlaing Conclusions/223

CONCLUSIONS

To implement the appropriate treatment and control measures for problems

associated with biofilm formation, there is a need for rapid bacterial identification.

The work presented in this thesis has focused on exploring the application of Raman

spectroscopy identification techniques to the challenging systems of life cycle

analysis, co-culture environments and biofilm formation. Much of the existing

Raman literature has focussed on the identification of bacteria in the planktonic state,

whereas it is known that bacteria actively modify their behaviour in order to adapt to

the constraints of biofilm consortia and survive in a range of environmental settings.

These adaptations generally involve changes in biochemical activity in the cell,

which are expected to modify the bacterial Raman spectrum and therefore may

interfere with the successful identification of the species. Therefore this work aimed

to evaluate the capacity of Raman spectroscopy to identify bacteria in biofilm and

when attaching to surfaces with a range of surface chemistries.

First, the Raman spectroscopy experimental methods were optimised in order to

analyse the Raman spectra quantitatively and consistently throughout this study.

These steps involved the implementation of a fluorescence background removal

method and validation of sample preparation methods for bacterial identification. An

improved background subtraction method using adaptive-weight penalised least

squares fitting was evaluated and implemented. With this method, the background

was successfully removed from Raman spectra taken from planktonic cells, colonies

and biofilms, providing a significant improvement for the performance of further

quantitative analysis of the Raman spectra (including multivariate analysis). From a

detailed analysis of the possible effects of different sample preparation procedures on

bacterial Raman spectra, the sample to sample variations were minimised during the

study.

In order to construct a database to model the individual cellular differences in

macromolecular composition within a bacterial population, Raman spectroscopy

experiments were set up for different time points of the planktonic growth cycle.

FSET PhD Thesis/224

With principal component analysis (PCA), the results revealed Raman spectral

changes in DNA/RNA synthesis and protein synthesis all the way through the growth

cycle for four species of interest (E. coli, V. vulnificus, P. aeruginosa and S. aureus).

Although PCA was not perfect in detecting all of these subtle changes in the Raman

spectra of P. aeruginosa cells during the growth cycle, it should be noted that such

differentiation (between stationary and decline phase) was achieved solely on the

basis of multivariate analysis of Raman data without a priori expectation of chemical

differences or an understanding of the biochemical-physiological pathways.

Moreover, the PCA presented here should still be considered as preliminary analysis

for differential identification. Nevertheless, the results from E. coli and V. vulnificus

species highlighted that Raman spectroscopy together with PCA analysis can detect

cellular differences from metabolic growth phases of single bacterial species.

As a consequence of cellular heterogeneity during the growth cycle, poor PCA

classification results were obtained for the data set collected from the whole growth

cycles of four bacterial species, although they were well-discriminated at a particular

growth time point. In contrast, analysis based on principal component and linear

discriminant analysis (PC-LDA) could successfully discriminate and classify the

diverse species in spite of these growth phase dependent physiological differences.

The validation of the constructed prediction model (PC-LDA planktonic model) with

new spectra from each individual species provided >80% classification accuracy.

Moreover, this PC-LDA model could detect the presence of E. coli and V. vulnificus

species from mixed culture. The results from the fluorescence in situ hybridisation

(FISH) technique with rRNA-targeted oligonucleotide (probe) for E. coli (ATCC

25922) further supported the validity of the identification results using the model.

These findings demonstrated that Raman spectroscopy with the application of a PC-

LDA model that incorporates chemotaxonomic data may provide valuable

applications in the rapid sensing of microbial cells in environmental and clinical

studies. However, the classification accuracy may be affected by intra-species and

inter-species variability. The effect of such inaccuracy may be more pronounced if

more species were added to the database. It is important to evaluate the application of

the PC-LDA planktonic model to the identification of real-world biofilm samples.


Raman spectroscopy experiments were thus performed on intact bacterial colonies

and biofilm as a step towards realistic settings by examining the cellular changes of

surface-attached bacterial cells. Analysis of Raman spectra taken from intact

bacterial colonies isolated on nitrocellulose membrane were complex. The

background peak removal of nitrocellulose membrane was extremely challenging.

Attempts were made to remove the peak by normalising with the intensity of the

nitrocellulose membrane signal at 1282 cm-1. While the membrane peak removal

method for membrane-grown colonies cells remains somewhat subjective and

requires further improvement, a high accuracy in differential identification was

achieved using the PC-LDA planktonic model for all species, except V. vulnificus.

