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Digital CRISPR-based diagnostics for quantification of Candida auris and resistance mutations

Nature Biomedical Engineering (2026)Cite this article

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Abstract

Candida auris, an increasingly prevalent fungal pathogen, requires both rapid identification and antifungal susceptibility testing to enable proper treatment. This study introduces digital SHERLOCK (dSHERLOCK), a platform that combines CRISPR/Cas nucleic acid detection, single-template quantification and real-time kinetics monitoring. Assays implemented on this platform display excellent sensitivity to C. auris from major clades 1–4, while maintaining specificity when challenged with common environmental and pathogenic fungi. dSHERLOCK detects C. auris within 20 min in minimally processed swab samples and achieves sensitive quantification (1 c.f.u. µl−1) within 40 min. To address antifungal susceptibility testing, we develop assays that detect mutations that are commonly associated with azole and echinocandin multidrug resistance. We use machine learning and real-time monitoring of reaction kinetics to achieve highly accurate simultaneous quantification of mutant and wild-type FKS1 SNP alleles in fungal populations with mixed antifungal susceptibility, which would be misdiagnosed as completely susceptible or resistant under standard reaction conditions. Our platform’s use of commercially available materials and common laboratory equipment makes C. auris diagnostics widely deployable in global healthcare settings.

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Fig. 1: Schematic representation of digital SHERLOCK assays.
Fig. 2: SHERLOCK enables specific and sensitive detection of C. auris.
Fig. 3: dSHERLOCK accurately quantifies C. auris in simulated and clinical swab samples.
Fig. 4: SNPs of the FKS1 resistance gene exhibit distinct kinetics in dSHERLOCK.
Fig. 5: dSHERLOCK simultaneously and rapidly quantifies SNP alleles in heterogeneous samples.

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Data availability

The data supporting the findings of this study are available within the paper and its Supplementary Information. Example data can be downloaded from figshare at https://doi.org/10.6084/m9.figshare.29944823 (ref. 51). Should any raw data files be needed in another format, they are available from the corresponding author on reasonable request.

Code availability

The image processing and machine learning scripts have been deposited in the open-access online repository GitHub and may be accessed at https://github.com/Walt-Lab/dSherlock (ref. 52). The source code is also available as a Code Ocean Compute Capsule at https://doi.org/10.24433/CO.6935105 (ref. 53). The image processing and machine learning scripts have been deposited in GitHub at https://github.com/Walt-Lab/dSherlock (ref. 52) and in Zenodo at https://doi.org/10.5281/zenodo.17153993 (ref. 54).

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Acknowledgements

Digital Droplet PCR measurements were performed by the Boston Children’s Hospital Intellectual and Developmental Disabilities Research Center Molecular Genetics Core Facility. The research described in this manuscript was supported by grants from The Wyss Institute for Bioinspired Engineering (J.C.R., A.T., D.R.W.), Health Research Inc. (GR110013801, N.E.W., N.K., H.d.P., X.T., J.J.C.), the New York State Department of Health, and the Wadsworth Center Division of Infectious Diseases (WC-2019-01, N.E.W., N.K., H.d.P., X.T., J.J.C.). N.K. was supported by the Wyss Technology Development Fellowship. X.T. acknowledges funding from the NIH NIDDK (5K08DK132516).

Author information

Author notes

  1. Nicole E. Weckman

    Present address: Department of Chemical Engineering and Applied Chemistry, Institute for Studies in Transdisciplinary Engineering Education and Practice, University of Toronto, Toronto, Ontario, Canada

  2. Vishnu Chaturvedi

    Present address: Westchester Medical Center/New York Medical College, Valhalla, NY, USA

  3. These authors contributed equally: Justin C. Rolando, Anton Thieme, Nicole E. Weckman.

  4. These authors jointly supervised this work: James J. Collins, David R. Walt.

Authors and Affiliations

  1. Department of Pathology, Brigham and Women’s Hospital, Boston, MA, USA

    Justin C. Rolando & David R. Walt

  2. Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA

    Justin C. Rolando, Anton Thieme, Nicole E. Weckman, Nayoung Kim, Helena de Puig, Xiao Tan, James J. Collins & David R. Walt

  3. Harvard Medical School, Boston, MA, USA

    Justin C. Rolando, Anton Thieme, Nicole E. Weckman, Nayoung Kim, Xiao Tan & David R. Walt

  4. Institute for Medical Engineering and Science, Department of Biological Engineering, and Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, USA

    Nayoung Kim, Helena de Puig, Xiao Tan & James J. Collins

  5. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Nayoung Kim, Helena de Puig & James J. Collins

  6. Division of Gastroenterology, Massachusetts General Hospital, Boston, MA, USA

    Xiao Tan

  7. Wadsworth Center Mycology Lab, New York State Department of Health, Albany, NY, USA

    Emily Cotnoir & Vishnu Chaturvedi

  8. Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

    James J. Collins

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Contributions

J.C.R, A.T. and N.E.W. conceived the technology, designed and conducted experiments, analysed results and prepared the manuscript. N.K. participated in study design, conducted experimental work, contributed to data analysis and provided materials essential to the study. H.d.P. and X.T. submitted the initial proposal and performed computational primer analyses. H.d.P. designed gRNA sequences and primers, conducted initial experiments and analysed the resulting data. E.C. prepared simulated swab samples, collected residual clinical composite surveillance swabs and characterized their molecular properties. V.C. helped design initial experiments, data analysis and interpretations. J.J.C. and D.R.W. jointly supervised the research. All authors reviewed and approved the final manuscript.

Corresponding authors

Correspondence to James J. Collins or David R. Walt.

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Competing interests

J.J.C. and D.R.W. had financial interests in and served on the board of Sherlock Biosciences Corporation, a company that develops CRISPR diagnostics; Sherlock Biosciences was acquired by OraSure Technologies. D.R.W.’s interests are reviewed and managed by Mass General Brigham and Harvard University in accordance with their conflict-of-interest policies. Harvard College and Brigham and Women’s Hospital have filed Provisional Patent 63/764,169, on behalf of J.C.R., A.T. and D.R.W. related to the multiplexed single-molecule CRISPR detection described in this manuscript. The remaining authors declare no competing interests.

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Nature Biomedical Engineering thanks Ruijie Dengvand, Tony Hu and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Rolando, J.C., Thieme, A., Weckman, N.E. et al. Digital CRISPR-based diagnostics for quantification of Candida auris and resistance mutations. Nat. Biomed. Eng (2026). https://doi.org/10.1038/s41551-025-01597-0

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