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.
This is a preview of subscription content, access via your institution
Open Access articles citing this article.
Brain microvascular endothelial cell dysfunction in an isogenic juvenile iPSC model of Huntington’s disease
Fluids and Barriers of the CNS Open Access 30 June 2022
Cellular and Molecular Life Sciences Open Access 20 June 2022
Subscribe to Nature+
Get immediate online access to the entire Nature family of 50+ journals
Subscribe to Journal
Get full journal access for 1 year
only $3.90 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Get time limited or full article access on ReadCube.
All prices are NET prices.
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. 1–5 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.
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.
Sweeney, M. D., Zhao, Z., Montagne, A., Nelson, A. R. & Zlokovic, B. V. Blood-brain barrier: from physiology to disease and back. Physiol. Rev. 99, 21–78 (2019).
Saunders, A. et al. Molecular diversity and specializations among the cells of the adult mouse brain. Cell 174, 1015–1030 (2018).
Sabbagh, M. F. et al. Transcriptional and epigenomic landscapes of CNS and non-CNS vascular endothelial cells. eLife 7, e36187 (2018).
Vanlandewijck, M. et al. A molecular atlas of cell types and zonation in the brain vasculature. Nature 554, 475–480 (2018).
Montagne, A. et al. Blood-brain barrier breakdown in the aging human hippocampus. Neuron 85, 296–302 (2015).
Sweeney, M. D., Kisler, K., Montagne, A., Toga, A. W. & Zlokovic, B. V. The role of brain vasculature in neurodegenerative disorders. Nat. Neurosci. 21, 1318–1331 (2018).
Lee, Y.-K., Uchida, H., Smith, H., Ito, A. & Sanchez, T. The isolation and molecular characterization of cerebral microvessels. Nat. Protoc. 14, 3059–3081 (2019).
Mathys, H. et al. Single-cell transcriptomic analysis of Alzheimer’s disease. Nature 570, 332–337 (2019).
Lee, H. et al. Cell type-specific transcriptomics reveals that mutant Huntingtin leads to mitochondrial RNA release and neuronal innate immune activation. Neuron 107, 891–908 (2020).
Bennett, D. A. et al. Religious Orders Study and Rush Memory and Aging Project. J. Alzheimer’s Dis. 64, S161–S189 (2018).
Zhao, Z. et al. Central role for PICALM in amyloid-β blood-brain barrier transcytosis and clearance. Nat. Neurosci. 18, 978–987 (2015).
Lim, Y.-H. et al. Identification of long noncoding RNAs involved in muscle differentiation. PLoS ONE 13, e0193898 (2018).
Joutel, A. et al. Notch3 mutations in CADASIL, a hereditary adult-onset condition causing stroke and dementia. Nature 383, 707–710 (1996).
Park, C., Kim, T. M. & Malik, A. B. Transcriptional regulation of endothelial cell and vascular development. Circ. Res. 112, 1380–1400 (2013).
Ley, K., Laudanna, C., Cybulsky, M. I. & Nourshargh, S. Getting to the site of inflammation: the leukocyte adhesion cascade updated. Nat. Rev. Immunol. 7, 678–689 (2007).
Attwell, D., Mishra, A., Hall, C. N., O’Farrell, F. M. & Dalkara, T. What is a pericyte? J. Cereb. Blood Flow Metab. 36, 451–455 (2016).
Morrone, C. D., Bishay, J. & McLaurin, J. Potential role of venular amyloid in Alzheimer’s disease pathogenesis. Int. J. Mol. Sci. 21, 1985 (2020).
Rajan, A. M., Ma, R. C., Kocha, K. M., Zhang, D. J. & Huang, P. Dual function of perivascular fibroblasts in vascular stabilization in zebrafish. PLoS Genet. 16, e1008800 (2020).
Muhl, L. et al. Single-cell analysis uncovers fibroblast heterogeneity and criteria for fibroblast and mural cell identification and discrimination. Nat. Commun. 11, 3953 (2020).
Fernández-Klett, F. et al. Early loss of pericytes and perivascular stromal cell-induced scar formation after stroke. J. Cereb. Blood Flow Metab. 33, 428–439 (2013).
Zeisel, A. et al. Molecular architecture of the mouse nervous system. Cell 174, 999–1014 (2018).
Dorrier, C. E. et al. CNS fibroblasts form a fibrotic scar in response to immune cell infiltration. Nat. Neurosci. 24, 234–244 (2021).
Bonney, S. K., Sullivan, L. T., Cherry, T. J., Daneman, R. & Shih, A. Y. Distinct features of brain perivascular fibroblasts and mural cells revealed by in vivo two-photon imaging. J. Cereb. Blood Flow Metab., https://doi.org/10.1177/0271678X211068528 (2021).
Chinnery, P. F. et al. Clinical features and natural history of neuroferritinopathy caused by the FTL1 460InsA mutation. Brain 130, 110–119 (2007).
