How to do successful gene expression analysis Jan Hellemans, PhD - - PowerPoint PPT Presentation

How to do successful gene expression analysis

Jan Hellemans, PhD Center for Medical Genetics Biogazelle

qPCR meeting – June 25th 2010 – Sienna, Italy

• qPCR: reference technology for nucleic acid

quantification

• sensitivity and specificity
• wide dynamic range
• speed
• relative low cost
• conceptual and practical simplicity
• easy to perform ≠ easy to do it right
• many steps involved
• all need to be right

Introduction

Introduction

Choice of chemistry Choice of RT RNA quality assessment Sample selection and handling Data reporting Sample extraction RT and PCR primer design cDNA synthesis strategy Assay validation Data analysis

prepare – cycle – report

Prepare

experiment design

• power analysis
• sample vs gene maximization
• run layout

samples

• preparation
• quality control
• pre amplification

assays

• design
• in silico validation
• empirical validation

reference gene

• selection
• validation

Prepare

experiment design

• power analysis
• sample vs gene maximization
• run layout

samples

• preparation
• quality control
• pre amplification

assays

• design
• in silico validation
• empirical validation

reference gene

• selection
• validation

Power analysis

• determination of the number of data points

needed to reach statistical significance for a given

• difference
• variability
• technical constraints
• confidence interval (CI)

 3 (~ critical t-value t*) CI = SEM x t*

0,00 2,00 4,00 6,00 8,00 10,00 12,00 14,00 2 3 4 5 10 20 100

critical t-value number of datapoints

Power analysis

• determination of the number of data points

needed to reach statistical significance for a given

• difference
• variability
• technical constraints
• confidence interval (CI)  3
• Mann-Whitney test: nA + nB  8
• Wilcoxon test:  6 pairs
• http://www.cs.uiowa.edu/~rlenth/Power/

• how to set-up an experiment with
• 3 genes of interest (GOI) & 3 reference genes (REF)
• 11 samples (S) & 1 no template control (NTC)

Sample vs gene maximization

S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11NTC S1 S2 S3 S4 S5 S6 S7 NTC S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11NTC S1 S2 S3 S8 S9 S10 S11 NTC

sample maximization

GOI2 GOI3 REF1 REF2 REF3 GOI1

gene maximization

REF1 REF2 REF3 GOI1 GOI2 GOI3 GOI2 GOI3 REF1 REF2 REF3 GOI1

Sample vs gene maximization

• sample maximization – to be preferred
• no increase in variation due to absence of inter-run

variation

• suitable for retrospective studies and controlled

experiments

• gene maximization
• introduces (under-estimated) inter-run variation
• applicable for prospective studies or large studies in

which the number of samples do not fit in the run anymore

• inter-run variation can be measured and corrected for

using inter-run calibrators (IRC) through a procedure called inter-run calibration

Prepare

experiment design

• power analysis
• sample vs gene maximization
• run layout

samples

• preparation
• quality control
• pre amplification

assays

• design
• in silico validation
• empirical validation

reference gene

• selection
• validation

Preparation

• cDNA synthesis
• most variable step in the workflow (> RT replicates)
• different performance of the enzymes
• linearity and yield are important
• DNase treament
• retropseudogenes (15%) and single exon genes (5%)
• on column vs. in solution
• verify absence of DNA
• qPCR for genomic DNA target on RNA as input

• Evaluate integrity of 18S and 28S rRNA
• Agilent Bioanalyzer
• Bio-Rad Experion
• Caliper GX
• Qiagen QIAxcel
• Shimadzu MultiNA

Quality control – RNA integrity value

• universally expressed low abundant reference
• anchored oligo(dT) reverse transcription
• increasing delta-Cq values upon artificial RNA

degradation

Quality control – 5’-3’ ratio

AAAAAA 5’ 3’ Cq 5’ Cq 3’

1 2 3 4 5 6 7 8 9 109 109* 109** 275 275* 275** 539 539* 539** samples 5'-3' delta Ct

• spiking of synthetic sequence lacking homology

with any known human sequence into RNA Quality control – SPUD assay for inhibition

SPUD + H2O SPUD + heparin SPUD + RNA1 SPUD + RNA2 SPUD + RNA3 Cq 22 Cq 27 Cq 22 Cq 25 Cq 22 ΔCq > 1: presence of inhibitors

• -----------RT-qPCR---------

• methods
• WT-Ovation (NuGEN)
• limited cycle PCR (PreAmp - Applied Biosystems)
• preservation of differential expression (fold

changes) before (B) and after (A) sample pre- amplification

• (G1S1)B/(G1S2) B = (G1S1) A/(G1S2) A
• G1B/G2B < > G1A/G2A
• gene G, sample S, before B, after A

Pre amplification

Prepare

experiment design

• power analysis
• sample vs gene maximization
• run layout

samples

• preparation
• quality control
• pre amplification

assays

• design
• in silico validation
• empirical validation

reference gene

• selection
• validation

http://www.rtprimerdb.org

Assay design guidelines

• location
• sequence repeats, protein domains
• splice variants
• intron spanning vs intra exonic
• short amplicons: 80-150bp
• SNPs
• primers
• dTm < 2°C
• identical Tm for all assays
• maximum 2 GC in last 5 nucleotides
• use software to design assays
• Primer3(Plus), BeaconDesigner, RTprimerDB

In silico assay validation

• do thorough in silico assay evaluation
• BLAST/BiSearch specificity analysis
• mfold secondary structure
• SNP analysis of primer annealing regions
• splice variant specificity
• streamline in silico analyses with RTprimerDB

pipeline

Empirical assay validation

• specificity
• size analysis (only once)
• agarose or polyacrylamide gel
• capillary electrophoresis
• melting curves (SYBR, repeated)
• [sequence / restriction digest]
• amplification efficiency
• standard curve
• range & number dilution points
• representative sample
• [single curve efficiency algorithms]
• for absolute quantification
• linear range and limit of detection

