Multiplexed CRISPR-based microfluidic platform for clinical testing of respiratory viruses and SARS-CoV-2 variants

  1. N.L. Welch
  2. M. Zhu
  3. C. Hua
  4. J. Weller
  5. M. Ezzaty Mirhashemi
  6. S. Mantena
  7. T.G. Nguyen
  8. B.M. Shaw
  9. C.M. Ackerman
  10. S.G. Thakku
  11. M.W. Tse
  12. J. Kehe
  13. M.R. Bauer
  14. M-M. Uwera
  15. J.S. Eversley
  16. D.A. Bielwaski
  17. G. McGrath
  18. J. Braidt
  19. J. Johnson
  20. F. Cerrato
  21. B.A. Petros
  22. G.L. Gionet
  23. S.K. Jalbert
  24. M.L. Cleary
  25. K.J. Siddle
  26. C.T. Happi
  27. D.T. Hung
  28. M. Springer
  29. B.L. MacInnis
  30. J.E. Lemieux
  31. E. Rosenberg
  32. J.A. Branda
  33. P.C. Blainey
  34. P.C. Sabeti
  35. C Myhrvold

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Abstract

The COVID-19 pandemic has demonstrated a clear need for high-throughput, multiplexed, and sensitive assays for detecting SARS-CoV-2 and other respiratory viruses as well as their emerging variants. Here, we present microfluidic CARMEN (mCARMEN), a cost-effective virus and variant detection platform that combines CRISPR-based diagnostics and microfluidics with a streamlined workflow for clinical use. We developed the mCARMEN respiratory virus panel (RVP) and demonstrated its diagnostic-grade performance on 533 patient specimens in an academic setting and then 166 specimens in a clinical setting. We further developed a panel to distinguish 6 SARS-CoV-2 variant lineages, including Delta and Omicron, and evaluated it on 106 patient specimens, with near-perfect concordance to sequencing-based variant classification. Lastly, we implemented a combined Cas13 and Cas12 approach that enables quantitative measurement of viral copies in samples. mCARMEN enables high-throughput surveillance of multiple viruses and variants simultaneously.

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  1. ScreenIT

    SciScore for 10.1101/2021.12.14.21267689: (What is this?)

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

    Table 1: Rigor

    EthicsConsent: Human specimens from patients with SARS-CoV-2, HCoV-HKU1, HCoV-NL63, FLUAV, FLUBV, HRSV, and HMPV were obtained under a waiver of consent from the Mass General Brigham IRB Protocol #2019P003305.
    Sex as a biological variablenot detected.
    RandomizationThe final droplet pool was pipetted up and down gently to fully randomize the arrangement of the droplets in the pool.
    Blindingnot detected.
    Power Analysisnot detected.
    Cell Line Authenticationnot detected.

    Table 2: Resources

    Experimental Models: Cell Lines
    SentencesResources
    HCoV-229E, HCoV-HKU1, HCoV-NL63, HCoV-OC43, FLUAV, FLUBV, HMPV, HRSV, HPIV-1,2,3,4, AdV, HEV-A,B,C,D, SARS-CoV, MERS-CoV, and HRV.
    HCoV-NL63
    suggested: RRID:CVCL_RW88)
    Preparation of contrived samples prior to extraction: Contrived patient samples of viruses HCoV-HKU1, HCoV-OC43, HCoV-NL63, FLUAV-g4, HPIV-3, and HMPV were prepared by diluting either viral seed stock (HCoV-OC43 and HPIV-3) or template RNA (HCoV-HKU1 and HCoV-NL63).
    HCoV-OC43
    suggested: None
    Software and Algorithms
    SentencesResources
    These aligned sequences were then fed into ADAPT for crRNA design with high coverage using the ‘minimize guides’ objective (>90% of sequences detected).
    ADAPT
    suggested: (ADAPT, RRID:SCR_006769)
    Target control - PIC1 and PIC2: The consensus sequences generated directly above after multiple genome alignment with MAFFT were used to order a 500 bp dsDNA fragment encompassing the primer and crRNA binding sites.
    MAFFT
    suggested: (MAFFT, RRID:SCR_011811)
    In brief, pre-merge imaging data was processed using custom Python scripts to detect fluorescently-encoded droplets in microwells and identify their inputs based on their fluorescence intensity in three encoding channels, 647 nm, 594 nm, and 555 nm.
    Python
    suggested: (IPython, RRID:SCR_001658)

    Results from OddPub: Thank you for sharing your code and data.

    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.

    Results from scite Reference Check: We found no unreliable references.

    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 10.1101/2021.12.14.21267689v1 on medRxiv