Extracting interpretable single-cell metabolic states with graph-guided representation learning

Metabolism shapes cellular function and state, yet measuring single-cell metabolic states at scale remains a challenge. We present Metabolic Representation Net (MeRN), a graph-guided variational autoencoder that leverages prior metabolic knowledge as a topology graph to learn latent representations of metabolic state and reaction activity from single-cell transcriptomes. MeRN's scalable estimation of reaction activity enables the definition of data-driven pathways (DDPs): context-specific metabolic modules supported by transcriptomic evidence and agnostic of standard pathway definitions. Using DDPs, we introduce the weakest link analysis to identify metabolic network rewiring. MeRN recovers metabolic zonation in the mouse intestine, links a folate deficiency-induced break in de novo purine synthesis to embryonic neural tube defects, shows cytokines with similar non-metabolic effects can elicit divergent T cell metabolism, and identifies metabolic drivers of T cell exhaustion and therapy response in human cancers. Our results establish MeRN as a unified method for metabolic analysis of single-cell transcriptomes.

MeRN leverages the metabolic topology to comprehensively predict reaction- and pathway-level metabolic activitiesMeRN enables data-driven pathways (DDPs) that capture empirically supported cell-type-specific metabolic modules, agnostic of standard pathway definitions MeRN-based DDPs identify metabolic network rewiring of de novo purine synthesis due to folate deficiency during embryonic neural tube development MeRN identifies human pan-cancer metabolic drivers of T cell exhaustion and Treg-specific metabolic adaptations.