Clinical Chemistry
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Clinical Chemistry 55: 1816-1823, 2009. First published July 30, 2009; 10.1373/clinchem.2009.126201
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(Clinical Chemistry. 2009;55:1816-1823.)
© 2009 American Association for Clinical Chemistry, Inc.

Molecular Diagnostics and Genetics

Design and Optimization of Reverse-Transcription Quantitative PCR Experiments

Ales Tichopad1,2,a, Rob Kitchen3, Irmgard Riedmaier1, Christiane Becker1, Anders Ståhlberg2,4 and Mikael Kubista2,5

1 Physiology Weihenstephan, Technical University Munich, Freising, Germany;2 TATAA Biocenter, Göteborg, 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 Gothenburg, Gothenburg, Sweden;5 Institute of Biotechnology AS CR, Prague, Czech Republic.

aAddress correspondence to this author at: Technical University Munich, Physiology Weihenstephan, Weihenstephaner Berg 3, 85354 Freising, Germany. Fax +49-8161714204; e-mail ales{at}tichopad.de.

Background: Quantitative PCR (qPCR) is a valuable technique for accurately and reliably profiling and quantifying gene expression. Typically, samples obtained from the organism of study have to be processed via several preparative steps before qPCR.

Method: We estimated the errors of sample withdrawal 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 differences became easily confounded by sample heterogeneity for single cells and solid tissue. In cell cultures and blood, the noise from the RT and qPCR steps contributed substantially to the overall error because the sampling noise was less pronounced.

Conclusions: We recommend the use of sample replicates preferentially to any other replicates when working 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. .






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Copyright © 2009 by the American Association for Clinical Chemistry.