Pan Yuchen, Huang Yuefan, Mohanty Vakul, Chen Ken
Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Department of Biostatistics & Data Science, School of Public Health, The University of Texas Health Science Center at Houston (UTHealth), Houston, TX, USA.
Methods Mol Biol. 2025;2932:187-202. doi: 10.1007/978-1-0716-4566-6_10.
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.
代谢失调是恶性细胞的一个标志，它对肿瘤的增殖、持续存在和治疗抗性有显著贡献。此外，恶性细胞与肿瘤微环境（TME）中的细胞之间的代谢相互作用对肿瘤表型有重大影响。因此，研究肿瘤和TME内代谢途径的重新配置对于理解癌症生物学和改善患者护理至关重要。然而，代谢组学技术目前的局限性限制了对肿瘤代谢组的广泛而深入的表征。为了填补这一空白，我们开发了METAFlux（代谢通量平衡分析），这是一种计算技术，它使用通量平衡分析（FBA）从批量和单细胞RNA测序数据（scRNA-seq）推断代谢反应的活性或通量。在这里，我们描述了工作流程，并对使用METAFlux从批量RNA-seq和scRNA-seq数据计算代谢通量以及扩展以表征细胞类型之间的代谢异质性和代谢相互作用进行了详细的逐步解释。
