Genetically informed single-cell and spatial mapping of metabolic programs in human health and disease
Abstract
Defining cell-type-specific endogenous metabolic features, the spatial distribution of cell-level metabolic states and cellular responses to exogenous metabolites is very important for understanding disease mechanisms. However, existing transcriptome-based metabolic models primarily infer intracellular reaction or pathway-level activities, and therefore cannot directly assess associations between individual metabolite levels and cellular states, particularly for metabolites that act extracellularly as signalling molecules rather than entering cells as metabolic substrates. To overcome this problem, we introduce the gmMAP (Genetically informed metabolite trait mapping across single-cell and spatial tissues), a framework that integrates metabolite GWAS summary statistics with single-cell and spatial transcriptomes to map metabolic programmes at cellular and spatial resolution. Notably, the gmMAP enables the prediction of endogenous metabolic process activation while also revealing intrinsic associations between exogenous metabolites and diverse cellular functional states. To further capture the connectivity of cellular metabolic networks, we incorporated a constraint-based metabolic flux model to evaluate global metabolic activity. To evaluate the accuracy and generalizability of gmMAP, we applied the framework across representative biological contexts spanning human development, physiological homeostasis, inflammation and cancer. In human kidney development, the gmMAP captured dynamic metabolic programmes, which was validated using paired transcriptomic and metabolomic reference datasets, supporting its reliability in metabolite identification and metabolic-flow inference. At the organ level, the gmMAP reconstructed spatial metabolite distribution patterns across 17 mouse organs under homeostatic and autoimmune inflammatory conditions, and further extension of gmMAP to 24 normal human tissues generated a multi-scale metabolic atlas at both organ and cellular resolutions. In disease settings, gmMAP revealed metabolic reprogramming across 29 pan-cancer cell populations, and identified potential links between exogenous metabolites and inflammation-associated stromal metabolic remodelling in ulcerative colitis. Together, gmMAP can consistently connect genetically informed metabolite traits with cell states, spatial tissue organization and disease pathology.
Full-text reasoning
From the deep-tier full-text analysis of this preprint.
Reasoning review — 8 key claims, 2 well-supported, 6 with gaps
- supported gmMAP integrates metabolite GWAS-derived genetic signatures with single-cell and spatial transcriptomics for metabolic inference.
- unsupported gmMAP reduces the impact of single-cell transcriptomic sparsity and technical noise on metabolite-trait inference. gap: No evidence is presented to demonstrate that gmMAP actually reduces sparsity/noise impact compared to other methods or a baseline.
- partial gmMAP enables systematic inference of diverse metabolite-associated features at cellular resolution, extending beyond canonical intracellular networks. gap: The claim of "systematic inference" and "extending beyond canonical networks" is broad, and the excerpt lacks specific data or examples to fully demonstrate this capability across all mentioned metabolite types or the extent of the extensi…
- supported gmMAP incorporates multi-dimensional biological priors to derive pathway-level metabolic-flow activities across cells, tissues, and developmental trajectories.
- partial gmMAP achieved high-specificity prediction of spatially resolved metabolite distribution patterns and reconstructed whole-organ spatial metabolic features across 17 mouse organs. gap: The claim of "high-specificity prediction" is made without presenting the actual evidence or metrics that define this specificity.
- partial gmMAP's accuracy in metabolite identification and relative metabolic-flow inference was validated using paired transcriptomic and metabolomic reference datasets in renal development. gap: While validation is claimed, the specific results, metrics, or methodology of this validation are not detailed in the excerpt, making it impossible to assess the extent of "accuracy."
- partial gmMAP revealed extensive metabolic rewiring across 29 pan-cancer cell populations and decoded inflammation-associated stromal metabolic remodelling in ulcerative colitis. gap: These are presented as findings, but the excerpt does not provide the underlying data, analysis, or specific results to support the claims of "extensive rewiring" or "decoding remodelling."
- unsupported gmMAP provides a versatile framework for connecting metabolic programmes with tissue physiology, disease pathology, and therapeutic intervention. gap: This is a very broad claim of versatility and impact, for which the excerpt provides no direct, comprehensive evidence or demonstration across all listed applications.
Claims and gaps are read from the full text by a language model, shown for transparency; they do not affect ranking or selection.
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#SingleCell preprints @prepub-singlecell.bsky.social · 721 followers neutral
Genetically informed single-cell and spatial mapping of metabolic programs in human health and disease #SingleCell 🧪🧬🖥️ https://www.biorxiv.org/content/10.64898/2026.06.25.734643v1
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#SingleCell preprints @prepub-singlecell.bsky.social · 721 followers neutral
Genetically informed single-cell and spatial mapping of metabolic programs in human health and disease #SingleCell 🧪🧬🖥️ https://www.biorxiv.org/content/10.64898/2026.06.25.734643v1
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bioRxiv Genomics @biorxiv-genomic.bsky.social · 6974 followers neutral
Genetically informed single-cell and spatial mapping of metabolic programs in human health and disease https://www.biorxiv.org/content/10.64898/2026.06.25.734643v1
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bioRxivpreprint @biorxivpreprint.bsky.social · 8895 followers neutral
Genetically informed single-cell and spatial mapping of metabolic programs in human health and disease https://www.biorxiv.org/content/10.64898/2026.06.25.734643v1