A Versatile Pipeline for High-fidelity Imaging and Analysis of Vascular Networks Across the Body

Structural and functional changes in vascular networks play a vital role during development, causing or contributing to the pathophysiology of injury and disease. Current methods to trace and image the vasculature in laboratory settings have proven inconsistent, inaccurate, and labor intensive, lacking the inherent three-dimensional structure of vasculature. Here, we provide a robust and highly reproducible method to image and quantify changes in vascular networks down to the capillary level. The method combines vasculature tracing, tissue clearing, and three-dimensional imaging techniques with vessel segmentation using AI-based convolutional reconstruction to rapidly process large, unsectioned tissue specimens throughout the body with high fidelity. The practicality and scalability of our protocol offer application across various fields of biomedical sciences. Obviating the need for sectioning of samples, this method will expedite qualitative and quantitative analyses of vascular networks. Preparation of the fluorescent gel perfusate takes < 30 min per study. Transcardiac perfusion and vasculature tracing takes approximately 20 min, while dissection of tissue samples ranges from 5 to 15 min depending on the tissue of interest. The tissue clearing protocol takes approximately 24–48 h per whole-tissue sample. Lastly, three-dimensional imaging and analysis can be completed in one day. The entire procedure can be carried out by a competent graduate student or experienced technician. Key features • This robust and highly reproducible method allows users to image and quantify changes in vascular networks down to the capillary level. • Three-dimensional imaging techniques with vessel segmentation enable rapid processing of large, unsectioned tissue specimens throughout the body. • It takes approximately 2–3 days for sample preparation, three-dimensional imaging, and analysis. • The user-friendly pipeline can be completed by experienced and non-experienced users.