Updated project metadata. Gliomas represent 80% of all malignant cerebral tumors and are classified within different malignity grade. Glioblastoma, the most aggressive group, represent more than half of all gliomas but remain a heterogeneous group. Indeed, patient survival can reach from several months to a few years after surgery and chemotherapy. To study gliomas, MALDI mass spectrometry imaging is an interesting technique, allowing the analysis of tumor heterogeneity. In this study, MALDI-MSI is coupled with spatially-resolved microproteomic within the objective to identify subgroups of glioblastomas patients in order to help diagnosis and prognostic. Molecular images are generated from thin tissue section in order to determine the spatial localization of digested peptides. Thanks to unsupervised statistical analysis, we then generated hierarchical clustering of homogeneous molecular regions. According to these regions, spatially resolved proteomic gave access to large scale protein identification and relative quantification. Hierarchical clustering reveal three molecular region within all the tumors sample : region red which represent 33% of surface of all sample, region yellow (25.7%) and region blue (40.8%). Consequently, 3 groups of patient can be determined: group A composed mostly of region red (13/50 patients), group B composed mostly of region yellow (9/50 patients) and group C composed mostly of region blue (24/50 patients). Spatially resolved proteomic was performed and the 147 extractions were analyzed by nano-LC MS/MS. Two-way ANOVA test reveal panel of proteins specifically overexpressed in each region. Protein overexpressed in region red are associated with neurogenesis; protein overexpressed in region yellow are associated with an activation of the immune system and protein overexpressed in region blue are involved with viral processes. To conclude, the combination of MALDI-MSI and microproteomic provides new information on these tumors, allowing us to differentiate three group of patient. In the future, these data will permit to build a more precise classification, a better medical care for patients and the identification of potential new therapeutic targets.