The Ultimate qPCR Experiment: Producing Publication Quality, Reproducible Data the First Time

• Published in: Trends in biotechnology (Regular ed.) Vol. 37; no. 7; pp. 761 - 774
• Main Authors: Taylor, Sean C., Nadeau, Katia, Abbasi, Meysam, Lachance, Claude, Nguyen, Marie, Fenrich, Joshua
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.07.2019; Elsevier Limited
• Subjects: accuracy; Annealing; Chemical contaminants; data analysis; data normalization; experimental design; Experiments; Genes; Internal Medicine; Laboratories; MIQE guidelines; Molecular biology; Nucleic acids; Optimization; Proteins; qPCR; quantitative polymerase chain reaction; Reagents; reproducibility; RT-qPCR; Software; data normalization; experimental design; qPCR; data analysis; RT-qPCR; MIQE guidelines
• ISSN: 0167-7799; 1879-3096; 0167-9430; 1879-3096
• DOI: 10.1016/j.tibtech.2018.12.002

Quantitative PCR (qPCR) is one of the most common techniques for quantification of nucleic acid molecules in biological and environmental samples. Although the methodology is perceived to be relatively simple, there are a number of steps and reagents that require optimization and validation to ensure reproducible data that accurately reflect the biological question(s) being posed. This review article describes and illustrates the critical pitfalls and sources of error in qPCR experiments, along with a rigorous, stepwise process to minimize variability, time, and cost in generating reproducible, publication quality data every time. Finally, an approach to make an informed choice between qPCR and digital PCR technologies is described. qPCR is more complex than perceived by many scientists. The production of an amplification curve and an associated quantitative cycle value does not necessarily mean interpretable data. The MIQE guidelines and associated methodology articles published thereafter, underline the ongoing drive to help scientists produce reproducible data from qPCR, culminating in a simple, stepwise methodology to ensure high-quality, reproducible data from qPCR experiments. The concept of data normalization has led to the ongoing publication of articles solely focused on this subject for various sample types and experimental parameters. The analysis of qPCR data can be challenging, especially as experiments grow in sample number and complexity of biological groups. A defined approach to qPCR data analysis is necessary to clarify gene expression analysis.