Related Experiment Video
Updated: Jan 30, 2026
11:37
Cellular Redox Profiling Using High-content Microscopy
Published on: May 14, 2017
11.5K
Integrated Multiparametric High-Content Profiling of Endothelial Cells
Erika Wiseman1,2, Annj Zamuner3,4, Zuming Tang1,5
11 Stem Cell Hotel-Cell Phenotyping Platform, Centre for Stem Cells & Regenerative Medicine, King's College London, London, UK.
SLAS Discovery : Advancing Life Sciences R & D
|January 26, 2019
Summary
This study introduces a new method to analyze endothelial cells (ECs) using key markers. This approach helps distinguish different EC populations for cell therapy and research.
Area of Science:
- Cell Biology
- Regenerative Medicine
Background:
- Endothelial cells (ECs) exhibit significant heterogeneity, impacting their functions.
- Induced pluripotent stem cell-derived ECs (iPSC-ECFCs) and primary human umbilical vein ECs (HUVECs) are crucial research models.
- High-content analysis has been used for EC morphology and immunofluorescence studies.
Purpose of the Study:
- To develop and validate a tailored workflow for characterizing endothelial cells.
- To assess the utility of specific markers (Hoechst, vascular endothelial cadherin, activated NOTCH) in distinguishing EC populations.
- To benchmark ECs under varying conditions, including vascular endothelial growth factor (VEGF) presence.
Main Methods:
- High-resolution imaging with high-magnification water-immersion objectives.
- Staining with Hoechst, vascular endothelial cadherin (VEC), and activated NOTCH.
- Quantitative high-content multiparametric profiling using cell population software analysis.
Main Results:
- The developed workflow successfully phenotyped HUVECs and iPSC-ECFCs.
- Key markers effectively distinguished between different endothelial cell populations.
- The study presents the first parallel quantitative high-content multiparametric profiling of EC models.
Conclusions:
- A simple, effective strategy for benchmarking endothelial cells has been established.
- This approach facilitates new avenues for biological research and translational applications.
- The findings support advancements in cell therapies, disease modeling, and drug discovery.
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