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用于高通量药物筛选的二维和三维患者来源细胞模型的集成、自动化维护、扩展和分化。

Integrated, automated maintenance, expansion and differentiation of 2D and 3D patient-derived cellular models for high throughput drug screening.

机构信息

Luxembourg Centre for Systems Biomedicine, Translational Neuroscience, University of Luxembourg, Luxembourg, Luxembourg.

Disease Modeling and Screening Platform (DMSP), Luxembourg Centre of Systems Biomedicine (Biomedicine), University of Luxembourg and Luxembourg Institute of Health (LIH), 6 Avenue du Swing, 4367, Belvaux, Luxembourg.

出版信息

Sci Rep. 2021 Jan 14;11(1):1439. doi: 10.1038/s41598-021-81129-3.

DOI:10.1038/s41598-021-81129-3
PMID:33446877
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7809482/
Abstract

Patient-derived cellular models become an increasingly powerful tool to model human diseases for precision medicine approaches. The identification of robust cellular disease phenotypes in these models paved the way towards high throughput screenings (HTS) including the implementation of laboratory advanced automation. However, maintenance and expansion of cells for HTS remains largely manual work. Here, we describe an integrated, complex automated platform for HTS in a translational research setting also designed for maintenance and expansion of different cell types. The comprehensive design allows automation of all cultivation steps and is flexible for development of methods for variable cell types. We demonstrate protocols for controlled cell seeding, splitting and expansion of human fibroblasts, induced pluripotent stem cells (iPSC), and neural progenitor cells (NPC) that allow for subsequent differentiation into different cell types and image-based multiparametric screening. Furthermore, we provide automated protocols for neuronal differentiation of NPC in 2D culture and 3D midbrain organoids for HTS. The flexibility of this multitask platform makes it an ideal solution for translational research settings involving experiments on different patient-derived cellular models for precision medicine.

摘要

基于患者来源的细胞模型正成为精准医疗方法模拟人类疾病的有力工具。在这些模型中鉴定出稳健的细胞疾病表型,为高通量筛选(HTS)铺平了道路,包括实验室先进自动化的实施。然而,用于 HTS 的细胞的维持和扩增在很大程度上仍是手动操作。在这里,我们描述了一种用于转化研究环境中的 HTS 的集成的、复杂的自动化平台,该平台也设计用于维持和扩增不同的细胞类型。综合设计允许对所有培养步骤进行自动化,并且针对不同细胞类型的方法开发具有灵活性。我们展示了用于受控细胞接种、人成纤维细胞、诱导多能干细胞(iPSC)和神经祖细胞(NPC)的分裂和扩增的方案,这些方案允许随后分化为不同的细胞类型和基于图像的多参数筛选。此外,我们提供了 NPC 在 2D 培养和 3D 中脑类器官中的神经元分化的自动化方案,用于 HTS。该多功能平台的灵活性使其成为涉及不同患者来源的细胞模型的精准医疗实验的转化研究环境的理想解决方案。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/534b/7809482/dfa5ce0db454/41598_2021_81129_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/534b/7809482/130b41950b7d/41598_2021_81129_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/534b/7809482/febd4fb8d37c/41598_2021_81129_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/534b/7809482/dcd5b391d558/41598_2021_81129_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/534b/7809482/55a1b0f23808/41598_2021_81129_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/534b/7809482/dfa5ce0db454/41598_2021_81129_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/534b/7809482/130b41950b7d/41598_2021_81129_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/534b/7809482/febd4fb8d37c/41598_2021_81129_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/534b/7809482/dcd5b391d558/41598_2021_81129_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/534b/7809482/55a1b0f23808/41598_2021_81129_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/534b/7809482/dfa5ce0db454/41598_2021_81129_Fig5_HTML.jpg

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