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  4. Minimally instrumented SHERLOCK (miSHERLOCK) for CRISPR-based point-of-care diagnosis of SARS-CoV-2 and emerging variants

Minimally instrumented SHERLOCK (miSHERLOCK) for CRISPR-based point-of-care diagnosis of SARS-CoV-2 and emerging variants

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Author(s)
de Puig, Helena
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Lee, Rose A.
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Najjar, Devora
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Tan, Xiao
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Soekensen, Luis R.
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Angenent-Mari, Nicolaas M.
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Donghia, Nina M.
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Weckman, Nicole E.
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Ory, Audrey
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Ng, Carlos F.
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Date Issued
August 2021
Journal
Science Advances
Publisher
American Association for the Advancement of Science
Citation
de Puig, Helena et al. "Minimally instrumented SHERLOCK (miSHERLOCK) for CRISPR-based point-of-care diagnosis of SARS-CoV-2 and emerging variants." Science Advances 7, 32 (August 2021): eabh2944. © 2021 The Authors
Version
Final published version
Abstract
The COVID-19 pandemic highlights the need for diagnostics that can be rapidly adapted and deployed in a variety of settings. Several SARS-CoV-2 variants have shown worrisome effects on vaccine and treatment efficacy, but no current point-of-care (POC) testing modality allows their specific identification. We have developed miSHERLOCK, a low-cost, CRISPR-based POC diagnostic platform that takes unprocessed patient saliva; extracts, purifies, and concentrates viral RNA; performs amplification and detection reactions; and provides fluorescent visual output with only three user actions and 1 hour from sample input to answer out. miSHERLOCK achieves highly sensitive multiplexed detection of SARS-CoV-2 and mutations associated with variants B.1.1.7, B.1.351, and P.1. Our modular system enables easy exchange of assays to address diverse user needs and can be rapidly reconfigured to detect different viruses and variants of concern. An adjunctive smartphone application enables output quantification, automated interpretation, and the possibility of remote, distributed result reporting.
MIT Department
Massachusetts Institute of Technology. Institute for Medical Engineering & Science
Massachusetts Institute of Technology. Department of Biological Engineering
Abdul Latif Jameel Clinic for Machine Learning in Health
Massachusetts Institute of Technology. Synthetic Biology Center
Harvard-MIT Program in Health Sciences and Technology
Massachusetts Institute of Technology. Media Laboratory
Terms of Use
Creative Commons Attribution NonCommercial License 4.0
https://creativecommons.org/licenses/by-nc/4.0/
Persistent DSpace Link
https://hdl.handle.net/1721.1/131169
DOI of Published Version
https://doi.org/10.1126/sciadv.abh2944
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