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Ein High-Throughput-Assay für die Vorhersage der chemischen Toxizität durch automatisierte phänotypischen Profilierung von Caenorhabditis elegans
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A High-throughput Assay for the Prediction of Chemical Toxicity by Automated Phenotypic Profiling of Caenorhabditis elegans
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Ein High-Throughput-Assay für die Vorhersage der chemischen Toxizität durch automatisierte phänotypischen Profilierung von Caenorhabditis elegans

7,071 Views

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09:01 min

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March 14, 2019

DOI:

10.3791/59082-v

DOI:
10.3791/59082-v

•

09:01 min
March 14, 2019

•

3 Views
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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

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Eine quantitative Methode wurde entwickelt, um zu identifizieren und die akute Toxizität von Chemikalien durch die automatisch analysieren die phänotypische Profilierung von Caenorhabditis ElegansVorhersagen. Dieses Protokoll beschreibt die Würmer mit Chemikalien zu behandeln, in einem 384-Well-Platte, Videos aufzunehmen und toxikologischen Verwandte Phänotypen zu quantifizieren.

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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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