TY - JOUR
T1 - Use and misuse of cq in qpcr data analysis and reporting
AU - Ruiz-Villalba, Adrián
AU - Ruijter, Jan M.
AU - van den Hoff, Maurice J. B.
N1 - Funding Information: A.R.V. is supported by funds from University of M?laga (Incorporaci?n de doctores from the I Plan Propio de Incorporaci?n de Doctores, 2020). Funding Information: Funding: A.R.V. is supported by funds from University of Málaga (Incorporación de doctores from the I Plan Propio de Incorporación de Doctores, 2020). Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2021
Y1 - 2021
N2 - 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 effi-ciency, 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.
AB - 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 effi-ciency, 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.
KW - Cq
KW - LOD
KW - LOQ
KW - PCR efficiency
KW - Pois-son variation
KW - QPCR analysis
KW - Quantification cycle
KW - Quantification threshold
UR - http://www.scopus.com/inward/record.url?scp=85107835232&partnerID=8YFLogxK
U2 - https://doi.org/10.3390/life11060496
DO - https://doi.org/10.3390/life11060496
M3 - Review article
C2 - 34072308
SN - 2075-1729
VL - 11
JO - Life (Basel, Switzerland)
JF - Life (Basel, Switzerland)
IS - 6
M1 - 496
ER -