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

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 di...

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Autores principales: 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.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10020645/
https://www.ncbi.nlm.nih.gov/pubmed/36937263
http://dx.doi.org/10.3389/fmicb.2023.1048661
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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.
author_facet 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.
author_sort Schmidt, Philip J.
collection PubMed
description 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. Estimation of concentrations using qPCR often features a log-linear standard curve model calibrating quantification cycle (Cq) values obtained from underlying fluorescence measurements to 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. Here, fundamental probabilistic modeling concepts from classical quantitative microbiology were integrated into standard curve modeling approaches by reflecting well-understood mechanisms for random error in microbial data. This work showed that data diverging from the log-linear regression model at low concentrations as well as non-detects can be seamlessly integrated into enhanced 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 modeling 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 modeling 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.
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spelling pubmed-100206452023-03-18 Realizing the value in “non-standard” parts of the qPCR standard curve by integrating fundamentals of quantitative microbiology 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. Front Microbiol Microbiology 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. Estimation of concentrations using qPCR often features a log-linear standard curve model calibrating quantification cycle (Cq) values obtained from underlying fluorescence measurements to 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. Here, fundamental probabilistic modeling concepts from classical quantitative microbiology were integrated into standard curve modeling approaches by reflecting well-understood mechanisms for random error in microbial data. This work showed that data diverging from the log-linear regression model at low concentrations as well as non-detects can be seamlessly integrated into enhanced 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 modeling 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 modeling 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. Frontiers Media S.A. 2023-03-03 /pmc/articles/PMC10020645/ /pubmed/36937263 http://dx.doi.org/10.3389/fmicb.2023.1048661 Text en Copyright © 2023 Schmidt, Acosta, Chik, D’Aoust, Delatolla, Dhiyebi, Glier, Hubert, Kopetzky, Mangat, Pang, Peterson, Prystajecky, Qiu, Servos and Emelko. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Microbiology
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.
Realizing the value in “non-standard” parts of the qPCR standard curve by integrating fundamentals of quantitative microbiology
title Realizing the value in “non-standard” parts of the qPCR standard curve by integrating fundamentals of quantitative microbiology
title_full Realizing the value in “non-standard” parts of the qPCR standard curve by integrating fundamentals of quantitative microbiology
title_fullStr Realizing the value in “non-standard” parts of the qPCR standard curve by integrating fundamentals of quantitative microbiology
title_full_unstemmed Realizing the value in “non-standard” parts of the qPCR standard curve by integrating fundamentals of quantitative microbiology
title_short Realizing the value in “non-standard” parts of the qPCR standard curve by integrating fundamentals of quantitative microbiology
title_sort realizing the value in “non-standard” parts of the qpcr standard curve by integrating fundamentals of quantitative microbiology
topic Microbiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10020645/
https://www.ncbi.nlm.nih.gov/pubmed/36937263
http://dx.doi.org/10.3389/fmicb.2023.1048661
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