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Single-Cell Transcriptome Atlas of Murine Endothelial Cells
Author(s) -
Joanna Kalucka,
Laura de Rooij,
Jermaine Goveia,
Kateřina Rohlenová,
Sébastien J. Dumas,
Elda Meta,
Nadine V. Conchinha,
Federico Taverna,
Laure-Anne Teuwen,
Koen Veys,
Melissa GarcíaCaballero,
Shawez Khan,
Vincent Geldhof,
Liliana Sokol,
Rongyuan Chen,
Lucas Treps,
Mila Borri,
Pauline de Zeeuw,
Charlotte Dubois,
Tobias K. Karakach,
Kim D. Falkenberg,
Magdalena Parys,
Xiangke Yin,
Stefan Vinckier,
Yuxiang Du,
Robert A. Fenton,
Luc Schoonjans,
Mieke Dewerchin,
Guy Eelen,
Bernard Thienpont,
Lin Lin,
Lars Bolund,
Xuri Li,
Yonglun Luo,
Peter Carmeliet
Publication year - 2020
Publication title -
cell
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 26.304
H-Index - 776
eISSN - 1097-4172
pISSN - 0092-8674
DOI - 10.1016/j.cell.2020.01.015
Subject(s) - biology , transcriptome , atlas (anatomy) , microbiology and biotechnology , cell , computational biology , genetics , gene , gene expression , anatomy
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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