RNA Quality Control - Comparing Different RNA Quality Indicators

Sample to Insight

1

Dr. Pierre-Henri FerdinandGlobal Product Management

[email protected]

Quality Control of RNA samples

RNA quality control – comparing different RNA quality indicators

mailto:[email protected]

Sample to Insight

Legal Disclaimer

2

QIAGEN products shown here are intended for molecular biology applications. These products are not intended for the diagnosis, prevention, or treatment of a disease.

For up-to-date licensing information and product-specific disclaimers, see the respective QIAGEN kit handbook or user manual. QIAGEN kit handbooks and user manuals are available at www.QIAGEN.com or can be requested from QIAGEN Technical Services or your local distributor.


http://www.qiagen.com/

Sample to Insight

Quality Control of RNA samples 3

Outline

Why is RNA quality control so important1

Main parameters for RNA QC2

Fast and reliable quality control3

Comparison of RIN and RIS4

Wrap up5

Sample to Insight

4

http://www.sciencemag.org/news/2015/06/study-claims-28-billion-year-spent-irreproducible-biomedical-researchhttp://www.nature.com/news/reproducibility-1.17552http://www.nature.com/news/1-500-scientists-lift-the-lid-on-reproducibility-1.19970http://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1002165


http://www.sciencemag.org/news/2015/06/study-claims-28-billion-year-spent-irreproducible-biomedical-research

http://www.nature.com/news/reproducibility-1.17552

http://www.nature.com/news/1-500-scientists-lift-the-lid-on-reproducibility-1.19970

http://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1002165

Sample to Insight

5

Non reproducibleReproducible biomedical

research

50%• Culture of “publish or perish”• Inefficiencies in designing, conducting, and reporting of

studies• Lack of standardization of sample quality for inter-lab

reproducibility

Some solutions for tackling the issue:• Implementing quality control procedures at key steps of laboratory workflows to help

standardizing the parameters of the samples, and thus the quality of the data generated.

• Combining lab automation with quality chemistries to enhance reproducibility of experiments, bringing confidence in data interpretation.


Sample to Insight

6

QC emphasizes on gaining insight into a sample quality and assessing its suitability for downstream applications and its potential for generating reliable results.

QC plays an important role when it comes to developing routine procedures or troubleshooting an experiment.

µg

?

Purity?

Quantity?

Integrity?

Size?

Are these parameters in the range of my experiment’s requirements for generating high-quality results?

RNA Quality?

What is RNA sample QC?


Sample to Insight

7

Why is sample QC so important?

Risk of failure Molecular biology workflows are complex, error- and failure-prone multi-

step procedures A lot of things can potentially go wrong during workflows RNA can be unstable: sensitive to heat, radiations and nucleases

Confidence in results At the end, only the quality of the final result matters The quality of the final results is highly influenced by quality of samples

Cost aspects Downstream applications get more sensitive but not necessarily more

robust to variations in samples quality Failure means waste of time and resources

Price per sample Time

qPCR 1.5 $US 4-5 hrs

Sanger Sequencing 5-6 $US 4-6 hrs

Next-Gen Sequencing >200 $US(5plex) 2-3 working days

Affymetrix GeneChip Analysis 500-800 $US/sample > 2 working days


Sample to Insight

8

Sample collection/

stabilization

RNA purification,

cDNA & cRNA synthesis

Quantification, Normalization, Fragmentation

Array Hybridization and scanning

Data analysis &

interpretation

Gene expression Microarrays

QC QC

Sample collection/

stabilization

RNA purification Amplification Analysis

Data analysis &

interpretation

Gene expression RT-PCR

QC

Sample collection/

stabilization

DNA/RNA purification

NGS Library Prep Sequencing

Data analysis &

interpretation

RNA-Seq

QC QC

RNA Quality Control is recommended in Gene expression workflows

RNA samples Quality Control and standardization


Sample to Insight


Outline





Wrap up5

Sample to Insight

Six parameters of prime relevance for sample Quality Control


QC Criteria UV spectro. Gels Dye-based spectro.

