Generative AI for Cell Type-Specific Fluorescence Image Generation of hPSC-derived Cardiac Organoid.

Human pluripotent stem cell (hPSC)-derived cardiac organoid is the most recent three-dimensional tissue structure that mimics the structure and functionality of the human heart and plays a pivotal role in modeling heart development and disease. The hPSC-derived cardiac organoids are commonly characterized by bright-field microscopic imaging for tracking daily organoid differentiation and morphology formation. Although the brightfield microscope provides essential information about hPSC-derived cardiac organoids, such as morphology, size, and general structure, it does not extend our understanding of cardiac organoids on cell type-specific distribution and structure. Then, fluorescence microscopic imaging is required to identify the specific cardiovascular cell types in the hPSC-derived cardiac organoids by fluorescence immunostaining fixed organoid samples or fluorescence reporter imaging of live organoids. Both approaches require extra steps of experiments and techniques and do not provide general information on hPSC-derived cardiac organoids from different batches of differentiation and characterization, which limits the biomedical applications of hPSC-derived cardiac organoids. This research addresses this limitation by proposing a comprehensive workflow for colorizing phase contrast images of cardiac organoids from brightfield microscopic imaging using conditional Generative Adversarial Networks (GANs) to provide cardiovascular cell type-specific information in hPSC-derived cardiac organoids. By infusing these phase contrast images with accurate fluorescence colorization, our approach aims to unlock the hidden wealth of cell type, structure, and further quantifications of fluorescence intensity and area, for better characterizing hPSC-derived cardiac organoids.

Organalysis: Multifunctional Image Preprocessing and Analysis Software for Cardiac Organoid Studies.

Due to a growing need in visualizing human pluripotent stem cell-derived organoids from recent advancements in the field, an efficient bulk-processing application is necessary to provide preprocessing and image analysis services. In this study, we developed Organalysis, a high-accuracy, multifunctional, and accessible application that meets these needs by providing the functionality of image manipulation and enhancement, organoid area and intensity calculation, fractal analysis, noise removal, and feature importance computation. The image manipulation feature includes brightness and contrast adjustment. The area and intensity calculation computes six values for each image: organoid area, total image area, percentage of the image covered by organoid, the total intensity of organoid, the total intensity of organoid-by-organoid area, and total intensity of organoid by total image area. The fractal analysis function computes the fractal dimension value for each image. The noise removal function removes superfluous marks from the input images, such as bubbles and other unwanted noise. The feature importance function trains a lasso-regularized linear regression machine learning algorithm to identify cardiac growth factors that are the strongest determinants for cell differentiation. The batch processing of this application further builds on existing services like ImageJ to provide a more convenient way to process multiple images. Collectively, the versatility and preciseness of Organalysis demonstrate novelty, since no other current imaging software combines the capability of batch processing and the breadth of feature analysis. Therefore, Organalysis provides unique functions in cardiac organoid research and proves to be invaluable in regenerative medicine.

Bioengineering Systems for Modulating Notch Signaling in Cardiovascular Development, Disease, and Regeneration.

The Notch intercellular signaling pathways play significant roles in cardiovascular development, disease, and regeneration through modulating cardiovascular cell specification, proliferation, differentiation, and morphogenesis. The dysregulation of Notch signaling leads to malfunction and maldevelopment of the cardiovascular system. Currently, most findings on Notch signaling rely on animal models and a few clinical studies, which significantly bottleneck the understanding of Notch signaling-associated human cardiovascular development and disease. Recent advances in the bioengineering systems and human pluripotent stem cell-derived cardiovascular cells pave the way to decipher the role of Notch signaling in cardiovascular-related cells (endothelial cells, cardiomyocytes, smooth muscle cells, fibroblasts, and immune cells), and intercellular crosstalk in the physiological, pathological, and regenerative context of the complex human cardiovascular system. In this review, we first summarize the significant roles of Notch signaling in individual cardiac cell types. We then cover the bioengineering systems of microfluidics, hydrogel, spheroid, and 3D bioprinting, which are currently being used for modeling and studying Notch signaling in the cardiovascular system. At last, we provide insights into ancillary supports of bioengineering systems, varied types of cardiovascular cells, and advanced characterization approaches in further refining Notch signaling in cardiovascular development, disease, and regeneration.