Spatial multi omics enables single cell transcriptome metabolome inference

Abstract

Joint single cell transcriptomic metabolomic profiling remains technically intractable. Here we present CHIMERA (Cell-level Hybrid Inference of Metabolome Embedded on RNA Atlas), a data-driven framework that learns transcriptome to metabolome mappings from spatially paired multi omics data and transfers them to unpaired scRNAseq. CHIMERA generates quantitative, database independent single cell metabolite abundances and, by pairing them with the measured transcriptome of the same cells, enables joint co embedding of genes and metabolites for the discovery of differential metabolites and co regulated gene metabolite modules. Using 10x Visium paired with MALDI MSI from murine liver sections and a matched scRNAseq reference, CHIMERA achieves a per-metabolite median Pearson r = 0.285 with positive cross-section generalization. On an independent Liver Cell Atlas Western diet cohort, CHIMERA recovers metabolic reprogramming that recapitulate published non-alcoholic fatty liver disease pathophysiology. Applied to a Rarres2 (chemerin) knock down hepatocellular carcinoma model, CHIMERA uncovers metabolic heterogeneity among tumour associated macrophages, resolving four metabolic subclusters (MC-0 to MC-3); Rarres2 appears to drive macrophage polarization from an LAM-like MC-3 state toward Spp1+ like MC-0/MC-2 by modulating a co-regulated gene metabolite module a dual omics phenotype undetectable by either modality alone. CHIMERA is the first data-driven framework for quantitative single cell metabolome inference, opening joint transcriptomic metabolomic analyses inaccessible to either experimental or knowledge based computational approaches.