Freitas-Andrade Moises, Comin Cesar H, da Silva Matheus Viana, Costa Luciano da F, Lacoste Baptiste
The Ottawa Hospital Research Institute, Neuroscience Program, Ottawa, Ontario, Canada.
Federal University of São Carlos, Department of Computer Science, São Carlos, Brazil.
Neurophotonics. 2022 Jul;9(3):031916. doi: 10.1117/1.NPh.9.3.031916. Epub 2022 May 18.
A growing body of research supports the significant role of cerebrovascular abnormalities in neurological disorders. As these insights develop, standardized tools for unbiased and high-throughput quantification of cerebrovascular structure are needed. We provide a detailed protocol for performing immunofluorescent labeling of mouse brain vessels, using thin ( ) or thick (50 to ) tissue sections, followed respectively by two- or three-dimensional (2D or 3D) unbiased quantification of vessel density, branching, and tortuosity using digital image processing algorithms. Mouse brain sections were immunofluorescently labeled using a highly selective antibody raised against mouse Cluster of Differentiation-31 (CD31), and 2D or 3D microscopy images of the mouse brain vasculature were obtained using optical sectioning. An open-source toolbox, called Pyvane, was developed for analyzing the imaged vascular networks. The toolbox can be used to identify the vasculature, generate the medial axes of blood vessels, represent the vascular network as a graph, and calculate relevant measurements regarding vascular morphology. Using Pyvane, vascular parameters such as endothelial network density, number of branching points, and tortuosity are quantified from 2D and 3D immunofluorescence micrographs. The steps described in this protocol are simple to follow and allow for reproducible and unbiased analysis of mouse brain vascular structure. Such a procedure can be applied to the broader field of vascular biology.
越来越多的研究支持脑血管异常在神经系统疾病中的重要作用。随着这些见解的发展，需要用于无偏倚和高通量量化脑血管结构的标准化工具。我们提供了一个详细的方案，用于对小鼠脑血管进行免疫荧光标记，使用薄（ ）或厚（50至 ）组织切片，然后分别使用数字图像处理算法对血管密度、分支和曲折度进行二维或三维（2D或3D）无偏倚量化。使用针对小鼠分化簇31（CD31）产生的高选择性抗体对小鼠脑切片进行免疫荧光标记，并使用光学切片获得小鼠脑血管系统的2D或3D显微镜图像。开发了一个名为Pyvane的开源工具箱，用于分析成像的血管网络。该工具箱可用于识别脉管系统、生成血管的中轴线、将血管网络表示为图形，并计算有关血管形态的相关测量值。使用Pyvane，可从2D和3D免疫荧光显微照片中量化血管参数，如内皮网络密度、分支点数和曲折度。本方案中描述的步骤易于遵循，可对小鼠脑血管结构进行可重复和无偏倚的分析。这样的程序可应用于更广泛的血管生物学领域。