Prepare

experiment design

• power analysis
• sample vs gene maximization
• run layout

samples

• preparation
• quality control
• pre amplification

assays

• design
• in silico validation
• empirical validation

reference gene

• selection
• validation

Single reference gene

• quantitative RT-PCR analysis of 10 reference

genes (belonging to different functional and abundance classes) on 85 samples from 13 different human tissues

1 2 3 4

ACTB HMBS HPRT1 TBP UBC A B C D E F G

Single vs multiple reference genes

• single reference gene
• errors related to the use of a single reference gene:

> 3 fold in 25% of the cases > 6 fold in 10% of the cases

• multiple reference genes
• developed a robust algorithm for assessment of

expression stability of candidate reference genes

• proposed the geometric mean of at least 3 reference

genes for accurate and reliable normalisation

• geNorm analysis in pilot study
• Vandesompele et al. Genome Biol. 2002 Jun

18;3(7):RESEARCH0034.

geNorm

• validation
• insensitive to outliers
• reduce most of the variation
• statistically more significant results
• accurate assessment of small expression differences
• de facto standard for reference gene validation
• 2 400 citations of the geNorm technology
• ~12 000 geNorm software downloads in 112 countries

genormPLUS

genormPLUS

genormPLUS

Cycle cycle

• instrument
• chemistry
• controls

• fast PCR
• fast ramping ≠ fast qPCR experiment
• 96-well vs 384-well
• 384-well system is slightly more expensive
• 384-well plates harder to pipet (multichannel pipets or

pipetting robot)

• 384-well run gives 4x more data in same time
• 384-well plates require smaller volumes
• plate homogeneity test

Instrument

Chemistry

• choose probes for
• multiplexing
• genotyping
• absolute sensitivity (detection past cycle 40) (e.g.

clinical-diagnostic setting, GMO detection)

• choose SYBR Green I for
• all other applications
• low cost
• seeing what you do

• melting curve
• unique melt peak for all samples?
• replicates
• delta-Cq < 0.5 cycles?
• controls
• negative control really blank

delta-Cq samples/NTC > 5?

• positive controls with expected Cq?
• amplification plot shape (kinetic outlier detection)

Controls

Report

relative quantification

• efficiency correction
• multiple reference gene normalization
• inter-run calibration
• error propagation

bio statistical analysis

• biological replicates
• log transform data
• selection of statistical test

reporting guidelines

• RDML
• MIQE

Report

relative quantification

• efficiency correction
• multiple reference gene normalization
• inter-run calibration
• error propagation

bio statistical analysis

• biological replicates
• log transform data
• selection of statistical test

reporting guidelines

• RDML
• MIQE

Calculation methods

Cq RQ NRQ CNRQ

Normalization Inter-run calibration

Cq

E RQ





n ref n i toi

i

RQ RQ NRQ  

n irc n i soi

i

NRQ NRQ CNRQ  

Hellemans et al. Genome Biol. 2007;8(2):R19.

ref toi

RQ RQ NRQ 

Cq

RQ



 2

irc soi

NRQ NRQ CNRQ 

Data processing - relative quantification

qbasePLUS

Quality controls

• PCR replicates
• ∆Cq < 0.5 cycles
• no template control
• no signal (no Cq value)
• Cq (NTC) > Cq (samples) + 5
• reference gene stability
• M < 0.5

M < 1 for heterogeneous samples

• CV < 25%

CV < 50% for heterogeneous samples

• normalization factors
• no unexpected high variation

Report

relative quantification

• efficiency correction
• multiple reference gene normalization
• inter-run calibration
• error propagation

bio statistical analysis

• biological replicates
• log transform data
• selection of statistical test

reporting guidelines

• RDML
• MIQE

Replicates

• technical vs biological replicates
• repeated measures vs. replication
• PCR replicates (pipetting error & Poisson’s law)
• RT replicates
• repeated RNA extraction from same sample
• repeated cell cultures / patient sampling
• true biological replicates (from different subjects)
• no statistics on repeated measures
• type of replicates dictates conclusions that can be

drawn

• relative quantities are not normally distributed
• log transformation makes them more symmetrical
• relevant tests in the field of relative quantification
• comparison of 2 unpaired groups
• t test
• Mann-Whitney
• randomization test
• comparison of 2 paired groups
• ratio t test (paired t test on log values)
• Wilcoxon rank sum test
• correlation analysis
• Pearson
• Spearman
• linear regression
• correct for multiple testing

Statistical tests

Report

relative quantification

• efficiency correction
• multiple reference gene normalization
• inter-run calibration
• error propagation

bio statistical analysis

• biological replicates
• log transform data
• selection of statistical test

reporting guidelines

• RDML
• MIQE

MIQE

• http://www.rdml.org/miqe
• Bustin et al. Clin Chem. 2009 Apr;55(4):611-22.
• authors
• improve quality of qPCR experiments
• reliable and unequivocal interpretation of results
• reviewers and editors
• assess technical merit
• full disclosure of reagents and analysis methods
• consumers of published research
• published results easier to reproduce

MIQE checklist for authors, reviewers and editors

• experimental design
• sample
• nucleic acid extraction
• reverse transcription
• target information
• ligonucleotides
• qPCR protocol
• qPCR validation
• data analysis
• E – essential
• D – desirable

RDML

• http://www.rdml.org
• Lefever et al. Nucleic Acids Res. 2009

Apr;37(7):2065-9.

acknowledgements

• Jo Vandesompele
• Stefaan Derveaux

http://www.biogazelle.com - Jan.Hellemans@UGent.be