Rapid processing of SARS-CoV-2 containing specimens for direct RT-PCR

  1. Piotr Chomczynski
  2. Peter W. Chomczynski
  3. Amy Kennedy
  4. Michal Rymaszewski
  5. William W. Wilfinger
  6. Judith A. Heiny
  7. Karol Mackey

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Abstract

Widespread diagnostic testing is needed to reduce transmission of COVID-19 and manage the pandemic. Effective mass screening requires robust and sensitive tests that reliably detect the SARS-CoV-2 virus, including asymptomatic and pre-symptomatic infections with a low viral count. Currently, the most accurate tests are based on detection of viral RNA by RT-PCR. We developed a method to process COVID-19 specimens that simplifies and increases the sensitivity of viral RNA detection by direct RT-qPCR, performed without RNA purification. In the method, termed Alkaline-Glycol Processing (AG Processing), a SARS-CoV-2-containing biological specimen, such as saliva or a swab-collected suspension, is processed at pH 12.2 to 12.8 for 5 min at room temperature. An aliquot of the AG-processed specimen is used for detection of SARS-CoV-2 RNA by direct RT-qPCR. AG processing effectively lyses viruses and reduces the effect of inhibitors of RT-PCR that are present in biological specimens. The sensitivity of detecting viral RNA using AG processing is on par with methods that include a viral RNA purification step. One copy of SARS-CoV-2 virus per reaction, equivalent to 300 copies per ml of saliva, is detectable in the AG-processed saliva. The LOD is 300 viral copies per ml of initial saliva specimen. AG processing works with saliva specimens or swab specimens collected into Universal Transport Medium, is compatible with heat treatment of saliva, and was confirmed to work with a range of CDC-approved RT-qPCR products and kits. Detection of SARS-CoV-2 RNA using AG processing with direct RT-qPCR provides a reliable and scalable diagnostic test for COVID-19 that can be integrated into a range of workflows, including automated settings.

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  1. Version published to 10.1371/journal.pone.0246867
  2. ScreenIT

    SciScore for 10.1101/2020.12.14.20248183: (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
    Reference materials: The following materials were used as substitutes for SARS-CoV-2 live virus: NIAID,
    NIAID
    suggested: (NIAID, RRID:SCR_016598)

    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.
    • No funding statement was detected.
    • 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 10.1101/2020.12.14.20248183v1 on medRxiv