Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

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

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

Subjects

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

This article has been updated

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.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

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.

Similar content being viewed by others

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.

Change history

References

  1. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Sweeney, M. D., Sagare, A. P. & Zlokovic, B. V. Blood–brain barrier breakdown in Alzheimer disease and other neurodegenerative disorders. Nat. Rev. Neurol. 14, 133–150 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Vanlandewijck, M. et al. A molecular atlas of cell types and zonation in the brain vasculature. Nature 554, 475–480 (2018).

    Article  CAS  PubMed  Google Scholar 

  4. Garcia, F. J. et al. Single-cell dissection of the human brain vasculature. Nature 603, 893–899 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Yang, A. C. et al. A human brain vascular atlas reveals diverse mediators of Alzheimer’s risk. Nature 603, 885–892 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Wilhelm, I., Nyúl-Tóth, Á., Suciu, M., Hermenean, A. & Krizbai, I. A. Heterogeneity of the blood–brain barrier. Tissue Barriers 4, e1143544 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Tran, K. A. et al. Endothelial β-catenin signaling is required for maintaining adult blood–brain barrier integrity and central nervous system homeostasis. Circulation 133, 177–186 (2016).

    Article  CAS  PubMed  Google Scholar 

  8. Slater, S. C. et al. MicroRNA-532-5p regulates pericyte function by targeting the transcription regulator BACH1 and Angiopoietin-1. Mol. Ther. 26, 2823–2837 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Zhang, X. et al. High-resolution mapping of brain vasculature and its impairment in the hippocampus of Alzheimer’s disease mice. Natl Sci. Rev. 6, 1223–1238 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Lau, S.-F., Cao, H., Fu, A. K. Y. & Ip, N. Y. Single-nucleus transcriptome analysis reveals dysregulation of angiogenic endothelial cells and neuroprotective glia in Alzheimer’s disease. Proc. Natl Acad. Sci. USA 117, 25800–25809 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Velmeshev, D. et al. Single-cell genomics identifies cell type–specific molecular changes in autism. Science 364, 685–689 (2019).

  12. Ruzicka, W. B. et al. Single-cell dissection of schizophrenia reveals neurodevelopmental-synaptic axis and transcriptional resilience. Preprint at medRxiv https://doi.org/10.1101/2020.11.06.20225342 (2020).

  13. Montagne, A. et al. Blood–brain barrier breakdown in the aging human hippocampus. Neuron 85, 296–302 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Elali, A. & Rivest, S. The role of ABCB1 and ABCA1 in beta-amyloid clearance at the neurovascular unit in Alzheimer’s disease. Front. Physiol. 4, 45 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. van Assema, D. M. E. et al. Blood–brain barrier P-glycoprotein function in Alzheimer’s disease. Brain 135, 181–189 (2012).

    Article  PubMed  Google Scholar 

  16. Hamilton, N. B., Attwell, D. & Hall, C. N. Pericyte-mediated regulation of capillary diameter: a component of neurovascular coupling in health and disease. Front. Neuroenergetics 2, 5 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  17. Nortley, R. et al. Amyloid β oligomers constrict human capillaries in Alzheimer’s disease via signaling to pericytes. Science 365, eaav9518 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Wang, F. et al. RNAscope: a novel in situ RNA analysis platform for formalin-fixed, paraffin-embedded tissues. J. Mol. Diagn. 14, 22–29 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Bhatia, S., Kim, W. S., Shepherd, C. E. & Halliday, G. M. Apolipoprotein D upregulation in Alzheimer’s disease but not frontotemporal dementia. J. Mol. Neurosci. 67, 125–132 (2019).

    Article  CAS  PubMed  Google Scholar 

  20. Leng, K. et al. Molecular characterization of selectively vulnerable neurons in Alzheimer’s disease. Nat. Neurosci. 24, 276–287 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Szklarczyk, D. et al. The STRING database in 2021: customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res. 49, D605–D612 (2021).

    Article  CAS  PubMed  Google Scholar 

  22. Kim, K.-J. et al. STAT3 activation in endothelial cells is important for tumor metastasis via increased cell adhesion molecule expression. Oncogene 36, 5445–5459 (2017).

    Article  CAS  PubMed  Google Scholar 

  23. Wei, D. et al. Stat3 activation regulates the expression of vascular endothelial growth factor and human pancreatic cancer angiogenesis and metastasis. Oncogene 22, 319–329 (2003).

