Remotely Controlled 3D-Engineered Scaffolds for Biomimetic In Vitro Investigations on Brain Cell Cocultures

IF 6.8 Q1 AUTOMATION & CONTROL SYSTEMS Advanced intelligent systems (Weinheim an der Bergstrasse, Germany) Pub Date : 2024-06-03 DOI:10.1002/aisy.202400261
Daniele De Pasquale, Attilio Marino, Carlotta Pucci, Omar Tricinci, Carlo Filippeschi, Pietro Fiaschi, Edoardo Sinibaldi, Gianni Ciofani
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Abstract

Most in vitro studies regarding new anticancer treatments are performed on 2D cultures, despite this approach imposes several limitations in recapitulating the real tumor behavior and in predicting the effects of therapy on both cancer and healthy tissues. Herein, advanced in vitro models based on scaffolds that support the 3D growth of glioma cells, further allowing the cocultures with healthy brain cells, are presented. These scaffolds, doped with superparamagnetic iron oxide nanoparticles and obtained through 2-photon polymerization, can be remotely manipulated thanks to an external magnet, thus obtaining biomimetic 3D organization recapitulating the brain cancer microenvironment. From a geometric point of view, the structure is functional to both cell culture on individual unit scaffolds and to tailored cocultures fostered by magnetic-driven unit assembly, also allowing for cell migration thanks to passages/fenestrations on adjacent structures. Leveraging magnetic dragging, for which a mathematical model is introduced, multiple cocultures are achieved, highlighting the high versatility and the user-friendly character of the proposed platform that can help overcome the current challenges in 3D cocultures handling, and open the way to the construction of increasingly biomimetic artificial systems.

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遥控三维工程支架用于仿生脑细胞体外培养研究
大多数有关新型抗癌疗法的体外研究都是在二维培养基上进行的,尽管这种方法在再现真实的肿瘤行为以及预测疗法对癌症和健康组织的影响方面存在一些局限性。本文介绍了基于支架的先进体外模型,这种支架可支持胶质瘤细胞的三维生长,并进一步允许与健康脑细胞共培养。这些支架掺杂了超顺磁性氧化铁纳米粒子,通过双光子聚合技术获得,可以通过外部磁铁进行远程操控,从而获得重现脑癌微环境的仿生三维组织。从几何角度看,这种结构既能在单个单元支架上进行细胞培养,也能通过磁力驱动的单元组装进行量身定制的共培养,还能通过相邻结构上的通道/瘘管实现细胞迁移。利用磁力拖曳(引入了一个数学模型),可以实现多重共培养,这凸显了所提议平台的高度通用性和用户友好性,有助于克服当前在处理三维共培养方面的挑战,并为构建日益仿生的人工系统开辟道路。
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