Pre-treatment of the clinical sample with Proteinase K allows detection of SARS-CoV-2 in the absence of RNA extraction

  1. Larissa Mallmann
  2. Karoline Schallenberger
  3. Meriane Demolliner
  4. Ana Karolina Antunes Eisen
  5. Bruna Saraiva Hermann
  6. Fágner Henrique Heldt
  7. Alana Witt Hansen
  8. Fernando Rosado Spilki
  9. Juliane Deise Fleck

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Abstract

COVID-19 (Coronavirus Disease 2019) outbreak was declared a pandemic, by World Health Organization, on March 11, 2020. Viral detection using RT-qPCR has been among the most important factors helping to control local spread of SARS-CoV-2 and it is considered the “gold standard” for diagnosis. Nevertheless, the RNA extraction step is both laborious and expensive, thus hampering the diagnosis in many places where there are not laboratory staff of funds enough to contribute for diagnosis efforts. Thus, the need to simplify procedures, reduce costs of the techniques used, and expand the capacity of the number of diagnostics of COVID-19 is imperative. In this study, detection of SARS-CoV-2 in the absence of RNA extraction has been successfully achieved through pre-treatment of the clinical sample with Proteinase K. The results show that only the use of proteinase K, without the need to perform the whole standard protocol for sample extraction and purification, can be an efficient technique for the diagnosis of COVID-19, since 91% of the samples matched the results with the standard procedure, with an average increase of 5.64 CT in the RT-qPCR.

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    SciScore for 10.1101/2020.05.07.083139: (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

    No key resources detected.

    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.

  2. Version published to 10.1101/2020.05.07.083139v1 on bioRxiv