There is currently little information available about how individual cell types contribute to Alzheimer’s disease. Here we applied single-nucleus RNA sequencing to entorhinal cortex samples from control and Alzheimer’s disease brains (n = 6 per group), yielding a total of 13,214 high-quality nuclei. We detail cell-type-specific gene expression patterns, unveiling how transcriptional changes in specific cell subpopulations are associated with Alzheimer’s disease. We report that the Alzheimer’s disease risk gene APOE is specifically repressed in Alzheimer’s disease oligodendrocyte progenitor cells and astrocyte subpopulations and upregulated in an Alzheimer’s disease-specific microglial subopulation. Integrating transcription factor regulatory modules with Alzheimer’s disease risk loci revealed drivers of cell-type-specific state transitions towards Alzheimer’s disease. For example, transcription factor EB, a master regulator of lysosomal function, regulates multiple disease genes in a specific Alzheimer’s disease astrocyte subpopulation. These results provide insights into the coordinated control of Alzheimer’s disease risk genes and their cell-type-specific contribution to disease susceptibility. These results are available at http://adsn.ddnetbio.com.
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All single-cell RNA sequencing data are available from the Gene Expression Omnibus (GEO) under the accession number GSE138852. Data can also be visualized via the interactive web application at adsn.ddnetbio.com. Single-cell gene expression data and metadata can also be downloaded directly via adsn.ddnetbio.com.
Code is available from the authors by reasonable request.
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The authors acknowledge Flowcore and Micromon, Monash University, for the provision of instrumentation, training, and technical support. Tissues were received from the Victorian Brain Bank, supported by The Florey Institute of Neuroscience and Mental Health, The Alfred and the Victorian Institute of Forensic Medicine, and funded in part by Parkinson’s Victoria and MND Victoria. The Australian Regenerative Medicine Institute is supported by grants from the State Government of Victoria and the Australian Government. This work was supported by a Dementia Australia Research Foundation Grant to A.G., J.M.P., and E.P., a Monash Network of Excellence grant to J.M.P., E.P., and O.J.L.R., and a Yulgilbar Foundation grant to A.G. and G.S. A.G. was supported by a National Health and Medical Research Council (NHMRC) and Australian Research Council (ARC) Dementia Research Development Fellowship (GNT1097461). J.M.P. was supported by a Sylvia-Charles Viertel Fellowship. O.J.L.R. and J.F.O. were supported by a Singapore National Research Foundation Competitive Research Programme (NRF-CRP20-2017-0002). S.B. was supported by a National Health and Medical Research Council (NHMRC) and Australian Research Council (ARC) Dementia Research Development Fellowship (GNT1111206). This work was supported by a Sylvia and Charles Viertel Senior Medical Research Fellowship to R.L. and and a Howard Hughes Medical Institute International Research Scholarship to R.L. We acknowledge S. Freytag (University of Western Australia) for her advice regarding disentangling individuals from mixed-individual libraries.
O.J.L.R. and J.M.P. are co-inventors of the patent (WO/2017/106932) and are co-founders, shareholders, and directors of Mogrify Ltd., a cell therapy company. All other authors declare no competing interests.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Nuclei isolated using the EZ-Prep kit from entorhinal cortex of AD and control patients were FACS sorted on BD Influx using 70 μm nozzle, 21–22 psi. Single DAPI+ events were considered nuclei.
a, cell proportion plots by library for eight libraries showing exclusion of two libraries due to high neuronal count and comparison of cell type proportions recovered in this study compared to cell proportions in other single nuclei studies24 and52. Cell proportions are shown by sequencing library, where this information is available. b, Frequency histogram of number of Unique Molecular Identifiers (UMIs) across all 8 cell type groups. c, Frequency histogram of number of detected genes across all 8 cell type groups. d, Barplot showing number of top two identified cell types in hybrid cell types in AD and control libraries as proportion. e, UpSetR69 plot showing the BRETIGEA number of distinct and overlapping cell type markers across the six major cell types - microglia, astrocyte, neuron, oligodendrocyte, OPC, and endothelial cells.
Extended data Fig. 3 Comparison of differentially expressed genes (DEGs) within microglia, OPCs and oligodendrocytes with Mathys et al. 2019.
a, d, g, Venn diagram showing overlap of DEGs detected in Mathys et al. (n = 24 AD-pathology vs n = 24 no-pathology individuals) and those identified in the present study (n = 6 AD vs n = 6 control individuals) in microglia (a), OPCs (d) and oligodendrocytes (g), and the table shows the number of and concordance of dysregulation in AD of genes within the overlap of DEGs in both studies. Hypergeometric test was used to test for significance of overlap. b, e, h, List of concordant and discordant overlapping DEGs in microglia (b), OPCs (e) and oligodendrocytes (h). c, f, i, Significant gene ontology (GO) terms for each gene set comparison in microglia (c), OPCs (f) and oligodendrocytes (i). Multiple testing correction was done using the Benjamini–Hochberg method. Enrichment analysis was performed using hypergeometric test.
