Single-cell dissection of the human brain vasculature
Name
2021.04.26.440975v1.full.pdf
Description
Submitted version
Size
1.71 MB
Format
Adobe PDF
Checksum (MD5)
647f6f9997effe3e7c71b824eef916ac
Author(s)
Garcia, Francisco J
Sun, Na
Lee, Hyeseung
Godlewski, Brianna
Mathys, Hansruedi
Galani, Kyriaki
Zhou, Blake
Jiang, Xueqiao
Ng, Ayesha P
Mantero, Julio
Date Issued
2022
Journal
Nature
Publisher
Springer Science and Business Media LLC
Citation
Garcia, Francisco J, Sun, Na, Lee, Hyeseung, Godlewski, Brianna, Mathys, Hansruedi et al. 2022. "Single-cell dissection of the human brain vasculature." Nature, 603 (7903).
Version
Original manuscript
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.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Picower Institute for Learning and Memory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1038/S41586-022-04521-7
