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Single-nucleus multiregion transcriptomic analysis of brain vasculature in Alzheimer’s disease

Nature Neuroscience volume 26, pages 970–982 (2023)Cite this article

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An Author Correction to this article was published on 31 October 2023

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Abstract

Cerebrovascular dysregulation is a hallmark of Alzheimer’s disease (AD), but the changes that occur in specific cell types have not been fully characterized. Here, we profile single-nucleus transcriptomes in the human cerebrovasculature in six brain regions from 220 individuals with AD and 208 age-matched controls. We annotate 22,514 cerebrovascular cells, including 11 subtypes of endothelial, pericyte, smooth muscle, perivascular fibroblast and ependymal cells. We identify 2,676 differentially expressed genes in AD, including downregulation of PDGFRB in pericytes, and of ABCB1 and ATP10A in endothelial cells, and validate the downregulation of SLC6A1 and upregulation of APOD, INSR and COL4A1 in postmortem AD brain tissues. We detect vasculature, glial and neuronal coexpressed gene modules, suggesting coordinated neurovascular unit dysregulation in AD. Integration with AD genetics reveals 125 AD differentially expressed genes directly linked to AD-associated genetic variants. Lastly, we show that APOE4 genotype-associated differences are significantly enriched among AD-associated genes in capillary and venule endothelial cells, as well as subsets of pericytes and fibroblasts.

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Fig. 1: Brain vasculature characterization across six brain regions.
Fig. 2: Cell type-specific brain vasculature changes in AD.
Fig. 3: Upstream regulators of DEGs in AD.
Fig. 4: Dynamics of cell–cell communication between vascular cell types and neuron and glial cells in AD.
Fig. 5: AD GWAS loci directly linked to brain vascular adDEGs.
Fig. 6: AD GWAS loci are indirectly linked to brain vascular adDEGs.
Fig. 7: APOE4-associated transcriptional changes and cognitive decline.

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Data availability

Count matrices and metadata for all cells analyzed in this study are available at http://compbio.mit.edu/scADbbb/. The interactive website is linked from http://compbio2.mit.edu/scadbbb/. ROSMAP data can be requested at https://www.radc.rush.edu. Raw data are available upon request from https://www.synapse.org/#!Synapse:syn51015750.

Code availability

The code used in this study is available at http://compbio.mit.edu/scADbbb/ and https://github.com/nasunmit/scadbbb.

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Acknowledgements

We thank the study participants and staff of the Rush Alzheimer’s Disease Center. This work was supported in part by National Institutes of Health grant nos. AG054012, AG058002, AG062377, NS110453, NS115064, AG062335 and NS127187 (M.K., L.-H.T.); grant nos. AG067151, MH109978, MH119509, HG008155 and DA053631 (M.K.); grant nos. P30AG10161, P30AG72975, R01AG15819, R01AG17917, U01AG46152, U01AG61356 and R01AG57473 (D.A.B.); and the Cure Alzheimer’s Foundation CIRCUITS consortium (M.K., L.-H.T); the JPB Foundation (L.-H.T.); and Robert A. and Renee Belfer (L.-H.T.). N.S. was supported by a Takeda Fellowship from the Takeda Pharmaceutical Company. We thank C. A. Boix, L. Hou, A. Grayson and P. Purcell for scientific suggestions.

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Author notes

  1. These authors jointly supervised this work: Li-Huei Tsai, Manolis Kellis.

Authors and Affiliations

  1. MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA

    Na Sun, Leyla Anne Akay, Yongjin Park, Kyriaki Galani & Manolis Kellis

  2. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Na Sun, Leyla Anne Akay, Yongjin Park, Kyriaki Galani, Li-Huei Tsai & Manolis Kellis

  3. Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA

    Leyla Anne Akay, Mitchell H. Murdock, Fabiola Galiana-Melendez, Adele Bubnys, Hansruedi Mathys, Xueqiao Jiang, Ayesha P. Ng & Li-Huei Tsai

  4. Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA

    Leyla Anne Akay, Mitchell H. Murdock, Fabiola Galiana-Melendez, Adele Bubnys, Hansruedi Mathys, Xueqiao Jiang, Ayesha P. Ng & Li-Huei Tsai

  5. Department of Pathology and Laboratory Medicine, Department of Statistics, University of British Columbia, Vancouver, British Columbia, Canada

