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

Yuefan Huang, Vakul Mohanty, Merve Dede, Kyle Tsai, May Daher, Li Li, Katayoun Rezvani and Ken Chen ()
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Yuefan Huang: The University of Texas MD Anderson Cancer Center
Vakul Mohanty: The University of Texas MD Anderson Cancer Center
Merve Dede: The University of Texas MD Anderson Cancer Center
Kyle Tsai: The University of Texas MD Anderson Cancer Center
May Daher: The University of Texas MD Anderson Cancer Center
Li Li: The University of Texas MD Anderson Cancer Center
Katayoun Rezvani: The University of Texas MD Anderson Cancer Center
Ken Chen: The University of Texas MD Anderson Cancer Center

Nature Communications, 2023, vol. 14, issue 1, 1-16

Abstract: Abstract Cells often alter metabolic strategies under nutrient-deprived conditions to support their survival and growth. Characterizing metabolic reprogramming in the tumor microenvironment (TME) is of emerging importance in cancer research and patient care. However, recent technologies only measure a subset of metabolites and cannot provide in situ measurements. Computational methods such as flux balance analysis (FBA) have been developed to estimate metabolic flux from bulk RNA-seq data and can potentially be extended to single-cell RNA-seq (scRNA-seq) data. However, it is unclear how reliable current methods are, particularly in TME characterization. Here, we present a computational framework METAFlux (METAbolic Flux balance analysis) to infer metabolic fluxes from bulk or single-cell transcriptomic data. Large-scale experiments using cell-lines, the cancer genome atlas (TCGA), and scRNA-seq data obtained from diverse cancer and immunotherapeutic contexts, including CAR-NK cell therapy, have validated METAFlux’s capability to characterize metabolic heterogeneity and metabolic interaction amongst cell types.

Date: 2023
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DOI: 10.1038/s41467-023-40457-w

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