Radiation induces acute and subacute vascular regression in a three-dimensional microvasculature model.

Radiation treatment is one of the most frequently used therapies in patients with cancer, employed in approximately half of all patients. However, the use of radiation therapy is limited by acute or chronic adverse effects and the failure to consider the tumor microenvironment. Blood vessels substantially contribute to radiation responses in both normal and tumor tissues. The present study employed a three-dimensional (3D) microvasculature-on-a-chip that mimics physiological blood vessels to determine the effect of radiation on blood vessels. This model represents radiation-induced pathophysiological effects on blood vessels in terms of cellular damage and structural and functional changes. DNA double-strand breaks (DSBs), apoptosis, and cell viability indicate cellular damage. Radiation-induced damage leads to a reduction in vascular structures, such as vascular area, branch length, branch number, junction number, and branch diameter; this phenomenon occurs in the mature vascular network and during neovascularization. Additionally, vasculature regression was demonstrated by staining the basement membrane and microfilaments. Radiation exposure could increase the blockage and permeability of the vascular network, indicating that radiation alters the function of blood vessels. Radiation suppressed blood vessel recovery and induced a loss of angiogenic ability, resulting in a network of irradiated vessels that failed to recover, deteriorating gradually. These findings demonstrate that this model is valuable for assessing radiation-induced vascular dysfunction and acute and chronic effects and can potentially improve radiotherapy efficiency.

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.