These results are encouraging further extension of the Raman spectroscopy

application to detect and identify membrane-grown bacteria in food-processing

environments and water analysis. Furthermore, with this model, it was possible to

evaluate the population behaviour of the membrane-attached cells from intact colony.

If reference methods can be applied to confirm the classification results in future

work, this approach will have a great potential to study the bacterial population

behaviour resulting from different nutrient utilisation.

To apply the PC-LDA classification approach for rapid bacterial identification from

biofilm consortia in real-world situations, Raman spectra taken from throughout the

stages of biofilm growth were analysed, using a similar approach to the analysis done

in planktonic cells. It was found that there were Raman identifiable changes in

DNA/RNA and protein-related peaks in surface-attached cells of the individual

species. Given that biofilm cells are believed to be different from their planktonic

counterparts, it was perhaps unsurprising that ineffective classification results were

obtained using the PC-LDA planktonic model. Instead, a PC-LDA biofilm model

that could match the surface-attached biofilm cells was constructed. The PC-LDA

biofilm model was validated on new spectra of E. coli surface-attached cells from

single-species biofilm, which were grown on quartz substrates and provided high

accuracy in prospective classification. This interpretation and classification result did

not take into account the possible misclassification with closely related species that

were not included in the current database and hence may overestimate the accuracy

FSET PhD Thesis/226

of the model. Furthermore, it is possible that the model can only be applied to a

specific substrate where the biofilm was grown. As a change in the surface chemistry

will probably alter the accuracy of the model, it was highlighted that a new model

might be required for each surface of interest, significantly reducing the viability of

the direct surface analysis approach. A validation of the constructed PC-LDA biofilm

model was performed with the new spectra collected from dual-species biofilms. The

results revealed 75% sensitivity in detecting the presence of E. coli and V. vulnificus

species in dual-species biofilms. With this approach, species interactions could

potentially be better understood, thereby assisting in the study of biofilm formation

with species of interests in more complex situations.

The positive results from the surface-attached E. coli cells on quartz substrate

encouraged us to examine the effects of different surface chemistries on bacterial

identification by Raman spectroscopy and to examine the Raman identifiable

macromolecular changes in the cells. Therefore, the interaction of E. coli cells with

plasma polymer thin films containing hydrocarbon, amine and carboxyl groups were

investigated. The functional groups on these surfaces provided different wettabilities,

ranging from a more hydrophobic hydrocarbon-rich surface to more hydrophilic

surface carboxyl/ester containing films. The results from microscopic examinations

using CLSM illustrated the differences in cell attachment phenotypes, cell viability

and subsequent biofilm formation, which were associated with different surface

chemistries and surface hydrophobicity. These results showed that bacteria adhered

preferentially to the more hydrophobic CH2 exposed surface (i.e. ppOD) than to the

more hydrophilic OH- exposed surface (i.e. ppAAc). The same phenomenon was

seen for biofilm formation and cell viability. However, the less cell viability was

seen on the amine exposed containing polymer surface (i.e. ppAAm) compared to

ppOD although there were higher cell adhesion and biofilm formation. Therefore,

the next step was to investigate whether these cellular differences in the surface-

attached bacteria influenced their identification by Raman spectroscopy.

While investigating the Raman spectra taken from cells attached to the polymer

surfaces, it was found that the spectra were convoluted with polymer and quartz

background. The identification of surface-attached cells from polymer surfaces using


PC-LDA model was thus challenged by weak spectral features resulting from

background interference. The lack of success with the direct identifications of the

cells from the polymer surfaces prompted another approach that used transferred

cells for identification purposes. In particular, cells from the polymer surfaces were

transferred to a CaF2 substrate for Raman measurement. Raman spectra from these

transferred cells showed the characteristic peak assignments of E. coli cells and the

results were comparable with those from planktonic cells. Correct identification

outcome were also achieved for these surface-attached cells using the PC-LDA

planktonic model. This outcome suggested that transferred cells should be used to

increase the chance of successful Raman spectroscopy analysis of any surface-

attached cells.

