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高内涵细胞器运输使疾病状态分析成为疾病建模的有力工具。

High content organelle trafficking enables disease state profiling as powerful tool for disease modelling.

机构信息

Division of Neurodegenerative Diseases, Department of Neurology, Technische Universität Dresden, Dresden, Germany.

German Center for Neurodegenerative Diseases (DZNE) Dresden, Dresden, Germany.

出版信息

Sci Data. 2018 Nov 13;5:180241. doi: 10.1038/sdata.2018.241.

Abstract

Neurodegenerative diseases pose a complex field with various neuronal subtypes and distinct differentially affected intra-neuronal compartments. Modelling of neurodegeneration requires faithful in vitro separation of axons and dendrites, their distal and proximal compartments as well as organelle tracking with defined retrograde versus anterograde directionality. We use microfluidic chambers to achieve compartmentalization and established high throughput live organelle imaging at standardized distal and proximal axonal readout sites in iPSC-derived spinal motor neuron cultures from human amyotrophic lateral sclerosis patients to study trafficking phenotypes of potential disease relevance. Our semi-automated pipeline of organelle tracking with FIJI and KNIME yields quantitative, multiparametric high content phenotypic signatures of organelle morphology and their trafficking in axons. We provide here the resultant large datasets to enable systemic signature interrogations for comprehensive and predictive disease modelling, mechanistic dissection and secondary hit validation (e.g. drug screens, genetic screens). Due to the nearly complete coverage of analysed motility events, our quantitative method yields a bias-free statistical power superior over common analyses of a handful of manual kymographs.

摘要

神经退行性疾病是一个复杂的领域,涉及多种神经元亚型和明显不同的神经元内隔室受影响情况。神经退行性变的建模需要忠实于体外分离轴突和树突、它们的远端和近端隔室以及细胞器跟踪,具有定义的逆行和顺行方向。我们使用微流控室来实现隔室化,并在源自人类肌萎缩侧索硬化症患者的 iPSC 衍生脊髓运动神经元培养物中的标准化远端和近端轴突读出点建立高通量活细胞器成像,以研究潜在疾病相关的运输表型。我们使用 FIJI 和 KNIME 的半自动化细胞器跟踪流水线可提供细胞器形态及其在轴突中运输的定量、多参数高内涵表型特征。我们在此提供由此产生的大型数据集,以实现系统的特征分析,用于全面和预测性疾病建模、机制剖析和二次命中验证(例如药物筛选、遗传筛选)。由于分析的运动事件几乎完全涵盖,我们的定量方法产生的无偏统计能力优于少数手动描绘轨迹的常见分析。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fc1a/6233479/c91444f8af34/sdata2018241-f1.jpg

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