Dataset Information

Quantifying Vascular Density in Tissue Engineered Constructs Using Machine Learning.

ABSTRACT: Given the considerable research efforts in understanding and manipulating the vasculature in tissue health and function, making effective measurements of vascular density is critical for a variety of biomedical applications. However, because the vasculature is a heterogeneous collection of vessel segments, arranged in a complex three-dimensional architecture, which is dynamic in form and function, it is difficult to effectively measure. Here, we developed a semi-automated method that leverages machine learning to identify and quantify vascular metrics in an angiogenesis model imaged with different modalities. This software, BioSegment, is designed to make high throughput vascular density measurements of fluorescent or phase contrast images. Furthermore, the rapidity of assessments makes it an ideal tool for incorporation in tissue manufacturing workflows, where engineered tissue constructs may require frequent monitoring, to ensure that vascular growth benchmarks are met.

SUBMITTER: Strobel HA

PROVIDER: S-EPMC8110917 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Quantifying Vascular Density in Tissue Engineered Constructs Using Machine Learning.

Strobel Hannah A HA Schultz Alex A Moss Sarah M SM Eli Rob R Hoying James B JB

Frontiers in physiology 20210427

Given the considerable research efforts in understanding and manipulating the vasculature in tissue health and function, making effective measurements of vascular density is critical for a variety of biomedical applications. However, because the vasculature is a heterogeneous collection of vessel segments, arranged in a complex three-dimensional architecture, which is dynamic in form and function, it is difficult to effectively measure. Here, we developed a semi-automated method that leverages m ...[more]

PMID: 33986691

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Project description:Vascularization remains a substantial limitation to the viability of engineered tissue. By comparing in vivo vascularization dynamics of a self-assembled prevascular endothelial-fibroblast model to avascular grafts, we explore the vascularization rate limitations in implants at early time intervals, during which tissue hypoxia begins to affect cell viability. Scaffold-free prevascular endothelial-fibroblast constructs (SPECs) may serve as a modular and reshapable vascular bed in replacement tissues. SPECs, fibroblast-only spheroids (FOS), and silicone implants were implanted in 54 Sprague Dawley rats and harvested at 6, 12, and 24 h (n = 5 per time point and implant type). We hypothesized that the primary endothelial networks of the SPECs allow earlier anastomosis and increased vessel formation in the interior of the implant compared to FOS and silicone implants within a 24 h window. All constructs were encapsulated by an endothelial lining at 6 h postimplantation and SPEC internal cords inosculated with the host vascular network by this time point. SPECs had a significantly higher microvascular area fraction and branch/junction density of penetrating cords at 6-12 h compared with other constructs. In addition, SPECs demonstrated perivascular cell recruitment, lumen formation, and network remodeling consistent with vessel maturation at 12-24 h; however, these implants were poorly perfused within our observation window, suggesting poor lumen patency. FOS vascular characteristics (microvessel area and penetrating cord density) increased within the 12-24 h period to represent those of the SPEC implants, suggesting a 12 h latency in host response to avascular grafts compared to prevascular grafts. Knowledge of this temporal advantage in in vitro prevascular network self-assembly as well as an understanding of the current limitations of SPEC engraftment builds on our theoretical temporal model of tissue graft vascularization and suggests a crucial time window, during which technological improvements and vascular therapy can improve engineered tissue survival.

Dataset Information

Quantifying Vascular Density in Tissue Engineered Constructs Using Machine Learning.

Publications

Quantifying Vascular Density in Tissue Engineered Constructs Using Machine Learning.

Similar Datasets

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