Distinguishing subtypes of endothelial cells in the mouse aorta

This study reanalyzed endothelial cell (EC) gene expression in the mouse aorta utilizing eight single-cell RNA-sequencing datasets. Contrary to the assumption that all ECs originated from the luminal surface, we identified two distinct sites of origin: aortic lumen (<i>Cytl1</i>-positive ECs) and peri-aortic microvascular (<i>Gpihbp1</i>-positive ECs). The proportions of these EC subtypes varied extensively across the eight datasets, likely due to differing experimental procedures. We deduced complete transcriptomes for each EC subtype, revealing differential gene expression and predicted functional properties. We provide marker lists and an accessible online database for gene-by-gene analysis to aid in correct cell identification and in gauging the impact of patho/physiological parameters on aortic EC gene expression.

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Publication Details

Journal
Proceedings of the National Academy of Sciences
Published
2025-12-04
DOI
https://doi.org/10.1073/pnas.2525755122
Primary Topic
Single-cell and spatial transcriptomics
Type
article
Field-Weighted Citation Impact
0.00

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article

Distinguishing subtypes of endothelial cells in the mouse aorta

Riikka Pietilä, Liqun He, Marie Jeansson, Lars Muhl et al.
Proceedings of the National Academy of Sciences
Single-cell and spatial transcriptomics
article

Distinguishing subtypes of endothelial cells in the mouse aorta

Riikka Pietilä, Liqun He, Marie Jeansson, Lars Muhl, Maarja Andaloussi Mäe, Christer Betsholtz, Loren G. Fong, Elisa Vázquez-Liébanas, Stephen G. Young, Yuyang Miao
article en

Abstract

This study reanalyzed endothelial cell (EC) gene expression in the mouse aorta utilizing eight single-cell RNA-sequencing datasets. Contrary to the assumption that all ECs originated from the luminal surface, we identified two distinct sites of origin: aortic lumen (<i>Cytl1</i>-positive ECs) and peri-aortic microvascular (<i>Gpihbp1</i>-positive ECs). The proportions of these EC subtypes varied extensively across the eight datasets, likely due to differing experimental procedures. We deduced complete transcriptomes for each EC subtype, revealing differential gene expression and predicted functional properties. We provide marker lists and an accessible online database for gene-by-gene analysis to aid in correct cell identification and in gauging the impact of patho/physiological parameters on aortic EC gene expression.

Proceedings of the National Academy of SciencesVol. 122(49)
Uppsala University (SE), University of California, Los Angeles (US), Karolinska Institutet (SE), Integrated Cardio Metabolic Centre (SE), University of Bergen (NO)
Fondation Leducq, Cancerfonden, Knut och Alice Wallenbergs Stiftelse, Vetenskapsrådet, Uppsala Universitet, Uppsala Multidisciplinary Center for Advanced Computational Science, National Heart, Lung, and Blood Institute
Openalex Percentile: Top 65%
Single-cell and spatial transcriptomics
DOIOpen Access PDFOpenAlex
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