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大规模三维自组装血管网络的生成及形态学定量分析

Generation and morphological quantification of large scale, three-dimensional, self-assembled vascular networks.

作者信息

Shirazi Jasmine, Morgan Joshua T, Comber Erica M, Gleghorn Jason P

机构信息

Dept. of Biomedical Engineering, University of Delaware, Newark, DE, United States.

出版信息

MethodsX. 2019 Aug 23;6:1907-1918. doi: 10.1016/j.mex.2019.08.006. eCollection 2019.

Abstract

One of the largest issues facing the field of tissue engineering is scaling due to tissue necrosis as a result of a lack of vascularization. We have developed an accessible method for generating large scale vascular networks of arbitrary geometries through the self-assembly of endothelial cells in a collagen gel, similar to vasculogenesis that occurs in the developing embryo. This system can be applied to a wide range of collagen concentrations and seeding densities, resulting in networks of varying phenotypes, lending itself to the recapitulation of vascular networks that mimic those found across different tissues. Methods are thus described for the generation and imaging of these self-assembled three-dimensional networks in addition to image processing methods for rigorous quantitative measurement of various morphological parameters. There are several advantages to the system described herein. •Varied molding procedures allow for irregular geometries, similar to those that would be required for tissue grafts.•Robust network formation translates into centimeter scale constructs.•Whereas similar processes suffer from a high degree of variability and inconsistent characterization, our method employs image analysis techniques to stringently characterize each network based on several objective characteristics.

摘要

组织工程领域面临的最大问题之一是由于缺乏血管化导致组织坏死而产生的规模扩大问题。我们开发了一种可实现的方法,通过内皮细胞在胶原凝胶中的自组装来生成任意几何形状的大规模血管网络,这类似于发育胚胎中发生的血管生成过程。该系统可应用于广泛的胶原浓度和接种密度,从而产生具有不同表型的网络,有助于重现模仿不同组织中发现的血管网络。本文除了介绍用于严格定量测量各种形态参数的图像处理方法外,还描述了生成和成像这些自组装三维网络的方法。本文所述系统具有几个优点。•多样的成型程序允许形成不规则几何形状,类似于组织移植所需的形状。•强大的网络形成能力可转化为厘米级构建体。•尽管类似的过程存在高度变异性和表征不一致的问题,但我们的方法采用图像分析技术,根据几个客观特征对每个网络进行严格表征。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/15de/6812393/a621e8d90603/fx1.jpg

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