Rapid Detection of 2019 Novel Coronavirus SARS-CoV-2 Using a CRISPR-based DETECTR Lateral Flow Assay

  1. James P. Broughton
  2. Xianding Deng
  3. Guixia Yu
  4. Clare L. Fasching
  5. Jasmeet Singh
  6. Jessica Streithorst
  7. Andrea Granados
  8. Alicia Sotomayor-Gonzalez
  9. Kelsey Zorn
  10. Allan Gopez
  11. Elaine Hsu
  12. Wei Gu
  13. Steve Miller
  14. Chao-Yang Pan
  15. Hugo Guevara
  16. Debra A. Wadford
  17. Janice S. Chen
  18. Charles Y. Chiu

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Abstract

An outbreak of novel betacoronavirus, SARS-CoV-2 (formerly named 2019-nCoV), began in Wuhan, China in December 2019 and the COVID-19 disease associated with infection has since spread rapidly to multiple countries. Here we report the development of SARS-CoV-2 DETECTR, a rapid (∼30 min), low-cost, and accurate CRISPR-Cas12 based lateral flow assay for detection of SARS-CoV-2 from respiratory swab RNA extracts. We validated this method using contrived reference samples and clinical samples from infected US patients and demonstrated comparable performance to the US CDC SARS-CoV-2 real-time RT-PCR assay.

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  1. ScreenIT

    SciScore for 10.1101/2020.03.06.20032334: (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

    Software and Algorithms
    SentencesResources
    Briefly, viral genomes were aligned using Clustal Omega.
    Clustal Omega
    suggested: (Clustal Omega, RRID:SCR_001591)

    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 found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).

    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 10.1101/2020.03.06.20032334v2 on medRxiv
  3. Version published to 10.1101/2020.03.06.20032334v1 on medRxiv