Abstract
Pathological events often impact tissue regions in a spatially variable manner, making it challenging to identify therapeutic targets. Spatial transcriptomics (ST) is a powerful technology to map spatially variable molecular mechanisms, yet suitable analytical methods have been lacking. We introduce spatially resolved pathology score (SPaSE), an optimal transport-based algorithm to compare ST data from diseased and control tissues. SPaSE computes a "pathology score" for each spot in the diseased sample, quantifying the pathological impact at that spot. In post-myocardial infarction (post-MI) mouse hearts, these scores delineated zones that matched independent expert annotations. Modeling pathology scores from gene expression revealed signatures predictive of varying pathological severity. The scoring model learned from mouse data showed accurate predictions on human post-MI data. We also demonstrated SPaSE's efficacy on additional simulated and real ST data from traumatic brain injury and Duchenne muscular dystrophy mouse models. SPaSE is a useful addition to the existing ST algorithms. A record of this paper's transparent peer review process is included in the supplemental information.