    Article  CAS  PubMed  Google Scholar 

  24. Zhang, B. et al. A dynamic H3K27ac signature identifies VEGFA-stimulated endothelial enhancers and requires EP300 activity. Genome Res. 23, 917–927 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Marconcini, L. et al. c-fos-induced growth factor/vascular endothelial growth factor D induces angiogenesis in vivo and in vitro. Proc. Natl Acad. Sci. USA 96, 9671–9676 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Battle, T. E., Lynch, R. A. & Frank, D. A. Signal transducer and activator of transcription 1 activation in endothelial cells is a negative regulator of angiogenesis. Cancer Res. 66, 3649–3657 (2006).

    Article  CAS  PubMed  Google Scholar 

  27. Paneni, F. et al. Deletion of the activated protein-1 transcription factor JunD induces oxidative stress and accelerates age-related endothelial dysfunction. Circulation 127, 1229–1240 (2013).

    Article  CAS  PubMed  Google Scholar 

  28. Wang, N. et al. c-Jun triggers apoptosis in human vascular endothelial cells. Circ. Res. 85, 387–393 (1999).

    Article  CAS  PubMed  Google Scholar 

  29. Mahamud, M. R. et al. GATA2 controls lymphatic endothelial cell junctional integrity and lymphovenous valve morphogenesis through miR-126. Development 146, dev184218 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Chen, M. B. et al. Brain endothelial cells are exquisite sensors of age-related circulatory cues. Cell Rep. 30, 4418–4432 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Zhao, L. et al. Pharmacologically reversible zonation-dependent endothelial cell transcriptomic changes with neurodegenerative disease associations in the aged brain. Nat. Commun. 11, 4413 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Ohnesorge, N. et al. Erk5 activation elicits a vasoprotective endothelial phenotype via induction of Kruppel-like factor 4 (KLF4). J. Biol. Chem. 285, 26199–26210 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Pugacheva, E. M. et al. CTCF mediates chromatin looping via N-terminal domain-dependent cohesin retention. Proc. Natl Acad. Sci. USA 117, 2020–2031 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Xu, L., Nirwane, A. & Yao, Y. Basement membrane and blood–brain barrier. Stroke Vasc. Neurol. 4, 78–82 (2019).

    Article  PubMed  Google Scholar 

  35. McMillin, M. A. et al. TGFβ1 exacerbates blood–brain barrier permeability in a mouse model of hepatic encephalopathy via upregulation of MMP9 and downregulation of claudin-5. Lab. Invest. 95, 903–913 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Rustenhoven, J. et al. TGF-beta1 regulates human brain pericyte inflammatory processes involved in neurovasculature function. J. Neuroinflammation 13, 37 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  37. Kashima, R. & Hata, A. The role of TGF-β superfamily signaling in neurological disorders. Acta Biochim. Biophys. Sin. 50, 106–120 (2018).

    Article  CAS  PubMed  Google Scholar 

  38. van Cruijsen, H., Giaccone, G. & Hoekman, K. Epidermal growth factor receptor and angiogenesis: opportunities for combined anticancer strategies. Int. J. Cancer 117, 883–888 (2005).

    Article  CAS  PubMed  Google Scholar 

  39. Jansen, I. E. et al. Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer’s disease risk. Nat. Genet. 51, 404–413 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Kunkle, B. W. et al. Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing. Nat. Genet. 51, 414–430 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Kang, S. et al. Potential novel genes for late-onset Alzheimer’s disease in East-Asian descent identified by APOE-stratified genome-wide association study. J. Alzheimers Dis. 82, 1451–1460 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Pallejà, A., Horn, H., Eliasson, S. & Jensen, L. J. DistiLD database: diseases and traits in linkage disequilibrium blocks. Nucleic Acids Res. 40, D1036–D1040 (2012).

    Article  PubMed  Google Scholar 

  43. Buniello, A. et al. The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Res. 47, D1005–D1012 (2019).

    Article  CAS  PubMed  Google Scholar 

  44. Chang, T.-Y., Yamauchi, Y., Hasan, M. T. & Chang, C. Cellular cholesterol homeostasis and Alzheimer’s disease. J. Lipid Res. 58, 2239–2254 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Sun, Y. et al. Nuclear receptor RORα regulates pathologic retinal angiogenesis by modulating SOCS3-dependent inflammation. Proc. Natl Acad. Sci. USA 112, 10401–10406 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Saint-Pol, J. et al. Brain pericytes ABCA1 expression mediates cholesterol efflux but not cellular amyloid-β peptide accumulation. J. Alzheimers Dis. 30, 489–503 (2012).

