Gao Shan, Chen Weiyang, Zhang Nan, Xu Chi, Jing Haiming, Zhang Wenjing, Han Gaochao, Flavel Matthew, Jois Markandeya, Zeng Yingxin, Han Jing-Dong J, Xian Bo, Li Guojun
Beijing Key Laboratory of Diagnostic and Traceability Technologies for Food Poisoning, Beijing Center for Disease Prevention and Control/Beijing Center of Preventive Medicine Research, China.
College of Computer Science and Technology, Qilu University of Technology(Shandong Academy of Sciences), China.
J Vis Exp. 2019 Mar 14(145). doi: 10.3791/59082.
Applying toxicity testing of chemicals in higher order organisms, such as mice or rats, is time-consuming and expensive, due to their long lifespan and maintenance issues. On the contrary, the nematode Caenorhabditis elegans (C. elegans) has advantages to make it an ideal choice for toxicity testing: a short lifespan, easy cultivation, and efficient reproduction. Here, we describe a protocol for the automatic phenotypic profiling of C. elegans in a 384-well plate. The nematode worms are cultured in a 384-well plate with liquid medium and chemical treatment, and videos are taken of each well to quantify the chemical influence on 33 worm features. Experimental results demonstrate that the quantified phenotype features can classify and predict the acute toxicity for different chemical compounds and establish a priority list for further traditional chemical toxicity assessment tests in a rodent model.
由于小鼠或大鼠等高等生物寿命长且存在饲养问题，对其进行化学物质毒性测试既耗时又昂贵。相反，线虫秀丽隐杆线虫（C. elegans）具有诸多优势，使其成为毒性测试的理想选择：寿命短、易于培养且繁殖效率高。在此，我们描述了一种在384孔板中对秀丽隐杆线虫进行自动表型分析的方案。线虫在含有液体培养基并经过化学处理的384孔板中培养，对每个孔拍摄视频以量化化学物质对33种线虫特征的影响。实验结果表明，量化的表型特征可以对不同化合物的急性毒性进行分类和预测，并为在啮齿动物模型中进一步进行传统化学毒性评估测试建立优先级列表。
