Abstract Microbial communities function as dynamic societies where intercellular communication governs collective behaviors. However, mapping these interaction networks has remained a fundamental challenge in microbiology. This study aims to decode the social networks of complex bacterial communities at single-cell resolution by developing BACON, a computational framework that infers quorum sensing-mediated communication from single-microbe transcriptomic data. The approach combines a curated database of signaling systems with a statistical model that quantifies communication strength through coordinated expression of signal synthesis and receptor genes. Validation in model systems demonstrated BACON’s precision in reconstructing density-dependent communication trajectories in Bacillus subtilis and capturing rapid network reorganization in Escherichia coli under antibiotic stress, revealing distinct sender-receiver subpopulations. Applied to human gut microbiomes, BACON unveiled diurnal fluctuations in cross-species signaling that transcend enterotype boundaries and uncovered conserved metabolic specialization in signal-responsive bacteria. In a clinical context, analysis of an ICU patient’s gut microbiome revealed how Pseudomonas aeruginosa establishes a self-reinforcing communication circuit that upregulates virulence pathways. This work provides a unified framework for analyzing bacterial social interactions across diverse ecosystems. It opens new avenues for understanding microbial sociology, combating antimicrobial resistance, and engineering synthetic communities.
Qu et al. (Sun,) studied this question.