bioRxivBiochemistryPreprint
Characterization of metabolic compartmentalization in the liver using spatially resolved metabolomics
J. van der Reest, S. A. Stopka, W. M. Abdelmoula, D. F. Ruiz, S. Joshi, A. Ringel, M. C. Haigis, N. Y. R. Agar
Abstract
Cells adapt their metabolism to physiological stimuli, and metabolic heterogeneity exists between cell types, within tissues, and subcellular compartments. The liver plays an essential role in maintaining whole-body metabolic homeostasis and is structurally defined by metabolic zones. These zones are well-understood on the transcriptomic level, but have not been comprehensively characterized on the metabolomic level. Mass spectrometry imaging (MSI) can be used to map hundreds of metabolites directly from a tissue section, offering an important advance to investigate metabolic heterogeneity in tissues compared to extraction-based metabolomics methods that analyze tissue metabolite profiles in bulk. We established a workflow for the preparation of tissue specimens for matrix-assisted laser desorption/ionization (MALDI) MSI and achieved broad coverage of central carbon, nucleotide, and lipid metabolism pathways. We used this approach to visualize the effect of nutrient stress and excess on liver metabolism. Our data revealed a highly organized metabolic compartmentalization in livers, which becomes disrupted under nutrient stress conditions. Fasting caused changes in glucose metabolism and increased the levels of fatty acids in the circulation. In contrast, a prolonged high-fat diet (HFD) caused lipid accumulation within liver tissues with clear zonal patterns. Fatty livers had higher levels of purine and pentose phosphate related metabolites, which generates reducing equivalents to counteract oxidative stress. This MALDI MSI approach allowed the visualization of liver metabolic compartmentalization at high resolution and can be applied more broadly to yield new insights into metabolic heterogeneity in vivo.
- organism: mouse
- assay: metabolomics
- research focus: metabolism & bioenergetics
- research focus: spatial domain detection
- approach: experimental assay
- approach: microscopy & imaging analysis
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