    Yongjin Park

  6. Department of Molecular Oncology, BC Cancer, Vancouver, British Columbia, Canada

    Yongjin Park

  7. Department of Neurobiology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA

    Hansruedi Mathys

  8. Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL, USA

    David A. Bennett

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Contributions

N.S., M.K. and L.-H.T. conceived and designed the study. M.K. and L.-H.T. supervised the study. N.S. developed the computational framework and conducted the data analysis with assistance from Y.P., H.M. and K.G. X.J. and A.P.N. performed the snRNA-seq profiling. L.A.A., M.H.M. and F.G.-M. performed the in situ hybridization and quantification with help from A.B. D.A.B. provided the postmortem samples and scientific input. N.S. and M.K. wrote the paper with comments from all authors.

Corresponding authors

Correspondence to Li-Huei Tsai or Manolis Kellis.

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The authors declare no competing interests.

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Nature Neuroscience thanks Nancy Ip and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Brain vasculature characterization across six brain regions.

a. Markers for vascular cell types (left) and cell subtypes (right). The z-scores are shown at the pseudo-bulk level. The top 5 markers for each cell type or subtype with highest fold change and the known markers are shown along the right side of the heatmap. b-c. UMAPs showing the comparison of brain vascular nuclei in this study (labeled as ‘ROSMAP’) and Yang et al. at both the cell type (b) and subtype (c) levels. d. Heatmaps showing the significant overlap of marker genes at the subtype level between this study (labeled as ‘ROSMAP’) and Yang et al. (top: significance; bottom: the fraction of overlapped genes). The top heatmap shows the fraction of overlapped genes. The -log10(adj. p-value) are shown in the bottom heatmap to represent the significance (Fisher’s exact test, two-sided adjusted p-value < 0.05 as a cutoff). e. UMAPs showing the correspondence of pericyte subtypes between this study and Yang et al. The top UMAP shows the two subtypes of pericytes identified in this study. The bottom two UMAPs show the T-pericyte and M-pericyte signature score distribution in this study. The signature genes of T-pericyte and M-pericyte were defined in Yang et al.

Extended Data Fig. 2 Cell fraction analysis.

a. Distribution of cell fraction across six brain regions in cell subtypes. The stars represent the significance of cell types enriched(red star) or depleted(blue star) in specific regions compared to the overall fraction by the Wilcoxon rank test adjusted p-value <0.05. n = 409 in PFC; 47 in angular gyrus, mid-temporal cortex and thalamus; 45 in entorhinal cortex; 84 in hippocampus. The box starts in the first quantile (25%) and ends in the third (75%). The line inside represents the median. Two whiskers represent the maximum and minimum without outliers. b-c. Cerebrovascular cell distribution by sex. b. UMAP of brain vascular nuclei colored by sex. c. Cell fraction across sex for each cell type (left) and across cell types for male and female individuals (right). d-e. Cerebrovascular cell distribution by AD classification. d. UMAP of brain vascular nuclei colored by AD classification. e. cell fraction across AD classification for each cell type (left) and across cell types for AD and control individuals (right). f. Cell fraction distribution between control and AD individuals in overall vascular cells (left) and each cell type (middle: in all cells; right: in vascular cells) (n = 220 for AD and n = 208 for nonAD). The box starts in the first quantile (25%) and ends in the third (75%). The line inside represents the median. Two whiskers represent the maximum and minimum without outliers. g-h. UMAP of brain vascular nuclei colored by age (g) and PMI (h) showing no difference of cell distribution with age or PMI. i-j. Comparison of cell fraction between this study (labeled ‘ROSMAP’) and Yang et al. in hippocampus (i) and prefrontal cortex (j). Stars represent the statistical significance by the Wilcoxon rank test (*: adjusted p-value < 0.05, ***: adjusted p-value < 0.001). Blue stars indicate a higher fraction in Yang et al. and red stars represent a higher fraction in this study. For hippocampus, n = 42 for ROSMAP data, n = 17 for Yang et al. data; for PFC, n = 233 for ROSMAP data, n = 8 for Yang et al. data. The box starts in the first quantile (25%) and ends in the third (75%). The line inside represents the median. Two whiskers represent the maximum and minimum without outliers.

