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March 14, 2019
DOI:
1Beijing Key Laboratory of Diagnostic and Traceability Technologies for Food Poisoning,Beijing Center for Disease Prevention and Control/Beijing Center of Preventive Medicine Research, China, 2College of Computer Science and Technology,Qilu University of Technology(Shandong Academy of Sciences), China, 3Key Laboratory of Computational Biology, CAS Center for Excellence in Molecular Cell Science, Collaborative Innovation Center for Genetics and Developmental Biology, Chinese Academy of Sciences-Max Planck Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences,Chinese Academy of Sciences, China, 4Beijing Key Laboratory of Environmental Toxicology, School of Public Health,Capital Medical University, China, 5School of Life Sciences,La Trobe University, Australia
Summary
Automatically generated
通过对线虫表型分析的自动分析, 提出了一种定量鉴别和预测化学品急性毒性的方法。该协议描述了如何在384孔板中使用化学物质处理蠕虫, 捕获视频, 并量化与毒理学相关的表型。
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Cite this Article
Gao, S., Chen, W., Zhang, N., Xu, C., Jing, H., Zhang, W., Han, G., Flavel, M., Jois, M., Zeng, Y., Han, J. J., Xian, B., Li, G. A High-throughput Assay for the Prediction of Chemical Toxicity by Automated Phenotypic Profiling of Caenorhabditis elegans. J. Vis. Exp. (145), e59082, doi:10.3791/59082 (2019).
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