Sensitive detection and quantification of SARS-CoV-2 in saliva

  1. Mustafa Fatih Abasiyanik
  2. Blake Flood
  3. Jing Lin
  4. Sefika Ozcan
  5. Sherin J. Rouhani
  6. Athalia Pyzer
  7. Jonathan Trujillo
  8. Chaojie Zhen
  9. Ping Wu
  10. Stephen Jumic
  11. Andrew Wang
  12. Thomas F. Gajewski
  13. Peng Wang
  14. Madeline Hartley
  15. Bekim Ameti
  16. Rachael Niemiec
  17. Marian Fernando
  18. Vasudha Mishra
  19. Peter Savage
  20. Bulent Aydogan
  21. Cindy Bethel
  22. Scott Matushek
  23. Kathleen G. Beavis
  24. Nishant Agrawal
  25. Jeremy Segal
  26. Savaş Tay
  27. Evgeny Izumchenko

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Abstract

Saliva has significant advantages as a test medium for detection of SARS-CoV-2 infection in patients, such as ease of collection, minimal requirement of supplies and trained personnel, and safety. Comprehensive validation in a large cohort of prospectively collected specimens with unknown SARS-CoV-2 status should be performed to evaluate the potential and limitations of saliva-based testing. We developed a saliva-based testing pipeline for detection of SARS-CoV-2 nucleic acids using real-time reverse transcription PCR (RT-PCR) and droplet digital PCR (ddPCR) readouts, and measured samples from 137 outpatients tested at a curbside testing facility and 29 inpatients hospitalized for COVID-19. These measurements were compared to the nasal swab results for each patient performed by a certified microbiology laboratory. We found that our saliva testing positively detects 100% (RT-PCR) and 93.75% (ddPCR) of curbside patients that were identified as SARS-CoV-2 positive by the Emergency Use Authorization (EUA) certified nasal swab testing assay. Quantification of viral loads by ddPCR revealed an extremely wide range, with 1 million-fold difference between individual patients. Our results demonstrate for both community screening and hospital settings that saliva testing reliability is on par with that of the nasal swabs in detecting infected cases, and has potential for higher sensitivity when combined with ddPCR in detecting low-abundance viral loads that evade traditional testing methods.

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  1. Version published to 10.1038/s41598-021-91835-7
  2. ScreenIT

    SciScore for 10.1101/2020.12.04.20241059: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: Sample collection: This study was approved by the Institutional Review Board (IRB) of the University of Chicago (IRB 20-0520), and we obtained informed written consent from all patients prior to sample collection.
    Consent: Sample collection: This study was approved by the Institutional Review Board (IRB) of the University of Chicago (IRB 20-0520), and we obtained informed written consent from all patients prior to sample collection.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Statistical analysis: All figures were plotted with MATLAB or R software.
    MATLAB
    suggested: (MATLAB, RRID:SCR_001622)

    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.

  3. Version published to 10.1101/2020.12.04.20241059v1 on medRxiv