biostudies-literatureUnknown24(1)Lagache LPrediction of proteins and associated biological pathways from lipid analyses via matrix-assisted laser desorption/ionization (MALDI) MSI is a pressing challenge. We introduced "dry proteomics," using MALDI MSI to validate spatial localization of identified optimal clusters in lipid imaging. Consistent cluster appearance across omics images suggests association with specific lipid and protein in distinct biological pathways, forming the basis of dry proteomics. The methodology was refined using rat brain tissue as a model, then applied to human glioblastoma, a highly heterogeneous cancer. Sequential tissue sections underwent omics MALDI MSI and unsupervised clustering. Spatial omics analysis facilitated lipid and protein characterization, leading to a predictive model identifying clusters Molecular & cellular proteomics : MCP100891https://www.ebi.ac.uk/biostudies/studies/S-EPMC11773152biostudies-literaturePredicting Protein Pathways Associated to Tumor Heterogeneity by Correlating Spatial Lipidomics and Proteomics: The Dry Proteomic Concept.PMC11773152Lagache LZirem YLe Rhun ESalzet MFournier IfalsePredicting Protein Pathways Associated to Tumor Heterogeneity by Correlating Spatial Lipidomics and Proteomics: The Dry Proteomic Concept.Prediction of proteins and associated biological pathways from lipid analyses via matrix-assisted laser desorption/ionization (MALDI) MSI is a pressing challenge. We introduced "dry proteomics," using MALDI MSI to validate spatial localization of identified optimal clusters in lipid imaging. Consistent cluster appearance across omics images suggests association with specific lipid and protein in distinct biological pathways, forming the basis of dry proteomics. The methodology was refined using rat brain tissue as a model, then applied to human glioblastoma, a highly heterogeneous cancer. Sequential tissue sections underwent omics MALDI MSI and unsupervised clustering. Spatial omics analysis facilitated lipid and protein characterization, leading to a predictive model identifying clusters 2025-01-01T00:00:00Z2025 Jan2026-05-26T20:02:19.376Z2025-04-03T23:26:48.193ZS-EPMC117731523964492410.1016/j.mcpro.2024.100891