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Real-time qPCR Experimental Design Considerations

Real-time qPCR Experimental Design Considerations. DNA Analysis Facility User Educational Series December 11, 2009. Real-time qPCR:Experimental Design Topics to be covered. Basic Experimental Design for real-time qPCR experiments Identify the sources of variation in these experiments

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Real-time qPCR Experimental Design Considerations

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  1. Real-time qPCR Experimental Design Considerations DNA Analysis Facility User Educational Series December 11, 2009

  2. Real-time qPCR:Experimental DesignTopics to be covered Basic Experimental Design for real-time qPCR experiments Identify the sources of variation in these experiments Make recommendations

  3. Real-time qPCR: Experimental DesignData to be presented Presented at qPCR Symposium 2009 San Francisco CA Nov 9-10, 2009 Tichopad A, Kitchen R, Riedmaier I, Becker C, Stahlberg A, Kubista M. Design and Optimization of Reverse-Transcription Quantitative PCR Experiments. Clinical Chemistry 2009;55: 1816-1823

  4. Real-time qPCR:Experimental DesignqPCR Experiments • Real-time qPCR has many applications: • Viral Load detection • Genotyping • SNP detection • ChIP Assays • miRNA analysis • Gene expression studies • Gene Expression experiments are typically designed to test a hypothesis that a difference in gene expression exists between groups of biological subjects exposed to different treatments.

  5. Real-time qPCR:Experimental DesignqPCR Experiments: pre-qPCR steps • Sampling • Collection of samples • Storage of samples prior to extraction • Nucleic Acid Extraction • Method of extraction • Presence of inhibitors • Storage of RNA prior to RT Reaction • Nucleic Acid Quality and Quantification • Check RNA Quality • Good Quantification in order to balance RT Rxn. • Reverse Transcription • Selection of enzyme and priming strategy • gDNA contamination? • Presence of inhibitors?

  6. Real-time qPCR:Experimental DesignqPCR Step • Real-time qPCR • Assay validation • Choice of Chemistry • Choice of primers/probes • PCR efficiency • Dynamic Range of Assay • Choice of Endogenous Control • All of these steps impact the end result of the qPCR measurement, and they all have the potential to add noise to the experimental data.

  7. Real-time qPCR:Experimental DesignSources of Variation • Studied Variance • The treatment effect can only be resolved if it is larger than the random noise within the groups due to the confounding noise. • Confounding Variance • Biological or Inter-subject Variance • This is the random difference between individuals • Processing Variance • These are technical variances due to processing of samples, extractions, RT and qPCR reactions.

  8. Real-time qPCR:Experimental DesignGoals of Experimental Design Goal of Experimental Design is to optimize your treatment effect relative to the confounding effect of your biological and processing noise. This requires knowing where your sources of variation are likely to occur and accounting for these with you data analysis. Being cost effective with your choices.

  9. Real-time qPCR:Experimental DesignDetermine Sources of Variation Kubista’s group designed an experiment to look at the sources of variation that are found in a typical qPCR experiment. Tichopad et al. Design and Optimization of Reverse-Transcription Quantitative PCR Experiments. Clinical Chemistry 2009;55: 1816-1823

  10. Real-time qPCR:Experimental DesignDetermine Sources of Variation Tichopad et al. Design and Optimization of Reverse-Transcription Quantitative PCR Experiments. Clinical Chemistry 2009;55: 1816-1823 • Liver Tissue • qPCR Assays: ACTB, IL1B, CASP3, FGF7 • Blood • qPCR Assays : ACTB, IL1B, CASP3, IFNG • Cell Cultures • qPCR Assays : ACTB, H3F3A, BCL2, IL8 • Single Cells: individual astrocytes from mouse brain • qPCR Assays: 18s

  11. Real-time qPCR:Experimental DesignStatistical Analysis Total Variance Variance contribution from processing steps: Subject Sample/Extraction RT qPCR Tichopad et al. Design and Optimization of Reverse-Transcription Quantitative PCR Experiments. Clinical Chemistry 2009;55: 1816-1823

