Raw Data Library
About
Aims and ScopeAdvisory Board Members
More
Who We Are?
User Guide
Green Science
​
​
EN
Sign inGet started
​
​

About
Aims and ScopeAdvisory Board Members
More
Who We Are?
User GuideGreen Science

Language

Sign inGet started
RDL logo

Verified research datasets. Instant access. Built for collaboration.

Navigation

About

Aims and Scope

Advisory Board Members

More

Who We Are?

Contact

Add Raw Data

User Guide

Legal

Privacy Policy

Terms of Service

Support

Got an issue? Email us directly.

Email: info@rawdatalibrary.netOpen Mail App
​
​

© 2026 Raw Data Library. All rights reserved.
PrivacyTermsContact
  1. Raw Data Library
  2. /
  3. Publications
  4. /
  5. Single-Cell RNA Sequencing Reveals Renal Endothelium Heterogeneity and Metabolic Adaptation to Water Deprivation

Verified authors • Institutional access • DOI aware
50,000+ researchers120,000+ datasets90% satisfaction
Article
en
2019

Single-Cell RNA Sequencing Reveals Renal Endothelium Heterogeneity and Metabolic Adaptation to Water Deprivation

0 Datasets

0 Files

en
2019
Vol 31 (1)
Vol. 31
DOI: 10.1681/asn.2019080832

Get instant academic access to this publication’s datasets.

Create free accountHow it works

Frequently asked questions

Is access really free for academics and students?

Yes. After verification, you can browse and download datasets at no cost. Some premium assets may require author approval.

How is my data protected?

Files are stored on encrypted storage. Access is restricted to verified users and all downloads are logged.

Can I request additional materials?

Yes, message the author after sign-up to request supplementary files or replication code.

Advance your research today

Join 50,000+ researchers worldwide. Get instant access to peer-reviewed datasets, advanced analytics, and global collaboration tools.

Get free academic accessLearn more
✓ Immediate verification • ✓ Free institutional access • ✓ Global collaboration
Access Research Data

Join our academic network to download verified datasets and collaborate with researchers worldwide.

Get Free Access
Institutional SSO
Secure
This PDF is not available in different languages.
No localized PDFs are currently available.
Peter Carmeliet
Peter Carmeliet

Aarhus University

Verified
Sébastien J. Dumas
Elda Meta
Mila Borri
+24 more

Abstract

Significance Statement The specialized vessels comprising the renal vasculature are characterized by highly differentiated renal endothelial cell types, but this heterogeneity has been poorly inventoried. Using single-cell RNA sequencing, the authors developed a high-resolution atlas of mouse renal endothelial cells. They also investigated how medullary renal endothelial cells adapt to a switch from diuresis to antidiuresis. This study describes the molecular and metabolic adaptation of medullary renal endothelial cells to dehydration, and uncovers a role for mitochondrial oxidative phosphorylation in hyperosmolarity conditions to allow for urine concentration. The authors’ atlas of mouse renal endothelial cells provides a resource for future studies, and their findings may provide insights into cardiometabolic or kidney diseases involving hyperosmolarity and dehydration, in which urine concentration capacity is perturbed. Background Renal endothelial cells from glomerular, cortical, and medullary kidney compartments are exposed to different microenvironmental conditions and support specific kidney processes. However, the heterogeneous phenotypes of these cells remain incompletely inventoried. Osmotic homeostasis is vitally important for regulating cell volume and function, and in mammals, osmotic equilibrium is regulated through the countercurrent system in the renal medulla, where water exchange through endothelium occurs against an osmotic pressure gradient. Dehydration exposes medullary renal endothelial cells to extreme hyperosmolarity, and how these cells adapt to and survive in this hypertonic milieu is unknown. Methods We inventoried renal endothelial cell heterogeneity by single-cell RNA sequencing >40,000 mouse renal endothelial cells, and studied transcriptome changes during osmotic adaptation upon water deprivation. We validated our findings by immunostaining and functionally by targeting oxidative phosphorylation in a hyperosmolarity model in vitro and in dehydrated mice in vivo . Results We identified 24 renal endothelial cell phenotypes (of which eight were novel), highlighting extensive heterogeneity of these cells between and within the cortex, glomeruli, and medulla. In response to dehydration and hypertonicity, medullary renal endothelial cells upregulated the expression of genes involved in the hypoxia response, glycolysis, and—surprisingly—oxidative phosphorylation. Endothelial cells increased oxygen consumption when exposed to hyperosmolarity, whereas blocking oxidative phosphorylation compromised endothelial cell viability during hyperosmotic stress and impaired urine concentration during dehydration. Conclusions This study provides a high-resolution atlas of the renal endothelium and highlights extensive renal endothelial cell phenotypic heterogeneity, as well as a previously unrecognized role of oxidative phosphorylation in the metabolic adaptation of medullary renal endothelial cells to water deprivation.

How to cite this publication

Sébastien J. Dumas, Elda Meta, Mila Borri, Jermaine Goveia, Kateřina Rohlenová, Nadine V. Conchinha, Kim D. Falkenberg, Laure-Anne Teuwen, Laura de Rooij, Joanna Kalucka, Rongyuan Chen, Shawez Khan, Federico Taverna, Weisi Lu, Magdalena Parys, Carla De Legher, Stefan Vinckier, Tobias K. Karakach, Luc Schoonjans, Lin Lin, Lars Bolund, Mieke Dewerchin, Guy Eelen, Ton J. Rabelink, Xuri Li, Yonglun Luo, Peter Carmeliet (2019). Single-Cell RNA Sequencing Reveals Renal Endothelium Heterogeneity and Metabolic Adaptation to Water Deprivation. , 31(1), DOI: https://doi.org/10.1681/asn.2019080832.

Related publications

Why join Raw Data Library?

Quality

Datasets shared by verified academics with rich metadata and previews.

Control

Authors choose access levels; downloads are logged for transparency.

Free for Academia

Students and faculty get instant access after verification.

Publication Details

Type

Article

Year

2019

Authors

27

Datasets

0

Total Files

0

Language

en

DOI

https://doi.org/10.1681/asn.2019080832

Join Research Community

Access datasets from 50,000+ researchers worldwide with institutional verification.

Get Free Access