A digital manufactured microfluidic platform for flexible construction of 3D co-culture tumor model with spatiotemporal resolution

微流控 纳米技术 计算机科学 高分辨率 材料科学 分辨率(逻辑) 系统工程 工程制图 生物医学工程 人工智能 遥感 工程类 地质学
作者
Chao Han,Renchao Zhang,Xiwen He,Yuan Fang,Gang Cen,Weidong Wu,Chenyang Huang,Xiang Chen
出处
期刊:Biofabrication [IOP Publishing]
卷期号:17 (1): 015029-015029
标识
DOI:10.1088/1758-5090/ad9636
摘要

Abstract The specific spatiotemporal distribution of diverse components in tumor microenvironment plays a crucial role in the cancer progression. In vitro three-dimensional (3D) tumor models with polydimethylsiloxane (PDMS) based microfluidic platform have been applied as useful tool to conduct studies from cancer biology to drug screening. However, PDMS has not been welcomed as a standardized commercial application for preclinical screening due to inherent limitations in scale-up production and molecule absorption. Here, we present a novel microfluidic platform to flexibly construct 3D co-culture models with spatiotemporal resolution by using multiple digital manufacturing technologies. The platform, which consist of reduplicative microfluidic chips, is made of biocompatible poly methyl methacrylate by fast laser cutting. Each replica includes a simple microfluidic chamber without internal structures which can be flexibly post-fabricated according to various research requirements. Digital light processing based 3D bioprinting was used to pattern fine hydrogel structures for post-fabrication on-chip. By multi-step bioprinting and automatic image alignment, we show that this approach provides sufficient design flexibility to construct 3D co-culture tumor model with spatiotemporal resolution to replicate microarchitecture of tumor microtissue in situ . And the tumor model has the potential to mimic tumor biology behaviors which can be used for mechanism study and drug test. Our microengineered tumor model may serve as an enabling tool to recapitulate pathophysiological complexity of tumor, and to systematically examine the contribution of the tumor microenvironment to the cancer progression. The proposed strategy can also be applied to help engineer diverse meaningful in vitro models for extensive biomedical applications, from physiology and disease study to therapy evaluation.
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