Multiplex metal-detection based assay (MMDA) for COVID-19 diagnosis and identification of disease severity biomarkers†‡
文献类型：期刊论文
| 作者 | Zhou, Ying; Yuan, Shuofeng,,; To, Kelvin Kai-Wang,,,; Xu, Xiaohan ; Li, Hongyan; Cai, Jian-Piao,; Luo, Cuiting; Hung, Ivan Fan-Ngai,; Chan, Kwok-Hung,,; Yuen, Kwok-Yung,,,,,
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| 刊名 | Chemical Science
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| 出版日期 | 2022 |
| 卷号 | 13期号:11页码:3216-3226 |
| ISSN号 | 20416520 |
| DOI | 10.1039/d1sc05852e |
| 文献子类 | Article |
| 英文摘要 | The ongoing COVID-19 pandemic caused by SARS-CoV-2 highlights the urgent need to develop sensitive methods for diagnosis and prognosis. To achieve this, multidimensional detection of SARS-CoV-2 related parameters including virus loads, immune response, and inflammation factors is crucial. Herein, by using metal-tagged antibodies as reporting probes, we developed a multiplex metal-detection based assay (MMDA) method as a general multiplex assay strategy for biofluids. This strategy provides extremely high multiplexing capability (theoretically over 100) compared with other reported biofluid assay methods. As a proof-of-concept, MMDA was used for serologic profiling of anti-SARS-CoV-2 antibodies. The MMDA exhibits significantly higher sensitivity and specificity than ELISA for the detection of anti-SARS-CoV-2 antibodies. By integrating the high dimensional data exploration/visualization tool (tSNE) and machine learning algorithms with in-depth analysis of multiplex data, we classified COVID-19 patients into different subgroups based on their distinct antibody landscape. We unbiasedly identified anti-SARS-CoV-2-nucleocapsid IgG and IgA as the most potently induced types of antibodies for COVID-19 diagnosis, and anti-SARS-CoV-2-spike IgA as a biomarker for disease severity stratification. MMDA represents a more accurate method for the diagnosis and disease severity stratification of the ongoing COVID-19 pandemic, as well as for biomarker discovery of other diseases. This journal is © The Royal Society of Chemistry |
| 电子版国际标准刊号 | 20416539 |
| 语种 | 英语 |
| WOS记录号 | WOS:000769646600001 |
| 源URL | [http://ir.ihep.ac.cn/handle/311005/299341]  |
| 专题 | 高能物理研究所_多学科研究中心 |
| 作者单位 | 中国科学院高能物理研究所 |
| 推荐引用方式 GB/T 7714 | Zhou, Ying,Yuan, Shuofeng,,,To, Kelvin Kai-Wang,,,,et al. Multiplex metal-detection based assay (MMDA) for COVID-19 diagnosis and identification of disease severity biomarkers†‡[J]. Chemical Science,2022,13(11):3216-3226. |
| APA | Zhou, Ying.,Yuan, Shuofeng,,.,To, Kelvin Kai-Wang,,,.,Xu, Xiaohan.,Li, Hongyan.,...&Sun, Hongzhe.(2022).Multiplex metal-detection based assay (MMDA) for COVID-19 diagnosis and identification of disease severity biomarkers†‡.Chemical Science,13(11),3216-3226. |
| MLA | Zhou, Ying,et al."Multiplex metal-detection based assay (MMDA) for COVID-19 diagnosis and identification of disease severity biomarkers†‡".Chemical Science 13.11(2022):3216-3226. |
入库方式： OAI收割
来源：高能物理研究所
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