谷歌Chrome浏览器插件
订阅小程序
在清言上使用

Nanobody-based strategy for rapid and accurate pathogen detection: a case of COVID-19 testing

Wenjin Hu, Yichen Liu, Xi Li,Liusheng Lei,Huai Lin,Qingbin Yuan,Daqing Mao,Yi Luo

Biosensors and Bioelectronics(2024)

引用 0|浏览2
暂无评分
摘要
Antibody pairs-based immunoassay platforms served as essential and effective tools in the field of pathogen detection. However, the cumbersome preparation and limited detection sensitivity of antibody pairs challenge in establishment of a highly sensitive detection platform. In this study, using COVID-19 testing as a case, we utilized readily accessible nanobodies as detection antibodies and further proposed an accurate design concept with a more scientific and efficient screening strategy to obtain ultrasensitive antibody pairs. We employed nanobodies capable of binding different antigenic epitopes of the nucleocapsid (NP) or receptor-binding domain (RBD) antigens sandwich as substitutes for monoclonal antibodies (mAbs) sandwich in fast detection formats and utilized time-resolved fluorescence (TRF) microspheres as the signal probe. Consequently, we developed a multi-epitope nanobody sandwich-based fluorescence lateral flow immunoassay (FLFA) strip. Our results suggest that the NP antigen had a detection limit of 12.01 pg/mL, while the RBD antigen had a limit of 6.51 pg/mL using our FLFA strip. Based on double mAb sandwiches, the values presented herein demonstrated 4 to 32-fold enhancements in sensitivity, and 32 to 256-fold enhancements compared to commercially available antigen lateral flow assay kits. Furthermore, we demonstrated the excellent characteristics of the proposed test strip, including its specificity, stability, accuracy, and repeatability, which underscores its the prospective utility. Indeed, these findings indicate that our established screening strategy along with the multi-epitope nanobody sandwich mode provides an optimized strategy in the field of pathogen detection.
更多
查看译文
关键词
Antibody pairs,Pathogen detection,Nanobodies,Screening strategy,Lateral flow immunoassay
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要