Team Member Thesis Defense: Bioinformatics Methods to Studying Metabolism in Glioblastoma

On December 11th 2025, the defense of Johanna Galvis’ Thesis, co-supervised by Macha Nikolski and Thomas Daubon, was held at the IBGC conference room, with the presence of the Jury members: Mohamed Elati, Fabien Jourdan, Florence Cavalli, Lucie Brisson and Nicolas Papadakis.

Glioblastoma (GB), the most aggressive form of brain cancer, remains incurable to this day. Malignant cells proliferate within a hypoxic and highly acidic tumor microenvironment, adapting through pronounced metabolic plasticity: they exploit alternative nutrient sources and infiltrate myelin-rich, lipid-dense brain structures. To characterize lactate utilization in GB, stable isotope-resolved metabolomics (SIRM) was applied to glioblastoma spheroids and complemented by transcriptomic profiling. However, dedicated bioinformatic tools for analyzing SIRM datasets—particularly those derived from multifactorial experimental designs—remain scarce. To address this gap, the first part of this thesis introduces DIMet, a computational framework for differential analysis of labeled metabolomics data, supporting isotopologue enrichment. DIMet enables statistically rigorous comparisons, and its integrative extension combining SIRM and transcriptomic data uncovers dynamic mechanisms underlying lactate fueling in GB.

In a second phase, spatial lipidomic profiling of xenografted GB brain tissues exposed major analytical challenges: high signal noise, extensive isomerism, and limited functional annotation. A key unmet need in spatial metabolomics (SM) was to extend metabolic pathway coverage and enable functional interpretation at spatial resolution. Leveraging cheminformatics resources and incorporating spatial and ion-level quality metrics, this thesis presents SpacePath—a computational and functional analysis framework for SM data. SpacePath accounts for annotation ambiguity and rescues previously unmapped metabolites, enabling the discovery of spatially organized metabolic programs. In summary, the bioinformatic methods developed in this thesis—DIMet and SpacePath—provide robust analytical frameworks for interpreting complex labeled and spatial metabolomics data, with the goal of advancing our understanding of the metabolic underpinnings of glioblastoma.

Keywords: Bioinformatics, Glioblastoma, SIRM, spatial metabolomics