Article Dans Une Revue Molecular and Cellular Proteomics Année : 2024

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

Résumé

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 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.
Fichier principal
Vignette du fichier
PIIS1535947624001816.pdf (6) Télécharger le fichier
Origine Fichiers éditeurs autorisés sur une archive ouverte

Dates et versions

hal-04918138 , version 1 (29-01-2025)

Licence

Identifiants

Citer

Laurine Lagache, Yanis Zirem, Emilie Le Rhun, Isabelle Fournier, Michel Salzet. Heterogeneity Assessment and Protein Pathway Prediction via Spatial Lipidomic and Proteomic Correlation: Advancing Dry Proteomics concept for Human Glioblastoma Prognosis.. Molecular and Cellular Proteomics, 2024, Molecular and Cellular Proteomics, 24 (1), pp.100891. ⟨10.1016/j.mcpro.2024.100891⟩. ⟨hal-04918138⟩

Collections

UNIV-LILLE
0 Consultations
0 Téléchargements

Altmetric

Partager

More