4.1 Article

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

期刊

METHODSX
卷 6, 期 -, 页码 1907-1918

出版社

ELSEVIER
DOI: 10.1016/j.mex.2019.08.006

关键词

Vasculogenesis; Endothelial cell; Self-assembly; Multiscale; Tissue engineering; Image processing

资金

  1. National Institutes of Health [R01HL133163, R21ES027962, P30GM110759, P20GM103446, U54GM104941, S10OD016361]
  2. National Science Foundation [1537256, 1144726]
  3. University of Delaware Research Foundation
  4. Oak Ridge Associated Universities Ralph E. Powe Junior Faculty Enhancement Award
  5. March of Dimes Basil O'Connor Award [5-FY16-33]
  6. Direct For Education and Human Resources
  7. Division Of Graduate Education [1144726] Funding Source: National Science Foundation
  8. Directorate For Engineering
  9. Div Of Civil, Mechanical, & Manufact Inn [1537256] Funding Source: National Science Foundation

向作者/读者索取更多资源

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. (C) 2019 The Authors. Published by Elsevier B.V.

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