Improved Scalability of Neuron-Based Phenotypic Screening Assays for Therapeutic Discovery in Neuropsychiatric Disorders.

Molecular Neuropsychiatry Pub Date : 2018-02-01 Epub Date: 2017-11-17 DOI:10.1159/000481731
Timothy P Spicer, Christopher Hubbs, Thomas Vaissiere, Deanna Collia, Camilo Rojas, Murat Kilinc, Kyle Vick, Franck Madoux, Pierre Baillargeon, Justin Shumate, Kirill A Martemyanov, Damon T Page, Sathya Puthanveettil, Peter Hodder, Ronald Davis, Courtney A Miller, Louis Scampavia, Gavin Rumbaugh
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引用次数: 14

Abstract

There is a pressing need to improve approaches for drug discovery related to neuropsychiatric disorders (NSDs). Therapeutic discovery in neuropsychiatric disorders would benefit from screening assays that can measure changes in complex phenotypes linked to disease mechanisms. However, traditional assays that track complex neuronal phenotypes, such as neuronal connectivity, exhibit poor scalability and are not compatible with high-throughput screening (HTS) procedures. Therefore, we created a neuronal phenotypic assay platform that focused on improving the scalability and affordability of neuron-based assays capable of tracking disease-relevant phenotypes. First, using inexpensive laboratory-level automation, we industrialized primary neuronal culture production, which enabled the creation of scalable assays within functioning neural networks. We then developed a panel of phenotypic assays based on culturing of primary neurons from genetically modified mice expressing HTS-compatible reporters that capture disease-relevant phenotypes. We demonstrated that a library of 1,280 compounds was quickly screened against both assays using only a few litters of mice in a typical academic laboratory setting. Finally, we implemented one assay in a fully automated high-throughput academic screening facility, illustrating the scalability of assays designed using this platform. These methodological improvements simplify the creation of highly scalable neuron-based phenotypic assays designed to improve drug discovery in CNS disorders.

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神经精神疾病治疗发现中基于神经元的表型筛选测定的改进可扩展性。
迫切需要改进与神经精神疾病(NSDs)相关的药物发现方法。神经精神疾病的治疗发现将受益于能够测量与疾病机制相关的复杂表型变化的筛选分析。然而,追踪复杂神经元表型(如神经元连接)的传统分析方法表现出较差的可扩展性,并且与高通量筛选(HTS)程序不兼容。因此,我们创建了一个神经元表型分析平台,专注于提高能够跟踪疾病相关表型的基于神经元的分析的可扩展性和可负担性。首先,使用廉价的实验室级自动化,我们工业化了初级神经元培养生产,这使得在功能神经网络中创建可扩展的分析成为可能。然后,我们开发了一组表型分析,基于表达hts兼容报告的转基因小鼠的原代神经元的培养,捕获疾病相关表型。我们证明,在典型的学术实验室环境中,仅使用几窝小鼠,就可以快速筛选1280种化合物的文库。最后,我们在全自动高通量学术筛选设施中实施了一项检测,说明了使用该平台设计的检测的可扩展性。这些方法上的改进简化了高度可扩展的基于神经元的表型分析的创建,旨在改善中枢神经系统疾病的药物发现。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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