By using the transferred cells, a detailed analysis of specific peaks was possible

without any disturbance from superimposed polymer and quartz background. The

results of this analysis showed that the spectral profiles of the surface-attached cells

were subtly different from those of the planktonic cells. Furthermore, there were

spectral intensity changes among the surface-attached cells due to their interactions

with the different surfaces. It was found that the relative intensity changes for

DNA/RNA and protein-related peaks in the cells from the ppAAm surface were the

highest among the surface-attached cells. This finding correlated with the results of

reduced cell viability seen on the ppAAm surface compared to those on ppOD. It will

be interesting to evaluate phenotypic variability between species and to identify the

diversity of macromolecules throughout biofilm development on these polymer

surfaces.

In summary, this thesis illustrated the potential to apply Raman spectroscopy in

combination with multivariate analysis for bacterial identification in real world

settings. The results from optimising the effective sample storage and preparation

highlighted the role of bacterial EPS in response to environmental stresses. This

finding further suggested studying EPS-specific Raman markers to understand their

role in biofilm formation process of different bacterial species. The constructed PC-

LDA planktonic model of four bacterial species in this study showed promising

outcomes for differential identification. Some factors which might influence the

FSET PhD Thesis/228

identification, such as cellular heterogeneity throughout the life cycle, cell-cell

interactions within consortium biofilm and cell-surface interactions, were studied.

The ongoing challenge lies on the development of this approach for application on

the industrial scale. Therefore, further studies to validate the PC-LDA planktonic

model with more bacterial species are suggested. The effectiveness and accuracy of

the PC-LDA planktonic model for the identification of biofilm forming bacterial

species can also be studied by adding more bacterial species which are involved in

specific processes, such as microbiologically-influenced corrosion. More bacterial

species can be added to the database by categorising the groups which include EPS-

producing bacteria, acid-producing bacteria, sulphur oxidising bacteria, iron-

precipitating bacteria and sulfate-reducing bacteria. In addition, environmental

factors (such as temperature, pH and nutrient composition) can also be taken into

accounts for future study of factors influencing bacterial identification. The method

for membrane peak removal from the Raman spectra of intact colonies could also be

improved. The method conducted in this thesis was done by manual normalisation of

the intensity of the membrane peak. If it is possible, future work should develop a

reliable computational method which can automatically remove the membrane peak.

With a range of parameter settings for good performance in membrane peak removal,

the approach of using membrane-grown cells will be useful for a wide range of

applications in food-processing industries.

The promising classification results from the study of surface chemistry effects on

bacterial identification suggested a further study to be tested with Gram-positive

bacteria. Finally, the Raman spectral fluctuations observed among surface-attached

cells suggest the need to test the model with other antimicrobial coated thin films to

characterise the patterns of spectral variation between surface-attached cells and

planktonic cells. These fingerprint patterns can be applied to assess the stability and

antimicrobial property of coated films in clinical and environmental applications.

Finally, it should be noted that the time taken to collect high quality Raman spectra

from bacteria remains a limiting factor, particularly in terms of collecting a

significantly larger number of spectra for more reliable PCA training sets. Future

improvements in Raman spectroscopy equipment might assist in this regard.

Mya Myintzu Hlaing References/229

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Mya Myintzu Hlaing Appendix/259

APPENDIX

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APPENDIX A

Nucleotide sequence of E. coli ATCC 25922, 16S rRNA (X80724, GI: 1240023)

The DNA sequence presented starts with nucleotide number as described in 16sRNA

sequence of E. coli ATCC 25922 (Accession: X80724, GI: 1240023, 1452 base

pairs, genomic DNA). The primer, EC1_485, 5’ GTATCTAATCCTGTTTGCTCCC

-3’ which were used in Section 2.2.4.3 is indicated by horizontal small arrow and

highlighted in grey colour.

