Imaging, viral, and machine learning approaches in stem cell biology

Abstract

Pluripotent stem cells can be used for both regenerative medicine and in vitro modeling for diseases and drug screening. For regenerative medicine, pluripotent stem cells need to differentiate into a specific cell type that is required for transplantation therapy. Cell products for transplantation therapy have lot-to-lot variations in quality, which affect their efficacy and side effects as well as the cost of the products. Quality evaluation of cell products requires non-invasive methods although well-used methods, such as expression analysis of genes and proteins, deconstruct cell products. To evaluate the quality of cell products in a non-deconstructive way, we developed a machine learning model that predicts the function of cell products using label-free images of cell morphology. Next, for in vitro modeling, we sought to recapitulate the interaction of brain regions by fusing brain-region-specific organoids. Imaging and viral vectors allowed for the evaluation of in vitro interaction of brain regions. In this talk, we will introduce our recent work with multidisciplinary approaches.