A three‐dimensional method for morphological analysis and flow velocity estimation in microvasculature on‐a‐chip

Abstract

Three‐dimensional (3D) imaging techniques (e.g., confocal microscopy) are commonly used to visualize in vitro models, especially microvasculature on‐a‐chip. Conversely, 3D analysis is not the standard method to extract quantitative information from those models. We developed the μVES algorithm to analyze vascularized in vitro models leveraging 3D data. It computes morphological parameters (geometry, diameter, length, tortuosity, eccentricity) and intravascular flow velocity. μVES application to microfluidic vascularized in vitro models shows that they successfully replicate functional features of the microvasculature in vivo in terms of intravascular fluid flow velocity. However, wall shear stress is lower compared to in vivo references. The morphological analysis also highlights the model's physiological similarities (vessel length and tortuosity) and shortcomings (vessel radius and surface‐over‐volume ratio). The addition of the third dimension in our analysis produced significant differences in the metrics assessed compared to 2D estimations. It enabled the computation of new indices, such as vessel eccentricity. These μVES capabilities can find application in analyses of different in vitro vascular models, as well as in vivo and ex vivo microvasculature.

Targeting blood brain barrier—Remote ischemic conditioning alleviates cognitive impairment in female APP/PS1 rats

“…Finally, the images were obtained by micro‐CT (NEMO‐II Micro CT (NMC‐200), China). To further quantitatively evaluate the vascular unit, the 3D‐micro‐CT photographs were reconstructed and transformed into TIFF format using the Avatar 3 software (NEMO‐II Micro CT (NMC‐200), China), and the processing of the blood vessels further quantitatively analyze using Angio Tool software according to previous description 50 , 51 , 52 AngioTool is a free practical tool that can comprehensively and quantitatively evaluate various blood vessel morphology and spatial parameters, including blood vessel length and density, branch index, and space. 53 To begin the analysis using the Angio Tool, the TIFF CT image is opened, and the software was used to perform an initial selection of vessels based on estimated parameters, which were identified by a yellow overlay.…”

Engineering microvascular networks using a KLF2 reporter to probe flow-dependent endothelial cell function

“…We used a computational approach 25 to determine the magnitudes of the wall shear stresses within the MVNs exposed to flow ( Fig. 3 ).…”

“…Confocal images of static MVNs perfused with dextran were acquired as described above and tiled to span the entire width of the vasculature between the media channels. We used the micro-Vasculature Evaluation System algorithm to perform 3D vessel segmentation, skeletonization, and calculation of flow rates, speeds, and wall shear stresses in each branch 25 . For each MVN, boundary conditions were set such that the pressure across the network resulted in an average vessel flow speed of 2 mm/s.…”

“…Images were processed using AutoTube software 57 , with the following settings: pre-processing by adaptive histogram equalization, illumination correction, and image denoising through Block-Matching and 3D Filtering, tube detection with the Multi-Otsu thresholding, and tube analysis with removal of short ramification that are less than 20 pixels of length and merging of branch points within a radius of 22 pixels. Number of branches, branch length, and diameter were calculated by analyzing confocal z-stacks of MVNs perfused with dextran with the micro-Vasculature Evaluation System algorithm, which performs vessel segmentation, skeletonization, branch point analysis, and calculation of vascular morphological perameters in 3D 25 . To visualize vascular diameters, MVN surfaces were constructed from dextran images using Imaris software (Oxford Instruments) and clipped along the xz plane to reveal vessel openings.…”

Label-free phenotyping of human microvessel networks

“…Although such methods yield a varied set of morphometrics associated with various vascular drug responses 17,18 , they either limit the analysis to fixed and permeabilized samples or disrupt sample homeostasis, ultimately impeding the live examination of unaltered samples over time. Furthermore, current techniques often omit spatial heterogeneity, as they focus on segmented cropped areas rather than the entire vascular network 19 . Doing so ultimately leads to a skewed understanding of the complex heterogeneity across and within individual vascular networks, as interpretations are biased by the limited fields of view selected from the sample cohort.…”