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Single-cell dissection of the human brain vasculature

Abstract

Despite the importance of the cerebrovasculature in maintaining normal brain physiology and in understanding neurodegeneration and drug delivery to the central nervous system1, human cerebrovascular cells remain poorly characterized owing to their sparsity and dispersion. Here we perform single-cell characterization of the human cerebrovasculature using both ex vivo fresh tissue experimental enrichment and post mortem in silico sorting of human cortical tissue samples. We capture 16,681 cerebrovascular nuclei across 11 subtypes, including endothelial cells, mural cells and three distinct subtypes of perivascular fibroblast along the vasculature. We uncover human-specific expression patterns along the arteriovenous axis and determine previously uncharacterized cell-type-specific markers. We use these human-specific signatures to study changes in 3,945 cerebrovascular cells from patients with Huntington’s disease, which reveal activation of innate immune signalling in vascular and glial cell types and a concomitant reduction in the levels of proteins critical for maintenance of blood–brain barrier integrity. Finally, our study provides a comprehensive molecular atlas of the human cerebrovasculature to guide future biological and therapeutic studies.

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Fig. 1: snRNA-seq profiling of the human cerebrovasculature.
Fig. 2: Integrative analysis of human ex vivo, post mortem and mouse cerebrovascular cell types.
Fig. 3: Molecular zonation of human brain endothelial and mural cells.
Fig. 4: Perivascular fibroblasts in the human cerebrovasculature.
Fig. 5: Innate immune activation related to cerebrovascular dysfunction in HD.

Data availability

Count matrices for all cells analysed in this study have been uploaded with this submission at http://compbio.mit.edu/scBBB/. An interactive website is available at https://nsun.shinyapps.io/scbbb/. Raw sequencing data associated with Figs. 15 are available in the National Center for Biotechnology Information Gene Expression Omnibus under accession number GSE173731. Sample identifiers for ROSMAP and HD human tissue samples are listed in Supplementary Table 1. With these identifiers, data/sample acquisition for the HD samples can be accessed at https://neurobiobank.nih.gov/. ROSMAP samples and data can be accessed at https://www.radc.rush.edu. This website includes detailed documentation on variables and cross-calculations of selected variables, and the relevant Data Use Agreement and Material Transfer Agreement can also be downloaded from this site. Samples (and data regarding them) from the Boston Children’s Hospital were collected for the use of Boston Children’s Hospital investigators and their collaborators, and are not freely available. Further enquiries regarding these samples can be directed to the corresponding authors.

Code availability

The code used in this study is available at http://compbio.mit.edu/scBBB/. Code used in this study is also available upon reasonable request from the corresponding authors.

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Acknowledgements

This research was supported in part by the Intellectual and Developmental Disability Research Center (financed by NIH U54 HD090255 and P50 HD105351) and the Rosamund Stone Zander Translational Neuroscience Center at the Boston Children’s Hospital (to M.S.), a Picower Institute Innovation Fund Award and a Walter B. Brewer (1940) MIT Fund Award (to M.H.), and NIH AG054012, AG058002, AG062377, NS110453, NS115064, AG067151, AG062335, MH109978, MH119509 and HG008155 and Cure Alzheimer’s Fund CureAlz-CIRCUITS (to M.K.). ROSMAP was supported by National Institute on Aging grants P30AG10161, R01AG15819, R01AG17917 and U01AG61356. We thank A. H. Effenberger for assistance in graphics design; S. S. Pineda for assistance with differential gene expression analysis; P. Ge for assistance with the western blot experiments; and L.-L. Ho and Z. Peng, for collaborative and scientific input on experimental profiling. We also thank the NIH NeuroBioBank and the University of Alabama at Birmingham for providing the human HD and control samples used in this study.

Author information

Authors and Affiliations

Authors

Contributions

F.J.G. designed the human and mouse studies and developed the BVE protocol; N.S. conducted data analysis with assistance from F.J.G. and H.L.; B.G. and M.S. assisted in ex vivo human tissue sample acquisition; H.L. assisted with BVE snRNA-seq sample preparation; B.Z. performed vascular-related experiments and quantification; K.G. and J.M. conducted snRNA-seq post mortem sample profiling; H.M., X.J., A.P.N. and L.-H.T. provided pre-publication ROSMAP data; D.A.B. provided post mortem samples; F.J.G., N.S., M.K. and M.H. wrote the paper with comments from all authors; and M.K. and M.H. supervised the project.

