AUTHOR=Schmidt Philip J. , Acosta Nicole , Chik Alex H. S. , D’Aoust Patrick M. , Delatolla Robert , Dhiyebi Hadi A. , Glier Melissa B. , Hubert Casey R. J. , Kopetzky Jennifer , Mangat Chand S. , Pang Xiao-Li , Peterson Shelley W. , Prystajecky Natalie , Qiu Yuanyuan , Servos Mark R. , Emelko Monica B. 

TITLE=Realizing the value in “non-standard” parts of the qPCR standard curve by integrating fundamentals of quantitative microbiology

JOURNAL=Frontiers in Microbiology

VOLUME=Volume 14 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2023.1048661

DOI=10.3389/fmicb.2023.1048661

ISSN=1664-302X

ABSTRACT=The real-time polymerase chain reaction (PCR), commonly known as quantitative PCR (qPCR), is increasingly common in environmental microbiology applications. During the COVID-19 pandemic, qPCR combined with reverse transcription (RT-qPCR) has been used to detect and quantify SARS-CoV-2 in clinical diagnoses and wastewater monitoring of local trends. Conventional eEstimation of concentrations using qPCR often features a log-linear regression standard curve model relatingof quantification cycle (Cq) values obtained from underlying fluorescence measurements toand standard concentrations. This process works well at high concentrations within a linear dynamic range but has diminishing reliability at low concentrations because it cannot explain “non-standard” data such as Cq values reflecting increasing variability at low concentrations or non-detects that do not yield Cq values at all. This study integrates fundamental concepts from classical quantitative microbiology into standard curve modelling approaches by reflecting well-understood mechanisms for random error in microbial data. Data diverging from the log-linear regression model at low concentrations as well as non-detects can thus be seamlessly integrated to enhance standard curve analysis. The newly developed model provides improved representation of standard curve data at low concentrations while converging asymptotically upon conventional log-linear regression at high concentrations and adding no fitting parameters. Such modelling facilitates exploration of the effects of various random error mechanisms in experiments generating standard curve data, enables quantification of uncertainty in standard curve parameters, and is an important step toward quantifying uncertainty in qPCR-based concentration estimates. Improving understanding of the random error in qPCR data and standard curve modelling is especially important when low concentrations are of particular interest and inappropriate analysis can unduly affect interpretation, conclusions regarding lab performance, reported concentration estimates, and associated decision-making.