Spatial mapping of the brain metabolome lipidome and glycome

Harrison A. Clarke, Xin Ma, Cameron J. Shedlock, Terrymar Medina, Tara R. Hawkinson, Lei Wu, Roberto A. Ribas, Shannon Keohane, Sakthivel Ravi, Jennifer L. Bizon, Sara N. Burke, Jose Francisco Abisambra, Matthew E. Merritt, Boone M. Prentice, Craig W. Kooi, Matthew S. Gentry, Li Chen and Ramon C. Sun ()
Additional contact information
Harrison A. Clarke: University of Florida
Xin Ma: University of Florida
Cameron J. Shedlock: University of Florida
Terrymar Medina: University of Florida
Tara R. Hawkinson: University of Florida
Lei Wu: University of Florida
Roberto A. Ribas: University of Florida
Shannon Keohane: University of Florida
Sakthivel Ravi: University of Florida
Jennifer L. Bizon: University of Florida
Sara N. Burke: University of Florida
Jose Francisco Abisambra: University of Florida
Matthew E. Merritt: University of Florida
Boone M. Prentice: University of Florida
Craig W. Kooi: University of Florida
Matthew S. Gentry: University of Florida
Li Chen: University of Florida
Ramon C. Sun: University of Florida

Nature Communications, 2025, vol. 16, issue 1, 1-13

Abstract: Abstract Metabolites, lipids, and glycans are fundamental but interconnected classes of biomolecules that form the basis of the metabolic network. These molecules are dynamically channeled through multiple pathways that govern cellular physiology and pathology. Here, we present a framework for the simultaneous spatial analysis of the metabolome, lipidome, and glycome from a single tissue section using mass spectrometry imaging. This workflow integrates a computational platform, the Spatial Augmented Multiomics Interface (Sami), which enables multiomics integration, high-dimensional clustering, spatial anatomical mapping of matched molecular features, and metabolic pathway enrichment. To demonstrate the utility of this approach, we applied Sami to evaluate metabolic diversity across distinct brain regions and to compare wild-type and Ps19 Alzheimer’s disease (AD) mouse models. Our findings reveal region-specific metabolic demands in the normal brain and highlight metabolic dysregulation in the Ps19 model, providing insights into the biochemical alterations associated with neurodegeneration.

Date: 2025
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DOI: 10.1038/s41467-025-59487-7

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