De Temmerman, M.; Vandemoortele, B.; Vermeirssen, V.

Metabolic reprogramming is a hallmark of glioblastoma, yet how distinct malignant and tumor microenvironment cell populations contribute to this metabolic heterogeneity remains poorly defined. Since direct single-cell metabolomics remains technically limited, transcriptomics-based computational inference offers a powerful alternative. Here we apply and systematically compare three complementary computational methods: (1) metabolic pathway activity scoring, (2) gene regulatory network inference focused on metabolic enzyme gene regulation, and (3) single-cell metabolic flux prediction. These methods were applied to snRNA-seq data from a set of GBM patient samples using the Human1 genome-scale metabolic model as a unified reaction and pathway annotation prior knowledge reference. Across all three methods, tumor-associated macrophages emerge as the metabolically dominant tumor microenvironment population, with prominent enrichment in lipid and fatty acid metabolism pathways. Methodologically, these three approaches provide complementary information. Pathway-level scoring effectively captured overall metabolic activity patterns and identified cell populations of interest but lacked mechanistic insight. Regulatory network analysis added a critical layer by revealing the transcriptional architecture controlling metabolic programs. Flux-based predictions provided the highest resolution view, distinguishing cell types based on single-cell metabolic profiles and revealing specific reaction modules driving cellular phenotypes. These findings demonstrate that multi-layered metabolic inference can resolve cell-type/state-specific dependencies in glioblastoma and highlight tumor-associated macrophage metabolism as a promising therapeutic target.