The metabolic heterogeneity and metabolic interplay between cells are known as significant contributors to disease treatment resistance. However, with the lack of a mature high-throughput single-cell metabolomics technology, we are yet to establish systematic understanding of the intra-tissue metabolic heterogeneity and cooperative mechanisms. To mitigate this knowledge gap, we developed a novel computational method, namely, single-cell flux estimation analysis (scFEA), to infer the cell-wise fluxome from single-cell RNA-sequencing (scRNA-seq) data. scFEA is empowered by a systematically reconstructed human metabolic map as a factor graph, a novel probabilistic model to leverage the flux balance constraints on scRNA-seq data, and a novel graph neural network–based optimization solver. The intricate information cascade from transcriptome to metabolome was captured using multilayer neural networks to capitulate the nonlinear dependency between enzymatic gene expressions and reaction rates. We experimentally validated scFEA by generating an scRNA-seq data set with matched metabolomics data on cells of perturbed oxygen and genetic conditions. Application of scFEA on this data set showed the consistency between predicted flux and the observed variation of metabolite abundance in the matched metabolomics data. We also applied scFEA on five publicly available scRNA-seq and spatial transcriptomics data sets and identified context- and cell group–specific metabolic variations. The cell-wise fluxome predicted by scFEA empowers a series of downstream analyses including identification of metabolic modules or cell groups that share common metabolic variations, sensitivity evaluation of enzymes with regards to their impact on the whole metabolic flux, and inference of cell–tissue and cell–cell metabolic communications.

中文翻译：

---

使用单细胞 RNA-seq 数据估计细胞代谢通量的图神经网络模型


细胞之间的代谢异质性和代谢相互作用被认为是疾病治疗抵抗的重要因素。然而，由于缺乏成熟的高通量单细胞代谢组学技术，我们尚未对组织内代谢异质性和协同机制建立系统的认识。为了缩小这一知识差距，我们开发了一种新的计算方法，即单细胞通量估计分析 (scFEA)，以从单细胞 RNA 测序 (scRNA-seq) 数据推断细胞通量组。 scFEA 由系统重建的人类代谢图作为因子图、利用 scRNA-seq 数据的通量平衡约束的新颖概率模型以及基于新颖的图神经网络的优化求解器提供支持。使用多层神经网络捕获从转录组到代谢组的复杂信息级联，以克服酶基因表达和反应速率之间的非线性依赖性。我们通过生成 scRNA-seq 数据集来实验验证 scFEA，该数据集与受扰动的氧和遗传条件的细胞的代谢组学数据相匹配。 scFEA 在此数据集上的应用显示了匹配代谢组数据中预测通量与观察到的代谢物丰度变化之间的一致性。我们还将 scFEA 应用于五个公开的 scRNA-seq 和空间转录组学数据集，并确定了特定背景和细胞组的代谢变化。 scFEA 预测的细胞通量组支持一系列下游分析，包括识别具有共同代谢变化的代谢模块或细胞群、酶对整个代谢通量影响的敏感性评估以及细胞-组织和细胞间相互作用的推断。细胞间的代谢通讯。