  12. Real-time qPCR:Experimental DesignDetermine Sources of Variation Total noise SD: Cumulative variance which is expressed as the SD of measured CT values. Highlighted figure is the mean of all 4 genes. Tichopad et al. Design and Optimization of Reverse-Transcription Quantitative PCR Experiments. Clinical Chemistry 2009;55: 1816-1823

  13. Real-time qPCR:Experimental Design Sources of Variation: Liver samples Subject Level: SD was negligible at this step Sampling Level: Largest SD was estimated for this step. Mean SD=1.2 Ct which is >2 fold variation. RT Level: 3 genes: mean SD=0.39CT’s 4th gene: SD= 0.9 CT’s qPCR Level: showed highest reproducibility. Mean SD=0.09 CT’s Total noise SD estimate ~1.5 CT’s Tichopad et al. Design and Optimization of Reverse-Transcription Quantitative PCR Experiments. Clinical Chemistry 2009;55: 1816-1823 • 3 subjects x 3 samples x 3 RT’s x 3 qPCR’s (81 CT’s measured)

  14. Real-time qPCR:Experimental Design Sources of Variation: Single cells Subject Level: SD was negligible. Sampling Level: SD=1.9 CT’s This is consistent with other studies that show mRNA levels vary greatly between individual cells. RT Level: SD=0.30 CT’s qPCR Level: SD=0.51 CT’s Total noise SD estimate ~2.0 CT’s Tichopad et al. Design and Optimization of Reverse-Transcription Quantitative PCR Experiments. Clinical Chemistry 2009;55: 1816-1823 • 3 subjects x 3 samples x 1 extractions x 3 RT’s x 3 qPCR’s (81 CT’s measured)

  15. Real-time qPCR:Experimental Design Sources of Variation: Blood samples Subject Level: Negligible for 2 genes, SD=1CT for other 2 genes. Sampling Level: Highest reproducibility SD=0.12 CT’s RT Level: Similar for all genes SD=0.24 qPCR Level: 3 higher expressor’s (CT’s 16-25) SD=0.17 CT’s Low expressor SD=0.4 CT’s Total noise SD estimate ~0.66 CT’s Tichopad et al. Design and Optimization of Reverse-Transcription Quantitative PCR Experiments. Clinical Chemistry 2009;55: 1816-1823 • 3 subjects x 1 samples x 3 extractions x 3 RT’s x 3 qPCR’s (81 CT’s measured)

  16. Real-time qPCR:Experimental Design Sources of Variation: Cell Cultures Subject Level: Cell cultures are unique at this level due to their clonal nature. Sampling Level: mean SD=0.27 CT’s RT Level: mean SD=0.31 CT’s qPCR Level: mean SD=0.14 CT’s Total noise SD estimate ~0.44 CT’s Tichopad et al. Design and Optimization of Reverse-Transcription Quantitative PCR Experiments. Clinical Chemistry 2009;55: 1816-1823 • 1 subject x 10 samples x 1 extraction x 3 RT’s x 3 qPCR’s • (90 CT’s measured)

  17. Real-time qPCR:Experimental Design qPCR Variance Tichopad et al. Design and Optimization of Reverse-Transcription Quantitative PCR Experiments. Clinical Chemistry 2009;55: 1816-1823 qPCR variance (mean SD=0.13 CT’s) is lower than the variance of other steps and does not depend on sample type. qPCR variance will be higher in samples with CT’s > 30. qPCR was done in duplicate in most publications, but without justification as to why selected. The use of single wells is indicated but does not insure against a failed reaction. If cDNA is limited, a single qPCR well is preferable because splitting into two wells will further reduce the cDNA available in the qPCR reaction.

  18. Real-time qPCR:Experimental Design Kubista General Recommendations Supplemental Table 1 Tichopad A, Kitchen R, Riedmaier I, Becker C, Stahlberg A, Kubista M. Design and Optimization of Reverse-Transcription Quantitative PCR Experiments. Clinical Chemistry 2009;55: 1816-1823

  19. Real-time qPCR:Experimental Design Conclusions Perform a fully nested pilot study to identify the sources of variation associated with your experiment. Cost-optimize the experimental design to include the optimal number of subjects and technical replicates you need to strengthen the power of your experiment.

  20. Real-time qPCR:Experimental Design Questions?

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