Single-Cell Analysis of the Normal Mouse Aorta Reveals Functionally Distinct Endothelial Cell Populations
Name
nihms-1534854.pdf
Description
Accepted version
Size
4.89 MB
Format
Unknown
Checksum (MD5)
7213e871088c7c8a240456ceb5bdb027
Author(s)
Kalluri, Aditya S.
Vellarikkal, Shamsudheen K.
Edelman, Elazer R.
Nguyen, Lan
Subramanian, Ayshwarya
Ellinor, Patrick T.
Regev, Aviv
Kathiresan, Sekar
Gupta, Rajat M.
Date Issued
July 2019
Journal
Circulation
Publisher
Ovid Technologies (Wolters Kluwer Health)
Version
Author's final manuscript
Abstract
Background: The cells that form the arterial wall contribute to multiple vascular diseases. The extent of cellular heterogeneity within these populations has not been fully characterized. Recent advances in single-cell RNA-sequencing make it possible to identify and characterize cellular subpopulations. Methods: We validate a method for generating a droplet-based single-cell atlas of gene expression in a normal blood vessel. Enzymatic dissociation of 4 whole mouse aortas was followed by single-cell sequencing of >10 000 cells. Results: Clustering analysis of gene expression from aortic cells identified 10 populations of cells representing each of the main arterial cell types: fibroblasts, vascular smooth muscle cells, endothelial cells (ECs), and immune cells, including monocytes, macrophages, and lymphocytes. The most significant cellular heterogeneity was seen in the 3 distinct EC populations. Gene set enrichment analysis of these EC subpopulations identified a lymphatic EC cluster and 2 other populations more specialized in lipoprotein handling, angiogenesis, and extracellular matrix production. These subpopulations persist and exhibit similar changes in gene expression in response to a Western diet. Immunofluorescence for Vcam1 and Cd36 demonstrates regional heterogeneity in EC populations throughout the aorta. Conclusions: We present a comprehensive single-cell atlas of all cells in the aorta. By integrating expression from >1900 genes per cell, we are better able to characterize cellular heterogeneity compared with conventional approaches. Gene expression signatures identify cell subpopulations with vascular disease-relevant functions.
MIT Department
Massachusetts Institute of Technology. Institute for Medical Engineering & Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1161/circulationaha.118.038362
