SARS-CoV-2 infection activates inflammatory macrophages in vascular immune organoids.

SARS-CoV-2 provokes devastating tissue damage by cytokine release syndrome and leads to multi-organ failure. Modeling the process of immune cell activation and subsequent tissue damage is a significant task. Organoids from human tissues advanced our understanding of SARS-CoV-2 infection mechanisms though, they are missing crucial components: immune cells and endothelial cells. This study aims to generate organoids with these components. We established vascular immune organoids from human pluripotent stem cells and examined the effect of SARS-CoV-2 infection. We demonstrated that infections activated inflammatory macrophages. Notably, the upregulation of interferon signaling supports macrophages' role in cytokine release syndrome. We propose vascular immune organoids are a useful platform to model and discover factors that ameliorate SARS-CoV-2-mediated cytokine release syndrome.

Analyzing angiogenesis on a chip using deep learning-based image processing.

Angiogenesis, the formation of new blood vessels from existing vessels, has been associated with more than 70 diseases. Although numerous studies have established angiogenesis models, only a few indicators can be used to analyze angiogenic structures. In the present study, we developed an image-processing pipeline based on deep learning to analyze and quantify angiogenesis. We utilized several image-processing algorithms to quantify angiogenesis, including a deep learning-based cell nuclear segmentation algorithm and image skeletonization. This method could quantify and measure changes in blood vessels in response to biochemical gradients using 16 indicators, including length, width, number, and nuclear distribution. Moreover, this procedure is highly efficient for the three-dimensional quantitative analysis of angiogenesis and can be applied to diverse angiogenesis investigations.