mosna reveals different types of cellular interactions predictive of response to immunotherapies in cancer

Single-cell spatially resolved proteomic or transcriptomic methods offer the opportunity to discover cell types interactions of biological or clinical importance. To extract relevant information from these data, we present mosna, a Python package to analyze spatially resolved experiments and discover patterns of cellular spatial organization. It includes the detection of preferential interactions between specific cell types and the discovery of cellular niches. We exemplify the proposed analysis pipeline on spatially resolved proteomic data from cancer patient samples annotated with clinical response to immunotherapy, and we show that mosna can identify a number of features describing cellular composition and spatial distribution that can provide biological hypotheses regarding factors that affect response to therapies.