A Streamlined Approach for Spatial Matrisomics with High-Confidence Annotation of Extracellular Protein Alterations in Functional Tissue Units

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.