Hierarchical imaging and computational analysis of three-dimensional vascular network architecture in the entire postnatal and adult mouse brain

Abstract

The formation of new blood vessels and the establishment of vascular networks are crucial during brain development, in the adult healthy brain, as well as in various diseases of the central nervous system (CNS). Here, we describe a method that enables hierarchical imaging and computational analysis of vascular networks in postnatal- and adult mouse brains. Resin-based vascular corrosion casting, scanning electron microscopy, synchrotron radiation and desktop uCT imaging, and computational network analysis are used. Combining these methods enables detailed visualization and quantification of the three-dimensional (3D) brain vasculature. Network features such as vascular volume fraction, branch point density, vessel diameter, -length, -tortuosity, and -directionality as well as extravascular distance can be obtained at any developmental stage from the early postnatal to the adult brain. Our method allows characterizing brain vascular networks separately for capillaries and non-capillaries. The entire protocol, from mouse perfusion to vessel network analysis, takes approximately 10 days.

Molecular atlas of the human brain vasculature at the single-cell level

“…We next addressed the distributions of EC clusters between the fetal, adult/control and pathological brains. Notably, capillaries accounted for ~61.6% of ECs, arterial ECs for 15.23%, and venous ECs for 15.19%, in agreement with 2,14 . Stem-to-EC clusters accounted for 1.92%, EndoMT clusters for 2.1% (Figure 2g,k, Extended Data Figure 11).…”

“…A better understanding of the underlying cellular and molecular mechanisms and architecture of the brain vasculature during brain development, in the healthy adult brain, as well as in vascular-dependent brain diseases, has broad implications for both the biological understanding as well as the therapeutic targeting of the pathological brain vasculature [9][10][11][12][13] . Vascular growth and network formation, involving endothelial cells (ECs) and other cells of the neurovascular unit (NVU), are highly dynamic during brain development, almost quiescent in the healthy adult brain, and reactivated in a variety of angiogenesis-dependent brain pathologies such as brain tumors and brain vascular malformations 2,6,14,15 , thereby activating ECs and perivascular cells (PVCs) of the NVU and other tissue-derived cells (collectively referred to hereafter as PVCs). However, which molecular signaling cascades are reactivated and how they regulate brain tumor and brain vascular malformation vascularization and growth is largely unknown.…”

Brain microvasculature has a common topology with local differences in geometry that match metabolic load

“…This results from both the collapse and close opposition of surface arteries and veins (Blinder et al, 2013) and the embedding of venous sinuses into the foramina (Cai et al, 2019;Herisson et al, 2018). Moving forward will require an in situ analysis of the vasculature, a challenge than may be met with all optical histology (Tsai et al, 2003) and/or X-ray tomographic techniques (Dyer et al, 2017;Quintana et al, 2019;W€ alchli et al, 2020). ll Article Relation to Krogh-Erlang model Over a century ago, August Krogh reported a formula for calculating the maximum oxygen tension between capillaries and muscle cells (Krogh, 1919).…”

Geometrically reconstructing confocal microscopy images for modelling the retinal microvasculature as a 3D cylindrical network

“…Their goal was to obtain detailed topographic spatial analyses of their microvasculature in 3D, but they did not test the utility of their algorithms for geometric reconstructions. Another protocol for the 3D reconstruction of microvascular networks was recently released by Wächli et al, which utilises corrosion casting, scanning electron microscopy, synchrotron radiation and desktop microcomputed tomography (µCT) imaging, and computational network analysis [Wälchli et al, 2021]. While this work could also be adapted to output 3D cylindrical network models, it does not in its presently published form.…”