CE / µ-fluidic UV/VISspectro.

Protein contaminants(A260/280) Salts & other contaminants(A260/230)

Quantity of dsDNA vs. other NA Yield () Degradation/Size distribution () Sizing

There is no one-for-all solution – 2 technologies cover all sample QC needs

purity

quantity

integrity

Sample to Insight

Main RNA quality parameters


Purity

Quantity

Integrity

What is the yield of my extraction? How much RNA is in the tube and at what concentration? Will I have enough material?

When the concentration of NA is overestimated, the input amount will be too low. This leads to weak amplification and weak signal strength.When the concentration of DNA is underestimated the input amount will be too high and can lead to overuse of precious samples, increased concentration of inhibitors, false priming, unfavorable ratios, etc.

What’s really in my tube? Could contamination or impurities interfere with my assay?

Phenol, ethanol, salts, etc. can jeopardize sensitivity and efficiency of downstream enzymatic reactions.Large amounts of unwanted ssDNA or dsDNA template lead to overestimation of the molecule of interest and interferes with downstream applications.

Does my RNA sample have the correct size distribution? Has RNA been degraded? RNA samples of poor integrity can affect PCR or sequencing reactions and lead to errors in replication and/or lower yields e.g. irrelevant CT values. Degraded samples can produce false negative results.

http://www.google.ch/url?sa=i&rct=j&q=&esrc=s&source=images&cd=&cad=rja&uact=8&ved=0ahUKEwjyt-uHv5DLAhWBWxoKHSKzD1IQjRwIBw&url=http://digitalsplashmedia.com/2014/04/free-health-icon-dna-molecule/&bvm=bv.114733917,d.ZWU&psig=AFQjCNGMDD4JoM3RgxS1JPHQ7mzi-k35Pw&ust=1456406462993670




Sample to Insight


• RNA QC requirements vary depending on downstream applications: RT-PCR, qRT-PCR, Microarray or RNA-Seq

• Small deviations in key sample parameters can have potential impact on final results

Implementation of QC procedures with relevant pass-criteria and ensuring RNA samples quality parameters are within acceptable range are essential to maximize success of experiments, thus saving time, money and peace of mind

Highest quality results are only achieved from quality samples Only quality results are worth sharing

Sample to Insight

Key quality indicators






There is no one-for-all solution

purity

quantity

integrity

Sample to Insight




QIAxcel Advanced QIAxpert



purity

quantity

integrity

There is no one-for-all solution – QIAGEN covers all your needs for sample Quality Control!

Sample to Insight


Outline





Wrap up5

Sample to Insight


Sample collection/

stabilization

RNA purification

Amplification/ NGS Library

PrepAnalysis

Data analysis &

interpretationQCQC

Quantity & Purity

Integrity & size distribution

QIAxpert QIAxcel Advanced

Sample to Insight

QIAxpert uses innovative microfluidic sample carrier

17

Key features:

1

2

34

5

1

2

3

4

5

Sample loading wellCapillariesMicrocuvetteOverflowVent hole for vacuum

Only 2 µl of sample required

DNA range: 1.5 ng/μl up to 2000 ng/μl dsDNA (A260)

Fast analysis, up to 16 samples in less than 2 minutes

Algorithm can unmixe sample’s spectra to differentiate contribution of different molecules.

QIAxpert is a high-speed spectrophotometer for DNA, RNA and

protein analysis. It is fast, accurate and sensitive.


