Comparison of different computational frameworks for metabolic modeling from single-cell transcriptomics data in glioblastoma

Abstract

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. Tumor-associated macrophages in mesenchymal-like tumors show coordinated transcriptional control of lipid metabolism by five recurrently active transcription factors. They also exhibit consistent nucleotide biosynthesis flux and glutamate-to-glutamine conversion potentially supporting malignant cells. 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