Gut single-microbe landscape in patients with major depressive disorder and bipolar disorder.

Microbial communities in the human gut are highly diverse and complex, and many play critical roles in health and disease. Their functioning depends not only on species composition and diversity but also on intra- and intercellular transcriptional dynamics. Robust technologies capable of capturing single-microbe RNA sequencing information are urgently needed to understand microbial heterogeneity and host interactions. In this exploratory study, we applied droplet-based single-microbe RNA sequencing (smRNA-seq2) to analyze gut microbiomes from five patients with major depressive disorder (MDD), five with bipolar disorder (BD), and five healthy participants (HP), generating a transcriptional atlas of 33,174 single microbial cells. Unsupervised clustering based on RNA expression profiles partitioned these cells into 37 distinct clusters, reflecting both taxonomic diversity and intra-species functional heterogeneity. The most dominant clusters were identified as Fusicatenibacter saccharivorans, Phocaeicola dorei, Enterocloster sp000431375, and Clostridium_Q sp003024715. Compositional and transcriptional differences were observed across diagnostic groups, with clustering patterns appearing to be influenced by disease, gender, and age. Functional heterogeneity was evident in oxidative stress and metabolic genes such as sodB, mdh2, and eno2 in Phocaeicola dorei, while stress-response genes (htpG_1, groL, dnaK, clpB) were upregulated in BD. Focusing on the ko03110 pathway (chaperones and folding catalysts), species-specific patterns emerged: Clostridium_Q_sp003024715 was positively associated with health but decreased in disease status; Enterocloster_sp000431375 and Fusicatenibacter saccharivorans were upregulated in BD; and Phocaeicola dorei was downregulated in MDD. Together, these findings suggest the presence of functional and phenotypic heterogeneity within the gut microbiome in mood disorders and identify exploratory microbial transcriptomic features that may be associated with group-level differences, warranting further validation in larger cohorts.