Laboratory for Innovations in Microengineering (LiME), University of Victoria, Victoria, BC, Canada.
Department of Mechanical Engineering, University of Victoria, Victoria, BC, Canada.
Methods Mol Biol. 2022;2515:281-296. doi: 10.1007/978-1-0716-2409-8_17.
Understanding the mechanisms underlying the formation and progression of brain diseases is challenging due to the vast variety of involved genetic/epigenetic factors and the complexity of the environment of the brain. Current preclinical monolayer culture systems fail to faithfully recapitulate the in vivo complexities of the brain. Organoids are three-dimensional (3D) culture systems that mimic much of the complexities of the brain including cell-cell and cell-matrix interactions. Complemented with a theoretical framework to model the dynamic interactions between different components of the brain, organoids can be used as a potential tool for studying disease progression, transport of therapeutic agents in tissues, drug screening, and toxicity analysis. In this chapter, we first report on the fabrication and use of a novel self-filling microwell arrays (SFMWs) platform that is self-filling and enables the formation of organoids with uniform size distributions. Next, we will introduce a mathematical framework that predicts the organoid growth, cell death, and the therapeutic responses of the organoids to different therapeutic agents. Through systematic investigations, the computational model can identify shortcomings of in vitro assays and reduce the time and effort required to improve preclinical tumor models' design. Lastly, the mathematical model provides new testable hypotheses and encourages mathematically driven experiments.
由于涉及的遗传/表观遗传因素种类繁多,大脑环境复杂,因此理解大脑疾病的形成和发展机制具有挑战性。目前的临床前单层培养系统无法真实再现大脑的体内复杂性。类器官是一种三维(3D)培养系统,可模拟大脑的许多复杂性,包括细胞-细胞和细胞-基质相互作用。与用于模拟大脑不同成分之间动态相互作用的理论框架相结合,类器官可用作研究疾病进展、治疗剂在组织中的转运、药物筛选和毒性分析的潜在工具。在本章中,我们首先报告了一种新型自填充微井阵列(SFMWs)平台的制造和使用情况,该平台可自填充,并可形成具有均匀尺寸分布的类器官。接下来,我们将介绍一个数学框架,该框架可预测类器官的生长、细胞死亡以及类器官对不同治疗剂的治疗反应。通过系统研究,该计算模型可以识别体外分析的缺点,并减少改进临床前肿瘤模型设计所需的时间和精力。最后,该数学模型提供了新的可测试假设,并鼓励进行基于数学的实验。