Design and Optimization of Reverse-Transcription Quantitative PCR Experiments Ales Tichopad, 1,2* Rob Kitchen, 3 Irmgard Riedmaier, 1 Christiane Becker, 1 Anders Ståhlberg, 2,4 and Mikael Kubista 2,5 BACKGROUND: Quantitative PCR (qPCR) is a valuable technique for accurately and reliably profiling and quantifying gene expression. Typically, samples ob- tained from the organism of study have to be processed via several preparative steps before qPCR. METHOD: We estimated the errors of sample with- drawal and extraction, reverse transcription (RT), and qPCR that are introduced into measurements of mRNA concentrations. We performed hierarchically arranged experiments with 3 animals, 3 samples, 3 RT reactions, and 3 qPCRs and quantified the expression of several genes in solid tissue, blood, cell culture, and single cells. RESULTS: A nested ANOVA design was used to model the experiments, and relative and absolute errors were calculated with this model for each processing level in the hierarchical design. We found that intersubject dif- ferences became easily confounded by sample hetero- geneity for single cells and solid tissue. In cell cultures and blood, the noise from the RT and qPCR steps con- tributed substantially to the overall error because the sampling noise was less pronounced. CONCLUSIONS: We recommend the use of sample repli- cates preferentially to any other replicates when work- ing with solid tissue, cell cultures, and single cells, and we recommend the use of RT replicates when working with blood. We show how an optimal sampling plan can be calculated for a limited budget. © 2009 American Association for Clinical Chemistry Experimental design, first developed by Sir Ronald A. Fisher (1), is a structured, organized method for deter- mining the relationship between the different factors affecting an experimental process and the output of that process. The use of quantitative PCR (qPCR) 6 to study gene expression (2–6) requires statistical consid- erations of all invoked factors: the treatment effect, the intersubject biological variance, and the noise due to sample processing. In addition, the gene-specific effect on the error structure must be considered. A typical qPCR experiment designed to test a hypothesis that a difference in gene expression exists between groups of biological subjects exposed to different treatments in- volves sampling the biological material, extracting the RNA, reverse transcription (RT) of the RNA into cDNA, and amplification of the cDNA by the qPCR. Too often, experiments are designed and conducted with excessive emphasis on the amplification step while ignoring the preceding steps and their contribution to the measurement error. The measured difference between any 2 groups has 3 contributions: the treatment effect, intersubject vari- ation, and processing noise (Table 1). Exact definitions for these variance contributions, as well as the models for their calculation, are given in Supplemental Text 1 in the Data Supplement that accompanies the online version of this article at http://www.clinchem.org/ content/vol55/issue10. The relationship between the effect, variation, and the number of subjects studied can be calculated with the power test, which is exem- plified in Fig. 2 in the online Data Supplement. In general, independent errors are additive. Fluc- tuations in yield due to pipetting errors, uncertainties in instrument readings, and chemical noise in the dif- ferent processing steps are expected to be independent. Interference due to inhibitors is not independent, how- ever, because any inhibiting substance present in a sample will propagate through the subsequent process- ing steps of that sample, although the inhibitor will gradually be removed and diluted. The noise observed for a given processing step is only partially attributable 1 Physiology Weihenstephan, Technical University Munich, Freising, Germany; 2 TATAA Biocenter, Go ¨ teborg, Sweden; 3 National e-Science Centre, School of Physics, University of Edinburgh, Edinburgh, UK; 4 Lundberg Laboratory for Cancer, Department of Pathology, Sahlgrenska Academy at University of Goth- enburg, Gothenburg, Sweden; 5 Institute of Biotechnology AS CR, Prague, Czech Republic. * Address correspondence to this author at: Technical University Munich, Phys- iology Weihenstephan, Weihenstephaner Berg 3, 85354 Freising, Germany. Fax +49-8161714204; e-mail ales@tichopad.de. Received February 26, 2009; accepted June 29, 2009. Previously published online at DOI: 10.1373/clinchem.2009.126201 6 Nonstandard abbreviations: qPCR, quantitative PCR; RT, reverse transcription; Cq, cycle of quantification; GOI, gene of interest. Clinical Chemistry 55:10 1816–1823 (2009) Molecular Diagnostics and Genetics 1816 Downloaded from https://academic.oup.com/clinchem/article/55/10/1816/5629422 by guest on 26 January 2023