Morphological Characterization of Imaged iPSC-Derived Endothelial Cells Using Machine Learning
London Central Secondary School
Somatic cells making up the body, such as skin and blood cells, can be reprogrammed into induced pluripotent stem cells (iPSCs). These cells can be differentiated into all other specialized cell types. Endothelial cells derived from iPSCs can be used to model vascular diseases and test personalized treatments. Methods used to characterize iPSC-derived cells, such as immunostaining, flow cytometry or qPCR, can be time-consuming and affect cell viability. Recent research has applied machine learning to analyzing imaged cells; however, few are specific to iPSC-derived cells. This project focuses on characterizing imaged iPSC-derived endothelial cells using machine learning based on morphology.
University of Alberta
University of Ottawa