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Updated: Jan 24, 2026

Flow Cytometry Analysis of Immune Cells Within Murine Aortas
Published on: July 1, 2011
Single-Cell Analysis of the Normal Mouse Aorta Reveals Functionally Distinct Endothelial Cell Populations
Aditya S Kalluri1,2, Shamsudheen K Vellarikkal1,3, Elazer R Edelman2,4
1Broad Institute of MIT and Harvard University, Cambridge, MA (A.S.K., S.K.V., L.N., A.S., P.T.E., A.R., S.K., R.M.G.).
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.
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