1 AGTTTGATCATGGCTCAGATTGAACGCTGGCGGCAGGCCTAACACATGCAAGTCGAACGG

61 TAACAGGAACGAGCTTGCTGCTTTGCTGACGAGTGGCGGACGGGTGAGTAATGTCTGGGA

121 AACTGCCTGATGGAGGGGGATAACTACTGGAAACGGTAGCTAATACCGCATAACGTCGCA

181 AGACCAAAGAGGGGGACCTTCGGGCCTCTTGCCATCGGATGTGCCCAGATGGGATTAGCT

241 AGTAGGTGGGGTAAAGGCTCACCTAGGCGACGATCCCTAGCTGGTCTGAGAGGATGACCA

301 GCCACACTGGAACTGAGACACGGTCCAGACTCCTACGGGAGGCAGCAGTGGGGAATATTG

361 CACAATGGGCGCAAGCCTGATGCAGCCATGCCGCGTGTATGAAGAAGGCCTTCGGGTTGT

421 AAAGTACTTTCAGCGGGGAGGAAGGGAGTAAAGTTAATACCTTTGCTCATTGACGTTACC

481 CGCAGAAGAANNACCGGCTAACTCCGTGCCAGCAGCCGCGGTAATACGGAGGGTGCAAGC

541 GTTAATCGGAATTACTGGGCGTAAAGNGCANGCAGGCGGTTTGTTAAGTCAGATGTGAAA

601 TCCCCGGGCTCAACCTGGGAACTGCATCTGATACTGGCAAGCTTGAGTCTCGTAGAGGGG

661 GGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAA

721 GGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGATT

781 AGATACCCTGGTAGTCCACGCCGTAAACGATGTCGACTTGGAGGTTGTGCCCTTGAGGCG

841 TGGCTTCCGGANNTAACGCGTTAAGTCGACCGCCTGGGGAGTACGGCCGCAAGGTTAAAA

901 CTCAAATGAATTGACGGGGGCCGCACAAGCGGTGGAGCATGTGGTTTAATTCGATGCAAC

961 GCGAAGAACCTTACCTGGTCTTGACATCCACGGAAGTTTTCAGAGATGAGAATGTGCCTT

1021 CGGGAACCGTGAGACAGGTGCTGCATGGCTGTCGTCAGCTCGTGTTGTGAAATGTTGGGT

1081 TAAGTCCCGCAACGAGCGCAACCCTTATCCTTTGTTGCCAGCGGTCCGGCCGGGAACTCA

1141 AAGGAGACTGCCAGTGATAAACTGGAGGAAGGTGGGGATGACGTCAAGTCATCATGGCCC

(See overleaf for continued sequence)

FSET PhD Thesis/262

1201 TTACGACCAGGGCTACACACGTGCTACAATGGCGCATACAAAGAGAAGCGACCTCGCGAG

1261 AGCAAGCGGACCTCATAAAGTGCGTCGTAGTCCGGATTGGAGTCTGCAACTCGACTCCAT

1321 GAAGTCGGAATCGCTAGTAATCGTGGATCAGAATGCCACGGTGAATACGTTCCCGGGCCT

1381 TGTACACACCGCCCGTCACACCATGGGAGTGGGTTGCAAAAGAAGTAGGTAGCTTAACCT

1441 TCGGGAGGGCGC


APPENDIX B

Curve-fitted spectrum and quantification parameters for the components using

CasaXPS software

Figure 1. Curve-fitted spectrum and the components.

0

10

20

30

40

50

10-2

2000 1600 1200 800

Wavenumber / cm-1

No

rma

lise

d R

am

an In

tensity

(a.u

)

FSET PhD Thesis/264

Figure 2. Quantification parameters for the components shown in Fig 1.


APPENDIX C

PCA of Raman spectra for planktonic E. coli cells taken from refrigerated sample

before cell washing steps

Figure 1. Flow chart diagram for the different sample preparations for planktonic E.

coli cells: (i) fresh sample, (ii) refrigerated sample after cell washing steps, (iii)

refrigerated sample before cell washing steps and (iv) frozen sample.

FSET PhD Thesis/266

Figure 2. Background subtracted Raman spectra from planktonic E. coli cells taken

from (i) fresh sample; (ii) refrigerated sample after cell washing steps; (iii)

refrigerated sample before cell washing steps and (iv) frozen sample. The dominant

peaks for spectra of DNA/RNA and proteins are shown with the wave number (cm-

1). The change in peak position attributed to phenylalanine is shown in the enlarged

picture with dotted line.

Figure 3. Principal component analysis of Raman spectra for planktonic E. coli cells

taken from (i) fresh sample; (ii) refrigerated sample after cell washing steps; (iii)

refrigerated sample before cell washing steps. (A) Average values plots and (B)

loading value plots for the first principal component (*** p value < 0.005).

Ram

an Inte

nsity (

Norm

alis

ed)

Wavenumber/cm-1

(i)

(ii)

(iii)

(iv)72

6

78

1-7

85

74

6

81

18

52

10

01

11

25

12

40

13

3714

47

-14

58

1900 1700 1500 1300 1100 900 700 500

0.5

0

1.5

1

2

66

86

17

-64

0

14

85

15

73

16

63

(i)

(ii)

(iii)

(iv)

First prin

cip

al co

mp

on

en

t (a

.u.)

