Detection and diagnosis of bloodborne pathogens are critical for patients and for preventing outbreaks, yet challenging due to these diseases’ nonspecific initial symptoms. We advanced CRISPR-based Combinatorial Arrayed Reactions for Multiplexed Evaluation of Nucleic acids (CARMEN) technology for simultaneous detection of pathogens on numerous samples. We developed three specialized panels that target viral hemorrhagic fevers, mosquito-borne viruses, and sexually transmitted infections, collectively identifying 23 pathogens. We used deep learning to design CARMEN assays with enhanced sensitivity and specificity, validating them and evaluating their performance on synthetic targets, spiked healthy normal serum samples, and patient samples for Neisseria gonorrhoeae in the United States and for Lassa and mpox virus in Nigeria. Our results show multiplexed CARMEN assays match or outperform individual assay RT-PCR in sensitivity, with matched specificity. These findings underscore CARMEN’s potential as a highly effective tool for rapid, accurate pathogen detection for clinical diagnosis and public health surveillance.
Competing Interest StatementN.L.W., and P.C.S. are coinventors on a patent related to this work. P.C.S. is a cofounder of and consultant to Sherlock Biosciences, and a board member of the Danaher Corporation, and holds equity in the companies. The other authors declare no competing interests.
Funding StatementThis work is made possible by support from Flu Lab and a cohort of generous donors through TEDs Audacious Project, including the ELMA Foundation, MacKenzie Scott, the Skoll Foundation, and Open Philanthropy. P.C.S. was supported by the Howard Hughes Medical Institute and Merck KGaA Future Insight Prize.
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