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Combining Automated Organoid Workflows with Artificial Intelligence‐Based Analyses: Opportunities to Build a New Generation of Interdisciplinary High‐Throughput Screens for Parkinson's Disease and Beyond

类有机物 工作流程 计算机科学 大数据 可扩展性 神经科学 数据科学 人工智能 生物 数据挖掘 数据库
作者
Henrik Renner,Hans R. Schöler,Jan M. Bruder
出处
期刊:Movement Disorders [Wiley]
卷期号:36 (12): 2745-2762 被引量:33
标识
DOI:10.1002/mds.28775
摘要

Abstract 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
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