Sample to Insight


Classic spectrophotometry

A230: impurities

A280: proteins

A260: nucleic acids

Sample to Insight


1. Absorbance measurement (and background correction)

2. Content profiling of the measured spectrum by fitting of reference spectra into

• Specific DNA or RNA spectrum• Impurities spectrum • Residue spectrum

3. Quality control• Impurities spectrum• Residue spectrum • Backround spectrum• A260/A280 ratio for protein

contamination

QIAxpert – Unique spectral content profiling protocols

Quantity of DNA or RNA you think you have

Quantity of DNA or RNA you actually have

Sample to Insight

With QIAxpert – tell RNA from DNA and other contaminants without a dye

20

High-speed microfluidics• No drop & clean action required, no cross contamination• Evaporation-safe for 2h• Flexible input & up to 16 samples in <2 min

Comprehensive Export• Reports can be viewed on any browser• USB and Network data output• No extra software or computer required

Easy interaction• Inbuilt touchscreen• Simple GUI • Variety of pre-installed protocols

Spectral content profiling• Differentiation between molecules• Quantify and subtract identified impurities• Give best insight into samples’ quality


Sample to Insight

Quantity & Purity !

QIAxpert – Unique spectral content profiling protocols

21

Classic spectrophotometry

Quantity

Spectral Content Profiling

For internal use only

• DNA or RNA spectrum• Impurities spectrum • Residue spectrum


Sample to Insight


Sample collection/

stabilization

RNA purification


PrepAnalysis

Data analysis &

interpretationQCQC

Quantity & Purity



Sample to Insight

QIAxcel Advanced allows separation, detection and analysis of NA

23

QIAxcel Advanced

Fully automated DNA and RNA analysis

Fast processing: 12 samples in 8 – 13 min

Up to 96 samples per run

Up to twenty 96-well plates with QIAxcel HT

High resolution down to 3 – 5 bp up to 500 bp

Safety and convenience with ready-to-use gel cartridges

Digital data output

QIAxcel Advanced is an automated capillary electrophoresis system for

DNA and RNA analysis.

QIAxcel Advanced allows analysis of gDNA, RNA and NGS libraries


Sample to Insight

QIAxcel Advanced capillary electrophoresis principle

24Quality Control of RNA samples

• Reusable 12-capillary cartridges

• Fast processing: 12 samples in 8-13 min

• Detection limit of 0.1 ng/µl

• Sample consumption < 0.1µl

Sample to Insight

How does it work?

Place the gel cartridge into the instrument

Load samples (tube strips or 96-well plates), buffers, and markers

Select the Process Profile of your choice... and GO!

Look at the results in real-time on the screen and report data just a few minutes later

Operating the QIAxcel Advanced system:


Sample to Insight

Total RNA quality is assessed by analysing the migration pattern

26

28S

18S

5S

18S/28S ratioSmears

Objectivity of the visual ratio estimation?Ratio is not always correlated to integrity!


Sample to Insight

RNA Quality Control - RNA Integrity Score (RIS)

27

A1 RIS: 9.5 A7 RIS: 5.8

A11 RIS: 3.6 Superimposed view

Lane Name RIS

A1 rat_liver _1 9.5

A7 rat_liver _4 5.8

A11 rat_liver _6 3.6


RIS10 1

RNA Integrity

Sample to Insight

28

The gel images of QIAxcel and Agilent 2100 are comparable Q

IAxc

elB

ioan

alyz

er


Sample to Insight

RIS and RIN can objectively assess RNA integrity

29

RIS : RNA Integrity Score, by QIAGENRIN : RNA Integrity Number, by Agilent

• Indicators reflecting RNA integrity • Intended to predict the validity of

downstream qPCR

Frame of reference of RIS and RIN:• Values range from 1 (highly degraded) to 10 (mostly intact)• Analyse several different electropherogram’s parameters, including 28S and

18S peaks analysis• Values between 7 and 10 (depending of what is achievable with samples) are

indicators of RNA quality suitable for downstream applications• Allow comparison of sample, standardization and repeatability of experiments


Sample to Insight


Outline





Wrap up5

Sample to Insight

RIS is more robust than RIN for assessing RNA integrity

31

• Depending on the degradation mechanism (heat, nuclease or UV), total RNA samples have different electrophoretic behavior

• Different RNA degradation methods result in different ranges of ΔΔCt values for identical RIS/RIN values

