Abstract
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. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000661984Publication status
publishedExternal links
Journal / series
Molecular Systems BiologyVolume
Pages / Article No.
Publisher
EMBO PressSubject
Spatial Transcriptomics; Co-occurrence Analysis; Cellular Networks; Ligand–Receptor Interaction; Ulcerative ColitisFunding
181249 - Functional consequences of single cell heterogeneity in cancer pathophysiology (SNF)
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Is new version of: https://doi.org/10.3929/ethz-b-000651655
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