摘要
为进一步建立克伦特罗(CL)残留检测方法,给制备CL残留检测试剂盒打下基础,进行了CL单克隆抗体(McAb)的制备研究。以重氮反应,将CL与牛血清白蛋白(BSA)、鸡卵清白蛋白(OVA)偶联,分别制备免疫原和检测原,并通过杂交瘤技术,获得了5株能稳定分泌特异结合CL小分子的单克隆抗体杂交瘤细胞株(1E9A9、2A9C1、3F2E9、6G2E9、6E10D9)。这5株细胞株经过体外传代培养和冻存复苏后均能稳定分泌抗体。用间接酶联免疫吸附试验(ELISA)测定的这5株细胞上清液抗体效价分别为1:1.28×10~5、1:5.12×10~5、1:1.28×10~5、1:5.12×10~5、1:2.56×10~5;选用3F2E9、6E10D9细胞株,制备CL单克隆抗体腹水并纯化,其纯化后的腹水抗体效价分别为1:1.28×10~5、1:2.56×10~5,BCA(2,2-联喹啉-4,4-二甲酸二钠)方法检测的浓度分别为1.60 mg/mL、1.54 mg/mL;最终通过斑点ELISA(Dot-ELISA)方法测定CL抗原与这2株抗体有较强的结合力。在上述方法的制备及验证下,成功获得了特异性较高的CL单克隆抗体,为进一步研制CL残留检测试剂盒奠定了基础。
In order to further develop the method for detecting residual Clenbuterol(CL),so as to lay foundation for research and development of the detection kits,monoclonal antibodies(m Abs)against CL were prepared. Through diazo reaction,CL was coupled with Bovine serum albumin(BSA)and Ovalbumin(OVA),the coupling products were used as immunogen and detection antigen,respectively. Using hybridoma technique,5 monoclonal hybridoma cell strains(1E9A9、2A9C1、3F2E9、6G2E9、6E10D9)which could secret antibodies against small CL molecules were obtained. During serial subcultivation in vitro and cryopreservation and resuscitation,the antibodies could be secreted stably. ELISA detection showed the antibody titers of supernatant of the 5 strains were 1:128 000,1:512 000,1:128 000,1:512 000 and 1:256 000,respectively. Ascites containing m Abs were induced by hybridoma cell strain 3F2E9 and 6E10D9 and the antibody titers in purifi ed ascites were 1:128 000 and 1:256 000. Measurement by BCA protein determination method showed their concentrations were 1.60 mg/mL and 1.54 mg/mL repectively. Through Dot-ELISA,good reactivity between the two m Abs and CL was verifi ed. As a conclusion,specifi c m Abs against CL were prepared successfully,which would lay foundation for further research of the kits for CL residue detection.
出处
《中国动物检疫》
CAS
2017年第2期91-95,105,共6页
China Animal Health Inspection
基金
"十三五"国家重点研发计划(2016YFD0500708)
山东省现代农业产业技术体系疫病控制岗位(SDAIT-08-07)
山东省农业重大应用技术创新课题
山东省科技发展计划(2014GNC111011)
山东省农业科学院青年英才培养工程
山东省农业科学院青年科研基金项目(2016YQN53)