A simple, safe and sensitive method for SARS-CoV-2 inactivation and RNA extraction for RT-qPCR

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Abstract

The SARS-CoV-2 pandemic has created an urgent need for large amounts of diagnostic tests to detect viral RNA, which commercial suppliers are increasingly unable to deliver. In addition to the lack of availability, the current methods do not always fully inactivate the virus. Together, this calls for the development of safer methods for extraction and detection of viral RNA from patient samples that utilise readily available reagents and equipment present in most standard laboratories. We present a rapid and straightforward RNA extraction protocol for inactivating the SARS-CoV-2 virus that uses standard lab reagents. This protocol expands analysis capacity as the inactivated samples can be used in RT-qPCR detection tests at laboratories not otherwise classified for viral work. The method circumvents the need for commercial RNA purification kits, takes about 30 minutes from swab to PCR-ready viral RNA, and enables downstream detection of SARS-CoV-2 by RT-qPCR with very high sensitivity (~4 viral RNA copies per RT-qPCR). In summary, we present a rapid, safe and sensitive method for high-throughput detection of SARS-CoV-2, that can be conducted in any laboratory equipped with a qPCR machine.

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  1. SciScore for 10.1101/2020.06.29.179176: (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.
    Cell Line Authenticationnot detected.

    Table 2: Resources

    Experimental Models: Cell Lines
    SentencesResources
    Negative controls were reactions without the RT enzyme and without sample, and the positive control was RNA isolated from HeLa cells.
    HeLa
    suggested: CLS Cat# 300194/p772_HeLa, RRID:CVCL_0030)

    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.