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用于类器官表型分析的自动化 3D 高内涵细胞筛选平台的开发。

Development of an automated 3D high content cell screening platform for organoid phenotyping.

机构信息

The Jackson Laboratory for Genomic Medicine, Farmington, CT, United States; Yale School of Medicine, Yale University, New Haven, CT, United States; Department of Biomedical Engineering, School of Engineering and Applied Sciences, Yale University, New Haven, CT, United States.

The Jackson Laboratory for Genomic Medicine, Farmington, CT, United States.

出版信息

SLAS Discov. 2024 Oct;29(7):100182. doi: 10.1016/j.slasd.2024.100182. Epub 2024 Sep 6.

Abstract

The use of organoid models in biomedical research has grown substantially since their inception. As they gain popularity among scientists seeking more complex and biologically relevant systems, there is a direct need to expand and clarify potential uses of such systems in diverse experimental contexts. Herein we outline a high-content screening (HCS) platform that allows researchers to screen drugs or other compounds against three-dimensional (3D) cell culture systems in a multi-well format (384-well). Furthermore, we compare the quality of robotic liquid handling with manual pipetting and characterize and contrast the phenotypic effects detected by confocal imaging and biochemical assays in response to drug treatment. We show that robotic liquid handling is more consistent and amendable to high throughput experimental designs when compared to manual pipetting due to improved precision and automated randomization capabilities. We also show that image-based techniques are more sensitive to detecting phenotypic changes within organoid cultures than traditional biochemical assays that evaluate cell viability, supporting their integration into organoid screening workflows. Finally, we highlight the enhanced capabilities of confocal imaging in this organoid screening platform as they relate to discerning organoid drug responses in single-well co-cultures of organoids derived from primary human biopsies and patient-derived xenograft (PDX) models. Altogether, this platform enables automated, imaging-based HCS of 3D cellular models in a non-destructive manner, opening the path to complementary analysis through integrated downstream methods.

摘要

自从类器官模型问世以来,它们在生物医学研究中的应用已经大大增加。由于科学家们正在寻求更复杂和更具生物学相关性的系统,因此直接需要扩展和阐明此类系统在不同实验环境中的潜在用途。在此,我们概述了一种高内涵筛选 (HCS) 平台,该平台允许研究人员以多孔板格式(384 孔)对三维 (3D) 细胞培养系统进行药物筛选或其他化合物筛选。此外,我们比较了机器人液体处理与手动移液的质量,并对药物处理后通过共聚焦成像和生化分析检测到的表型效应进行了特征描述和对比。我们表明,与手动移液相比,由于提高了精度和自动化随机化能力,机器人液体处理在高通量实验设计中更具一致性和可扩展性。我们还表明,基于图像的技术比评估细胞活力的传统生化分析更能敏感地检测类器官培养物中的表型变化,支持将其整合到类器官筛选工作流程中。最后,我们强调了共聚焦成像在该类器官筛选平台中的增强功能,因为它们与辨别源自人体活检和患者来源异种移植 (PDX) 模型的类器官的单孔共培养物中的类器官药物反应有关。总之,该平台能够以非破坏性的方式自动进行基于成像的 3D 细胞模型 HCS,为通过集成的下游方法进行互补分析开辟了道路。

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