Despite its conceptual and practical simplicity, qPCR based expression analysis involves multiple steps, all of which need to be perfect in order to obtain reliable results in the end. This presentation describes points of attention, potential pitfalls and suggestions for improvements on every step along the workflow. By implementing these guidelines in your experiments you increase the chance of doing successful gene expression analysis.

1. How to do successful gene expression analysis Jan Hellemans, PhD Center for Medical Genetics Biogazelle qPCR meeting – June 25th 2010 – Sienna, Italy

2. 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

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

4. prepare – cycle – report

5. Prepare

6. Prepare

7. 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*

8. 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/

9. 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

10. 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

11. Prepare

12. 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

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

14. Quality control – 5’-3’ ratio 5’ 3’ AAAAAA Cq 5’ Cq 3’ universally expressed low abundant reference anchored oligo(dT) reverse transcription increasing delta-Cq values upon artificial RNA degradation

15. 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 ------------RT-qPCR--------- Cq 22 Cq 27 Cq 22 Cq 25 Cq 22 ΔCq > 1: presence of inhibitors

16. 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

17. Prepare

18. http://www.rtprimerdb.org

19. 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

20. In silicoassayvalidation 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

21. Empiricalassayvalidation 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

22. Prepare

23. 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

24. 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 Vandesompeleet al. Genome Biol. 2002 Jun 18;3(7):RESEARCH0034.

25. 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

26. genormPLUS

27. genormPLUS

28. genormPLUS

29. Cycle

30. 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

31. 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

32. melting curve unique melt peak for all samples? replicates delta-Cq < 0.5 cycles? controls negative control really blankdelta-Cq samples/NTC > 5? positive controls with expected Cq? amplification plot shape (kinetic outlier detection) Controls

33. Report

34. Report

35. Calculation methods Cq RQ NRQ CNRQ Normalization Inter-run calibration Hellemanset al. Genome Biol. 2007;8(2):R19.

36. Data processing - relative quantification

37. qbasePLUS

39. 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 < 0.5M < 1 for heterogeneous samples CV < 25%CV < 50% for heterogeneous samples normalization factors no unexpected high variation

40. Report

41. 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

42. 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

43. Report

44. MIQE http://www.rdml.org/miqe Bustinet al. ClinChem. 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

45. MIQE checklist for authors, reviewers and editors experimental design sample nucleic acid extraction reverse transcription target information oligonucleotides qPCR protocol qPCR validation data analysis E – essential D – desirable

46. RDML http://www.rdml.org Lefeveret al. NucleicAcidsRes. 2009 Apr;37(7):2065-9.

47. acknowledgements Jo Vandesompele StefaanDerveaux http://www.biogazelle.com - Jan.Hellemans@UGent.be