The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
Massively multiplexed nucleic acid detection with Cas13
Open accessAbstract
The great majority of globally circulating pathogens go undetected, undermining patient care and hindering outbreak preparedness and response. To enable routine surveillance and comprehensive diagnostic applications, there is a need for detection technologies that can scale to test many samples 1–3 while simultaneously testing for many pathogens 4–6. Here, we develop Combinatorial Arrayed Reactions for Multiplexed Evaluation of Nucleic acids (CARMEN), a platform for scalable, multiplexed pathogen detection. In the CARMEN platform, nanolitre droplets containing CRISPR-based nucleic acid detection reagents 7 self-organize in a microwell array 8 to pair with droplets of amplified samples, testing each sample against each CRISPR RNA (crRNA) in replicate. The combination of CARMEN and Cas13 detection (CARMEN–Cas13) enables robust testing of more than 4,500 crRNA–target pairs on a single array. Using CARMEN–Cas13, we developed a multiplexed assay that simultaneously differentiates all 169 human-associated viruses with at least 10 published genome sequences and rapidly incorporated an additional crRNA to detect the causative agent of the 2020 COVID-19 pandemic. CARMEN–Cas13 further enables comprehensive subtyping of influenza A strains and multiplexed identification of dozens of HIV drug-resistance mutations. The intrinsic multiplexing and throughput capabilities of CARMEN make it practical to scale, as miniaturization decreases reagent cost per test by more than 300-fold. Scalable, highly multiplexed CRISPR-based nucleic acid detection shifts diagnostic and surveillance efforts from targeted testing of high-priority samples to comprehensive testing of large sample sets, greatly benefiting patients and public health 9–11.
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Cites
MAFFT Multiple Sequence Alignment Software Version 7: Improvements in Performance and Usability
Katoh, K., & Standley, D. M. (2013) · Molecular Biology and Evolution · doi:10.1093/molbev/mst010 · Cited by 46,209
Primer3—new capabilities and interfaces
Untergasser, A., Cutcutache, I., Koressaar, T., Ye, J., Faircloth, B. C., Remm, M., & Rozen, S. G. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks596 · Cited by 9,461
Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets.
Macosko, E. Z., Basu, A., Satija, R., Nemesh, J., Shekhar, K., Goldman, M., Tirosh, I., Bialas, A. R., Kamitaki, N., Martersteck, E. M., et al. (2015) · Cell · doi:10.1016/j.cell.2015.05.002 · Cited by 7,110
CRISPR-Cas12a target binding unleashes indiscriminate single-stranded DNase activity
Chen, J. S., Ma, E., Harrington, L. B., Da Costa, M., Tian, X., Palefsky, J. M., & Doudna, J. A. (2018) · Science · doi:10.1126/science.aar6245 · Cited by 4,507
Nucleic acid detection with CRISPR-Cas13a/C2c2
Gootenberg, J. S., Abudayyeh, O. O., Lee, J. W., Essletzbichler, P., Dy, A. J., Joung, J., Verdine, V., Donghia, N., Daringer, N. M., Freije, C. A., et al. (2017) · Science · doi:10.1126/science.aam9321 · Cited by 3,844
Self-citationMultiplexed and portable nucleic acid detection platform with Cas13, Cas12a, and Csm6
Gootenberg, J. S., Abudayyeh, O. O., Kellner, M. J., Joung, J., Collins, J. J., & Zhang, F. (2018) · Science · doi:10.1126/science.aaq0179 · Cited by 2,638
C2c2 is a single-component programmable RNA-guided RNA-targeting CRISPR effector
Abudayyeh, O. O., Gootenberg, J. S., Konermann, S., Joung, J., Slaymaker, I. M., Cox, D. B. T., Shmakov, S., Makarova, K. S., Semenova, E., Minakhin, L., et al. (2016) · Science · doi:10.1126/science.aaf5573 · Cited by 2,336
Microfluidic Large-Scale Integration
Thorsen, T., Maerkl, S. J., & Quake, S. R. (2002) · Science · doi:10.1126/science.1076996 · Cited by 2,222
Field-deployable viral diagnostics using CRISPR-Cas13
Myhrvold, C., Freije, C. A., Gootenberg, J. S., Abudayyeh, O. O., Metsky, H. C., Durbin, A. F., Kellner, M. J., Tan, A. L., Paul, L. M., Parham, L. A., et al. (2018) · Science · doi:10.1126/science.aas8836 · Cited by 1,444
Self-citationTwo distinct RNase activities of CRISPR-C2c2 enable guide-RNA processing and RNA detection
East-Seletsky, A., O’Connell, M. R., Knight, S. C., Burstein, D., Cate, J. H. D., Tjian, R., & Doudna, J. A. (2016) · Nature · doi:10.1038/nature19802 · Cited by 1,236
Cited by
Kaminski, M. M., Abudayyeh, O. O., Gootenberg, J. S., Zhang, F., & Collins, J. J. (2021) · Nature Biomedical Engineering · doi:10.1038/s41551-021-00760-7 · Cited by 1,259
Diagnostics for SARS-CoV-2 infections
Kevadiya, B. D., Machhi, J., Herskovitz, J., Oleynikov, M. D., Blomberg, W. R., Bajwa, N., Soni, D., Das, S., Hasan, M., Patel, M., et al. (2021) · Nature Materials · doi:10.1038/s41563-020-00906-z · Cited by 833
Feng, W., Newbigging, A. M., Tao, J., Cao, Y., Peng, H., Le, C., Wu, J., Pang, B., Li, J., Tyrrell, D. L., et al. (2021) · Chemical Science · doi:10.1039/d0sc06973f · Cited by 312
High-fidelity Cas13 variants for targeted RNA degradation with minimal collateral effects
Tong, H., Huang, J., Xiao, Q., He, B., Dong, X., Liu, Y., Yang, X., Han, D., Wang, Z., Wang, X., et al. (2023) · Nature Biotechnology · doi:10.1038/s41587-022-01419-7 · Cited by 230
Advancing COVID-19 diagnosis with privacy-preserving collaboration in artificial intelligence
Bai, X., Wang, H., Ma, L., Xu, Y., Gan, J., Fan, Z., Yang, F., Ma, K., Yang, J., Bai, S., et al. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00421-z · Cited by 75
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Metadata
- Authors
- Ackerman, C. M., Myhrvold, C., Thakku, S. G., Freije, C. A., Metsky, H. C., Yang, D. K., Ye, S. H., Boehm, C. K., Kosoko-Thoroddsen, T.-S. F., Kehe, J., Nguyen, T. G., Carter, A., Kulesa, A., Barnes, J. R., Dugan, V. G., Hung, D. T., Blainey, P. C., & Sabeti, P. C.
- Venue
- Nature
- Published
- 2020-04-29
- Volume
- 582
- Issue
- 7811
- Pages
- 277-282
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W3020077371
- Cited by
- 803
- References
- 33
- FWCI
- 34.21
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Ackerman, C. M., Myhrvold, C., Thakku, S. G., Freije, C. A., Metsky, H. C., Yang, D. K., Ye, S. H., Boehm, C. K., Kosoko-Thoroddsen, T.-S. F., Kehe, J., Nguyen, T. G., Carter, A., Kulesa, A., Barnes, J. R., Dugan, V. G., Hung, D. T., Blainey, P. C., & Sabeti, P. C. (2020). Massively multiplexed nucleic acid detection with Cas13. Nature. https://doi.org/10.1038/s41586-020-2279-8
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