Corresponding authors

Correspondence to Manolis Kellis or Myriam Heiman.

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Extended data figures and tables

Extended Data Fig. 1 Validation of Blood Vessel Enrichment (BVE) protocol.

a. qPCR of canonical cell type markers for endothelial Cldn5 (p < 0.0001), Abcb1a (p = 0.0002), Mfsd2a (p = 0.9556), mural Pdgfrb (p = 0.0388), Acta2 (p < 0.0001), Myh11 (p < 0.0001), astrocytes Aqp4 (p >0.9999), Aldh1l1 (p >0.9999), oligodendrocytes Mog (p > 0.9999), neurons Rbfox3 (p >0.9999), and microglia Aif1 (p > 0.9999) from mouse cortex, ordinary one-way ANOVA, *p < 0.05, **p < 0.01, ***p < 0.001,****p < 0.0001, n.s. = not significant. Error bars denote standard deviation of the mean from n = 3 independent biological replicates. b. Representative immunofluorescence of blood vessels enriched from mouse cortex using the BVE protocol. n = 3 independent biological replicates for immunostaining. Brightness and contrast enhanced for visualization. Scale bar, 20 μm.

Extended Data Fig. 2 Characterization of human snRNA-seq data from human temporal cortex.

a. UMAP of ex vivo dataset by patient ID. b. UMAP of ex vivo dataset by experimental protocol. c. Heatmap of top cell-type differentially-expressed genes (ctDEGs) in major cell types from ex vivo human tissue. d. UMAP sub-clustering of excitatory neurons. e. UMAP sub-clustering of inhibitory neurons. f. Correlation heatmap between ex vivo and post mortem vascular cell types.

Extended Data Fig. 3 Integrative analysis of ex vivo, post mortem, and mouse datasets.

a. UMAP plot of integrated human snRNA-seq datasets without covariate correction shown by platform and cell type and b. with covariate correction by platform and cell type c. Cell fraction distribution of single nuclei across all datasets by cerebrovasculature cell type. d. Cell number distribution of single nuclei across all datasets by cerebrovasculature cell type. e. Venn diagram overlap of genes between human post mortem vs. mouse and human ex vivo vs. mouse. f. Cell fraction and g. gene comparison of vascular cell types between mouse and human datasets. h–i. Representative functional enriched terms of human- and mouse-specific/highly expressed genes in endothelial (h) and pericytes (i). Human-mouse differentially expressed genes (hmDEGs) smooth muscle cells (j, left), and fibroblast (k, left). X-axis represents the log-transformed fold change and y-axis represents the maximal expression level. The top genes are highlighted in blue for mouse and red for human. Genes that were also cell type markers are bolded. j–k. (right panels), the representative functional enriched terms of human- and mouse-specific/highly expressed genes.

Extended Data Fig. 4 Zonation gene expression analysis of human endothelial cells.

a. Heatmap of 147 zonated transcription factors along the endothelial gradient. b. Heatmap of 76 zonated transporters along the endothelial gradient.

Extended Data Fig. 5 Zonation in human brain endothelial cells.

a. Gene zonation analysis of mouse brain endothelial cells from Vanlandewjick et al. b. Integrated zonation analysis of human and mouse brain endothelial cell profiles. Pearson correlation coefficient of shared genes shown on right. c. Indirect immunofluorescence of TSHZ2 expression in human and mouse brain cortex. d. Indirect immunofluorescence of MT1E/MT1 expression in human and mouse brain cortex. e. Indirect immunofluorescence of MT2A/MT2 expression in human and mouse brain cortex. f. Enriched Gene Ontology terms in endothelial zones. Representative images in c., d., and e. from n = 3 independent biological replicates for each marker. Brightness and contrast enhanced for visualization. Scale bar, 20 μm.

Extended Data Fig. 6 Zonation gene expression analysis of human pericytes.

a. Heatmap of zonated transcription factors along the pericyte gradient. b. Heatmap of zonated transporters along the pericyte gradient.

