Use and Misuse of Cq in qPCR Data Analysis and Reporting

• Published in: Life (Basel, Switzerland) Vol. 11; no. 6; p. 496
• Main Authors: Ruiz-Villalba, Adrián, Ruijter, Jan M., van den Hoff, Maurice J. B.
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 29.05.2021; MDPI
• Subjects: Data analysis; Efficiency; Gene expression; PCR efficiency; Poisson variation; qPCR analysis; quantification cycle; quantification threshold; Review
• ISSN: 2075-1729; 2075-1729
• DOI: 10.3390/life11060496

In the analysis of quantitative PCR (qPCR) data, the quantification cycle (Cq) indicates the position of the amplification curve with respect to the cycle axis. Because Cq is directly related to the starting concentration of the target, and the difference in Cq values is related to the starting concentration ratio, the only results of qPCR analysis reported are often Cq, ΔCq or ΔΔCq values. However, reporting of Cq values ignores the fact that Cq values may differ between runs and machines, and, therefore, cannot be compared between laboratories. Moreover, Cq values are highly dependent on the PCR efficiency, which differs between assays and may differ between samples. Interpreting reported Cq values, assuming a 100% efficient PCR, may lead to assumed gene expression ratios that are 100-fold off. This review describes how differences in quantification threshold setting, PCR efficiency, starting material, PCR artefacts, pipetting errors and sampling variation are at the origin of differences and variability in Cq values and discusses the limits to the interpretation of observed Cq values. These issues can be avoided by calculating efficiency-corrected starting concentrations per reaction. The reporting of gene expression ratios and fold difference between treatments can then easily be based on these starting concentrations.