Extended Data Fig. 3 Differential gene analysis across brain regions.

a. Heatmap of the number of highly expressed brDEGs in each region and for each cell type. The intensity of the color corresponds to the quantity of brDEGs (indicated in each cell). b. Enriched Gene Ontology biological processes in each cell type. Heatmap of -log10(p-value) indicates the significance. Enrichr in R was used to perform GO enrichment (proportion test, adjusted p-value < 0.05 as cutoff, one-sided). Only regions with enriched terms were kept.

Extended Data Fig. 4 Cell-type-specific brain vasculature changes in AD.

a. The distribution of numbers of adDEGs in permutation and real analysis. The adDEGs were identified based on permuted AD classification for each individual, and the p-value was estimated by t-test. n = 428 individuals for permutation analysis. The box starts in the first quantile (25%) and ends in the third (75%). The line inside represents the median. Two whiskers represent the maximum and minimum without outliers. b. Heatmaps of the consistency of adDEGs using single-cell based (MAST) and pseudo-bulk based (edgeR) methods. The fraction of overlapped genes is shown in the top heatmap. The -log10(adj.p-value) by Fisher’s exact test represents the significance and is shown in the bottom heatmap (two-sided). c. The consistency of adDEGs in cEndo using different numbers of cells by downsampling analysis (1000, 2000, 3000, 4000, 5000 and the original 6195 cEndo cells). For each panel, the x-axis represents the significance of adDEGs in the downsampling condition listed at the top of the columns. The significance was measured by log(p-value), and the sign indicates the direction of effect size in this condition. The y-axis shows the effect size in the downsampling condition listed at the right of the rows. The effect size was measured by coefficient in MAST. d. Comparison of adDEGs across brain regions in each cell type. Each heatmap shows the significant overlap of adDEGs between brain regions in one cell type. Significance represented by -log10(adj.p-value) by Fisher’s exact test (two-sided). Six cell types with enough cells to identify adDEGs in each region are included in this analysis.

Extended Data Fig. 5 Functional enrichment of adDEGs.

a. Heatmap showing the significance of GO term overlap between adDEG sets. The -log10(adj.p-value) by Fisher’s exact test represents the significance (adj.p-value < 0.05 as a cutoff). b. Top enriched Gene Ontology biological processes in up-regulated adDEGs (left) and down-regulated adDEGs (right). Enrichr in R was used to perform GO enrichment (proportion test, adjusted p-value < 0.05 as cutoff, one-sided). The full list of enriched GO terms is shown in Supplementary Table 7.

Extended Data Fig. 6 Experimental validation of adDEGs.

a. Chromogenic RNAscope for ABCC9 and SLC6A1. Prefrontal cortex brain sections (n = 4 for AD and n = 4 for nonAD) were sectioned at 20 µm (3 images per slide), then stored at -80 °C. Sections were then prepared for RNAscope using the manufacturer’s instructions. Scale bar, 20 µm. b. Quantification of SLC6A and ABCC9 double-positive cells per image. P value was calculated by t-test. Data are presented as mean values +/− standard deviation. c. Collagen-4 and lectin-488 immunohistochemistry. Scale bar, 50 µm. d. Additional images of collagen-4 immunohistochemistry. Scale bar, 20 µm. e. Quantification of collagen-4 signal intensity. P value was calculated by t-test (n = 4 for AD and n = 4 for nonAD individuals). Data are presented as mean values +/− standard deviation.

Extended Data Fig. 7 Upstream regulators of adDEGs.

Regulator modules of adDEGs in 11 cell types. On the left of each heatmap, the first column shows if the regulator is significantly differentially expressed (adDEGs, labeled as DE) or just expressed (exp) in the corresponding cell type. The second column shows the significance of differential expression as represented by the coefficient calculated in MAST analysis. The heatmap shows -log10(p-value) to represent the significance of overlapping targets between regulators by Fisher’s exact test (two-sided).

Extended Data Fig. 8 Dynamics of cell-cell communications between vascular cell types and neuron/glial cells in AD.

