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

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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Publication Details

Journal
Advanced Healthcare Materials
Published
2025-11-21
DOI
https://doi.org/10.1002/adhm.202503665
Citations
1
Primary Topic
3D Printing in Biomedical Research
Type
article
Field-Weighted Citation Impact
0.67

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article

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

David Fernández Rivas, Riccardo Levato, Jos Malda, Núria Ginés Rodriguez et al.
1 citations
Advanced Healthcare Materials
3D Printing in Biomedical Research
0.67
article

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

David Fernández Rivas, Riccardo Levato, Jos Malda, Núria Ginés Rodriguez, Marcel Karperien, Jeroen Leijten, Ulisses J. Gutiérrez‐Hernández, Cécile Bosmans, Liliana Moreira Teixeira
article en
1 citations

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.

Advanced Healthcare MaterialsVol. 15(8)
Utrecht University (NL), University Medical Center Utrecht (NL), Nederlandse Vereniging voor Kindergeneeskunde (NL), Regenerative Medicine Institute (MX), University of Twente (NL)
European Commission, University of Twente, Nederlandse Organisatie voor Wetenschappelijk Onderzoek
Openalex Percentile: Top 24%
3D Printing in Biomedical Research
0.67
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