Wastewater surveillance using ddPCR reveals highly accurate tracking of Omicron variant due to altered N1 probe binding efficiency

This article has been Reviewed by the following groups

Read the full article See related articles

Abstract

Wastewater surveillance for SARS-CoV-2 is being used worldwide to understand COVID-19 infection trends in a community. We found the emergence and rapid timeline for dominance of the Omicron variant was accurately reflected in wastewater when measured with droplet digital (dd)PCR. We were able to distinguish Omicron from the circulating Delta variant because Omicron has a mutation in the N1 probe binding region that diminished the fluorescent signal within individual droplets. The ddPCR platform may be advantageous for wastewater surveillance since analysis of the data can segregate fluorescent signals from different individual templates. In contrast, platforms such as qPCR that rely solely on the intensity of fluorescence for quantification would not distinguish a subset of variants with mutations affecting the reaction and could underestimate SARS-CoV-2 concentrations. The proportion of Omicron in wastewater was tightly correlated to clinical cases in five cities and provided a higher resolution timeline of appearance and dominance (>75%) than sequenced clinical samples, which were limited in less populated areas. Taken together, this work demonstrates wastewater is a reliable metric for tracking SARS-CoV-2 at a population level.

Article activity feed

  1. SciScore for 10.1101/2022.02.18.22271188: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    No key resources detected.


    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).


    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.


    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.


    Results from JetFighter: We did not find any issues relating to colormaps.


    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.


    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.