2026-10-09T19:05:18Z https://api.figshare.com/v2/oai

oai:figshare.com:article/34059605 2026-10-02T21:07:25Z category_1 category_4 category_7 category_16 category_21 category_272 category_873 category_45 category_734 category_931 category_61 category_69 portal_63 item_type_3 month_year_10_2026

A Streamlined Approach for Spatial Matrisomics with High-Confidence Annotation of Extracellular Protein Alterations in Functional Tissue Units Brittney L. Gorman (13942566) Daniel J. Orton (789988) James M. Fulcher (11538264) Heidi Vandyk (25303649) Sarah M. Williams (8630691) Geremy Clair (5869625) Kumar Sharma (57347) Christopher R. Anderton (575201) Biophysics Biochemistry Medicine Physiology Biotechnology Environmental Sciences not elsewhere classified Chemical Sciences not elsewhere classified Sociology Biological Sciences not elsewhere classified Information Systems not elsewhere classified Developmental Biology Inorganic Chemistry spatially resolved analyses serves critical structural processing adjacent formalin enables confident identification enable future applications driven digital pathology based digestion strategy variable ecm patterns specific ecm profiles ecm peptide identifications including glomerular enrichment human kidney tissues extracellular protein alterations functional tissue units ecm peptides directly pairing msi data ms peptide identifications tubular enrichment kidney disease extracellular matrix col3a1 peptides tissue sections unified collagenase supports construction studying fibrosis streamlined approach spatial matrisomics spatial arrangement source mass resolve ftu ready samples msi features matching pipeline matched lc integrates lc increasing throughput fully elucidated enhancing confidence confidence annotation across samples The 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. 2026-10-02T00:00:00Z Dataset Dataset 10.1021/jasms.6c00180.s002 https://figshare.com/articles/dataset/A_Streamlined_Approach_for_Spatial_Matrisomics_with_High-Confidence_Annotation_of_Extracellular_Protein_Alterations_in_Functional_Tissue_Units/34059605 CC BY-NC 4.0