A Combined approach of MALDI-TOF Mass Spectrometry and multivariate analysis as a potential tool for the detection of SARS-CoV-2 virus in nasopharyngeal swabs

  1. María Florencia Rocca
  2. Jonathan Cristian Zintgraff
  3. María Elena Dattero
  4. Leonardo Silva Santos
  5. Martín Ledesma
  6. Carlos Vay
  7. Mónica Prieto
  8. Estefanía Benedetti
  9. Martín Avaro
  10. Mara Russo
  11. Fabiane Manke Nachtigall
  12. Elsa Baumeister

Reviewed by

Read the full article See related articles

Discuss this preprint

Start a discussion What are Sciety discussions?

Listed in

Log in to save this article

Abstract

Coronavirus disease 2019 (COVID-19) is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The rapid, sensitive and specific diagnosis of SARS-CoV-2 by fast and unambiguous testing is widely recognized to be critical in responding to the ongoing outbreak. Since the current testing capacity of RT-PCR-based methods is being challenged due to the extraordinary demand of supplies, such as RNA extraction kits and PCR reagents worldwide, alternative and/or complementary testing assays should be developed. Here, we exploit the potential of mass spectrometry technology combined with machine learning algorithms as an alternative fast tool for SARS-CoV-2 detection from nasopharyngeal swabs samples. According to our preliminary results, mass spectrometry-based methods combined with multivariate analysis showed an interesting potential as a complementary diagnostic tool and further steps should be focused on sample preparation protocols and the improvement of the technology applied.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.05.07.082925: (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.
    • 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.

  2. Version published to 10.1101/2020.05.07.082925 on bioRxiv