Spatial patterns of hepatocyte glucose flux revealed by stable isotope tracing and multi-scale microscopy

Metabolic homeostasis requires engagement of catabolic and anabolic pathways consuming nutrients that generate and consume energy and biomass. Our current understanding of cell homeostasis and metabolism, including how cells utilize nutrients, comes largely from tissue and cell models analyzed after...

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Veröffentlicht in:Nature communications Jg. 16; H. 1; S. 5850 - 16
Hauptverfasser: Habashy, Aliyah, Acree, Christopher, Kim, Keun-Young, Zahraei, Ali, Dufresne, Martin, Phan, Sebastien, Cutler, Melanie, Patterson, Emilee, Mulligan, Alexandra G., Burkewitz, Kristopher, Flynn, Charles Robert, Lantier, Louise, Deerinck, Thomas, McGuinness, Owen P., Spraggins, Jeffrey M., Ellisman, Mark H., Arrojo e Drigo, Rafael
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Nature Publishing Group UK 01.07.2025
Nature Publishing Group
Nature Portfolio
Schlagworte:
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Animals
Biomass energy production
Blood levels
Carbon Isotopes - metabolism
Catheters
Cell culture
Endoplasmic Reticulum - metabolism
Fluctuations
Fractionation
Gas detectors
Glucose
Glucose - metabolism
Glycogen - metabolism
Hepatocytes
Hepatocytes - metabolism
Hepatocytes - ultrastructure
Homeostasis
Humanities and Social Sciences
Humans
Image processing
Image segmentation
Isotopes
Learning algorithms
Lipid Droplets - metabolism
Lipids
Liver
Machine learning
Male
Metabolic flux
Metabolism
Metabolites
Mice
Microenvironments
Microscopy
Microscopy - methods
Mitochondria - metabolism
multidisciplinary
Nutrients
Organelles
Oxidation
Pattern formation
Patterning
Plasma
Proteins
Scanning electron microscopy
Science
Science (multidisciplinary)
Spatial analysis
Stable isotopes
ISSN:2041-1723, 2041-1723
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Zusammenfassung:Metabolic homeostasis requires engagement of catabolic and anabolic pathways consuming nutrients that generate and consume energy and biomass. Our current understanding of cell homeostasis and metabolism, including how cells utilize nutrients, comes largely from tissue and cell models analyzed after fractionation, and that fail to reveal the spatial characteristics of cell metabolism, and how these aspects relate to the location of cells and organelles within tissue microenvironments. Here we show the application of multi-scale microscopy, machine learning-based image segmentation, and spatial analysis tools to quantitatively map the fate of nutrient-derived 13 C atoms across spatiotemporal scales. This approach reveals the cellular and organellar features underlying the spatial pattern of glucose 13 C flux in hepatocytes in situ, including the timeline of mitochondria-ER contact dynamics in response to changes in blood glucose levels, and the discovery of the ultrastructural relationship between glycogenesis and lipid droplets. Most metabolic studies using traditional procedures fail to reveal the spatial patterning associated with metabolic flux and cellular metabolism within tissue microenvironments. Here, the authors show the application of multi-scale microscopy, machine learning-based image segmentation and spatial analysis to map the fate of nutrient-derived 13 C across spatiotemporal scales.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:2041-1723
2041-1723
DOI:10.1038/s41467-025-60994-w