Characterizing cancer metabolism from bulk and single-cell RNA-seq data using METAFlux

Area of Science:

• * Computational Biology
• * Cancer Research
• * Systems Biology

Background:

• * Cellular metabolism is crucial for survival and growth, especially under nutrient deprivation.
• * Understanding metabolic reprogramming in the tumor microenvironment (TME) is vital for cancer research and patient care.
• * Current technologies have limitations in measuring metabolites in situ and comprehensively.

Purpose of the Study:

• * To develop and validate a computational framework, METAFlux, for inferring metabolic fluxes from transcriptomic data.
• * To assess the reliability of computational methods for metabolic flux estimation, particularly in the TME.
• * To enable in situ characterization of metabolic heterogeneity and cell-cell interactions within the TME.

Main Methods:

• * Development of METAFlux, a computational framework utilizing flux balance analysis (FBA).
• * Application of METAFlux to bulk and single-cell RNA sequencing (scRNA-seq) data.
• * Large-scale validation using cell-lines, The Cancer Genome Atlas (TCGA), and diverse cancer/immunotherapy datasets (e.g., CAR-NK cell therapy).

Main Results:

• * METAFlux successfully infers metabolic fluxes from both bulk and scRNA-seq data.
• * Validation across multiple datasets confirms METAFlux's capability in characterizing metabolic heterogeneity.
• * Demonstrated ability to identify metabolic interactions among different cell types within the TME.

Conclusions:

• * METAFlux provides a reliable computational approach to analyze metabolic reprogramming in cancer.
• * The framework facilitates a deeper understanding of metabolic heterogeneity and interactions in the TME.
• * METAFlux has significant potential for advancing cancer research and informing patient care strategies.