4.8 Article

Large-scale perfused tissues via synthetic 3D soft microfluidics

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NATURE COMMUNICATIONS
卷 14, 期 1, 页码 -

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NATURE PORTFOLIO
DOI: 10.1038/s41467-022-35619-1

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By developing micro-perfused 2-photon printing of 3D microfluidics, the authors have successfully engineered large-scale, viable and functional neural and hepatic 3D tissues. They have achieved the perfusion of multi-mm(3) tissue constructs by generating networks of synthetic capillary-scale 3D vessels, addressing the challenge of vascularization in regenerative medicine.
Bioengineering live tissues has remained challenging due to limited nutrient exchange in the growing tissues. Here, the authors have developed micro-perfused 2-photon printing of 3D microfluidics, to engineer large-scale, viable and functional neural and hepatic 3D tissues. The vascularization of engineered tissues and organoids has remained a major unresolved challenge in regenerative medicine. While multiple approaches have been developed to vascularize in vitro tissues, it has thus far not been possible to generate sufficiently dense networks of small-scale vessels to perfuse large de novo tissues. Here, we achieve the perfusion of multi-mm(3) tissue constructs by generating networks of synthetic capillary-scale 3D vessels. Our 3D soft microfluidic strategy is uniquely enabled by a 3D-printable 2-photon-polymerizable hydrogel formulation, which allows for precise microvessel printing at scales below the diffusion limit of living tissues. We demonstrate that these large-scale engineered tissues are viable, proliferative and exhibit complex morphogenesis during long-term in-vitro culture, while avoiding hypoxia and necrosis. We show by scRNAseq and immunohistochemistry that neural differentiation is significantly accelerated in perfused neural constructs. Additionally, we illustrate the versatility of this platform by demonstrating long-term perfusion of developing neural and liver tissue. This fully synthetic vascularization platform opens the door to the generation of human tissue models at unprecedented scale and complexity.

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