ArticlebioRxiv : the preprint server for biology2026
MatriSpace: Identification and visualization of spatially resolved ECM gene expression patterns in health and disease.
Article in bioRxiv : the preprint server for biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
The extracellular matrix (ECM) is a highly dynamic network of proteins forming the structural organizer of all tissues. Different cell populations contribute to the assembly of the 150+ proteins of a functional ECM. In addition, different ECM subtypes, supporting distinct cellular functions, are found in every organ. Spatial transcriptomics (ST) provides a unique, yet untapped, opportunity to identify which cell populations contribute to ECM production with spatial context. Applied to healthy and diseased samples, this method can identify ECM changes that could be exploited for therapeutic purposes. Here, we introduce MatriSpace, a computational framework to mine ST datasets with a focus on ECM genes. MatriSpace offers two operating modes: researchers can either upload their own ST datasets or explore a large collection of public datasets. Upon analysis, MatriSpace returns spatially resolved maps of matrisome gene expression in relation to cell populations, at multiple levels: from single-gene analysis to tissue niches and functional ECM units. MatriSpace is available as an R package and an online Shiny App (https://matrinet.shinyapps.io/matrispace), making it accessible to all users regardless of their level of expertise.
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