PDF(Pubmed)
Abstract：
Reproducible functional assays to study in vitro neuronal networks represent an important cornerstone in the quest to develop physiologically relevant cellular models of human diseases. Here, we introduce DeePhys, a MATLAB-based analysis tool for data-driven functional phenotyping of in vitro neuronal cultures recorded by high-density microelectrode arrays. DeePhys is a modular workflow that offers a range of techniques to extract features from spike-sorted data, allowing for the examination of functional phenotypes both at the individual cell and network levels, as well as across development. In addition, DeePhys incorporates the capability to integrate novel features and to use machine-learning-assisted approaches, which facilitates a comprehensive evaluation of pharmacological interventions. To illustrate its practical application, we apply DeePhys to human induced pluripotent stem cell-derived dopaminergic neurons obtained from both patients and healthy individuals and showcase how DeePhys enables phenotypic screenings.
摘要：
用于研究体外神经元网络的可重复功能测定代表了寻求开发人类疾病的生理相关细胞模型的重要基石。这里,我们介绍DeePhys,基于MATLAB的分析工具，用于通过高密度微电极阵列记录的体外神经元培养物的数据驱动功能表型分析。DeePhys是一个模块化的工作流程，提供了一系列的技术来提取特征从尖峰排序的数据，允许在单个细胞和网络水平上检查功能表型，以及整个发展。此外,DeePhys具有集成新功能和使用机器学习辅助方法的能力，这有助于全面评估药理学干预措施。为了说明其实际应用，我们将DeePhys应用于从患者和健康个体获得的人类诱导多能干细胞衍生的多巴胺能神经元,并展示了DeePhys如何进行表型筛查.
