Spatial multi-omics enables single-cell transcriptome–metabolome inference

Abstract Joint single-cell transcriptomic–metabolomic profiling remains technically intractable. Here we present CHIMERA ( C ell-level H ybrid I nference of M etabolome E mbedded on R NA A tlas), a data-driven framework that learns transcriptome-to-metabolome mappings from spatially paired multi-omics data and transfers them to unpaired scRNA-seq. 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 scRNA-seq 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.