• “Overall, RIS was more robust than RIN for assessing RNA integrity” Unger, C et al., 2015

http://www.ncbi.nlm.nih.gov/pubmed/25998866www.researchgate.net/publication/277080839_Ultraviolet_C_radiation_influences_the_robustness_of_RNA_integrity_measurement


http://www.ncbi.nlm.nih.gov/pubmed/25998866

http://www.researchgate.net/publication/277080839_Ultraviolet_C_radiation_influences_the_robustness_of_RNA_integrity_measurement



Sample to Insight

Methods

32

RNA extractionRNA degradationx3 independent experiments

RNA integrity assessmentmeasured in duplicate

SYBR Green qRT-PCRRT2 RNA QC PCR Array, selective degraded RNA samples applied in triplicate

Goal of the study: compare RIS and RIN to the mRNA integrity observed by qRT-PCR in order to evaluate their respective ability to assess RNA integrity

Jurkat & HeLa S3

cells

RNase III0-7 min,

37°C

Heat0-120 min,

70°C

UV light0-220 min,

254 nm

Agilent 2100 Bioanalyzer

RIN

QIAxcel Advanced

RIS

JurkatΔΔCt of actb and hprt1


Sample to Insight

33

Figure : Decreasing RIS and RIN values were obtained by increasing time of heat degradation of total RNA. RIS and RIN were determined of duplicates from partially degraded RNA. In three independent experiments. The mean of RIS and RIN obtained for each cell type is presented over time of heat degradation (A). The correlation of RIS and RIN calculated with a regression analysis using Minitab 16 presented for degraded RNA from Jurkat (B) and HeLa S3 cells (C).

RIN and RIS have a high correlation for heat-degraded RNA


Sample to Insight

34

Figure : Increasing degradation level of total RNA by RNase digestion resulted in decreasing RIS and RIN. RIS and RIN were determined for duplicates of degraded RNA samples from three independent experiments; the mean was plotted as function of time of incubation with RNase (A). A linear regression analysis was performed with Minitab 16 to determine correlation of RIS and RIN for degraded RNA from Jurkat (B) and HeLa S3 cells (C).

RIN and RIS have a high correlation for RNase-digested RNA


Sample to Insight

35

RIS and RIN values are dissimilar after 100 minutes of UV exposure

Figure: RNA exposure to UV-irradiation resulted in decreasing RIS and RIN over time with high variances in lower RINvalues. RIS and RIN were determined of duplicates for each sample from three independent experiments. The mean values wereplotted as function of exposure time (A), the correlation between RIS and RIN was analyzed by a linear regression analysiswith Minitab 16 for Jurkat (B) and HeLa S3 cells (C). Presented are also screen shots of the 2100 Expert software showing theelectropherogram from duplicates of Jurkat RNA degraded for 185 min, resulting in RIN 2.6 (D) and 4.7 (E).


Sample to Insight

RIN algorithm struggles defining 28S peaks of highly UV-damaged RNA

36

Electropherograms of these duplicates look similar but 28S peaks are defined differently

(Jurkat RNA degraded for 185 min) resulting in RIN 2.6 (D) and 4.7 (E).


Sample to Insight

37

RIS>RIN RIS>RIN RIS>RIN

RNase digestion

Heat degradation

UV-lightexposure

Correlation ΔΔCt and RIS values

R²= 89,7% R²= 89,6% R²= 97,4%

Correlation ΔΔCt and RIN values

R²= 89,6% R²= 87,6% R²= 93,1%

Figure: the correlation of ΔΔCt-values with RIS and RIN values Calculated by a linear regression analysis with Minitab 16, to study whether RIS and RIN could predict the outcome of a qPCR. Fitted line plots with regression line and adjusted R2 values of RIS and RIN against ΔΔCt-values of ACTB


Sample to Insight

Heat

38

RNase UV light

1) RNase, heat and UV light have different degradation mechanisms on RNA

2) Different RNA degradation methods result in different ranges of ΔΔCt values at similar RNA

integrity values (1-log difference)

3) RIS is more robust than RIN to determine RNA integrity of RNA degraded by different methods

and to evaluate suitability of RNA sample for qRT-PCR High correlation between RIS and RIN values for heat- and RNase III-degraded RNA

Lower correlation between RIN and RIS values for UV-degraded RNA, due to high variation in

RIN values, but similar at a decision level of RIN/RIS 7


Sample to Insight


Outline





Wrap up5

Sample to Insight

Sample Insight

Sample collection/

stabilization

DNA/RNA purification


PrepAnalysis

Data analysis &

interpretation

Sample QC must be part of molecular biology workflows, not optional steps on top.

