BACON: decoding the dynamic social networks of complex microbial communities at single-cell resolution

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.