SARS-CoV-2 RNA Quantification Using Droplet Digital RT-PCR

  1. Natalie N. Kinloch
  2. Gordon Ritchie
  3. Winnie Dong
  4. Kyle D. Cobarrubias
  5. Hanwei Sudderuddin
  6. Tanya Lawson
  7. Nancy Matic
  8. Julio S.G. Montaner
  9. Victor Leung
  10. Marc G. Romney
  11. Christopher F. Lowe
  12. Chanson J. Brumme
  13. Zabrina L. Brumme

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Abstract

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  1. Version published to 10.1016/j.jmoldx.2021.04.014
  2. ScreenIT

    SciScore for 10.1101/2020.12.21.423898: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: Ethical Approval: This study was approved by the Providence Health Care/University of British Columbia and Simon Fraser University Research Ethics Boards under protocol H20-01055.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Reverse transcriptase droplet digital PCR (RT-ddPCR) for SARS-CoV-2 quantification: RT-ddPCR reactions were performed by combining relevant SARS-CoV-2 RNA template with target-specific primers and probe (900nM and 250nM, respectively, Integrated DNA Technologies; Table 1), One-Step RT-ddPCR Advanced Kit for Probes Supermix, Reverse Transcriptase and DTT (300nM) (all from BioRad), XhoI restriction enzyme (New England Biolabs), background nucleic acid (for reactions employing synthetic RNA template only, see above) and nuclease free water.
    BioRad
    suggested: None
    Analysis was performed on a QX200 Droplet Reader (BioRad) using QuantaSoft software (BioRad, version 1.7.4).
    QuantaSoft
    suggested: None
    Statistical Analysis: Statistical analysis was performed using GraphPad Prism (Version 8) or Microsoft Excel (Version 14.7.2).
    GraphPad Prism
    suggested: (GraphPad Prism, RRID:SCR_002798)
    Microsoft Excel
    suggested: (Microsoft Excel, RRID:SCR_016137)

    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: We detected the following sentences addressing limitations in the study:
    Some limitations merit mention. We only tested eight commonly-used SARS-CoV-2-specific primer/probe sets, and others may exist that adapt well to RT-ddPCR. Our assay performance estimates should be considered approximate, as the manufacturer-reported concentration of the synthetic SARS-CoV-2 RNA standards used in our study may vary by up to 20% error (Twist Bioscience, personal communication). Moreover, we solely evaluated a one-step RT-ddPCR protocol, and therefore assay performance estimates will likely differ from protocols that feature independent cDNA generation followed by ddPCR. We could not precisely define the upper boundary of the linear dynamic range of the E-Sarbeco, IP2 and IP4 RT-ddPCR assays as linearity was maintained at the maximum input of 114,286 target copies/reaction, which already exceeds the manufacturer’s estimated upper range of quantification in a ddPCR reaction (36). Our convenience panel of 48 SARS-CoV-2-positive diagnostic specimens also likely did not capture the full range of biological variation in viral loads, though data from larger cohorts (47) suggests that it was reasonably comprehensive. We also acknowledge that there is measurement uncertainty with real-time RT-PCR Ct values that may subtly affect the linear relationship between Ct value and RT-ddPCR-derived SARS-CoV-2 viral load described here. Finally, our estimates of assay performance may not completely reflect those of the entire diagnostic process, as the nucleic acid extraction st...

    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:
    • No conflict of interest statement was detected. If there are no conflicts, we encourage authors to explicit state so.
    • 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.

  3. Version published to 10.1101/2020.12.21.423898v1 on bioRxiv