A Quantitative Printability Framework for Programmable Assembly of Pre-Vascular Patterns via Laser-Induced Forward Transfer

基于激光诱导前向转移的可编程组装血管前体图案的定量可打印性框架

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Abstract

The defined vascularization of complex and intricate tissue constructs remains an unmet need in tissue engineering and regenerative medicine. While large constructs require vasculature for oxygen, nutrient supply, and waste clearance, their incorporation within biofabricated tissues is essential for developmental and disease modeling studies. There is, therefore, a critical demand to establish reproducible and organized vascular networks within in vitro models to ensure experimental robustness and quantitative interpretability. Current micropatterning and biofabrication strategies are limited in emulating native geometrical complexity, throughput, and resolution, while self-assembly approaches rely on inherently random network formation. Here, laser-induced forward transfer (LIFT) is utilized, offering high spatial resolution for deterministic micropatterning of cells with high viability. A unique droplet quality assessment framework is established through a multiparametric study to objectively identify a printability window, assigning a single-indexed score per printing condition. Within the optimal transfer regime, control over droplet concentration is demonstrated. The impact of pattern density on early vascular morphogenesis is explored, highlighting the effect of geometrical design on network formation. Finally, these findings are leveraged for the spatially controlled assembly of multicellular vascular patterns, offering a reproducible strategy for high-resolution micropatterning and addressing a key limitation in the biofabrication of physiologically relevant tissue models.

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