Integrated Multiparametric High-Content Profiling of Endothelial Cells

Erika Wiseman, Annj Zamuner, Zuming Tang, James Rogers, Sabrina Munir, Lucy Di Silvio, Davide Danovi, Lorenzo Veschini*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)
149 Downloads (Pure)


Endothelial cells (ECs) are widely heterogeneous at the cell level and serve different functions at the vessel and tissue levels. EC-forming colonies derived from induced pluripotent stem cells (iPSC-ECFCs) alongside models such as primary human umbilical vein ECs (HUVECs) are slowly becoming available for research with future applications in cell therapies, disease modeling, and drug discovery. We and others previously described high-content analysis approaches capturing unbiased morphology-based measurements coupled with immunofluorescence and used these for multidimensional reduction and population analysis. Here, we report a tailored workflow to characterize ECs. We acquire images at high resolution with high-magnification water-immersion objectives with Hoechst, vascular endothelial cadherin (VEC), and activated NOTCH staining. We hypothesize that via these key markers alone we would be able to distinguish and assess different EC populations. We used cell population software analysis to phenotype HUVECs and iPSC-ECFCs in the absence or presence of vascular endothelial growth factor (VEGF). To our knowledge, this study presents the first parallel quantitative high-content multiparametric profiling of EC models. Importantly, it highlights a simple strategy to benchmark ECs in different conditions and develop new approaches for biological research and translational applications for regenerative medicine.

Original languageEnglish
Pages (from-to)264-273
Number of pages10
JournalSLAS discovery
Issue number3
Early online date25 Jan 2019
Publication statusPublished - 1 Mar 2019


  • endothelial cells
  • high-content analysis
  • iPS
  • phenotyping
  • stem cells


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