A graph-theoretic framework for quantitative analysis of angiogenic networks.

Area of Science:

• Biomedical Engineering
• Cell Biology
• Computational Biology

Background:

• The endothelial tube formation assay is a standard in vitro model for studying angiogenesis.
• Current quantification methods for angiogenic behavior lack spatial, topological, and structural context.
• There is a need for quantitative frameworks to analyze complex angiogenic networks.

Purpose of the Study:

• To develop and validate a graph-theoretic framework for quantifying angiogenesis in vitro.
• To analyze network morphology, temporal dynamics, and spatial heterogeneity in tube formation assays.
• To provide a sensitive and scalable method for assessing angiogenic processes.

Main Methods:

• Simulated distinct angiogenic network morphologies using human umbilical vein endothelial cells (HUVECs) at varying densities.
• Imaged networks at 2, 4, and 18 hours post-seeding.
• Converted skeletonized images to mathematical graphs and extracted 11 graph-based metrics.

Main Results:

• The framework successfully captured morphological differences and temporal progression of networks.
• Sparse networks showed higher average node degree, clustering coefficient, and tortuosity.
• Dense networks exhibited greater node and edge counts, while networks integrated over time.
• Graph metrics effectively distinguished between sparse and dense morphologies and separated networks based on time points.

Conclusions:

• The graph-theoretic framework offers a robust method for quantifying angiogenic dynamics.
• This approach provides insights into therapeutic efficacy and disease-related vascular remodeling.
• The method enhances the analysis of in vitro angiogenesis models by incorporating structural and temporal information.