This study effectively distinguishes oxidative phosphorylation (OXPHOS) from glycolysis/pentose phosphate pathway and mitochondrial (GPM)-type glioblastoma using NADH-FLIM imaging technology, providing a potential tool for label-free identification of tumor metabolic states.
Literature Overview
The article "Deciphering metabolic cellular states in glioblastoma" published in Neuro-Oncology reviews and summarizes the classification of glioblastoma (GBM) metabolic states using RNA sequencing and NADH-FLIM imaging technology, as well as their applications in preclinical research. The article further explores metabolic differences between OXPHOS and GPM subtypes, establishing a theoretical foundation for future targeted therapies.
Background Knowledge
Glioblastoma (GBM) is the most common and aggressive primary brain tumor in adults, with extremely poor prognosis. Recent advances in single-cell analysis and metabolic research have revealed distinct metabolic dependencies in GBM, such as oxidative phosphorylation (OXPHOS) and glycolysis/pentose phosphate pathway with mitochondrial (GPM) subtypes. The OXPHOS subtype demonstrates sensitivity to mitochondrial inhibitors, suggesting metabolic reprogramming as a potential therapeutic breakthrough. However, non-invasive, rapid technologies for identifying GBM metabolic states remain lacking. This study employs NADH-FLIM (NADH fluorescence lifetime imaging) to quantitatively analyze differences in free and bound NADH distributions, exploring its potential as a metabolic classification tool to support precision therapy and molecular target screening.
Research Methods and Experiments
The research team conducted 3’ mRNA NGS analysis on 28 newly diagnosed IDHwt GBM patients to determine metabolic subtypes including OXPHOS and GPM. Subsequently, NADH-FLIM imaging was performed on five representative cases from each subtype, with quantitative analysis of NADH bound/free distribution curves. Ten to sixteen non-necrotic regions were analyzed per sample to exclude necrotic tissue interference. Multi-omics validation was also conducted for metabolic gene expression profiles, NADH homeostasis, mitochondrial density, and related indicators.
Key Conclusions and Perspectives
Research Significance and Prospects
This study represents the first application of NADH-FLIM imaging in FFPE tissue, establishing non-genome-dependent biomarkers for GBM metabolic subtyping. Future research should validate technical stability through larger cohorts and explore real-time monitoring capabilities in clinical settings. Integration with artificial intelligence could enable intraoperative rapid diagnosis and personalized treatment planning.
Conclusion
This study successfully identified distinct metabolic subtypes of glioblastoma through NADH-FLIM imaging, particularly highlighting NADH distribution differences between OXPHOS and GPM subtypes. The methodology offers advantages including rapid processing, label-free detection, and high-throughput capabilities without genetic sequencing requirements, providing novel insights for precision medicine and metabolic-targeted therapies. These findings suggest optical imaging techniques can identify tumor metabolic states, offering potential tools for intraoperative real-time monitoring and personalized treatment. The technology can also integrate with multi-omics data to further elucidate GBM metabolic heterogeneity, advancing precision oncology. Additionally, this approach can be extended to other tumor types, establishing a universal platform for cancer metabolism research.
