High-throughput cytological profiling uncovers genotype-phenotype associations in Mycobacterium tuberculosis clinical isolates.

Abstract

Understanding the functional impact of bacterial genetic diversity is crucial for linking pathogen variants to clinical outcomes. Here, we introduce a high-throughput cytological profiling pipeline optimized for Mycobacterium tuberculosis (Mtb) clinical strains, integrating OD-calibrated feature analysis and high-content microscopy. Our system quantifies single-bacterium morphological and physiological traits related to DNA replication, redox state, carbon metabolism, and cell envelope dynamics. Applied to 64 Mtb clinical isolates from lineages 1, 2, and 4, the approach revealed that cytological phenotypes recapitulate genetic relationships and exhibit both lineage- and density-dependent dynamics. Notably, we identified a link between a convergent "small cell" phenotype and a convergent ino1 mutation that is associated with the presence of an antisense transcript, suggesting a potential non-canonical regulatory mechanism under selection. In summary, we present a resource-efficient approach for mapping Mtb's phenotypic landscape, uncovering cellular traits that underlie its evolution and providing new insights into the functional consequences of bacterial genetic diversity.

Importance: Understanding how genetic variation in Mycobacterium tuberculosis (Mtb) shapes its physical traits is essential to unraveling the evolution of this global pathogen. Here, we introduce a systematically optimized, high-throughput imaging platform for the comprehensive characterization of Mtb clinical strains. We demonstrate that Mtb's phenotypic manifestation is shaped by both genetic background and culture density. By accounting for these factors, our analysis linked distinct cellular dynamics to specific lineages, sublineages, and even single nucleotide variations. Notably, we linked a recurring mutation to a unique cell-shortening phenotype, finding that it potentially acts by creating a cryptic antisense transcript. This platform provides a powerful framework for systematically dissecting the physiological dynamics underlying Mtb evolution and identifying new therapeutic vulnerabilities of this deadly pathogen.