Development of nucleocapsid-specific monoclonal antibodies for SARS-CoV-2 and their ELISA diagnostics on an automatic microfluidic device.

Sensors and actuators. B, Chemical(2023)

引用 4|浏览5
暂无评分
摘要
Coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has threatened public health globally, and the emergence of viral variants has exacerbated an already precarious situation. To prevent further spread of the virus and determine government action required for virus control, accurate and rapid immunoassays for SARS-CoV-2 diagnosis are urgently needed. In this study, we generated monoclonal antibodies (mAbs) against the SARS-CoV-2 nucleocapsid protein (NP), compared their reactivity using an enzyme-linked immunosorbent assay (ELISA), and selected four mAbs designated 1G6, 3E10, 3F10, and 5B6 which have higher reactivity to NP and viral lysates of SARS-CoV-2 than other mAbs. Using an epitope mapping assay, we identified that 1G6 detected the C-terminal domain of SARS-CoV-2 NP (residues 248-364), while 3E10 and 3F10 bound to the N-terminal domain (residues 47-174) and 3F10 detected the N-arm region (residues 1-46) of SARS-CoV-2 NP. Based on the epitope study and sandwich ELISA, we selected the 1G6 and 3E10 Abs as an optimal Ab pair and applied them for a microfluidics-based point-of-care (POC) ELISA assay to detect the NPs of SARS-CoV-2 and its variants. The integrated and automatic microfluidic system could operate the serial injection of the sample, the washing solution, the HRP-conjugate antibody, and the TMB substrate solution simply by controlling air purge via a single syringe. The proposed Ab pair-equipped microsystem effectively detected the NPs of SARS-CoV-2 variants as well as in clinical samples. Collectively, our proposed platform provides an advanced protein-based diagnostic tool for detecting SARS-CoV-2.
更多
查看译文
关键词
Antibody pair,Diagnosis,Microfluidic device,Nucleocapsid,Point-of-care testing,SARS-CoV-2
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