%0 Journal Article
%T Identifying Spatial Co-occurrence in Healthy and InflAmed tissues (ISCHIA).
%A Lafzi A
%A Borrelli C
%A Baghai Sain S
%A Bach K
%A Kretz JA
%A Handler K
%A Regan-Komito D
%A Ficht X
%A Frei A
%A Moor A
%J Mol Syst Biol
%V 20
%N 2
%D 2024 Feb 15
%M 38225383
%F 13.068
%R 10.1038/s44320-023-00006-5
%X Sequencing-based spatial transcriptomics (ST) methods allow unbiased capturing of RNA molecules at barcoded spots, charting the distribution and localization of cell types and transcripts across a tissue. While the coarse resolution of these techniques is considered a disadvantage, we argue that the inherent proximity of transcriptomes captured on spots can be leveraged to reconstruct cellular networks. To this end, we developed ISCHIA (Identifying Spatial Co-occurrence in Healthy and InflAmed tissues), a computational framework to analyze the spatial co-occurrence of cell types and transcript species within spots. Co-occurrence analysis is complementary to differential gene expression, as it does not depend on the abundance of a given cell type or on the transcript expression levels, but rather on their spatial association in the tissue. We applied ISCHIA to analyze co-occurrence of cell types, ligands and receptors in a Visium dataset of human ulcerative colitis patients, and validated our findings at single-cell resolution on matched hybridization-based data. We uncover inflammation-induced cellular networks involving M cell and fibroblasts, as well as ligand-receptor interactions enriched in the inflamed human colon, and their associated gene signatures. Our results highlight the hypothesis-generating power and broad applicability of co-occurrence analysis on spatial transcriptomics data.