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. 2023 Feb 22:16:1136398.
doi: 10.3389/fnmol.2023.1136398. eCollection 2023.

Single-nucleus transcriptional profiling uncovers the reprogrammed metabolism of astrocytes in Alzheimer's disease

Li-Yuan Fan  1   2   3 , Jing Yang  1 , Ming-Li Li  1   2 , Ruo-Yu Liu  1   2 , Ying Kong  1 , Su-Ying Duan  1 , Guang-Yu Guo  1   2 , Jing-Hua Yang  2 , Yu-Ming Xu  1
Affiliations

Single-nucleus transcriptional profiling uncovers the reprogrammed metabolism of astrocytes in Alzheimer's disease

Li-Yuan Fan et al. Front Mol Neurosci. .

Abstract

Astrocytes play an important role in the pathogenesis of Alzheimer's disease (AD). It is widely involved in energy metabolism in the brain by providing nutritional and metabolic support to neurons; however, the alteration in the metabolism of astrocytes in AD remains unknown. Through integrative analysis of single-nucleus sequencing datasets, we revealed metabolic changes in various cell types in the prefrontal cortex of patients with AD. We found the depletion of some important metabolites (acetyl-coenzyme A, aspartate, pyruvate, 2-oxoglutarate, glutamine, and others), as well as the inhibition of some metabolic fluxes (glycolysis and tricarbocylic acid cycle, glutamate metabolism) in astrocytes of AD. The abnormality of glutamate metabolism in astrocytes is unique and important. Downregulation of GLUL (GS) and GLUD1 (GDH) may be the cause of glutamate alterations in astrocytes in AD. These results provide a basis for understanding the characteristic changes in astrocytes in AD and provide ideas for the study of AD pathogenesis.

Keywords: Alzheimer’s disease; astrocyte; glutamate; glutamine; metabolism; neurodegenerative disease; single-nucleus transcriptome.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Single-nucleus transcriptional landscape of AD. (A) Flow chart of the experimental analysis. (B) Uniform manifold approximation and projection (UMAP) representation of the landscape of different cell types. (C) Features plots for the merged single-nucleus sequencing (snRNA-seq) data demonstrated the expressions of the markers in the different nuclei clusters. (D) Stacked bar plots of the differing cell-type proportions in emerged datasets.
Figure 2
Figure 2
Single-cell metabolic flux mapping of prefrontal cortex (PFC) revealed metabolism heterogeneity of AD. (A) Profile of the predicted flux of metabolic modules. (B) Profile of the predicted metabolites. (C) The statistical chart of some important metabolites. The ordinate represents the ratio of the metabolite contents of the disease group to the control group.
Figure 3
Figure 3
Demonstration of differentially expressed genes in astrocytes. (A) Volcano map revealing differences in genes expression in astrocytes. The nonparametric Wilcoxon rank sum test wae used for differential expression analysis. Differential expression genes with p_val_adj < 0.05 were then selected for analysis. The metabolism-associated enzymes and transporters that differ significantly are highlighted in the figure (take the intersection of differentially expressed genes and metabolism-related genes). (B) Cell trajectories are calculated based on pseudotime values. Pseudotime 0–20 represents the stages of progression in different states of the cell. Component represents the dimensions on the Monocle nonlinear dimensionality reduction space. (C) Cell trajectories are calculated based on pseudotime values, split by disease. Component represents the dimensions on the Monocle nonlinear dimensionality reduction space. (D) Heatmap of some enzymes and transporters expressed differently over pseudotime. (E) Cell trajectories are calculated based on pseudotime values, split by plaque stages of patients with AD and controls. Component represents the dimensions on the Monocle nonlinear dimensionality reduction space.
Figure 4
Figure 4
Weighted correlation network analysis (WGCNA) and gene set enrichment analysis (GSEA) analysis identified a particular module associated with metabolic shift. (A) Gene dendrogram with clustering based on consensus topological overlap. Lower color row: consensus modules following merging of similar modules. Five consensus non-gray modules across all samples were identified (represented by different colors). (B) Heatmap of the correlation of the modules with age, plaque stage, and tangle stage. This calculation process uses the Spearman correlation coefficient method of the Cor function. The correlation coefficient and p-value are shown. (C) The module-network analysis of the yellow module. Bold and marked in red are differentially expressed metabolism-related genes. (D) GSEA diagram of the genes in the yellow module. The results showed that the genes in the yellow module were closely related to metabolism pathway (p < 0.05).
Figure 5
Figure 5
Single-cell regulatory network inference and clustering (SCENIC) analysis identified transcriptional regulatory networks that related to the reprogrammed metabolism in astrocytes. (A) Hierarchical clustering heatmap of the discovered regulons, and 5 main modules are represented. (B) Transcriptional regulators that are significantly enriched in the yellow module. (C) The specificity scores of regulons in astrocytes. The top 20 genes with higher activity are noted. (D) The network of the main regulons in the yellow module, and the interaction diagram between some important enzymes (red font).
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