Electric field-driven microfluidics for rapid CRISPR-based diagnostics and its application to detection of SARS-CoV-2

  1. Ashwin Ramachandran
  2. Diego A. Huyke
  3. Eesha Sharma
  4. Malaya K. Sahoo
  5. ChunHong Huang
  6. Niaz Banaei
  7. Benjamin A. Pinsky
  8. Juan G. Santiago

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Abstract

Rapid, early-stage screening is crucial during pandemics for early identification of infected patients and control of disease spread. CRISPR biology offers new methods for rapid and accurate pathogen detection. Despite their versatility and specificity, existing CRISPR diagnostic methods suffer from the requirements of up-front nucleic acid extraction, large reagent volumes, and several manual steps—factors which prolong the process and impede use in low-resource settings. We here combine microfluidics, on-chip electric field control, and CRISPR to directly address limitations of current CRISPR diagnostic methods. We apply our method to the rapid detection of SARS-CoV-2 RNA in clinical samples. Our method takes about 35 min from raw sample to result, a significant improvement over existing nucleic acid-based diagnostic methods for COVID-19.

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  1. Version published to 10.1073/pnas.2010254117
  2. ScreenIT

    SciScore for 10.1101/2020.05.21.109637: (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
    Image analysis of fluorescence readouts: The fluorescence signal was calculated from raw experimental images using ImageJ software (National Institutes of Health)
    ImageJ
    suggested: (ImageJ, RRID:SCR_003070)

    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 10.1101/2020.05.21.109637v1 on bioRxiv