QC brings value to workflow and can save cost, time and peace of mind

QC QC


Sample to Insight



QC Criteria UV spectro. Gels Dye-based spectro




There is no one-for-all solution

purity

quantity

integrity

Sample to Insight




QIAxcel Advanced QIAxpert



purity

quantity

integrity

There is no one-for-all solution – QIAGEN covers all your needs for sample Quality Control!

Sample to Insight

Size distribution analysis

Quantity & Purity

QIAGEN covers all your needs for sample Quality Control

43

Spectral Content Profiling

For internal use only

Integrity



Sample to Insight

Sample QC can save you time, money, and peace of mind


Application Price per sample Time

Downstream application

qPCR 1.5 $US 4-5 hrs

Sanger Sequencing 5-6 $US 4-6 hrs

Next-Gen Sequencing >200 $US(5plex) 2-3 working days

Affymetrix GeneChip Analysis

500-800 $US/sample > 2 working days

Sample Quality Control

QIAxpert <1$US 90 seconds(16 samples in parallel)

QIAxcel Advanced <1$US 3-10 minutes(12 samples in parallel)

Purity & quantity

Integrity

QC gives insights into sample suitability for

downstream application

• Brings confidence in data analysis and interpretation, troubleshooting• Saves time, money, and peace of mind by excluding samples of poor quality

“Save time, money, and peace of mind only dealing with the samples you deserve: the ones of highest quality”

Sample to Insight

How does QIAGEN helps you achieving reliable QC?


Quantity & Purity



Let lab automation work for you and do the job they are excellent at: Delivering reliable and reproducible results

Perform accurate measurements, keep track of your sample quality, standardize your experiments and increase reproducibility of your results.

Increase your lab productivity: automation frees your hands and your mind!

Sample to Insight


Quantity & Purity



More info on:QIAGEN.com

Discover how these instruments can help you improve the reproducibility of your experiments and achieve higher quality results with our interactive online demo tools and request a free demo in your lab!

Sample to Insight

47

Questions?

Thank you for your attention!

Pierre-Henri FerdinandGlobal Product Management

[email protected]

Learn more about nucleic acids QC on: qiagen.com/QCSolutions


mailto:[email protected]

Sample to Insight


Back-up slides

Sample to Insight

Additional Methods & Materials

Title, Location, Date 49

Determination of RNA integrity

QIAxcel Advanced

QIAxcel RNA QC Kit v2.0

Method: CL-RNA

Software: QIAxcel ScreenGel

Output: RNA Integrity Score (RIS)

Agilent 2100 Bioanalyzer

Agilent RNA 6000 Nano Kit

Method: Eukaryote Total RNA Nano

Software: 2100 Expert

Output: RNA Integrity Number (RIN)

RNA extraction

Material: Jurkat and HeLa S3 cells

Homogenization: TissueRuptor

RNA extraction: RNeasy Mini kit, including DNase I digestion, on the

QIAcube

Determination of RNA concentration: Nanodrop 1000

Sample to Insight


RNA degradation

Three independent experiments for each degradation method, comparing RNA of Jurkat and HeLa

S3 cells.

