Parkinson's disease (PD) is the second most common neurodegenerative disease and primarily characterized by the loss of dopaminergic neurons in the substantia nigra pars compacta of the midbrain. Despite decades of research and the development of various disease model systems, there is no curative treatment. This could be due to current model systems, including cell culture and animal models, not adequately recapitulating human PD etiology. More complex human disease models, including human midbrain organoids, are maturing technologies that increasingly enable the strategic incorporation of the missing components needed to model PD in vitro. The resulting organoid-based biological complexity provides new opportunities and challenges in data analysis of rich multimodal data sets. Emerging artificial intelligence (AI) capabilities can take advantage of large, broad data sets and even correlate results across disciplines. Current organoid technologies no longer lack the prerequisites for large-scale high-throughput screening (HTS) and can generate complex yet reproducible data suitable for AI-based data mining. We have recently developed a fully scalable and HTS-compatible workflow for the generation, maintenance, and analysis of three-dimensional (3D) microtissues mimicking key characteristics of the human midbrain (called “automated midbrain organoids,” AMOs). AMOs build a reproducible, scalable foundation for creating next-generation 3D models of human neural disease that can fuel mechanism-agnostic phenotypic drug discovery in human in vitro PD models and beyond. Here, we explore the opportunities and challenges resulting from the convergence of organoid HTS and AI-driven data analytics and outline potential future avenues toward the discovery of novel mechanisms and drugs in PD research. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society

中文翻译：

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将自动化的类器官工作流程与基于人工智能的分析相结合：为帕金森病及其他疾病构建新一代跨学科高通量筛选的机会

帕金森病 (PD) 是第二常见的神经退行性疾病，主要特征是中脑黑质致密部中多巴胺能神经元的丧失。尽管进行了数十年的研究和各种疾病模型系统的开发，但还没有治愈性的治疗方法。这可能是由于当前的模型系统，包括细胞培养和动物模型，没有充分概括人类 PD 病因。更复杂的人类疾病模型，包括人类中脑类器官，是成熟的技术，越来越多地能够战略性地整合体外模拟 PD 所需的缺失组件. 由此产生的基于类器官的生物复杂性为丰富的多模式数据集的数据分析提供了新的机遇和挑战。新兴的人工智能 (AI) 功能可以利用大型、广泛的数据集，甚至可以跨学科关联结果。当前的类器官技术不再缺乏大规模高通量筛选 (HTS) 的先决条件，并且可以生成适用于基于 AI 的数据挖掘的复杂但可重复的数据。我们最近开发了一个完全可扩展且与 HTS 兼容的工作流程，用于生成、维护和分析模拟人类中脑关键特征的三维 (3D) 微组织（称为“自动化中脑类器官”，AMO）。AMO 构建了一个可重现的、体外PD 模型及其他模型。在这里，我们探讨了类器官 HTS 和人工智能驱动的数据分析的融合所带来的机遇和挑战，并概述了在 PD 研究中发现新机制和药物的潜在未来途径。© 2021 作者。Wiley Periodicals LLC 代表国际帕金森和运动障碍协会出版的运动障碍