(2

8%

)

(ii) (iii) (i)

(A)6

4

2

0

-2

-4

-6

***

1900 1700 1500 1300 1100 900 700 500

Ra

ma

n In

ten

sity

Wavenumber/cm-1

(B)0.15

0.1

0.05

0

-0.05

-0.1

-0.15

-0.2

1680

-1620

1391-1

260

1001

1573

1489-1

443

1125

640-6

20


Figure 4. Principal component analysis of Raman spectra for planktonic E. coli cells

taken from a (i) fresh sample; (ii) refrigerated sample after cell washing steps; (iii)

refrigerated sample before cell washing steps; (iv) frozen sample. (A) Average

values plots and (B) loading value plots for the first principal component (** p value

< 0.01).

(A)F

irst princip

al com

ponent (a

.u.)

(32.3

4%

)

(i) (ii) (iii) (iv)

6

4

2

0

-2

-4

-6

8

(B)

Ram

an Inte

nsity

Wavenumber/cm-1

0.2

0.1

0

-0.1

-0.2

1900 1700 1500 1300 1100 900 700 500

1573(1

580

-1520)

1673 (

1699

-1657)

1337 (

1323

-1375)

1001

1447 (

1469-1

434)

**

1640-1

620

FSET PhD Thesis/268

APPENDIX D

Figure 1. Comparison of normalised Raman spectra from E. coli ATCC 25922

colony grown on membrane after membrane peak normalisation and vector

correction.

500 1000 1500 2000

0.00

0.43

0.86

1.29

-2500

0

2500

5000N

orm

alis

ed Inte

nsity / A

rbitr.

Units

Wavenumber (cm-1)

Peak Intensity normalisation

Vector projection normalisation


Analysis of population behaviour of colony cells with the application PC-LDA

planktonic model

V. vulnificus

Reference

(PC-LDA

planktonic

model)

Met

ab

oli

c

ph

ase


V. vulnificus

Growth region


V. vulnificus

(6 cells)

EE 3 2

ME 1

LE

ES

MS

LS

ED

MD

LD

P. aeruginosa

Reference

(PC-LDA

planktonic

model)

Met

ab

oli

c

ph

ase


P. aeruginosa

Growth region


P. aeruginosa

(9 cells)

EE 2

ME

LE 1 1

ES

MS 1 1

LS 1 2

ED

MD

LD

FSET PhD Thesis/270

S. aureus

Reference

(PC-LDA

planktonic

model)

Met

ab

oli

c

ph

ase


S. aureus

Growth region


S. aureus

(8 cells)

EE 1 2

ME

LE 2

ES 2 1

MS

LS

ED

MD

LD

Abbreviations: EE; early exponential, ME; mid exponential, LE; late exponential,

ES; early stationary, MS; mid stationary, LE; late stationary, ED; early decline, MD;

mid decline, LD; late decline.

Mya Myintzu Hlaing Lists of Publications/271

LISTS OF PUBLICATIONS

1. Hlaing MM, Cadusch PJ, Wade SA, McArthur SL, Stoddart PR. Method for

Fluorescence Background Subtraction from Raman Spectra. International

Conference on Raman spectroscopy. August, 2012, India. Poster presentation

2. Cadusch PJ, Hlaing MM, Wade SA, McArthur SL, Stoddart PR. Improved

methods for fluorescence background subtraction from Raman spectra. Journal of

Raman Spectroscopy. 2013; 44(11):1587-95.

3. Hlaing MM, Dunn M, McArthur SL, Stoddart PR. Sample Preparation and

Optimization for Bacterial Identification by Raman Spectroscopy. AVS International

Symposium and Exhibition (60th ). October, 2013, California. Oral presentation

4. Hlaing MM, Dunn M, McArthur SL, Stoddart PR. Raman spectroscopy for

bacterial identification: Effects of sample preparation and storage. International

Journal of Integrative Biology. 2014; 15(1):11-7.

5. Hlaing MM, Dunn M, Stoddart PR, McArthur SL. Raman Spectroscopy for

Differential Identification of Bacterial Species. International Conference on Raman

spectroscopy (14th). August , 2014, Germany. Poster presentation

6. Hlaing MM, Dunn M, Wade SA, Stoddart PR, McArthur SL. Raman

Spectroscopy for Differential Identification of Bacterial Species. DMTC Students

Conference. October, 2014, Melbourne, Australia. Oral presentation

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