1. RNA digestion with ShortCut® RNase III (NEB) 90 µl reaction volume with:

9 µg RNA 0.1x Reaction buffer 0.2 units RNase III 0.1x MnCl2

Incubation at 37 °C for 0.5 / 1.5 / 3 / 5 and 7 min, stop reaction with 5 mM EDTA

Remove enzyme with RNeasy MinElute Cleanup kit

2. RNA degradation by heat (established by QIAGEN)

50 µl of 300 ng/µl RNA in RNase-free water

Incubation in a heat block at 75 °C for 0 / 20 / 40 / 60 / 80 / 100 and 120 min

3. RNA degradation by UV-irradiation (established by IBBL)

50 µl of 320 ng/µl RNA in RNase-free water

Exposure to UV-irradiation (254 nm)

10 time spans between 0 and 220 min

Sample to Insight

Correlation between RIN & RIS and ΔΔCt values were comparable


RIN RIS

ΔΔCt of actb

ΔΔCt of hprt1

Figure: For RNA degraded by RNase, the correlations between ΔΔCt‑values and RIS or RIN were comparable, ΔΔCt values were between 0 and 2.5. Selected samples of RNA from Jurkat cells degraded by ShortCut® RNase III were analyzed by qPCR. A linear regression analysis was performed using Minitab 16 to study the correlation between RIN (left) or RIS (right) and ΔΔCt‑values of the housekeeping genes actb (top) or hprt1 (bottom).

Sample to Insight

ΔΔCt values showed slightly higher correlation with RIS than RIN


RIN RIS

ΔΔCt of actb

ΔΔCt of hprt1

Figure: For heat degraded RNA, the correlation between ΔΔCt‑values and RIS was slightly higher compared to RIN, ΔΔCt values were between 0 and 1.6. Selected samples of heat degraded RNA from Jurkat cells were analyzed by qPCR and a linear regression analysis was performed using Minitab 16 to study the correlation between ΔΔCt‑values of the housekeeping genes actb (top) or hprt1 (bottom) and RIN (left) or RIS (right).

Sample to Insight

ΔΔCt values showed higher correlation with RIS than with RIN


RIN RIS

ΔΔCt of actb

ΔΔCt of hprt1

Figure: ΔΔCt values showed higher correlation with RIS than with RIN, ΔΔCt values were between 0 and 16. qRT-PCRs were run with selected samples of UV degraded RNA from Jurkat cells and a linear regression analysis was performed using Minitab 16 to study the correlation between ΔΔCt‑values of ACTB (top) or HPRT1 (bottom) and RIN (left) or RIS (right).

Sample to Insight

Total RNA and cRNA analysis

QIAxcel Advanced – Pure Excellence

Typical workflow within microarray analysis

Quality control of unlabelled cRNA generatedFrom yeast total RNAA gel view, B electropherogram view

Quality control of total RNA purified from yeast A gel view, B electropherogram view

Data kindly provided by D. van Leenen, Microarray Facility UMC Utrecht, NL

Sample to Insight

55

QIAxpert – excellent measurement accuracy

QIAxpert: lowest %CV value

Qubit: high mean variation

250 ng/µl reference RNA (Agilent Technologies)

AccuracyqPCR Human Reference Total RNA (Agilent Technologies) was diluted to 250 ng/µL (dilution from original solution in H2O). A total of 40 replicates were measured, each on the QIAxpert (RNA260 app), on a Nanodrop 8000, and the Qubit system.

Sample to Insight

56

QIAxpert – excellent linearity in RNA quantification

QIAxpert Nanodrop Qubit

0

200

400

600

800

1000

Nuc

leic

aci

ds (n

g/ul

)

0 200 400 600 800 1000Soll Konz (ng/µl)-1000

Excellent linearity Systematic overquantification

Systematic underquantification

Comparison of RNA linearity using different systems

LinearityHuman Reference RNA (Agilent) was diluted to 1000 ng/µl, 500 ng/µl, 100 ng/µl, 50 ng/µl, 10 ng/µl, 5 ng/µl, and 1.5 ng/µl. A total of 5 replicates of each dilution were measured using the QIAxpert system, a Nanodrop 8000, and the Qubit. Data shown for the QIAxpert reflects total NA measured with the RNeasy app.

RNA Quality Control - Comparing Different RNA Quality Indicators

Healthcare

Transcript of RNA Quality Control - Comparing Different RNA Quality Indicators