Nanozyme-catalysed CRISPR assay for preamplification-free detection of non-coding RNAs
Name
2022-NatNano-accepted.pdf
Description
Accepted version
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805.74 KB
Format
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Checksum (MD5)
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Author(s)
Broto, Marta
Kaminski, Michael M
Adrianus, Christopher
Kim, Nayoung
Greensmith, Robert
Dissanayake-Perera, Schan
Schubert, Alexander J
Tan, Xiao
Kim, Hyemin
Dighe, Anand S
Date Issued
2022
Journal
Nature Nanotechnology
Publisher
Springer Science and Business Media LLC
Citation
Broto, Marta, Kaminski, Michael M, Adrianus, Christopher, Kim, Nayoung, Greensmith, Robert et al. 2022. "Nanozyme-catalysed CRISPR assay for preamplification-free detection of non-coding RNAs." Nature Nanotechnology, 17 (10).
Version
Author's final manuscript
Abstract
CRISPR-based diagnostics enable specific sensing of DNA and RNA biomarkers associated with human diseases. This is achieved through the binding of guide RNAs to a complementary sequence that activates Cas enzymes to cleave reporter molecules. Currently, most CRISPR-based diagnostics rely on target preamplification to reach sufficient sensitivity for clinical applications. This limits quantification capability and adds complexity to the reaction chemistry. Here we show the combination of a CRISPR-Cas-based reaction with a nanozyme-linked immunosorbent assay, which allows for the quantitative and colorimetric readout of Cas13-mediated RNA detection through catalytic metallic nanoparticles at room temperature (CrisprZyme). We demonstrate that CrisprZyme is easily adaptable to a lateral-flow-based readout and different Cas enzymes and enables the sensing of non-coding RNAs including microRNAs, long non-coding RNAs and circular RNAs. We utilize this platform to identify patients with acute myocardial infarction and to monitor cellular differentiation in vitro and in tissue biopsies from prostate cancer patients. We anticipate that CrisprZyme will serve as a universally applicable signal catalyst for CRISPR-based diagnostics, which will expand the spectrum of targets for preamplification-free, quantitative detection.
MIT Department
Massachusetts Institute of Technology. Department of Biological Engineering
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1038/S41565-022-01179-0
