Multicellular, Biochemical, and Perfusion Effects on Vessel Network Morphogenesis in a Microfluidic...

Microvascular networks (MVNs) formed via endothelial cell self-assembly in 3D hydrogels have emerged as a widely used platform for modeling vascularized tissues and studying vascular pathophysiology. Conventional MVN systems incorporate supporting fibroblasts and may include biochemical cues such as VEGF, FGF, or S1P, as well as mechanical stimuli like luminal flow, yet the impact of these variables on MVN morphology and function remains incompletely understood. Here, we systematically investigated the effects of fibroblast concentration, fibroblast-conditioned media, angiogenic factors, and luminal flow on the morphology, perfusability, and vessel wall integrity of MVNs cultured in microfluidic vasculature-on-a-chip. In addition to standard branch-based metrics, such as vessel coverage area and vessel diameter, we developed and applied novel void-based morphological parameters that quantify the size, shape, and spatial distribution of vessel-free spaces. These metrics enabled us to capture subtle morphological differences across MVN culture conditions and to quantify the dynamic morphogenesis events that shaped the resulting MVNs including branch formation, vessel fusion, and pruning. Our results demonstrate that high fibroblast-to-endothelial cell ratios accelerate MVN formation but promote excessive vessel fusion, while MVNs cultured without fibroblasts─using only conditioned media or soluble factors─exhibited patch-like, nonphysiological morphology with reduced branch formation. Direct inclusion of fibroblasts proved to be essential for promoting the thin, interconnected vascular structures characteristic of in vivo microvasculature and could not be substituted by soluble cues alone. Overall, our void-based analysis method enabled more sensitive discrimination of MVN morphological features than traditional branch-based metrics and offers a reduced-data, high-content approach suitable for potential integration with machine learning and AI-assisted image analysis pipelines. This platform provides a new framework for optimizing MVN culture protocols and advancing vascular tissue engineering studies, particularly for the advancement of organ-on-a-chip (OOC) and microphysiological systems.