A Droplet Digital PCR Assay to Detect SARS-CoV-2 RNA

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Abstract

We describe a quantitative droplet digital PCR (ddPCR) assay for detection of SARS-CoV-2 viral ribonucleic acid (RNA) in total RNA extracted from human sputum. This method was validated using the guidance of the United States Food and Drug Administration’s Accelerated Emergency Use Authorization (EUA) Template for SARS-CoV-2 that Causes Coronavirus Disease (COVID-19) Molecular Testing of Respiratory Speciment in CLIA Certified High-Complexity Laboratories. Though our laboratory is not CLIA certified, this method met all criteria specified by the guidance document with a Limit of Detection (LOD) of 0.25 copies/μL in the final ddPCR (at least 19/20 replicates reactive), which we consider to be a Lower Limit of Quantification (LLOQ); inclusivity of all known annotated SARS-CoV-2 genomes; no cross-reactivity with other respiratory pathogens; and reactivity of all contrived positives at or above the LOD.

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  1. SciScore for 10.1101/2020.05.06.20090449: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The droplet reader as turned on and the QuantaSoft program was opened on the computer linked to the droplet reader.
    QuantaSoft
    suggested: None

    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.

    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.