Extended Data Fig. 7 Zonation gene expression analysis of human SMCs.

a. Heatmap of zonated transcription factors along the SMC gradient. b. Heatmap of zonated transporters along the SMC gradient. c. Overlap matrix across the zonated pericyte and SMC clusters.

Extended Data Fig. 8 Zonation in human brain mural cells.

a. Indirect immunofluorescence of SLC30A10 expression in human and mouse brain cortex. b. Indirect immunofluorescence of GRM8 expression in human and mouse brain cortex. c. Indirect immunofluorescence of FRMD3 expression in human and mouse brain cortex. d. Indirect immunofluorescence localization of FRMD3 on ACTA2+ (known SMC marker) vessels. e. Enriched Gene Ontology terms in mural zones. Representative images in a–d from n = 3 independent biological replicates for each marker. Brightness and contrast enhanced for visualization. Scale bar, 20 μm.

Extended Data Fig. 9 Validation and pathway analyses of perivascular fibroblast subtypes.

a. Immunofluorescence staining of Type III fibroblast marker KCNMA1 on ACTA2+ vessels in human. b. Enriched Gene Ontology analysis in perivascular fibroblast subtypes. c. Pseudotime analysis of ex vivo fibroblast subtypes. d. Pseudotime analysis of ex vivo fibroblast subtypes and Pericyte 2 (note: Pericyte 1 not shown as it did not fall within any pseudotime trajectory). Representative image in a. from n = 3 independent biological replicates for each marker. Brightness and contrast enhanced for visualization. Scale bar, 20 μm.

Extended Data Fig. 10 Cerebrovascular profiling in Huntington’s disease.

a. UMAP of integrated single nuclei from post mortem control and HD human patient samples. b. UMAP of integrated cerebrovasculature cells in post mortem control and HD human patients. c. Comparison of cerebrovasculature cell annotations (in cell numbers) in this study vs. Lee et al. d. UMAP analysis of astrocyte subclusters in HD. Vascular-related astrocytes outlined in blue. e. UMAP analysis of microglia subclusters in HD. Vascular-related microglia outlined in blue. f. ChEA prediction of top 10 regulators of upregulated genes in HD endothelial, mural, and fibroblasts cells. g. Pathway analysis of the top 10 enriched upregulated pathways in HD endothelial, mural, and fibroblasts cells. h. PKR immunoreactivity in the R6/2 HD mouse model engulfs blood vessels with low CLDN5 expression. i. Western blots for tight junction proteins CLDN5 and TJP1 from human HD and control samples. Representative images in h. from n = 3 independent biological replicates for each immunostaining. Brightness and contrast in immunofluorescence enhanced for visualization. Scale bar, 20 μm.

Supplementary information

Reporting Summary

Supplementary Table 1

Patient and sample information for ex vivo, post mortem ROSMAP, and post mortem HD and controls.

Supplementary Table 2

DEGs in cerebrovasculature cell types, as reported by snRNA-seq from ex vivo and post mortem samples. Differential expression analysis is based on the non-parametric Wilcoxon rank sum test.

Supplementary Table 3

DEGs from comparisons between cross-modal ex vivo versus post mortem and cross-species human versus mouse, as reported by snRNA-seq. Differential expression analysis is based on the non-parametric Wilcoxon rank sum test.

Supplementary Table 4

DEGs in endothelial, mural and fibroblasts, as reported by snRNA-seq from the caudate nucleus and putamen of patients with HD (compared to controls), as well as WikiPathways, KEGG and GOBP analysis. The differential gene expression analysis was performed at cell-type-specific pseudo-bulk level using ACTIONet and limma with age, sex, PMI and disease group as design covariates and gene-wise single-cell-level variances as weights for the linear model.

Supplementary Table 5

DEGs in vascular-related astrocyte and microglia subclusters, as reported by snRNA-seq from the caudate nucleus and putamen of patients with HD (compared to controls). Differential expression analysis is based on the non-parametric Wilcoxon rank sum test.

Supplementary Table 6

List of antibody information.

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Garcia, F.J., Sun, N., Lee, H. et al. Single-cell dissection of the human brain vasculature. Nature 603, 893–899 (2022). https://doi.org/10.1038/s41586-022-04521-7

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