Heterogeneity Assessment and Protein Pathway Prediction via Spatial Lipidomic and Proteomic Correlation: Advancing Dry Proteomics concept for Human Glioblastoma Prognosis.

Autor:	Lagache L; Univ.Lille, Inserm, CHU Lille, U1192 - Proteomics Inflammatory Response Mass Spectrometry- PRISM, F-59000 Lille, France., Zirem Y; Univ.Lille, Inserm, CHU Lille, U1192 - Proteomics Inflammatory Response Mass Spectrometry- PRISM, F-59000 Lille, France., Le Rhun É; Univ.Lille, Inserm, CHU Lille, U1192 - Proteomics Inflammatory Response Mass Spectrometry- PRISM, F-59000 Lille, France; Department of Neurosurgery and Neurology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland., Fournier I; Univ.Lille, Inserm, CHU Lille, U1192 - Proteomics Inflammatory Response Mass Spectrometry- PRISM, F-59000 Lille, France; Institut Universitaire de France, 75000 Paris. Electronic address: isabelle.fournier@univ-lille.fr., Salzet M; Univ.Lille, Inserm, CHU Lille, U1192 - Proteomics Inflammatory Response Mass Spectrometry- PRISM, F-59000 Lille, France; Institut Universitaire de France, 75000 Paris. Electronic address: michel.salzet@univ-lille.fr.
Jazyk:	angličtina
Zdroj:	Molecular & cellular proteomics : MCP [Mol Cell Proteomics] 2024 Dec 05, pp. 100891. Date of Electronic Publication: 2024 Dec 05.
DOI:	10.1016/j.mcpro.2024.100891
Abstrakt:	Prediction of proteins and associated biological pathways from lipid analyses via 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 in any tissue based on unique lipid signatures and predicting associated protein pathways. Application to rat brain slices revealed diverse tissue subpopulations, including successfully predicted cerebellum areas. Similarly, the methodology was applied to a dataset from a cohort of 50 glioblastoma patients, reused from a previous study. However, among the 50 patients, only 13 lipid signatures from MALDI MSI data were available, allowing for the identification of lipid-protein associations that correlated with patient prognosis. For cases lacking lipid imaging data, a classification model based on protein data was developed from dry proteomic results to effectively categorize the remaining cohort. Competing Interests: CONFLICT OF INTEREST The authors declare no competing interests. (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)
Databáze:	MEDLINE
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