Related Experiment Video
Updated: Sep 12, 2025
10:19
Measurement of Energy Metabolism in Explanted Retinal Tissue Using Extracellular Flux Analysis
Published on: January 7, 2019
9.6K
Inferring Metabolic Flux from Gene Expression Data Using METAFlux.
Yuchen Pan1,2, Yuefan Huang1,2, Vakul Mohanty3
1Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Methods in Molecular Biology (Clifton, N.J.)
|August 8, 2025
Summary
We developed METAFlux, a computational method to analyze metabolic pathways in tumors using RNA sequencing data. This tool helps understand cancer metabolism and interactions within the tumor microenvironment for better patient care.
Area of Science:
- Oncology
- Computational Biology
- Metabolomics
Background:
- Metabolic dysregulation is crucial in cancer, affecting tumor growth, survival, and treatment response.
- Interactions between cancer cells and the tumor microenvironment (TME) significantly influence tumor characteristics.
- Current metabolomic methods have limitations in comprehensively analyzing tumor metabolism.
Purpose of the Study:
- To introduce METAFlux, a novel computational technique for analyzing metabolic fluxes.
- To provide a detailed workflow for calculating metabolic fluxes from RNA sequencing data.
- To enable characterization of metabolic heterogeneity and cell-cell metabolic interactions within tumors.
Main Methods:
- Developed METAFlux, a computational technique utilizing flux balance analysis (FBA).
- Applied FBA to infer metabolic reaction activity from bulk and single-cell RNA sequencing (scRNA-seq) data.
- Established a workflow for calculating metabolic fluxes and analyzing metabolic interactions.
Main Results:
- Demonstrated the capability of METAFlux to infer metabolic fluxes from RNA-seq data.
- Showcased the application of METAFlux for characterizing metabolic heterogeneity.
- Enabled the analysis of metabolic interplay between different cell types in the TME.
Conclusions:
- METAFlux offers a powerful computational approach to overcome limitations in traditional metabolomic studies.
- Understanding tumor and TME metabolic reprogramming is vital for advancing cancer biology and patient treatment.
- This technique facilitates deeper insights into cancer metabolism and intercellular metabolic communication.
