A 1-year study on SARS-CoV-2 variant shifts in wastewater using dPCR: comparison with clinical and GISAID data

As clinical specimens are collected and analyzed less for SARS-CoV-2, variant detection in wastewater provides a readily accessible and rich source of information on SARS-CoV-2 evolution. The detection of pathogen targets in wastewater samples using PCR assays is a sensitive, cost-effective way to monitor the levels of infectious diseases, like SARS-CoV-2, in a community. Unfortunately, because PCR-based methods are typically not used to distinguish between viral variants, most wastewater testing labs must rely on more expensive, time-consuming, and resource-intensive sequencing methods for these results. Building upon recent developments for variant detection using quantitative PCR, we developed and assessed a novel, customizable digital PCR-based genotyping method for SARS-CoV-2 variant detection in wastewater, which is more rapid, cost-effective, and accessible than sequencing. We characterize the method and offer insights for improvement in future implementations.

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Publication Details

Journal
mSystems
Published
2025-10-22
DOI
https://doi.org/10.1128/msystems.00229-25
Primary Topic
SARS-CoV-2 detection and testing
Type
article
Field-Weighted Citation Impact
0.00
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article

A 1-year study on SARS-CoV-2 variant shifts in wastewater using dPCR: comparison with clinical and GISAID data

Stephanie M. Greenwald, Christine L. Moe, Chi-Yu Lin, DAVID O. WHITE et al.
mSystems
SARS-CoV-2 detection and testing
article

A 1-year study on SARS-CoV-2 variant shifts in wastewater using dPCR: comparison with clinical and GISAID data

Stephanie M. Greenwald, Christine L. Moe, Chi-Yu Lin, DAVID O. WHITE, Robbie Barbero, Lauren P. Saunders, Elizabeth Donahue, Anurag Patnaik, Sarah A. Kane, Sarah M. Owens, Patrick Andersen, Stephen Patrick Hilton, Dagmara S. Antkiewicz, Orlando Sablon, Pengbo Liu, Rachel Poretsky, Olivia Swahn, Andrew Jones, Daniel Goldfarb, Kayley H. Janssen, Chamodya Ruhunusiri, Adélaïde Roguet, Tim Wesselman, Ian Bradley, Marlene K. Wolfe, James Erickson, Jean Lozach, Stephanie M. Barksdale, Patrick T. Acer, Denton Munns, Yinyin Ye, Jeremy Davis‐Turak, Ross Dunlap, Tara Jones-Roe, Michael Kilroy, Modou Lamin Jarju, Hien Thi Thu Le, Yuke Wang, Jim Huang, Emily Milich, Jared Obermeyer, Ganit Pricer, Marion T. Reven, Kalpita Vengurlekar, Aouda Patricia Flores-Baffi, Siya Kashwala, Erica Camarato, Regan Wied, Sophia Bellakbira, Michael Secreto, Dalton Bunde, Mayumi E. Pascual, Johannah Gillespie, Benjamin Lepene, Dolores Sanchez Gonzalez, Dalaun Richardson, Shreya Shrestha, Sayed Mohammad R. Mosavi, Ayan Mohamed, Soumya Sibani Sahoo, Sydney Gallo, Rosemarie Wilton, Aaron Stanz
article en

Abstract

As clinical specimens are collected and analyzed less for SARS-CoV-2, variant detection in wastewater provides a readily accessible and rich source of information on SARS-CoV-2 evolution. The detection of pathogen targets in wastewater samples using PCR assays is a sensitive, cost-effective way to monitor the levels of infectious diseases, like SARS-CoV-2, in a community. Unfortunately, because PCR-based methods are typically not used to distinguish between viral variants, most wastewater testing labs must rely on more expensive, time-consuming, and resource-intensive sequencing methods for these results. Building upon recent developments for variant detection using quantitative PCR, we developed and assessed a novel, customizable digital PCR-based genotyping method for SARS-CoV-2 variant detection in wastewater, which is more rapid, cost-effective, and accessible than sequencing. We characterize the method and offer insights for improvement in future implementations.

mSystemsVol. 10(11)
Argonne National Laboratory (US), University of Wisconsin–Madison (US), Emory University (US), University of Illinois Chicago (US), Engineering Data Management (US), Rosalind Franklin Institute (GB), Ceres Nanosciences (US), Water Sanitation and Hygiene Institute (IN), University at Buffalo, State University of New York (US)
Openalex Percentile: Top 64%
SARS-CoV-2 detection and testing
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