““Standardising Analytical Standardising Analytical

MetabonomicsMetabonomics””““Standardising Analytical Standardising Analytical

MetabonomicsMetabonomics””

AUTh bioAnalytical group Metabonomics

AUTh bioAnalytical group Metabonomics

• Fundamental / Developmental workNew Methods (Targeted, Untargeted)

New Materials

Validation

• Clinical StudiesRheumatoid Arthritis

Physical Exercise

Frailty

EmbryoMetabolomics

Sepsis/NEC newborns

Metabolic profiling-analytical metabolomicsMetabolic profiling-analytical metabolomics

Analytical procedure

Sample collection

Data extraction

Data mining

Cons•Unstable, irreproducible, temperamental•Several different mass analysers and ionisation possibilities.•Not really robust

• Pros• Sensitive, specific, accurate• Widely available, several

different mass analysers and ionisation possibilities

• Multitude of information

LC-MS

Tools

Bottlenecks in analytical procedureBottlenecks in analytical procedure

• LC-MS instrumentation variability: Drifts in Rt, mass, sensitivity• Need for long analytical batches• Unknown trends /unknown components-analytes• Instrument calibration along the run• Different instrumentations/architecture • Wide spectrum of analytes • Huge span in concentration: 7 orders of magnitude

• Full scan mode aqcuisition

Bottlenecks in data treatmentBottlenecks in data treatment

• Big datasets• Impractical to correlate-combine data• Various pick picking and treatment algorithms • Filtering of noise analytically-oriented • lack of data repositories and databases • lack of commercial or wide-use LC-MS spectra libraries• metID

• Analytical Chemists, Informaticians, Chemometricians, biochemists still speak different language

-day-to day precision?

Need for standardization & harmonisation Need for standardization & harmonisation

Establishing guidelines/ SOPs

• Data quality (accuracy and precision of measurements)

• QC procedures

• Instrument performance and maintenance

• Sample collection/storage

• Sample treatment

• Data acquisition protocols

• Data manipulation

How can we validate a metabolic profiling method when we don’t know the analytes in advance

•Implementation of QC (pooled study sample analyzed at regular intervals) •Synthetic mixtures injections•Randomisation of injection order•Technical replicates

Integration of classical analytical strategies with modern unbiased data analysis

QC pipeline

Gika et al J Proteome Res 2007

QC pipeline

Gika et al J Proteome Res 2007

The projectThe projectThe projectThe project

““Standardising Analytical Metabonomics”Standardising Analytical Metabonomics”

Co-funded by European social fund and national sourcesCo-funded by European social fund and national sources

Scope Scope

• To promote standardization and quality control• To address major bottlenecks in analytical practice (development of advanced analytical methodologies

forMS-based metabolic profiling) • To develop informatics tools to improve the quality of

the extracted information

analytical procedure

sample collection

data extraction

data analysis

study design

Data mining, chemometrics

biomarkers IDs

sample prepanalysis

Project focusProject focus

WP1: Development of Analytical MethodologiesWP1: Development of Analytical Methodologies

• Profiling methods with complementary/orthogonal selectivities -HILIC/MS-MS for quantitative determination of ca. 140 primary metabolites -Implementation of other HILIC chemistries eg zwitterionic, diol, RP-WAX - Computational approach for column selection for metabolic profiling

• Protocols for sample extraction -Optimization studies on extraction of feces samples, tissue etc (e.g. different pH values, organic solvent composition, mass to

volume ratio) -Liquid and Solid Phase Extraction (SPE) assays for the fractionation of the extract minimising ion suppression effects/compatibility

with MS Derivatisation conditions optimization for GC-MS

Method robustness

Extraction efficiency

Metabolome coverage

ExtractionExtraction

WP 2: Data extractionWP 2: Data extraction

• Evaluation of various data extraction software (free and commercial: XCMS, MarkerLynx, MarkerView, Profiler and others) in real metabonomics studies. • Spiking experiments (comparison of sensitivity and reliability of the data treatment software)

• Development of intranet platform for the extraction of information from MS-profiling data (rules for monitoring and reporting the various alterations and parameter selection to improve standardization in data extraction and reporting

WP3: Quality Control and standardisation protocolsWP3: Quality Control and standardisation protocols

• Scripts for QC in holistic MS data

• Examine data in depth and applying rules by automated scripts

• Correction for retention time drift to improve peak alignment in

feature detection.

• Unifying these utilities in one program to standardize promote and

consolidate quality control and minimize error possibilities.

WP 4: Data fusionWP 4: Data fusion

• Software tools to fuse data from different methods LC-MS/MS + GC-MSLC-MS/MS + NMR HILIC-MS + RPLC-MS +evi ESi/ -evi ESI • link data • combine into one table of features or metabolites (?)

WP5: Metabolite IdentificationWP5: Metabolite Identification

MetID the major bottleneck in LC-MS metabonomics • scripts for adduct identification to reduce the number of detected features : +Na+, + NH4+ , dimers etc• MS spectra by analysis of standards (in-house MS

database). • Scripts for automated searches in local and internet-

based spectral/biochemistry libraries. • Compare isotope patterns between peaks in samples

and standards

WP6 : Retention Time PredictionWP6 : Retention Time Prediction

• Incorporating Rt data to assists MetID • Use of data from orthogonal chromatographic systems:

chemical information (polarity, LogP etc)• Rule out candidate IDs Retention time prediction algorithm in HILIC

• software to organise the necessary analyses and data treatment for metID within an easy to use platform.

SummarySummary

• Strong need for Standardisation • LC-MS is a major part of the solution (and the problem!)• Metabolomics is analytically dependent • Intelligent tools are needed to go through data and

efficiently check data quality

The major aim is to find biomarkers – when you’ve found them the real work begins.

Auth• Dr. H. Gika• Dr. G. Theodoridis• Prof. A. Papa• Dr. N. Raikos• Dr. C. Zisi• Dr. C. Liambas• O. Deda MSc• S. Fasoula MSc• A. C. Hatzioannou MSc • D. Palachanis MSc• C. Virgiliou MSc• I. Sampsonidis MSc

External collaborators• I. D. Wilson Imperial college London UK• P. Vorkas Imperial college London UK• P. Francheshi IASMA Trento Italy

The group