Unbiased analysis of mouse brain endothelial networks from two- or three-dimensional fluorescence images

Abstract

. Significance: 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. Aim: 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. Approach: 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. Results: Using Pyvane, vascular parameters such as endothelial network density, number of branching points, and tortuosity are quantified from 2D and 3D immunofluorescence micrographs. Conclusions: 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.

Using digital pathology to analyze the murine cerebrovasculature

“…65 Similarly, Freitas-Andrade published a protocol for analyzing cerebrovascular microvessels in a relatively unbiased manner, using immunofluorescence with subsequent 3 D digital reconstruction. 66 Further, several studies have employed image segmentation software to analyze vessel morphometry directly. 57,67,68 Despite progress in the field, our study underscores that there remains a need for technical studies so that different investigators can ask a range of questions with tools that are optimized and, perhaps in the future, standardized.…”

A new sampling methodology for creating rich, heterogeneous, subsets of samples for training image segmentation algorithms

“…The total length of vessels is calculated as the sum of the arc-lengths of all vessel segments. This metric is also fully explained in [18].…”

“…This methodology is based on the adaptive thresholding of the original images, where the threshold was selected manually for each image. The full details on the segmentation procedure are described in [18].…”

“…For the task of segmentation in medical images, which is the focus, but not the only application of our study, the detection of the important structures is usually only the first step of a more elaborate procedure for measuring relevant properties such as size [15], regularity [16], length [17,18], and curvature [18,19]. Therefore, systematic segmentation mistakes might lead to undetected errors when characterizing samples for clinical diagnoses [20] and research purposes [11].…”

Special Section Guest Editorial: Imaging Neuroimmune, Neuroglial, and Neurovascular Interfaces

“…present an optimized approach to label the endothelium in fixed tissues and introduce software for analysis of vascular structure. 9 …”