AbstractThe extracellular matrix (ECM) serves critical structural and functional purposes within tissues, but its spatial arrangement and composition across functional tissue units (FTUs) have not been fully elucidated. Here, we present a streamlined workflow for spatial matrisomics that enables confident identification of ECM peptides directly from tissue sections. By processing adjacent formalin-fixed, paraffin-embedded (FFPE) tissue sections in parallel, we generate MALDI-MSI-ready samples and matched LC-MS/MS data sets using a unified collagenase-based digestion strategy. This parallelized approach reduces sample handling and supports construction of a robust ECM peptide reference library. We further introduce an automated, open-source mass-matching pipeline that integrates LC-MS/MS peptide identifications with MALDI-MSI features to facilitate high-confidence annotation. Applying this workflow to human kidney tissues and pairing MSI data with AI-driven digital pathology, we resolve FTU-specific ECM profiles, including glomerular enrichment of Tenascin-C and Albumin, and tubular enrichment of COL1A2 and COL3A1 peptides. Across samples, we observe both conserved and variable ECM patterns, emphasizing the importance of spatially resolved analyses for understanding tissue ECM heterogeneity. Altogether, this approach advances spatial matrisomics by increasing throughput, reducing manual data curation, and enhancing confidence in ECM peptide identifications, which can enable future applications in studying fibrosis and kidney disease, for example.

The extracellular matrix (ECM) is a complex assembly of proteins surrounding cells. It is a critical component of the tumor microenvironment that plays an active role in tumor progression and modulati...