Spatial transcriptomics identifies novel Pseudomonas aeruginosa virulence factors.

To examine host-pathogen interactions, we leveraged a dual spatial transcriptomics approach that simultaneously captures the expression of Pseudomonas aeruginosa genes alongside the entire host transcriptome using a murine model of ocular infection. This method revealed differential pathogen- and host-specific gene expression patterns in infected corneas, which generated a unified transcriptional map of infection. By integrating these data, we developed a predictive ridge regression model trained on images from infected tissues. The model achieved an R(2) score of 0.923 in predicting bacterial burden distributions and identifying novel biomarkers associated with disease severity. Among iron acquisition pathogen-specific gene transcripts that showed significant enrichment at the host-pathogen interface, we discovered the novel virulence mediator PA2590, which was required for bacterial virulence. This study therefore highlights the power of combining bacterial and host spatial transcriptomics to uncover complex host-pathogen interactions and identify potentially druggable targets.