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Updated: Jan 17, 2026

Advancements in the Metabolic Profiling of Three-Dimensional Brain Tumor Spheroids for Drug Screening
Published on: September 5, 2025
Advancements in the Metabolic Profiling of Three-Dimensional Brain Tumor Spheroids for Drug Screening
Lijing Yang1, Xiaojuan Ma1, Decao Yang1
1Institute of Medical Innovation and Research, Peking University Third Hospital; Cancer Center of Peking University Third Hospital.
Abstract:
Brain tumors, especially gliomas, are challenging to treat because of their aggressive nature, complex tumor microenvironment, and resistance to conventional therapies. Traditional two-dimensional (2D) cell cultures often fail to replicate the true tumor environment, leading to inaccurate predictions of drug efficacy. Extracellular flux analysis technology, typically used for real-time metabolic analysis in 2D cultures, measures key metabolic parameters, such as the extracellular acidification rate (ECAR) and oxygen consumption rate (OCR), providing insights into cellular metabolism. The use of 3D models represents a significant advancement, as they more accurately mimic the in vivo tumor environment. The extracellular flux analyzer was adapted to three-dimensional (3D) glioma cell models, enabling the analysis of critical metabolic pathways, including glycolysis and oxidative phosphorylation, in a more physiologically relevant context. U87 cells were seeded at appropriate densities in a 96-well low-attachment plate and cultured for 5 days. On day 5, 3D spheroid formation was observed via high-content imaging. The successfully formed spheroids were then transferred to a metabolic assay plate coated with poly-L-lysine for metabolic analysis. To improve the accuracy of these measurements, high-content imaging systems assess 3D cell size, allowing for precise normalization of extracellular flux data and minimizing metabolic variations due to differences in cell size. This integrated approach provides a more reliable analysis of glioma cell metabolic responses to drug treatments, revealing potential mechanisms of drug resistance. Ultimately, this methodology offers valuable insights into the metabolic dynamics of gliomas and supports the development of novel, clinically relevant therapeutic strategies.
Insights
This study adapted extracellular flux analysis for 3D glioma models, improving drug efficacy prediction. This new method offers insights into brain tumor metabolism and aids in developing new therapies.
Area of Science:
- Oncology
- Cell Biology
- Biochemistry
Background:
- Brain tumors like gliomas are difficult to treat due to their aggressive nature and resistance to therapies.
- Traditional 2D cell cultures do not accurately mimic the in vivo tumor microenvironment, leading to unreliable drug efficacy predictions.
- Extracellular flux analysis measures cellular metabolism (ECAR, OCR) but is typically limited to 2D cultures.
Purpose of the Study:
- To adapt extracellular flux analysis for 3D glioma models to better mimic the in vivo tumor environment.
- To enable the analysis of critical metabolic pathways in a more physiologically relevant context.
- To improve the accuracy of drug response predictions and understand glioma drug resistance mechanisms.
Main Methods:
- U87 glioma cells were cultured in 3D spheroid models for 5 days.
- Extracellular flux analysis was performed on 3D spheroids using a specialized metabolic assay plate.
- High-content imaging was used to assess spheroid size for accurate data normalization.
Main Results:
- The adapted method allowed for real-time metabolic analysis of 3D glioma spheroids.
- Key metabolic pathways like glycolysis and oxidative phosphorylation were analyzed in a physiologically relevant context.
- The approach enabled reliable assessment of glioma cell metabolic responses to drug treatments, revealing potential resistance mechanisms.
Conclusions:
- Adapting extracellular flux analysis to 3D glioma models provides a more accurate platform for studying tumor metabolism.
- This integrated approach enhances the understanding of glioma cell metabolic dynamics and drug resistance.
- The methodology supports the development of more effective and clinically relevant therapeutic strategies for brain tumors.

