25 Feb NYMC Develops CRISPR Platform to Quickly Identify Candida auris, a Potentially Lethal Hospital Acquired Infection

Candida auris CDC Image

MedicalResearch.com Interview with:

Vishnu Chaturvedi, Ph.D.

Vishnu Chaturvedi, Ph.D.
Professor of Pathology, Microbiology, and Immunology and of Medicine
New York Medical College

MedicalResearch.com: What is the background for this study?
Response: The research was prompted by the rise of Candida auris (C. auris) as a critical fungal pathogen that has caused global outbreaks in healthcare facilities with high mortality rates. C. auris is particularly difficult to control because it can survive on physical surfaces for extended periods. Current diagnostic methods (such as culture-based approaches or mass spectrometry) are often costly, slow, and require complex equipment in centralized laboratories, which delays effective clinical responses. There is an urgent need for rapid tests that can both identify the fungus and measure its level of drug resistance.

MedicalResearch.com: What are the main findings?

Response:  The researchers developed dSHERLOCK, a platform that combines CRISPR/Cas detection with single-template quantification.
Key findings include: 

• Speed: It detects auris in under 20 minutes and provides full quantification within 40 minutes.
• Accuracy and Sensitivity: The system can detect as little as 1 CFU μl⁻¹ (colony-forming unit per microliter) across the four major global clades of the fungus.
• Resistance Identification: Using machine learning and real-time monitoring, the platform can distinguish between drug-resistant and non-resistant (wild type) strains in mixed populations, which standard tests might misdiagnose.

MedicalResearch.com: What is the significance of Candida auris infections?

Response:  C. auris is significant due to its high mortality rate and its threat to specific patient groups. 

• Vulnerable Patients: It is especially problematic for those with compromised immune systems (e.g., organ transplant recipients, chemotherapy patients, or those with HIV).
• Invasive Devices: Patients requiring breathing tubes, feeding tubes, or catheters are at high risk.
• Severe Complications: If the infection enters the bloodstream or vital organs, it becomes life-threatening, causing immune reactions and symptoms like sepsis caused by bacteria or viruses.
• Drug Resistance: In the U.S., 90% of isolates are resistant to azole antifungals, and resistance to other preferred treatments is increasing.

MedicalResearch.com: Where is it usually detected?

Response:  C. auris is most often found in hospital patients and individuals living in long-term care facilities, usually by using surveillance skin swabs taken from the axilla (armpit) and groin areas. The fungus can also be present on surfaces in healthcare environments, potentially contributing to hospital outbreaks.

MedicalResearch.com: How does the Sherlock platform improve detection and resistance?

Response:  The platform improves detection by using isothermal amplification (Recombinase Polymerase Amplification or RPA) combined with CRISPR/Cas12a technology.

• Single-Molecule Tracking: By splitting the sample into thousands of tiny partitions (picoliter volumes), the system tracks the “kinetics” or reaction speed of individual molecules.
• Machine Learning: It uses machine learning to analyze these reaction speeds to identify specific mutations (SNPs) associated with multidrug resistance.
• Mixed Populations: Unlike standard tests, dSHERLOCK can quantify both resistant and susceptible alleles simultaneously in a single sample.

MedicalResearch.com: Would the platform be readily implementable in a variety of health care settings?

Response:  Yes. The researchers highlight that the platform uses commercially available materials and common laboratory equipment. Because it is isothermal (operates at a constant temperature) and requires minimal sample processing—directly using transport medium without needing dedicated DNA extraction—it is designed to be widely deployable in global healthcare settings.

MedicalResearch.com: What should readers take away from your report?

Response:  dSHERLOCK is a rapid, sensitive, and quantitative diagnostic tool for point-of-care detection of fungal loads and antifungal resistance. This technology could enable swift clinical decision-making and better containment of hospital outbreaks.

MedicalResearch.com: What should readers take away from your report?

Response: The researchers note that the approach is easily adaptable. They recommend: 

• Designing clade-specific targets to account for emerging genetic variations.
• Updating universal targets as new genomic data for specific strains (such as those from recent outbreaks) becomes available and confirmed.

MedicalResearch.com: Is there anything else you would like to add?

• Technology Licensing: Sherlock Biosciences has licensed the Wyss Institute technology mentioned in the report to create affordable molecular diagnostics.
• Collaboration: The work involved researchers from the Wyss Institute at Harvard, MIT, and the Wadsworth Center of the New York State Department of Health.
• Open Access: The scripts for image processing and machine learning from this study are available for public use in open-access repositories such as GitHub.

Citation:

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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Last Updated on February 25, 2026 by Marie Benz MD FAAD