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Significance: To explore brain architecture and pathology, a consistent and reliable methodology to visualize the three-dimensional cerebral microvasculature is beneficial. Perfusion-based vascular labeling is quick and easily deliverable. However, the quality of vascular labeling can vary with perfusion-based labels due to aggregate formation, leakage, rapid photobleaching, and incomplete perfusion. Aim: We describe a simple, two-day protocol combining perfusion-based labeling with a two-day clearing step that facilitates whole-brain, three-dimensional microvascular imaging and characterization. Approach: The combination of retro-orbital injection of Lectin-Dylight-649 to label the vasculature, the clearing process of a modified iDISCO+ protocol, and light-sheet imaging collectively enables a comprehensive view of the cerebrovasculature. Results: We observed ∼threefold increase in contrast-to-background ratio of Lectin-Dylight-649 vascular labeling over endogenous green fluorescent protein fluorescence from a transgenic mouse model. With light-sheet microscopy, we demonstrate sharp visualization of cerebral microvasculature throughout the intact mouse brain. Conclusions: Our tissue preparation protocol requires fairly routine processing steps and is compatible with multiple types of optical microscopy.

Cerebral microbleeds (CMB) are deposits of blood that accumulate within the brain. An increase in CMBs is associated with an increased risk of cognitive impairment and stroke. The types of vessels associated with CMB formation remains unclear. We recently demonstrated the combined use of exogenous labels, vessel painting, and optical clearing to achieve three-dimensional views of blood vessels and CMBs. Here, we aimed to characterize brain vasculature by quantifying key vasculature-related metrics. An automated algorithm was developed to segment blood vessels within fluorescence images. An open-source neuron tracing software, neuTube, was then used to quantify blood vessel diameters.

We present a method for visualizing cerebral microvasculature in three-dimensions using an exogenous fluorescent label, tissue clearing, and confocal microscopy. A segmentation algorithm was developed for quantitative analysis of microvasculature.