    Article  CAS  PubMed  Google Scholar 

  47. Robert, J., Osto, E. & von Eckardstein, A. The endothelium is both a target and a barrier of HDL’s protective functions. Cells 10, 1041 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Husemann, J. & Silverstein, S. C. Expression of scavenger receptor class B, type I, by astrocytes and vascular smooth muscle cells in normal adult mouse and human brain and in Alzheimer’s disease brain. Am. J. Pathol. 158, 825–832 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Rothaug, M., Becker-Pauly, C. & Rose-John, S. The role of interleukin-6 signaling in nervous tissue. Biochim. Biophys. Acta 1863, 1218–1227 (2016).

    Article  CAS  PubMed  Google Scholar 

  50. Choi, J. M., Rotimi, O. O., O’Carroll, S. J. & Nicholson, L. F. B. IL-6 stimulates a concentration-dependent increase in MCP-1 in immortalised human brain endothelial cells. F1000Res. 5, 270 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  51. Wei, G. et al. Ets1 and Ets2 are required for endothelial cell survival during embryonic angiogenesis. Blood 114, 1123–1130 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Park, R. et al. Yap is required for ependymal integrity and is suppressed in LPA-induced hydrocephalus. Nat. Commun. 7, 10329 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Xie, C. et al. Yap1 protein regulates vascular smooth muscle cell phenotypic switch by interaction with myocardin. J. Biol. Chem. 287, 14598–14605 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Shen, T. et al. YAP1 plays a key role of the conversion of normal fibroblasts into cancer-associated fibroblasts that contribute to prostate cancer progression. J. Exp. Clin. Cancer Res. 39, 36 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Go, G.-W. & Mani, A. Low-density lipoprotein receptor (LDLR) family orchestrates cholesterol homeostasis. Yale J. Biol. Med. 85, 19–28 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. Thambisetty, M., Beason-Held, L., An, Y., Kraut, M. A. & Resnick, S. M. APOE ε4 genotype and longitudinal changes in cerebral blood flow in normal aging. Arch. Neurol. 67, 93–98 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  57. Leaston, J. et al. Neurovascular imaging with QUTE-CE MRI in APOE4 rats reveals early vascular abnormalities. PLoS ONE 16, e0256749 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Montagne, A. et al. APOE4 leads to blood–brain barrier dysfunction predicting cognitive decline. Nature 581, 71–76 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Bennett, D. A. et al. Religious orders study and rush memory and aging project. J. Alzheimers Dis. 64, S161–S189 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  60. Bennett, D. A. et al. Natural history of mild cognitive impairment in older persons. Neurology 59, 198–205 (2002).

    Article  CAS  PubMed  Google Scholar 

  61. Bennett, D. A. et al. Apolipoprotein E ε4 allele, AD pathology, and the clinical expression of Alzheimer’s disease. Neurology 60, 246–252 (2003).

    Article  CAS  PubMed  Google Scholar 

  62. Bennett, D. A. et al. Neuropathology of older persons without cognitive impairment from two community-based studies. Neurology 66, 1837–1844 (2006).

    Article  CAS  PubMed  Google Scholar 

  63. Bennett, D. A., Schneider, J. A., Wilson, R. S., Bienias, J. L. & Arnold, S. E. Neurofibrillary tangles mediate the association of amyloid load with clinical Alzheimer disease and level of cognitive function. Arch. Neurol. 61, 378–384 (2004).

    Article  PubMed  Google Scholar 

  64. Bennett, D. A. et al. Decision rules guiding the clinical diagnosis of Alzheimer’s disease in two community-based cohort studies compared to standard practice in a clinic-based cohort study. Neuroepidemiology 27, 169–176 (2006).

    Article  PubMed  Google Scholar 

  65. Mathys, H. et al. Single-cell transcriptomic analysis of Alzheimer’s disease. Nature 570, 332–337 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Zheng, G. X. Y. et al. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8, 14049 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Wang, D. et al. Comprehensive functional genomic resource and integrative model for the human brain. Science 362, eaat8464 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. 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).

