Generative Ai for Cardiovascular Cell Type‐Specific Fluorescence Colorization of Live‐Cell hPSC‐Derived Cardiac Organoids

Human pluripotent stem cell (hPSC)-derived cardiac organoids (COs) are the most recent three-dimensional tissue structure that mimics the human heart's structure and functionality for modeling heart development and disease. Fluorescent labeling and imaging are commonly utilized to characterize the cellular information in COs. However, the additional step of fluorescence labeling and imaging is time-consuming, inefficient, and typically for end-timepoint characterization. Meanwhile, the COs are routinely examined by brightfield/phase contrast microscope to track live-cell organoid formation in structure and morphology. Although the brightfield microscope provides essential information about COs, such as morphology and overall structure, it limits our understanding of cardiovascular cells (e.g., cardiomyocytes, CMs and endothelial cells, ECs) and corresponding quantifications in COs. Is it possible to overcome these limitations of bright-field microscopic imaging and provide cardiovascular cell type-specific information similar to the fluorescence-labeled imaging acquisition in COs? This research addresses this limitation by proposing a generative AI system for colorizing phase contrast images of COs from bright-field microscopic imaging using conditional generative adversarial networks (cGANs) to generate cardiovascular cell type-specific fluorescence images of COs. By giving these phase contrast images with multichannel fluorescence colorization, this intelligence system unlocks cell type and quantifications of COs in high efficiency and accuracy.