Tadic, V. et al. Primary familial brain calcification with known gene mutations. JAMA Neurol. 72, 460–467 (2015).
MacDonald, M. E. et al. A novel gene containing a trinucleotide repeat that is expanded and unstable on Huntington’s disease chromosomes. Cell 72, 971–983 (1993).
Drouin-Ouellet, J. et al. Cerebrovascular and blood–brain barrier impairments in Huntington’s disease: potential implications for its pathophysiology. Ann. Neurol. 78, 160–177 (2015).
Chen, J. J., Salat, D. H. & Rosas, H. D. Complex relationships between cerebral blood flow and brain atrophy in early Huntington’s disease. Neuroimage 59, 1043–1051 (2012).
Harris, G. J. et al. Reduced basal ganglia blood flow and volume in pre-symptomatic, gene-tested persons at-risk for Huntington’s disease. Brain 122, 1667–1678 (1999).
Hua, J., Unschuld, P. G., Margolis, R. L., van Zijl, P. C. M. & Ross, C. A. Elevated arteriolar cerebral blood volume in prodromal Huntington’s disease. Mov. Disord. 29, 396–401 (2014).
Di Pardo, A. et al. Impairment of blood–brain barrier is an early event in R6/2 mouse model of Huntington disease. Sci Rep. 7, 41316 (2017).
Padel, T. et al. Brain pericyte activation occurs early in Huntington’s disease. Exp. Neurol. 305, 139–150 (2018).
Liu, H. et al. Huntingtin silencing delays onset and slows progression of Huntington’s disease: a biomarker study. Brain 144, 3101–3113 (2021).
Ben-Zvi, A. et al. Mfsd2a is critical for the formation and function of the blood–brain barrier. Nature 509, 507–511 (2014).
Lim, R. G. et al. Huntington’s disease iPSC-derived brain microvascular endothelial cells reveal WNT-mediated angiogenic and blood-brain barrier deficits. Cell Rep. 19, 1365–1377 (2017).
Daniels, B. P. & Klein, R. S. Viral sensing at the blood–brain barrier: new roles for innate immunity at the CNS vasculature. Clin. Pharmacol. Ther. 97, 372–379 (2015).
Song, H. W. et al. Transcriptomic comparison of human and mouse brain microvessels. Sci Rep. 10, 12358 (2020).
Mondo, E. et al. A developmental analysis of juxtavascular microglia dynamics and interactions with the vasculature. J. Neurosci. 40, 6503–6521 (2020).
Fujioka, T., Kaneko, N. & Sawamoto, K. Blood vessels as a scaffold for neuronal migration. Neurochem. Int. 126, 69–73 (2019).
Wingo, A. P. et al. Shared proteomic effects of cerebral atherosclerosis and Alzheimer’s disease on the human brain. Nat. Neurosci. 23, 696–700 (2020).
Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).
McGinnis, C. S., Murrow, L. M. & Gartner, Z. J. DoubletFinder: doublet detection in single-cell RNA sequencing data using artificial nearest neighbors. Cell Syst. 8, 329–337 (2019).
Wang, D. et al. Comprehensive functional genomic resource and integrative model for the human brain. Science 362, eaat8464 (2018).
Finak, G. et al. MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data. Genome Biol. 16, 278 (2015).
Chen, E. Y. et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinform. 14, 128 (2013).
Xie, Z. et al. Gene set knowledge discovery with Enrichr. Curr. Protoc. 1, e90 (2021).
Kuleshov, M. V. et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 44, W90–W97 (2016).
Ashburner, M. et al. Gene Ontology: tool for the unification of biology. Nat. Genet. 25, 25–29 (2000).
The Gene Ontology Consortium. The Gene Ontology resource: 20 years and still GOing strong. Nucleic Acids Res. 47, D330–D338 (2019).
Cao, J. et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature 566, 496–502 (2019).
Mohammadi, S., Davila-Velderrain, J. & Kellis, M. A multiresolution framework to characterize single-cell state landscapes. Nat. Commun. 11, 5399 (2020).
Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).
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.
The authors declare no competing interests.
Peer review information
Nature thanks the anonymous reviewers for their contribution to the peer review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data figures and tables
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.
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.
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.
a. Heatmap of 147 zonated transcription factors along the endothelial gradient. b. Heatmap of 76 zonated transporters along the endothelial gradient.
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.
a. Heatmap of zonated transcription factors along the pericyte gradient. b. Heatmap of zonated transporters along the pericyte gradient.
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.
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.
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.
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.
Patient and sample information for ex vivo, post mortem ROSMAP, and post mortem HD and controls.
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.
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.
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.
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.
List of antibody information.
About this article
Cite this article
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
This article is cited by
Brain microvascular endothelial cell dysfunction in an isogenic juvenile iPSC model of Huntington’s disease
Fluids and Barriers of the CNS (2022)
Nature Reviews Neurology (2022)
Cellular and Molecular Life Sciences (2022)