    Article  PubMed  PubMed Central  Google Scholar 

  72. Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

    Article  CAS  PubMed  Google Scholar 

  73. Bankhead, P. et al. QuPath: open source software for digital pathology image analysis. Sci. Rep. 7, 16878 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  74. Chen, E. Y. et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics 14, 128 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  75. Xie, Z. et al. Gene set knowledge discovery with Enrichr. Curr. Protoc. 1, e90 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Kuleshov, M. V. et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 44, W90–W97 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Ashburner, M. et al. Gene Ontology: tool for the unification of biology. Nat. Genet. 25, 25–29 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. The Gene Ontology Consortium. The Gene Ontology Resource: 20 years and still GOing strong. Nucleic Acids Res. 47, D330–D338 (2019).

    Article  Google Scholar 

  79. Jin, S. et al. Inference and analysis of cell–cell communication using CellChat. Nat. Commun. 12, 1088 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Efremova, M., Vento-Tormo, M., Teichmann, S. A. & Vento-Tormo, R. CellPhoneDB: inferring cell–cell communication from combined expression of multi-subunit ligand-receptor complexes. Nat. Protoc. 15, 1484–1506 (2020).

    Article  CAS  PubMed  Google Scholar 

  81. Shao, X. et al. CellTalkDB: a manually curated database of ligand–receptor interactions in humans and mice. Brief. Bioinform. 22, bbaa269 (2021).

    Article  PubMed  Google Scholar 

  82. Cabello-Aguilar, S. et al. SingleCellSignalR: inference of intercellular networks from single-cell transcriptomics. Nucleic Acids Res. 48, e55 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Park, Y. P. & Kellis, M. CoCoA-diff: counterfactual inference for single-cell gene expression analysis. Genome Biol. 22, 228 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Barber, R. F. & Candès, E. J. Controlling the false discovery rate via knockoffs. Ann. Stat. 43, 2055–2085 (2015).

    Article  Google Scholar 

  85. Wang, Y. & Blei, D. M. The blessings of multiple causes. J. Am. Stat. Assoc. 114, 1574–1596 (2019).

    Article  CAS  Google Scholar 

  86. Zhu, Z., Fan, Y., Kong, Y., Lv, J. & Sun, F. DeepLINK: deep learning inference using knockoffs with applications to genomics. Proc. Natl Acad. Sci. USA 118, e2104683118 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Jiang, T., Li, Y. & Motsinger-Reif, A. A. Knockoff boosted tree for model-free variable selection. Bioinformatics 37, 976–983 (2021).

    Article  CAS  PubMed  Google Scholar 

  88. Candès, E., Fan, Y., Janson, L. & Lv, J. Panning for gold: ‘model‐X’ knockoffs for high dimensional controlled variable selection. J. R. Stat. Soc. Series B Stat. Methodol. 80, 551–577 (2018).

    Article  Google Scholar 

  89. Kaul, A., Bhattacharyya, S. & Ay, F. Identifying statistically significant chromatin contacts from Hi-C data with FitHiC2. Nat. Protoc. 15, 991–1012 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Lalonde, S. et al. Integrative analysis of vascular endothelial cell genomic features identifies AIDA as a coronary artery disease candidate gene. Genome Biol. 20, 133 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  91. Niskanen, H. et al. Endothelial cell differentiation is encompassed by changes in long range interactions between inactive chromatin regions. Nucleic Acids Res. 46, 1724–1740 (2018).

    Article  CAS  PubMed  Google Scholar 

  92. Mumbach, M. R. et al. Enhancer connectome in primary human cells identifies target genes of disease-associated DNA elements. Nat. Genet. 49, 1602–1612 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Boix, C. A., James, B. T., Park, Y. P., Meuleman, W. & Kellis, M. Regulatory genomic circuitry of human disease loci by integrative epigenomics. Nature 590, 300–307 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Aguet, F. et al. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 369, 1318–1330 (2020).

    Article  CAS  Google Scholar 

Download references

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.

Author information

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

Authors
  1. Na Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Leyla Anne Akay
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mitchell H. Murdock
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yongjin Park
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Fabiola Galiana-Melendez
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Adele Bubnys
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Kyriaki Galani
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Hansruedi Mathys
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Xueqiao Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Ayesha P. Ng
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. David A. Bennett
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Li-Huei Tsai
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Manolis Kellis
    View author publications

    You can also search for this author in PubMed Google Scholar

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.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

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

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41593-023-01334-3

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing