Integrating single-nucleus sequence profiling to reveal the transcriptional dynamics of Alzheimer's disease, Parkinson's disease, and multiple sclerosis

Abstract

Background: Alzheimer's disease (AD), Parkinson's disease (PD), and multiple sclerosis (MS) are three nervous system diseases that partially overlap clinically and genetically. However, bulk RNA-sequencing did not accurately detect the core pathogenic molecules in them. The availability of high-quality single cell RNA-sequencing data of post-mortem brain collections permits the generation of a large-scale gene expression in different cells in human brain, focusing on the molecular features and relationships between diseases and genes. We integrated single-nucleus RNA-sequencing (snRNA-seq) datasets of human brains with AD, PD, and MS to identify transcriptomic commonalities and distinctions among them.

Methods: The snRNA-seq datasets were downloaded from Gene Expression Omnibus (GEO) database. The Seurat package was used for snRNA-seq data processing. The uniform manifold approximation and projection (UMAP) were utilized for cluster identification. The FindMarker function in Seurat was used to identify the differently expressed genes. Functional enrichment analysis was carried out using the Gene Set Enrichment Analysis (GSEA) and Gene ontology (GO). The protein-protein interaction (PPI) analysis of differentially expressed genes (DEGs) was analyzed using STRING database ( http://string-db.org ). SCENIC analysis was performed using utilizing pySCENIC (v0.10.0) based on the hg19-tss-centered-10 kb-10species databases. The analysis of potential therapeutic drugs was analyzed on Connectivity Map ( https://clue.io ).

Results: The gene regulatory network analysis identified several hub genes regulated in AD, PD, and MS, in which HSPB1 and HSPA1A were key molecules. These upregulated HSP family genes interact with ribosome genes in AD and MS, and with immunomodulatory genes in PD. We further identified several transcriptional regulators (SPI1, CEBPA, TFE3, GRHPR, and TP53) of the hub genes, which has important implications for uncovering the molecular crosstalk among AD, PD, and MS. Arctigenin was identified as a potential therapeutic drug for AD, PD, and MS.

Conclusions: Together, the integrated snRNA-seq data and findings have significant implications for unraveling the shared and unique molecular crosstalk among AD, PD, and MS. HSPB1 and HSPA1A as promising targets involved in the pathological mechanisms of neurodegenerative diseases. Additionally, the identification of arctigenin as a potential therapeutic drug for AD, PD, and MS further highlights its potential in treating these neurological disorders. These discoveries lay the groundwork for future research and interventions to enhance our understanding and treatment of AD, PD, and MS.

Keywords: Alzheimer’s diseases; Arctigenin; Crosstalk; HSPA1A; HSPB1; Multiple sclerosis; Parkinson’s disease; Ribosomal proteins; Single-cell sequence.

Fig. 1

The workflow of the study. After literature search, 64 cases from different single-nucleus sequence studies were selected. These studies were done on post-mortem human brain tissue, which were categorized into entorhinal cortex, prefrontal cortex, midbrain, and white matter, severely affected regions by AD, PD, and MS pathology, respectively

Fig. 2

Single cell transcriptional landscape of AD, PD, and MS. A Uniform Manifold Approximation and Projection (UMAP) plot of the combined datasets before (right) and after (light) batch correction with Harmony, colored by dataset source. B UMAP representation of the landscape of different CNS cell types. C Dot-plots for the merged snRNA-seq data demonstrates the marker expressions in the different nuclei clusters. D Stacked bar plots of the differing cell-type proportions in the merged dataset. MS_Active (active lesions), MS_CA (chronic active lesions), MS_CI (chronic inactive lesions), MS_NAWM (non-lesioned, normal appearing white matter), MS_ Remyelinating (remyelinating lesions), and Ctrl_NWM (normal white matter)

Fig. 3

Network of DEGs and enrichment analysis. A The network of upregulated genes across different cell types in AD, PD, and MS. B Enrichment plots from Gene set enrichment analysis (GSEA), the top five biological pathways sorted by normalized enrichment score across AD (left panel), PD (middle panel) and MS (right panel) are shown. The color of broken line represents different pathways

Fig. 4

Functional enrichment analysis of differentially expressed genes among three diseases. A Gene ontology (GO) functional enrichment analyze based on the hub genes. BP: biological process. CC: cellular components. MF: molecular function. B Network visualization of relationships of enriched functions and genes. C Protein–protein interaction (PPI) network of differentially expressed genes (DEGs) in AD brains. D PPI network of DEGs in PD brains. E PPI network of DEGs in MS brains

Fig. 5

SCENIC analysis showing distinct and shared regulons across different cell types. A Heatmap of the identified TFs with hierarchical clustering. Different colors represent different modules. B Regulons activity scores of different cell types among modules (M1, M4, and M7)

Fig. 6

Transcriptional regulatory network among AD, PD, and MS. A Specificity scores of regulons in microglia, oligodendrocytes, and astrocytes. The top 20 genes with higher activity are noted. B Network of TFs shared by the three diseases and their target genes (regulons), accompanied by the corresponding motifs of the TFs

Fig. 7

The shared 30 therapeutic drugs of AD, PD, and MS. Drug sensitivity analyses were performed using Connectivity Map (CMap). Threshold normalized connectivity score (normalized_cs) was set at < 0, and p < 0.05