期刊
CELL
卷 180, 期 4, 页码 764-+出版社
CELL PRESS
DOI: 10.1016/j.cell.2020.01.015
关键词
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资金
- Fonds voor Wetenschappelijk Onderzoek (FWO)
- AIAS-COFUND II [MSCA: 754513]
- Steno Diabetes Center Aarhus
- University of Antwerp
- Strategisch Basisonderzoek Fonds voor Wetenschappelijk Onderzoek-Vlaanderen (SB-FWO)
- Marie Curie-IEF Fellowship
- Kom op tegen Kanker (Stand up to Cancer, Flemish Cancer Society)
- Lundbeck Foundation [R219-2016-1375]
- DFF Sapere Aude starting grant [8048-00072A]
- Novo Nordisk Foundation
- Sanming Project of Medicine in Shenzhen [SZSM201612074]
- Guangdong Provincial Key Laboratory of Genome Read and Write [2017B030301011]
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center (Sun Yat-Sen University)
- National Natural Science Foundation of China [81670855]
- Key Program of Guangzhou Scientific Research Plan [3030901006074]
- VIB TechWatch program
- Methusalem funding
- FWOVlaanderen
- Foundation Against Cancer [2016-078]
- ERC Proof of Concept [ERC-713758]
- ERC Advanced Research Grant [EUERC743074]
- BGI-Research
The heterogeneity of endothelial cells (ECs) across tissues remains incompletely inventoried. We constructed an atlas of >32,000 single-EC transcriptomes from 11 mouse tissues and identified 78 EC subclusters, including Aqp7(+) intestinal capillaries and angiogenic ECs in healthy tissues. ECs from brain/testis, liver/spleen, small intestine/colon, and skeletal muscle/heart pairwise expressed partially overlapping marker genes. Arterial, venous, and lymphatic ECs shared more markers in more tissues than did heterogeneous capillary ECs. ECs from different vascular beds (arteries, capillaries, veins, lymphatics) exhibited transcriptome similarity across tissues, but the tissue (rather than the vessel) type contributed to the EC heterogeneity. Metabolic transcriptome analysis revealed a similar tissue-grouping phenomenon of ECs and heterogeneous metabolic gene signatures in ECs between tissues and between vascular beds within a single tissue in a tissue-type-dependent pattern. The EC atlas taxonomy enabled identification of EC subclusters in public scRNA-seq datasets and provides a powerful discovery tool and resource value.
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