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在微电极阵列上的人类神经元网络是一种高度稳健的工具,可用于在体外研究与疾病相关的特定基因型-表型相关性。

Human neuronal networks on micro-electrode arrays are a highly robust tool to study disease-specific genotype-phenotype correlations in vitro.

机构信息

Department of Human Genetics, Radboudumc, Donders Institute for Brain, Cognition, and Behavior, 6500 HB Nijmegen, the Netherlands; Department of Clinical Neurophysiology, University of Twente, 7522 NB Enschede, the Netherlands.

Department of Human Genetics, Radboudumc, Donders Institute for Brain, Cognition, and Behavior, 6500 HB Nijmegen, the Netherlands; Centre for Molecular and Biomolecular Informatics, Radboudumc, Radboud Institute for Molecular Life Sciences, 6500 HB Nijmegen, the Netherlands.

出版信息

Stem Cell Reports. 2021 Sep 14;16(9):2182-2196. doi: 10.1016/j.stemcr.2021.07.001. Epub 2021 Jul 29.

Abstract

Micro-electrode arrays (MEAs) are increasingly used to characterize neuronal network activity of human induced pluripotent stem cell (hiPSC)-derived neurons. Despite their gain in popularity, MEA recordings from hiPSC-derived neuronal networks are not always used to their full potential in respect to experimental design, execution, and data analysis. Therefore, we benchmarked the robustness of MEA-derived neuronal activity patterns from ten healthy individual control lines, and uncover comparable network phenotypes. To achieve standardization, we provide recommendations on experimental design and analysis. With such standardization, MEAs can be used as a reliable platform to distinguish (disease-specific) network phenotypes. In conclusion, we show that MEAs are a powerful and robust tool to uncover functional neuronal network phenotypes from hiPSC-derived neuronal networks, and provide an important resource to advance the hiPSC field toward the use of MEAs for disease phenotyping and drug discovery.

摘要

微电极阵列(MEA)越来越多地用于描述人诱导多能干细胞(hiPSC)衍生神经元的神经网络活动。尽管它们越来越受欢迎,但在实验设计、执行和数据分析方面,MEA 记录并不总是能充分发挥其潜力。因此,我们对来自十个健康个体对照系的 MEA 衍生神经元活性模式的稳健性进行了基准测试,并揭示了可比较的网络表型。为了实现标准化,我们提供了实验设计和分析方面的建议。通过这种标准化,MEA 可以作为一种可靠的平台,用于区分(疾病特异性)网络表型。总之,我们表明 MEA 是一种强大而稳健的工具,可以从 hiPSC 衍生的神经元网络中揭示功能性神经元网络表型,并为推进 hiPSC 领域使用 MEA 进行疾病表型分析和药物发现提供了重要资源。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/03ed/8452490/32e0f30e9b1a/gr1.jpg

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