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DNA Analysis Facility User Educational Series December 11, 2009 Basic Experimental Design for real-time qPCR experiments Identify the sources of variation in these experiments Make recommendations 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 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. 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? Real-time qPCR • Assay validation All Choice of Chemistry Choice of primers/probes PCR efficiency Dynamic Range of Assay Choice of Endogenous Control of these steps impact the end result of the qPCR measurement, and they all have the potential to add noise to the experimental data. 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. 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. 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 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 Tichopad et al. Design and Optimization of Reverse-Transcription Quantitative PCR Experiments. Clinical Chemistry 2009;55: 1816-1823 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 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 3 subjects x 3 samples x 3 RT’s x 3 qPCR’s (81 CT’s measured) 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 Total noise SD estimate ~1.5 CT’s qPCR Level: showed highest reproducibility. Mean SD=0.09 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) 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. Total noise SD estimate ~2.0 CT’s RT Level: SD=0.30 CT’s qPCR Level: SD=0.51 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) Total noise SD estimate ~0.66 CT’s 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 1625) SD=0.17 CT’s Low expressor SD=0.4 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) 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 Total noise SD estimate ~0.44 CT’s qPCR Level: mean SD=0.14 CT’s 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. Tichopad et al. Design and Optimization of Reverse-Transcription Quantitative PCR Experiments. Clinical Chemistry 2009;55: 1816-1823 Supplemental Table 1 General Recommendation Solid tissue Blood Cell culture Low copy transcript Upstream replicates are better than downstream replicates. Hence generally, including more subjects is superior to any other replicates and should be preferred as long as it is economically feasible. Several samples should be withdrawn from the same tissue and processed separately (sampling replicates). Other types or replicates are inferior Producing RT replicates is superior to any other types of replicates. The number of cell culture wells should be maximized prior to any other type of replicates. Secondarily, increasing the number of RT replicates should be considered. Replicates should be produced at the RT level rather than at any other. 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 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.