Energetic Profiles and Distribution of Neurons and Glia in the Human Brain
Presented During:
Friday, June 27, 2025: 11:30 AM - 12:45 PM
Brisbane Convention & Exhibition Centre
Room:
M1 & M2 (Mezzanine Level)
Poster No:
706
Submission Type:
Abstract Submission
Authors:
Laura Fraticelli1, Valentin Riedl2
Institutions:
1Technical University Munich, Munich, Bavaria, 2Friedrich-Alexander University, Erlangen, Bavaria
First Author:
Co-Author:
Introduction:
Glucose is the primary fuel of the human brain, and its metabolism is tightly regulated on a cellular level. Neurons and glial cells, including astrocytes (Ast), microglia (Mic), and oligodendrocytes (Oli), distribute heterogeneously across the brain and possess divergent metabolic profiles [1]. These metabolic profiles have not been studied in the human brain. Metabolic differences within the cell types emerge from differential gene expressions. We leveraged transcriptomic data of post-mortem brains from the Allen Human Brain Atlas (AHBA) to characterize the metabolic profiles of brain cells and investigate their expression patterns across the human cortex and subcortex [2].
Methods:
Fig. 1A) Spatial microarray data from the AHBA was processed through abagen [3]. Left hemisphere data was assigned to the HCPex parcellation [4]. Microarray data was normalized and aggregated across donors to generate expression maps of 15637 genes across 203 cortical and subcortical ROIs. Each cell-type and metabolic pathway was represented by a selected set of genes. Cell-type markers (inhibitory and excitatory neurons (ExNeu), Ast, Oli and Mic) were defined by Seidlitz et al. 2020 [5]. Metabolic gene sets were extracted from the MSigDB and included glycolysis (Gly), oxidative phosphorylation (OxPhos), Fatty acid (FA) metabolism, reactive oxygen species (ROS) and peroxisome (Per).
By calculating the median expression of each gene set per ROI, we generated expression maps of cell-types and metabolic pathways. We calculated the spearman correlation between the cell-type and metabolic expression maps for cortical and subcortical ROIs separately. Correlation was tested for significance through t-tests and p-values were Bonferroni adjusted. Next, we investigated how cell composition and metabolism contribute to cortical complexity. ROIs were ordered according to the anterior-posterior (AP) axis and genes with similar expression patterns across the axis were clustered hierarchically. For each cluster, we averaged the expression of the corresponding genes per ROI and analyzed the relation between median expression and AP axis localization by linear regression.
By calculating the median expression of each gene set per ROI, we generated expression maps of cell-types and metabolic pathways. We calculated the spearman correlation between the cell-type and metabolic expression maps for cortical and subcortical ROIs separately. Correlation was tested for significance through t-tests and p-values were Bonferroni adjusted. Next, we investigated how cell composition and metabolism contribute to cortical complexity. ROIs were ordered according to the anterior-posterior (AP) axis and genes with similar expression patterns across the axis were clustered hierarchically. For each cluster, we averaged the expression of the corresponding genes per ROI and analyzed the relation between median expression and AP axis localization by linear regression.
Results:
Neuronal and glial cells displayed opposing expression patterns in the cortex and subcortex (Fig. 1B). Cortical regions exhibited higher expression of neuronal genes and subcortical areas had increased glia-related gene expression. In the cortex, the average expression of ExNeu correlated with Gly, Oxphos and ROS, Ast correlated with Gly, FA and Per, and Mic with all metabolic gene sets (Fig. 1C). These correlation patterns did not occur in the subcortex. Expression gradients along the AP axis emerged following hierarchical clustering of cell type marker and metabolic gene expression (Fig. 2A, B). Cluster with an AP gradient were dominated by glia-related genes (Fig. 2C). Between 27% and 35% of the AP cluster expression variance is explained by their location on the AP axis.
Conclusions:
Our approach allowed the metabolic characterization of brain cells on a whole-brain level. We show fundamental differences between cortex and subcortex. The glia to neuron ratio is higher in subcortical regions and glia-related metabolism increases concordantly. We replicate previous hypothesis about the metabolic profiles of different brain cells in cortical, but not subcortical regions. In the cortex, ExNeu appear to be highly oxidative, Ast glycolytic and FA oxidizing, and Mic are metabolically versatile [1,6]. In the subcortex, these trends were absent. Our results raise new questions about the distinct cellular and metabolic organization of the human cortex and subcortex. Lastly, our hierarchical clustering revealed an evolutionary PA expression gradient of glia-related genes. Manifold roles have recently been attributed to glial cells in higher cognitive functions [7,8]. An increase of glia in higher-order association cortices might reflect their fundamental role in supporting cognitive processes.
Genetics:
Transcriptomics 1
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Cortical Anatomy and Brain Mapping 2
Subcortical Structures
Neuroinformatics and Data Sharing:
Workflows
Physiology, Metabolism and Neurotransmission:
Cerebral Metabolism and Hemodynamics
Keywords:
Astrocyte
Cortex
Data analysis
Neuron
Open Data
Statistical Methods
Sub-Cortical
Workflows
Other - Transcriptomics
1|2Indicates the priority used for review
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Provide references using APA citation style.
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Markello, R. D. (2021). Standardizing workflows in imaging transcriptomics with the abagen toolbox. elife, 10, e72129.
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Seidlitz, J. (2020). Transcriptomic and cellular decoding of regional brain vulnerability to neurogenetic disorders. Nature communications, 11(1), 3358.
Lauro, C. (2020). Metabolic reprograming of microglia in the regulation of the innate inflammatory response. Frontiers in Immunology, 11, 493.
Fields, R. D. (2014). Glial biology in learning and cognition. The neuroscientist, 20(5), 426-431.
Valk, S.. (2020). Shaping brain structure: Genetic and phylogenetic axes of macroscale organization of cortical thickness. Science advances, 6(39), eabb3417