Disease-Associated Neurotoxic Astrocyte Markers in Alzheimer Disease Based on Integrative Single-Nucleus RNA Sequencing

Abstract

Alzheimer disease (AD) is an irreversible neurodegenerative disease, and astrocytes play a key role in its onset and progression. The aim of this study is to analyze the characteristics of neurotoxic astrocytes and identify novel molecular targets for slowing down the progression of AD. Single-nucleus RNA sequencing (snRNA-seq) data were analyzed from various AD cohorts comprising about 210,654 cells from 53 brain tissue. By integrating snRNA-seq data with bulk RNA-seq data, crucial astrocyte types and genes associated with the prognosis of patients with AD were identified. The expression of neurotoxic astrocyte markers was validated using 5 × FAD and wild-type (WT) mouse models, combined with experiments such as western blot, quantitative real-time PCR (qRT-PCR), and immunofluorescence. A group of neurotoxic astrocytes closely related to AD pathology was identified, which were involved in inflammatory responses and pathways related to neuron survival. Combining snRNA and bulk tissue data, ZEP36L, AEBP1, WWTR1, PHYHD1, DST and RASL12 were identified as toxic astrocyte markers closely related to disease severity, significantly elevated in brain tissues of 5 × FAD mice and primary astrocytes treated with Aβ. Among them, WWTR1 was significantly increased in astrocytes of 5 × FAD mice, driving astrocyte inflammatory responses, and has been identified as an important marker of neurotoxic astrocytes. snRNA-seq analysis reveals the biological functions of neurotoxic astrocytes. Six genes related to AD pathology were identified and validated, among which WWTR1 may be a novel marker of neurotoxic astrocytes.

Keywords: Alzheimer disease; Biomarkers; Bulk RNA-seq; Neurotoxic astrocytes; snRNA-seq.

Fig. 1

Integration of AD snRNA-seq data and clustering of cells. A UMAP of GSE138852. B UMAP of GSE157827. C UMAP of GSE174367. D UMAP distribution of cells annotated by cohorts. 30 AD patient samples, including a total of 210,654 cells were included for down-stream analysis. Cells from different cohorts were evenly merged. E Dot plot depicting selected differentially expressed genes for each cluster. Dot size corresponds to the percentage of nuclei expressing the gene in each cluster; color represents the average gene expression level. F UMAP distribution of cells colored by the major cell types. G Histogram showing the percentage of cells from different cell types in AD and NC

Fig. 2

Reclustering of astrocytes and neurotoxic astrocyte subcluster identification. A UMAP plot showing the reclustering of astrocytes. B Violin plot represents the distribution of A1-specific transcript signature scores across subclusters of astrocytes. C UMAP illustration of the distribution of a1-specific transcript expression. Cluster 3 exhibits the highest distribution. D Violin plot of downregulated DAAs marker across subclusters of astrocytes. Cluster 3 exhibits the lowest distribution. E UMAP representation of downregulated DAAs marker expression. F Violin plot of upregulated DAAs marker across subclusters of astrocytes. Cluster 3 exhibits the highest distribution. G UMAP representation of upregulated DAAs marker expression. H Cluster 3 astrocytes are identified as neurotoxic astrocytes

Fig. 3

Enrichment analysis and identification of key genes in neurotoxic astrocytes. A the GO analysis of genes in cluster 3 neurotoxic astrocytes. B the KEGG pathway analysis of cluster 3 neurotoxic astrocytes. C The volcano plot of the genes in the PFC and EC. D Venn diagram analysis of the intersection between upregulated differential genes in bulk data and cluster 3 astrocytes. E PPI network of the 12 key genes and neurotoxic astrocyte-specific genes overlapping with upregulated DEGs

Fig. 4

Clinical correlation analysis, functional analysis, and expression verification of key genes. A Dot plot depicting 12 key genes expression in different astrocytes cluster. B Correlation heatmap illustrating the analysis of the relationship between 12 key genes and clinical disease severity markers based on GSE106241. C RT-qPCR validation of the expression of six clinical severity-related genes in the brain tissue of 5 × FAD mice (n = 6 per group). D RT-qPCR validation of the expression of six clinical severity-related genes in primary astrocytes treated with Aβ (n = 6 per group). E GSEA of six clinical severity-related genes based on GSE48350. (****p < 0.0001)

Fig. 5

Verification of WWTR1 expression and localization in 5 × FAD mice brain. A Western blot analysis confirming the expression changes of WWTR1 in 5 × FAD mice brain (n = 6 per group). B Western blot analysis confirming the expression changes of WWTR1 in Aβ treated primary astrocytes and control astrocytes (n = 7 per group). C Immunofluorescence images indicate that WWTR1 is not colocalized with IBA1 and NEUN, but colocalizes with GFAP in the hippocampus and cortex of the 5 × FAD and WT mice. (****p < 0.0001)

Fig. 6

Significant increase in WWTR1 expression in astrocytes of 5 × FAD mice and its role in driving pro-inflammatory responses. A Representative hemisphere stitches of WT and 5 × FAD (WWTR1 in green, GFAP in red, DAPI in blue). Scale bar = 1000 μm. B In GFAP + astrocytes of hippocampal and cortex tissue from 5 × FAD mice, wwtr1 expression is significantly increased. (wt, n = 5; 5 × FAD, n = 6). All stained slides were quantified with Visiopharm Integrator System software. C Representative high magnification images of Immunofluorescence staining in the CA1 of wt and 5 × FAD mice. Scale bar = 25 μm; Segmentation of the digital image using Visiopharm Image Analysis Software where red pixels designate WWTR1 + astrocytes and white pixels define WWTR- astrocytes. D RT-qPCR verification of overexpression efficiency of WWTR1 and expression of inflammatory factors in primary astrocytes (WWTR1, CCL2, IL1β: n = 7 per group; IL6, IL10: n = 6 per group; ***p = 0.0001, ****p < 0.0001)