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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Vydané v:Nature communications Ročník 16; číslo 1; s. 5850 - 16
Hlavní autori: 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
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London Nature Publishing Group UK 01.07.2025
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631/80/2373
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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Abstract 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.
AbstractList 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.
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 13C atoms across spatiotemporal scales. This approach reveals the cellular and organellar features underlying the spatial pattern of glucose 13C 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 13C across spatiotemporal scales.
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.
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 13C atoms across spatiotemporal scales. This approach reveals the cellular and organellar features underlying the spatial pattern of glucose 13C 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.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 13C atoms across spatiotemporal scales. This approach reveals the cellular and organellar features underlying the spatial pattern of glucose 13C 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.
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 C atoms across spatiotemporal scales. This approach reveals the cellular and organellar features underlying the spatial pattern of glucose 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.
Abstract 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 13C atoms across spatiotemporal scales. This approach reveals the cellular and organellar features underlying the spatial pattern of glucose 13C 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.
ArticleNumber 5850
Author Acree, Christopher
Kim, Keun-Young
Habashy, Aliyah
Patterson, Emilee
Ellisman, Mark H.
Zahraei, Ali
Lantier, Louise
Arrojo e Drigo, Rafael
Mulligan, Alexandra G.
Flynn, Charles Robert
Deerinck, Thomas
Cutler, Melanie
McGuinness, Owen P.
Dufresne, Martin
Phan, Sebastien
Spraggins, Jeffrey M.
Burkewitz, Kristopher
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  organization: Department of Molecular Physiology and Biophysics, Vanderbilt University, Center for Computational Systems Biology, Vanderbilt University, Diabetes Research and Training Center (DRTC), Vanderbilt University Medical Center
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38699373 - Res Sq. 2024 Apr 17:rs.3.rs-4096781. doi: 10.21203/rs.3.rs-4096781/v1.
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– reference: 38699373 - Res Sq. 2024 Apr 17:rs.3.rs-4096781. doi: 10.21203/rs.3.rs-4096781/v1.
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Snippet Metabolic homeostasis requires engagement of catabolic and anabolic pathways consuming nutrients that generate and consume energy and biomass. Our current...
Abstract Metabolic homeostasis requires engagement of catabolic and anabolic pathways consuming nutrients that generate and consume energy and biomass. Our...
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631/80/2373
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
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Title Spatial patterns of hepatocyte glucose flux revealed by stable isotope tracing and multi-scale microscopy
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