Inferring Metabolic Flux from Gene Expression Data Using METAFlux

Metabolic dysregulation is a hallmark of malignant cells, which contributes significantly to tumor proliferation, persistence, and therapeutic resistance. Further, metabolic interplay between malignant cells and cells in the tumor microenvironment (TME) has a significant impact on tumor phenotype. Examining the reconfiguration of metabolic pathways within tumors and TME is therefore critical to understand cancer biology and improve patient care. Current limitations of metabolomic techniques, however, restrict broad and deep characterization of tumor metabolome. To address this gap, we developed METAFlux (METAbolic Flux balance analysis), a computational technique that uses flux balance analysis (FBA) to infer activity or flux of metabolic reactions from bulk and single-cell RNA sequencing data (scRNA-seq). Here, we describe the workflow along with a detailed step-by-step explanation for calculating metabolic fluxes using METAFlux from bulk RNA-seq and scRNA-seq data and the extension to characterize metabolic heterogeneity and metabolic interaction among cell types.