Extended data Fig. 4 Comparison of differentially expressed genes (DEGs) within astrocytes, excitatory neurons and inhibitory neurons with Mathys et al. 2019.
a, d, g, Venn diagram showing overlap of DEGs detected in Mathys et al. (n = 24 AD-pathology vs n = 24 no-pathology individuals) and those identified in the present study (n = 6 AD vs n = 6 control individuals) in astrocytes (a), excitatory neurons (d), and inhibitory neurons (g), and the table shows the number of and concordance of dysregulation in AD of genes within the overlap of DEGs in both studies. Hypergeometric test was used to test for significance of overlap. b, e, h, List of concordant and discordant overlapping DEGs in astrocytes (b), excitatory neurons (e), and inhibitory neurons (h). c, f, i, Significant gene ontology (GO) terms for each gene set comparison in astrocytes (c), excitatory neurons (f), and inhibitory neurons (i). Multiple testing correction was done using the Benjamini–Hochberg method. Enrichment analysis was performed using hypergeometric test.
a, Box plot (center line: median, box limits: upper and lower quartiles, whiskers: 1.5x interquartile range, points outside whiskers: outliers) showing percentage variance explained by each covariate for genes that are differentially expressed between AD and Control groups in any of the identified cell types. b, Proportion of AD-vs-Control differentially expressed genes (DEGs) that are also individual-associated genes and genes that are only DE between AD and Control. c, Proportion of AD-vs-Control DEGs that are also sex-associated genes and genes that are only DE between AD and Control. The number of DEGs are given in parenthesis.
a–i, Bubble plot showing the top 15 differentially expressed genes (DEGs) within the DEG1-DEG9 groups (see Fig. 1f), where the colour represents the relative gene expression and the bubble size is the proportion of cells in the group expressing the gene.
Scatter plot showing the relationship between cell-type-specific and common differentially expressed genes (DEGs) in control and AD, excitatory and inhibitory neurons. Cutoff for significance is abs(log fold change) > 0.5 and false discovery rate (FDR) < 0.01.
Extended data Fig. 8 Single nuclei sequencing of human AD and control entorhinal cortex reveals homeostatic, AD-specific and shared ontological cell subclusters.
a,e,i, Uniform Manifold Approximation and Projection (UMAP) visualization of subclusters of microglia (a), OPCs (e) and endothelial cells (i) showing b, f, j, the composition of cells in subclusters by disease state, c, g, k, hierarchical clustering and heatmap colored by single-cell gene expression of subcluster-specific genes (top eight genes were shown per cluster). d, h, l, Gene Set Enrichment Analysis (GSEA) results of subcluster specific genes coloured by normalised gene set enrichment scores for the gene ontologies shown in each cell subcluster.
Comparison of astrocyte signatures to ai;17 aii25. b, Comparison of neuronal subcluster signatures to24(Ex1–8, In1–8). c, Functional annotation of gene regulatory networks (GRNs) controlled by TFs in Fig. 5a. GRNs are shown in Extended data Fig. 10, ETS2 (n = 9 genes), GTF2IRD1 (n = 339 genes), HIF3A-neuron (n = 112 genes), HIF3A-OPC (n = 96 genes), KDM5B (n = 44 genes), MAX (n = 273 genes), NKX6–2 (n = 332 genes), RELA (n = 224 genes), SOX10 (n = 114 genes), and ZKSCAN1 (n = 37 genes). Multiple correction testing was done using the Benjamini–Hochberg method. Enrichment analysis was performed using the hypergeometric test in R package GOstats.
Extended data Fig. 10 Gene regulatory network analysis predicts transcription factors regulating GWAS genes for conversion of control to AD subcluster signatures.
Trajectories of the top transcription factors (TFs): NKX6–2, ETS2, ZKSCAN1, HIF3A, GTF2IRD1, RELA, SOX10, KDM5B and MAX, with downstream GWAS gene targets. For each cell type, the reported TFs-driven gene regulatory networks include GWAS gene hits, which are direct target genes of the TF and have the highest average log fold change in gene expression between the source subcluster/s and target subcluster/s. Each gene is colored according to its average log fold change between the source subcluster/s and target subcluster/s. Only significantly differentially expressed GWAS gene hits are reported (false discovery rate < 0.05).
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Grubman, A., Chew, G., Ouyang, J.F. et al. A single-cell atlas of entorhinal cortex from individuals with Alzheimer’s disease reveals cell-type-specific gene expression regulation. Nat Neurosci 22, 2087–2097 (2019). https://doi.org/10.1038/s41593-019-0539-4
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