Integrated sample inactivation, amplification, and Cas13-based detection of SARS-CoV-2

  1. Jon Arizti-Sanz
  2. Catherine A. Freije
  3. Alexandra C. Stanton
  4. Chloe K. Boehm
  5. Brittany A. Petros
  6. Sameed Siddiqui
  7. Bennett M. Shaw
  8. Gordon Adams
  9. Tinna-Solveig F. Kosoko-Thoroddsen
  10. Molly E. Kemball
  11. Robin Gross
  12. Loni Wronka
  13. Katie Caviness
  14. Lisa E. Hensley
  15. Nicholas H. Bergman
  16. Bronwyn L. MacInnis
  17. Jacob E. Lemieux
  18. Pardis C. Sabeti
  19. Cameron Myhrvold

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Abstract

The COVID-19 pandemic has highlighted that new diagnostic technologies are essential for controlling disease transmission. Here, we develop SHINE (SHERLOCK and HUDSON Integration to Navigate Epidemics), a sensitive and specific integrated diagnostic tool that can detect SARS-CoV-2 RNA from unextracted samples. We combine the steps of SHERLOCK into a single-step reaction and optimize HUDSON to accelerate viral inactivation in nasopharyngeal swabs and saliva. SHINE’s results can be visualized with an in-tube fluorescent readout — reducing contamination risk as amplification reaction tubes remain sealed — and interpreted by a companion smartphone application. We validate SHINE on 50 nasopharyngeal patient samples, demonstrating 90% sensitivity and 100% specificity compared to RT-PCR with a sample-to-answer time of 50 minutes. SHINE has the potential to be used outside of hospitals and clinical laboratories, greatly enhancing diagnostic capabilities.

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    SciScore for 10.1101/2020.05.28.119131: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: Clinical samples and ethics statement: Clinical samples were acquired from clinical studies evaluated and approved by the Institutional Review Board/Ethics Review Committee of the Massachusetts General Hospital and Massachusetts Institute of Technology (MIT).
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The Wilcoxon rank sum test was conducted in MATLAB (MathWorks).
    MATLAB
    suggested: (MATLAB, RRID:SCR_001622)
    Schematics shown in Fig. 1A and Fig. 3A were created using BioRender.com.
    BioRender
    suggested: (Biorender, RRID:SCR_018361)
    All other schematics were generated in Adobe Illustrator (v24.1.2).
    Adobe Illustrator
    suggested: (Adobe Illustrator, RRID:SCR_010279)
    Data panels were primarily generated via Prism 8 (GraphPad), except Figure 3E which was generated using Python (version 3.7.2), seaborn (version 0.10.1) and matplotlib (version 3.2.1) (33, 34).
    Python
    suggested: (IPython, RRID:SCR_001658)
    matplotlib
    suggested: (MatPlotLib, RRID:SCR_008624)

    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: We detected the following sentences addressing limitations in the study:
    Sample collection without UTM (i.e., “dry swabs”) combined with spin-column-free extraction buffers, and incorporation of solution-based, colorimetric readouts could address these limitations (28–31). Together, these advances could greatly enhance the accessibility of diagnostic testing and provide an essential tool in the fight against infectious diseases. By reducing personnel time, equipment, and assay time-to-results without sacrificing sensitivity or specificity, we have taken steps towards the development of such a tool.

    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 10.1101/2020.05.28.119131v1 on bioRxiv