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 25 th 2010 – Sienna, Italy

Introduction  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 RNA quality RT and PCR Choice of Choice of assessment primer design RT chemistry cDNA synthesis strategy Sample extraction Sample selection Assay validation and handling Data Data reporting 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 14,00 12,00  technical constraints critical t-value 10,00  confidence interval (CI) 8,00  3 (~ critical t-value t*) 6,00 CI = SEM x t* 4,00 2,00 0,00 2 3 4 5 10 20 100 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: n A + n B  8  Wilcoxon test:  6 pairs  http://www.cs.uiowa.edu/~rlenth/Power/

Sample vs gene maximization  how to set-up an experiment with  3 genes of interest (GOI) & 3 reference genes (REF)  11 samples (S) & 1 no template control (NTC) gene maximization sample maximization REF1 REF2 REF3 GOI1 GOI2 GOI3 S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11NTC S1 REF1 S2 S3 REF2 S4 S5 REF3 S6 S7 GOI1 NTC REF1 REF2 REF3 GOI1 GOI2 GOI3 S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11NTC S1 GOI2 S2 S3 GOI3 S8 S9 S10 S11 NTC

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

Quality control – RNA integrity value  Evaluate integrity of 18S and 28S rRNA  Agilent Bioanalyzer  Bio-Rad Experion  Caliper GX  Qiagen QIAxcel  Shimadzu MultiNA

Quality control – 5’ - 3’ ratio  universally expressed low abundant reference  anchored oligo(dT) reverse transcription 5’ 3’ AAAAAA Cq 5’ Cq 3’ 9 8 7 6 5'-3' delta Ct 5 4 3 2 1 0 109 109* 109** 275 275* 275** 539 539* 539** samples  increasing delta-Cq values upon artificial RNA degradation

Quality control – SPUD assay for inhibition  spiking of synthetic sequence lacking homology with any known human sequence into RNA SPUD SPUD SPUD SPUD SPUD + + + + + H2O heparin RNA1 RNA2 RNA3 ------------RT-qPCR--------- Cq 22 Cq 27 Cq 22 Cq 25 Cq 22 Δ Cq > 1: presence of inhibitors

Pre amplification  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  G1 B /G2 B < > G1 A /G2 A  gene G, sample S, before B, after A

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 4 3 ACTB HMBS 2 HPRT1 TBP 1 UBC 0 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

genorm PLUS

genorm PLUS

genorm PLUS

Cycle cycle • instrument • chemistry • controls

Instrument  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

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

Controls  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)

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 NRQ NRQ RQ RQ   CNRQ     2 NRQ   Cq Cq soi soi toi toi CNRQ RQ RQ E NRQ NRQ n RQ n   irc ref NRQ n RQ n irc ref i i i i Hellemans et al . Genome Biol. 2007;8(2):R19.

Data processing - relative quantification

qbase PLUS