High-Content Screening for Protein-Protein Interaction Modulators Using Proximity Ligation Assay in Primary Neurons
Q3 Biochemistry, Genetics and Molecular Biology
Tiago Mendes, Adrien Herledan, Florence Leroux, Benoit Deprez, Jean-Charles Lambert, Devrim Kilinc
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Abstract
The proximity ligation assay (PLA) allows the detection and subcellular localization of protein-protein interactions with high specificity. We recently developed a high-content screening model based on primary hippocampal neurons cultured in 384-well plates and screened a library of ∼1100 compounds using a PLA between tau and bridging integrator 1, a genetic risk factor for Alzheimer's disease. We developed image-segmentation and spot-detection algorithms to delineate PLA signals in the axonal network, but not in cell bodies, from confocal images acquired via a high-throughput microscope. To compare data generated from different plates and through different experiments, we developed a computational routine to optimize the image analysis parameters for each plate and devised a range of quality-control measures to ultimately identify compounds that consistently increase or decrease our read-out. We provide the following protocols. © 2019 by John Wiley & Sons, Inc.
Basic Protocol 1 : Routine culture of rat postnatal hippocampal neurons in 384-well plates
Basic Protocol 2 : Compound incubation using the high-content screening platform
Support Protocol 1 : Preparation of intermediate plates for compound screening
Support Protocol 2 : Preparation of intermediate plates for hit validation (dose-response curves)
Basic Protocol 3 : Proximity ligation assay in 384-well plates
Basic Protocol 4 : Image acquisition and analysis
Support Protocol 3 : Optimization of analysis parameters
Basic Protocol 5 : Identification of hits
Basic Protocol 6 : Validation of hits based on dose-response curves
利用邻近结扎法在初级神经元中筛选高含量的蛋白质相互作用调节剂
近距离连接试验(PLA)允许检测和亚细胞定位具有高特异性的蛋白质-蛋白质相互作用。我们最近开发了一种基于384孔板中培养的初级海马神经元的高含量筛选模型,并使用tau和桥接整合子1(阿尔茨海默病的遗传危险因子)之间的PLA筛选了约1100种化合物库。我们开发了图像分割和点检测算法,从高通量显微镜获得的共聚焦图像中描绘轴突网络中的PLA信号,而不是细胞体中的PLA信号。为了比较不同板和不同实验产生的数据,我们开发了一个计算程序来优化每个板的图像分析参数,并设计了一系列质量控制措施,以最终确定持续增加或减少我们读出的化合物。我们提供以下协议。©2019 by John Wiley &基本方案1:在384孔板中常规培养大鼠出生后海马神经元基本方案2:使用高含量筛选平台进行复合培养支持方案1:制备用于复合筛选的中间板支持方案2:制备用于命中验证的中间板(剂量-反应曲线)基本方案3:384孔板中的邻近结扎试验基本方案4:图像采集和分析支持方案3:分析参数的优化基本方案5:药物的鉴定基本方案6:基于剂量-反应曲线的药物验证
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