Deciphering Metabolic Alterations Associated with Glioma Grading Using Hyperspectral Stimulated Raman Scattering Imaging

Metabolic dysregulation is a critical feature of various cancers, including brain tumors. Studying metabolic changes in tumor cells and tissues significantly improves our understanding of tumor development, progression, and treatment response. In this study, we utilize hyperspectral stimulated Raman scattering (SRS) imaging combined with biochemical spectral modeling to identify unique histological and molecular signatures linked to metabolic diversity across different glioma grades, without the need for labeling. By employing rapid label-free SRS histopathology and multivariate curve resolution analysis, we uncover changes in lipid profiles and varying levels of neuron demyelination from low-grade (LG) to high-grade (HG) gliomas. Quantitative analysis of key metabolites using non-negative least-squares regression spectral modeling reveals a significant increase in cellular proteins, DNA, and cholesterol levels, alongside a reduced redox ratio (flavin adenine dinucleotide (FAD)/NADH) in the glioblastoma (GBM, grade IV) tissue compared to pilocytic astrocytoma (PA, grade I) and healthy brain tissues, indicating a shift toward a pro-malignant metabolic state. A neural network diagnostic classifier, trained on 4547 SRS spectra (healthy: 1263; LG: 815; HG: 2469) from 45 patients with PA and GBM, achieves 99.6% accuracy in detecting and grading brain tumors. This study highlights the potential of hyperspectral SRS imaging for rapid, label-free, and spatially resolved analysis of metabolic heterogeneity in human gliomas, paving the way for metabolome-targeted therapeutic strategies in precision brain tumor treatment.