Microbial single-cell transcriptomics links gut microbiota functional states to metabolic changes in male mice.

Abstract

Increasing recognition that microorganisms within the same community can differ markedly in activity has motivated approaches that measure microbial function at single-cell resolution. However, microbial single-cell transcriptional profiling in mouse models remains limited. Here we show that the microbial single-cell RNA-seq platform smRandom-seq can be adapted to intestinal contents from male diabetic (db/db) and male control mice to profile microbial single-cell transcriptomes across the cecum, colon, and rectum. Using the species-identification workflow smClassify, together with an analysis strategy that integrates microbial transcriptomes with metabolomic profiles, we obtain functionally annotated single-microbe transcriptomes and characterize region- and phenotype-associated metabolic alterations. We also observe cross-species functional patterns that are associated with diabetes-related metabolic changes. Within-species analysis shows region-dependent transcriptional changes in carbohydrate and nitrogen pathways in Muribaculum gordoncarteri. This framework offers a practical approach for resolving microbial functional heterogeneity in the mouse gut and provides a basis for linking such heterogeneity to host metabolic changes, enabling the investigation of how single-microbe transcriptional states interface with host metabolism under diverse physiological and metabolic perturbations.