Analysis of Real-Time Quantitative PCR

1. Analysis of Real-Time Quantitative PCR Brent Kerbel MSc. Candidate Dr. Suraj Unniappan’s Lab Comparative Neuroendocrinology

2. Agenda What is qRT-PCR Primer Optimization Plate design Expression calculation Statistical Analysis Data Presentation

3. What is qRT-PCR?

4. Uses Sensitive and reliable quantitative method for gene expression analysis Detect absolute or relative expression levels Quantity of PCR products in the exponential phase is proportional to quantity of initial template Template = cDNA or genomic DNA

5. Uses Sensitive and reliable quantitative method for gene expression analysis Detect absolute or relative expression levels Quantity of PCR products in the exponential phase is proportional to quantity of initial template

6. Amplification Step

7. Cycle of Threshold (Ct) Value used to calculate absolute or relative expression Ct Cycle at which the fluorescence of the PCR reaction mixture is greater than background fluorescence

8. Primer Quantification

9. Primers Required One primer set for a reference gene Each gene of interest (GOI) needs its own primer set

10. Reference Gene Highly expressed in all cells Provides an indication of the amount of template present before the PCR Used to “normalize” the qRT-PCR Accounts for the variations of template quantity prior to PCR Allows you to proceed as if all samples had the same amount of template at the beginning of the PCR

11. Normalization

12. Primer Optimization Required to optimize amplification efficiency (E) Makes life easier when calculating expression 2 steps Optimize annealing temperature using gradient PCRs Optimize primer concentration Calculate E Create standard dilution of cDNA and plot Ct values E = 10 -1/slope of standard curve Calculate % Efficiency = (E-1) x 100% Ideal reaction E = 2 (PCR product doubles during each cycle; 2-fold increase in number of copies per cycle) %Efficiency = 100% Ideal reaction E = 2 (PCR product doubles during each cycle; 2-fold increase in number of copies per cycle) %Efficiency = 100%

13. Plate Design

14. Plate Design Each sample must be run in duplicate for the reference gene and GOI Duplicate Ct values are average for statistical analysis and presentation Single plate strategy Ideally, all primer pairs for GOI and reference genes on all samples would be analyzed on single plate Multiple plate balanced strategy At least one sample from each treatment on a plate Multiple plate unbalanced strategy Without at least one sample from each treatment on a plate Ensure that treatment comparisons of greatest interest are seen more frequently on same plate

15. Plate Design Each sample must be run in duplicate for the reference gene and GOI Duplicate Ct values are average for statistical analysis and presentation Single plate strategy Ideally, all primer pairs for GOI and reference genes on all samples would be analyzed on single plate Multiple plate balanced strategy At least one sample from each treatment on a plate Multiple plate unbalanced strategy Without at least one sample from each treatment on a plate Ensure that treatment comparisons of greatest interest are seen more frequently on same plate

16. Single Plate Design Required: n=3 required for each treatment group (including control)Required: n=3 required for each treatment group (including control)

17. Calculating Normalized Expression

18. Three Methods Livak method Assume amplification efficiencies near 100% and within 5% of each other Reference gene method Variation of Livak method but simpler Normalized Expression = 2[Ct(reference) – Ct(target)] Pfaffl method Use if amplification efficiencies of the target and reference gene are not the same

19. Statistical Analysis

20. Transformation First transform normalized expression Ct’ = log2(Normalized Expression) Data is nonlinear Typically suffers from heterogeneity of variance across biological replicates within treatments and across treatments Conduct stats on Ct’ and corresponding standard error of Ct’ Stat test dependent on sample size and plate design Samples are paired Each sample provides material for GOI and reference gene Samples are paired Each sample provides material for GOI and reference gene

21. Analysis Two-sample T test Following single-plate experiment with only two treatments ANOVA Following single-plate or balanced-design experiment across a number of plates Benefits Can assess the variation due to multiple different treatments and the interaction between them Automatically accounts for block effects (i.e. Inter-plate variation)

22. Unbalanced Design Requires modeling to estimate the means one would have expected if the design had been balanced Modeling called Residual maximum Refer to Hellmemans et al. 2007 for more information ANOVA can be used on data after modeling

23. Common Problem Low Transcript Number High Ct values (27=Ct =30) Large errors Variance and heterogeneity not removed after log transformation Non parametric tests (all require balanced design) Friedman’s ANOVA (accounts for block effects) Kruskal-Wallis ANOVA (does not account for block effects) Mann-Whitney test (for only two treatments in one block)

24. Presentation Normalized expression must first be corrected for block effects (for multiple plate design) Balanced design Sample Ct’ in a block – average Ct’ of the same block Graph the mean normalized expression and corresponding standard error of the means for each treatment relative to the control Rescaled so that control equals 1 Control/control; treatment/control

25. Conclusion Optimize primers and design experiment so that you can run a high efficiency PCR on one plate or a balanced multiple-plate design Use the reference gene method to calculate normalized expression Normalized Expression = 2[Ct(reference) – Ct(target)] Log transform normalized expression for statistical analysis Ct’ = log2(Normalized Expression) Use two-sample T test or ANOVA to determine statistical significance. Present mean normalized expression as a ratio relative to the control

26. Sample qRT-PCR Analysis

27. Sample qRT-PCR Analysis

28. Two-Sample (one-tail) T test 1 = treatment mean 2 = control mean S1 = SEM treatment S2 = SEM control n1 = sample number treatment n2 = sample number control df = n1 + n2 – 2 ? 4

29. Two-Sample (one-tail) T test

31. References Bio-Rad. 2006. Real-time PCR applications guide. Pg. 4-6, 40-44. Hellemans J, Mortier G, De Paepe A, Speleman F, Vandesompele J. 2007. qBase relative quantification framework and software for management and automated analysis of real-time quantitative PCR data. Genome Biology. 8(2):R19-R32. Patterson HD, and Thompson R. 1971. Recovery of inter-block information when block sizes are unequal. Biometrika. 58(3): 545 - 554 Pfaffl M. 2001. A new mathematical model for relative quantification in real-time RT-PCR. Nucleic Acids Research. 29(9): 2002-2007. Rieu I. 2009. Real-Time quantitative RT-PCR: Design, calculations and statistics. The Plant Cell. 21: 1031-1033. Yuan JS, Reed A, Chen F and Stewart CN Jr. 2006. Statistical analysis of real-time PCR data. BMC Bioinformatics. 7: 85-97.

32. Primer Design Primers should be: 150-300 bp 17-28 bp long G + C: 50-60% Tms between 55-80 C Avoid complementarity of forward and reverse primers (avoid dimers) Prevent mispriming by avoiding runs of 3 or more C’s or G’s at 3’-end Avoid hair-pin structures (self-complementarity) End in G, C, GC or CG Use online primer design freeware IDT (http://www.idtdna.com/Scitools/Applications/RealTimePCR/) BioSearch Technologies (http://www.biosearchtech.com/realtimedesign)