Direct clinical identification of mycobacteria via droplet-encoded specificity

The global incidence and mortality of nontuberculous mycobacterial (NTM) infections have risen sharply with population aging. In some regions, they are now surpassing Mycobacterium tuberculosis complex (MTBC) infections, imposing a substantial clinical and economic burden. Because NTMs exhibit species-level heterogeneity and require prolonged culture for identification, their diagnosis remains slow and is frequently inaccurate. Here we describe a multiplexed CRISPR (clustered regularly interspaced short palindromic repeats)-assisted nanodroplet differential identification (CANDI) diagnostic platform that integrates species-agnostic target amplification with species-specific CRISPR-associated protein 12a (Cas12a) detection in fluorescence-barcoded nanodroplets. By spatially compartmentalizing CRISPR reactions into color-encoded nanodroplets, CANDI overcomes the multiplexing limitations of conventional CRISPR diagnostics and enables simultaneous interrogation of multiple mycobacterial targets within a single assay. We designed a 16-plex panel that distinguishes 15 clinically relevant Mycobacterium species and subspecies. CANDI achieved high analytical sensitivity, and accurate discrimination in samples containing coinfections with multiple species or subspecies. When applied to 230 clinical specimens, including sputum, tracheal aspirates, and other respiratory fluids, CANDI delivered subspecies-level results within 3.5 hours, achieving 97.3% sensitivity and 99.7% specificity relative to culture-based identification. By combining multiplexed, high-specificity CRISPR detection with scalable droplet-based engineering, CANDI could overcome the culture dependency of current diagnostics and enable species- and subspecies-level identification across the genetically complex Mycobacterium genus, offering a clinically adaptable framework for rapid, precision diagnosis of NTM infections.