a. Computational framework to infer cell-cell communications. For each cell type, a set of genes was clustered into a number of co-expressed modules. The pairwise Pearson correlation coefficient was calculated between modules for each pair of cell types. The significantly correlated modules, functional enrichment, and ligand-receptor pairs were integrated into the prediction of cell-cell communication. The output includes the interacting cell types, ligand, ligand-involved functions, receptor, receptor-involved functions, potential targets in signal receiver cell type, and direction of cell-cell communication (as determined by the changes of ligand-receptor and their co-expressed genes in the same module) in AD. b. Bar plot showing the statistical significance of the cell-cell communications, as represented by the adjusted p-value using a permutation test in each pair of interacting cell types. An adjusted p-value of 0.01 (dashed line) was used as a cutoff. The three cell pairs which had the most cell-cell communication (Ex_Per1, Astro_cEndo and cEndo_Ex) are highlighted in bold. c. Heatmap showing the up- and down-regulation of both forward and reverse cell-cell communications in AD. The purple indicates the number of forward interactions (from cell type on the left of the plot to cell type on the bottom of the plot) that were upregulated in AD (upper triangle in each square) and downregulated in AD (lower triangle in each square). The green indicates the number of reverse interactions (from the cell type on the bottom of the plot to the cell type on the left of the plot) that were upregulated in AD (upper triangle in each square) and downregulated in AD (lower triangle in each square). d. Heatmap showing the forward and reverse cell-cell communications that are both up- and down-regulated in AD. The red indicates the number of upregulated interactions in AD that are forward interactions (from cell type on the left of the plot to cell type on the bottom of the plot, lower triangle) and reverse interactions (from the cell type on the bottom of the plot to the cell type on the left of the plot, upper triangle). The blue indicates the number of downregulated interactions in AD that are forward interactions (from cell type on the left of the plot to cell type on the bottom of the plot, lower triangle) and reverse interactions (from the cell type on the bottom of the plot to the cell type on the left of the plot, upper triangle).

Extended Data Fig. 9 AD GWAS loci linked to brain vascular adDEGs.

a. Three proposed types of regulatory mechanisms to interpret the association between adDEGs and AD genetic variants: (1) SNP directly (cis) regulates adDEG; (2) SNP indirectly (trans) regulate adDEG; (3) SNP indirectly (ligand-receptor signaling) regulates adDEG. b-g. Examples of adDEGs directly regulated by AD-associated variants through linking (eQTLs, Hi-C, promoter-enhancer correlation) along with the expression changes in vascular cell types in AD shown in the boxplots on the right (n = 10,272 and 12,242 nuclei in AD and control individuals). The box starts in the first quantile (25%) and ends in the third (75%). The line inside represents the median. Two whiskers represent the maximum and minimum without outliers. Likelihood ratio test is used here (two-sided, adjusted p-value < 0.05 as cutoff. P-value is shown in the barplot.). h. The number of targets regulated by three, two, or only one of the regulatory mechanisms.

Extended Data Fig. 10 APOE4-associated DEGs and cognitive decline.

a. The number of individuals with each combination of AD classification and APOE genotype: nonAD with ε33 alleles, nonAD with ε4 allele, AD with ε33 alleles, and AD with ε4 allele. b. The comparison of apoeDEGs correlation with cognitive decline between up-regulated and down-regulated apoeDEGs in the APOE4 group in each cell type.

Supplementary information

Supplementary Information

Supplementary Figs. 1–3.

Reporting Summary

Supplementary Table 1

Metadata for the ROSMAP samples and cell fractions

Supplementary Table 2

Marker genes for cell types and subtypes

Supplementary Table 3

Functional enrichment for cell type markers

Supplementary Table 4

Brain region DEGs

Supplementary Table 5

Functional enrichment for brain region DEGs

Supplementary Table 6

adDEGs in each cell type

Supplementary Table 7

Functional enrichment of adDEGs

Supplementary Table 8

Predicted regulators of adDEGs and their targets

Supplementary Table 9

Predicted cell–cell interactions

Supplementary Table 10

One hundred and twenty-five GWAS genes and their variants, linking evidence and functional enrichment

Supplementary Table 11

GWAS TFs, targets and functions

Supplementary Table 12

GWAS ligands, cell-cell interactions and their targets

Supplementary Table 13

Summary of AD GWAS-associated vascular adDEGs

Supplementary Table 14

APOE DEGs between APOE3 and APOE4

Supplementary Table 15

Functional enrichment for APOE DEGs between APOE3 and APOE4

Supplementary Table 16

APOE genotype-dependent, cognitive decline-associated genes

Supplementary Table 17

Functional enrichment for APOE genotype-dependent, cognitive decline-associated genes

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Sun, N., Akay, L.A., Murdock, M.H. et al. Single-nucleus multiregion transcriptomic analysis of brain vasculature in Alzheimer’s disease. Nat Neurosci 26, 970–982 (2023). https://doi.org/10.1038/s41593-023-01334-3

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