细胞经常在缺乏营养条件下改变代谢策略，以支持其生存和生长。对肿瘤微环境（TME）中的代谢重编程进行表征在癌症研究和患者护理中具有新兴的重要性。然而，最近的技术只能测量代谢产物的子集，并且无法提供原位测量。计算方法，如通量平衡分析（FBA），已经发展出来，可以从批量RNA测序（RNA-seq）数据估计代谢通量，并且可能扩展到单细胞RNA测序（scRNA-seq）数据。然而，目前的方法的可靠性尤其在TME表征中还不清楚。在这里，我们提出了一个计算框架METAFlux（METAbolic Flux平衡分析），从批量或单细胞转录组数据推断代谢通量。使用细胞系、癌症基因组图谱（TCGA）和从不同癌症和免疫治疗背景中获得的CAR-NK细胞疗法的单细胞RNA测序数据等大规模实验证实了METAFlux表征细胞类型之间代谢异质性和代谢相互作用的能力。© 2023. Springer Nature Limited.

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.© 2023. Springer Nature Limited.