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Published on: October 23, 2011
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Multiplexed detection of febrile infections using CARMEN
M Kamariza1,2, K McMahon3, L Kim3
1Broad Institute of MIT and Harvard, Cambridge, MA, USA. mkamariz@broadinstitute.org.
Nature Communications
|December 9, 2025
Summary
This study introduces advanced CRISPR-based Combinatorial Arrayed Reactions for Multiplexed Evaluation of Nucleic acids (CARMEN) panels for detecting 23 pathogens. The new CARMEN assays show high sensitivity and specificity for diagnosing infectious diseases and supporting public health surveillance.
Area of Science:
- Molecular Biology
- Infectious Diseases
- Bioinformatics
Background:
- Accurate detection of bloodborne pathogens is crucial but challenging due to non-specific symptoms.
- Existing methods may lack the multiplexing capability for simultaneous detection of numerous pathogens.
- CRISPR-based Combinatorial Arrayed Reactions for Multiplexed Evaluation of Nucleic acids (CARMEN) technology offers a platform for broad-range pathogen identification.
Purpose of the Study:
- To develop and validate novel CARMEN panels for detecting viral hemorrhagic fevers, mosquito-borne viruses, and sexually transmitted infections.
- To enhance CARMEN assay sensitivity and specificity using deep learning algorithms.
- To evaluate the performance of multiplexed CARMEN assays against established diagnostic methods in diverse sample types.
Main Methods:
- Development of three CARMEN panels targeting a total of 23 distinct pathogens.
- Application of deep learning for optimizing CARMEN assay design.
- Validation using synthetic targets, spiked serum samples, and clinical samples from Nigeria and the United States.
- Comparative performance analysis against RT-qPCR.
Main Results:
- Multiplexed CARMEN panels successfully identified pathogens across viral hemorrhagic fevers, mosquito-borne viruses, and sexually transmitted infections.
- Deep learning-enhanced CARMEN assays demonstrated matched or superior sensitivity compared to individual RT-qPCR assays.
- Specificity of the CARMEN assays was comparable to RT-qPCR.
- Successful validation on clinical samples for Neisseria gonorrhoeae, Lassa virus, and mpox virus.
Conclusions:
- Multiplexed CARMEN assays represent a highly sensitive and specific tool for pathogen detection.
- CARMEN technology shows significant potential for advancing clinical diagnostics and public health surveillance.
- The integration of deep learning further enhances the capabilities of CARMEN for infectious disease detection.
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