ELISA detection of SARS-CoV-2 antibodies in saliva

  1. Melanie A. MacMullan
  2. Albina Ibrayeva
  3. Kylie Trettner
  4. Laura Deming
  5. Sudipta Das
  6. Frances Tran
  7. Jose Ricardo Moreno
  8. Joseph G. Casian
  9. Prithivi Chellamuthu
  10. Jeffrey Kraft
  11. Kenneth Kozak
  12. Fred E. Turner
  13. Vladimir I. Slepnev
  14. Lydia M. Le Page

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Abstract

To facilitate containment of the COVID-19 pandemic currently active in the United States and across the world, options for easy, non-invasive antibody testing are required. Here we have adapted a commercially available, serum-based enzyme-linked immunosorbent assay (ELISA) for use with saliva samples, achieving 84.2% sensitivity and 100% specificity in a set of 149 clinical samples. This strategy will enable widespread, affordable testing for patients who experienced this disease, whilst minimizing exposure risk for healthcare workers.

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  1. ScreenIT

    SciScore for 10.1101/2020.08.17.20176594: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board StatementIRB: Post-COVID-19 serum and saliva samples: Clinical samples were collected under UCLA Institutional Review Board approved study protocol IRB#20–000703.
    Consent: The UCLA IRB determined the protocol was minimal risk and verbal informed consent was sufficient for the research under 45 CFR 46.117(c)(2).
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Saliva ELISA optimizations: Statistical analysis: ROC curves were generated in GraphPad Prism (GraphPad Prism Version 8.4.3, San Diego, USA), with a 95% confidence interval.
    GraphPad Prism
    suggested: (GraphPad Prism, RRID:SCR_002798)
    GraphPad
    suggested: (GraphPad Prism, RRID:SCR_002798)

    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 10.1038/s41598-020-77555-4
  3. Version published to 10.1101/2020.08.17.20176594 on medRxiv