Real-time qPCR Experimental Design Considerations

Real-time qPCR Experimental Design ConsiderationsDNA Analysis FacilityUser Educational SeriesDecember 11, 20091Real-time qPCR:Experimental DesignTopics to be coveredBasic Experimental Design for real-time qPCR experiments

Identify the sources of variation in these experiments

Make recommendations 2Real-time qPCR: Experimental DesignData to be presented

Presented at qPCR Symposium 2009San Francisco CA Nov 9-10, 2009Tichopad 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

3Real-time qPCR:Experimental DesignqPCR ExperimentsReal-time qPCR has many applications:Viral Load detectionGenotypingSNP detectionChIP AssaysmiRNA analysisGene expression studiesGene 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.

4Real-time qPCR:Experimental DesignqPCR Experiments: pre-qPCR stepsSamplingCollection of samplesStorage of samples prior to extraction

Nucleic Acid ExtractionMethod of extractionPresence of inhibitorsStorage of RNA prior to RT Reaction

Nucleic Acid Quality and QuantificationCheck RNA QualityGood Quantification in order to balance RT Rxn.

Reverse TranscriptionSelection of enzyme and priming strategygDNA contamination?Presence of inhibitors?

5Real-time qPCR:Experimental DesignqPCR StepReal-time qPCR Assay validationChoice of ChemistryChoice of primers/probesPCR efficiencyDynamic Range of AssayChoice 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.

6Real-time qPCR:Experimental DesignSources of VariationStudied VarianceThe treatment effect can only be resolved if it is larger than the random noise within the groups due to the confounding noise.Confounding VarianceBiological or Inter-subject VarianceThis is the random difference between individualsProcessing VarianceThese are technical variances due to processing of samples, extractions, RT and qPCR reactions.

7Real-time qPCR:Experimental DesignGoals of Experimental DesignGoal 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.8Real-time qPCR:Experimental DesignDetermine Sources of Variation

Kubistas 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: 1816-18239Real-time qPCR:Experimental DesignDetermine Sources of VariationLiver TissueqPCR Assays: ACTB, IL1B, CASP3, FGF7

BloodqPCR Assays : ACTB, IL1B, CASP3, IFNG

Cell CulturesqPCR Assays : ACTB, H3F3A, BCL2, IL8

Single Cells: individual astrocytes from mouse brainqPCR Assays: 18sTichopad et al. Design and Optimization of Reverse-Transcription Quantitative PCR Experiments. Clinical Chemistry 2009;55: 1816-182310Real-time qPCR:Experimental DesignStatistical Analysis

Total Variance

Variance contribution from processing steps:

SubjectSample/ExtractionRTqPCR

Tichopad et al. Design and Optimization of Reverse-Transcription Quantitative PCR Experiments. Clinical Chemistry 2009;55: 1816-182311Real-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: 1816-182312Real-time qPCR:Experimental Design Sources of Variation: Liver samples3 subjects x 3 samples x 3 RTs x 3 qPCRs (81 CTs 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=0.39CTs 4th gene: SD= 0.9 CTs

qPCR Level: showed highest reproducibility.Mean SD=0.09 CTs

Total noise SD estimate ~1.5 CTsTichopad et al. Design and Optimization of Reverse-Transcription Quantitative PCR Experiments. Clinical Chemistry 2009;55: 1816-182313Real-time qPCR:Experimental Design Sources of Variation: Single cells3 subjects x 3 samples x 1 extractions x 3 RTs x 3 qPCRs (81 CTs measured)

Subject Level: SD was negligible.

Sampling Level: SD=1.9 CTsThis is consistent with other studies that show mRNA levels vary greatly between individual cells.

RT Level: SD=0.30 CTsqPCR Level: SD=0.51 CTs

Total noise SD estimate ~2.0 CTsTichopad et al. Design and Optimization of Reverse-Transcription Quantitative PCR Experiments. Clinical Chemistry 2009;55: 1816-182314Real-time qPCR:Experimental Design Sources of Variation: Blood samples3 subjects x 1 samples x 3 extractions x 3 RTs x 3 qPCRs (81 CTs measured)

Subject Level:Negligible for 2 genes, SD=1CT for other 2 genes.Sampling Level:Highest reproducibility SD=0.12 CTsRT Level: Similar for all genesSD=0.24qPCR Level:3 higher expressors (CTs 16-25)SD=0.17 CTsLow expressor SD=0.4 CTs

Total noise SD estimate ~0.66 CTsTichopad et al. Design and Optimization of Reverse-Transcription Quantitative PCR Experiments. Clinical Chemistry 2009;55: 1816-182315Real-time qPCR:Experimental Design Sources of Variation: Cell Cultures1 subject x 10 samples x 1 extraction x 3 RTs x 3 qPCRs (90 CTs measured)

Subject Level: Cell cultures are unique at this level due to their clonal nature.

Sampling Level: mean SD=0.27 CTs

RT Level: mean SD=0.31 CTs

qPCR Level: mean SD=0.14 CTs

Total noise SD estimate ~0.44 CTsTichopad et al. Design and Optimization of Reverse-Transcription Quantitative PCR Experiments. Clinical Chemistry 2009;55: 1816-182316Real-time qPCR:Experimental Design qPCR VarianceqPCR variance (mean SD=0.13 CTs) is lower than the variance of other steps and does not depend on sample type.

qPCR variance will be higher in samples with CTs > 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: 1816-182317Real-time qPCR:Experimental Design Kubista General RecommendationsGeneralRecommendationUpstream 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.Solid tissueSeveral samples should be withdrawn from the same tissue andprocessed separately (sampling replicates). Other types or replicates are inferiorBloodProducing RT replicates is superior to any other types of replicates. Cell cultureThe 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.Low copytranscriptReplicates should be produced at the RT level rather than at any other.Supplemental Table 1Tichopad A, Kitchen R, Riedmaier I, Becker C, Stahlberg A, Kubista M. Design and Optimization of Reverse-Transcription Quantitative PCR Experiments. Clinical Chemistry 2009;55: 1816-182318Real-time qPCR:Experimental Design ConclusionsPerform 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.

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