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

Quantitative viral load assays have transformed our understanding of – and ability to manage − viral diseases. They hold similar potential to advance COVID-19 control and prevention, but SARS-CoV-2 viral load tests are not yet widely available. SARS-CoV-2 molecular diagnostic tests, which typically employ real-time reverse transcriptase-polymerase chain reaction (RT-PCR), yield semi-quantitative results only. Reverse transcriptase droplet digital PCR (RT-ddPCR), a technology that partitions each reaction into 20,000 nanolitre-sized droplets prior to amplification, offers an attractive platform for SARS-CoV-2 RNA quantification. We evaluated eight primer/probe sets originally developed for real-time RT-PCR-based SARS-CoV-2 diagnostic tests for use in RT-ddPCR, and identified three (Charité-Berlin E-Sarbeco and Pasteur Institute IP2 and IP4) as the most efficient, precise and sensitive for RT-ddPCR-based SARS-CoV-2 RNA quantification. Analytical efficiency of the E-Sarbeco primer/probe set, for example, was ~83%, while assay precision, as measured by the coefficient of variation, was ~2% at 1000 input copies/reaction. Lower limits of quantification and detection for this primer/probe set were 18.6 and 4.4 input SARS-CoV-2 RNA copies/reaction, respectively. SARS-CoV-2 RNA viral loads in a convenience panel of 48 COVID-19-positive diagnostic specimens spanned a 6.2log 10 range, confirming substantial viral load variation in vivo . We further calibrated RT-ddPCR-derived SARS-CoV-2 E gene copy numbers against cycle threshold (C t ) values from a commercial real-time RT-PCR diagnostic platform. The resulting log-linear relationship can be used to mathematically derive SARS-CoV-2 RNA copy numbers from C t values, allowing the wealth of available diagnostic test data to be harnessed to address foundational questions in SARS-CoV-2 biology.

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    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.

  2. Version published to 10.1101/2020.12.21.423898 on bioRxiv