Whole-Brain Vasculature Reconstruction at the Single Capillary Level

Abstract

The distinct organization of the brain’s vascular network ensures that it is adequately supplied with oxygen and nutrients. However, despite this fundamental role, a detailed reconstruction of the brain-wide vasculature at the capillary level remains elusive, due to insufficient image quality using the best available techniques. Here, we demonstrate a novel approach that improves vascular demarcation by combining CLARITY with a vascular staining approach that can fill the entire blood vessel lumen and imaging with light-sheet fluorescence microscopy. This method significantly improves image contrast, particularly in depth, thereby allowing reliable application of automatic segmentation algorithms, which play an increasingly important role in high-throughput imaging of the terabyte-sized datasets now routinely produced. Furthermore, our novel method is compatible with endogenous fluorescence, thus allowing simultaneous investigations of vasculature and genetically targeted neurons. We believe our new method will be valuable for future brain-wide investigations of the capillary network.

Automated analysis of whole brain vasculature using machine learning

“…To assess the importance of integrating information from both, WGA and EB, we designed a control network that only has access to the commonly used WGA signal, which reduced the quality of the vessel segmentation (accuracy: 0.90 ± 0.08; F1-Score 0.74 ± 0.07). As further controls, we implemented alternative state-of-the-art methods and found that our network outperforms classical Frangi filters 13 (accuracy: 0.85 ± 0.03; F1-Score 0.47 ± 0.18), as well as recent methods considering local spatial context via Markov random fields 15,36 (accuracy: 0.85 ± 0.03; F1-Score 0.48 ± 0.04).…”

“…Importantly, our findings on vasculature length are in line with the predictions in the literature obtained in small volumes, confirming the robustness of our method. For example, previous studies quantifying small patches estimated the density of cortical blood vessels as 0.922 m/mm³, 0.444 m/mm³ or 0.471 m/mm³ in the cortex 12,17,37 . Using VesSAP, calculating centerline density as described above, and accounting for the 30 % isotropic tissue shrinkage in DISCO clearing 38 , we found an average vascular density of 0.473 ± 0.161 m/mm³ over the whole mouse cortex.…”

“…Thresholding methods are not capable of segmenting these large scans, whereas shape-based filtering approaches such as Frangi filters cannot reliably identify vessels from background 13,14 . To overcome these limitations more advanced image processing methods with local spatial regularization have been proposed for processing light-sheet scans 15 . However, such methods including local spatial regularization cannot segment large vascular networks across changing intensity distributions.…”

“…However, such methods including local spatial regularization cannot segment large vascular networks across changing intensity distributions. Finally, the size of the acquired datasets poses a difficulty to assess the organization of the whole vascular network; therefore, such methods can only segment small volumes [15][16][17][18][19] .…”

Contrast-enhanced serial optical coherence scanner with deep learning network reveals vasculature and white matter organization of mouse brain

“…Microvasculature in thick slices of mouse brain has been visualized by using a fluorescent lipophilic dye and FocusClear, a water-soluble clearing agent, followed by confocal fluorescence microscopy. 12 Light-sheet microscopy combined with hydrogel-based clearing method 13,14 and solvent-based clearing agent 15 have been demonstrated to reconstruct cerebral vasculature of the whole mouse brain at the single capillary level. These imaging techniques provide a brain vasculature atlas at micrometer resolution in ex-vivo configuration.…”