Unleashing the potential of mRNA-seq to uncover the microbiome structure and their crosstalk with host cells: the vulvar case

To comprehensively describe both host gene expression and microbiome composition in a single sample, parallel experimental and computational workflows -mRNA sequencing and either 16S or metagenomics- have been traditionally applied. Here, following in vitro validation of the reliability of Poly(A)-enriched mRNA sequencing in reconstructing the microbiota composition on a defined microbial mock community, we process a cohort of 30 vulvar mRNA-seq samples to fully analyze not only the transcriptome of the vulvar cells, but also the composition of microbial communities and their parallel changes. This three-level analysis on the very same specimens further enables a gene-level exploration of the reciprocal molecular crosstalk between host and microbes. The vulvar milieu represents an area of emerging research for its role in health and disease. Being at the anatomical intersection between the vaginal milieu and the perineal area, the vulvar microbiome displays an intermediate signature, with contamination from both ecosystems. Using this unified framework, we reveal marked heterogeneity and high inter-individual variability in the vulvar microbiota of healthy individuals, identifying community state types that mirror those described in the vaginal ecosystem. Importantly, we show that distinct microbial configurations are associated with specific host transcriptional programs: Lactobacillus crispatus dominated communities correlate with epithelial differentiation and barrier integrity, whereas communities enriched in taxa associated with dysbiosis exhibit transcriptional signatures linked to inflammation. Beyond providing new biological insights into an understudied anatomical niche, our study introduces a broadly applicable strategy with substantial impact for the field. With tens of thousands of human RNA-seq datasets already available in public repositories, our approach enables retrospective extraction of microbiome information and host-microbe interaction signals from existing transcriptomic data, without the need for additional sequencing or specialized microbiome protocols. This unlocks a powerful and cost-effective opportunity to revisit archived RNA-seq studies across tissues, diseases, and low-biomass environments, revealing previously inaccessible layers of host-microbiome crosstalk and maximizing the scientific value of data that already exist.