Multiplex metal-detection based assay (MMDA) for COVID-19 diagnosis and identification of disease severity biomarkers. Issue 11 (28th February 2022)
- Record Type:
- Journal Article
- Title:
- Multiplex metal-detection based assay (MMDA) for COVID-19 diagnosis and identification of disease severity biomarkers. Issue 11 (28th February 2022)
- Main Title:
- Multiplex metal-detection based assay (MMDA) for COVID-19 diagnosis and identification of disease severity biomarkers
- Authors:
- 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
Li, Yu-Feng
Chan, Jasper Fuk-Woo
Sun, Hongzhe - Abstract:
- Abstract : A MMDA platform is developed by using metal-tagged antibodies as reporting probes combined with machine learning algorithms, as a general strategy for highly multiplexed biofluid assay. Abstract : 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 severityAbstract : A MMDA platform is developed by using metal-tagged antibodies as reporting probes combined with machine learning algorithms, as a general strategy for highly multiplexed biofluid assay. Abstract : 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. … (more)
- Is Part Of:
- Chemical science. Volume 13:Issue 11(2022)
- Journal:
- Chemical science
- Issue:
- Volume 13:Issue 11(2022)
- Issue Display:
- Volume 13, Issue 11 (2022)
- Year:
- 2022
- Volume:
- 13
- Issue:
- 11
- Issue Sort Value:
- 2022-0013-0011-0000
- Page Start:
- 3216
- Page End:
- 3226
- Publication Date:
- 2022-02-28
- Subjects:
- Chemistry -- Periodicals
540.5 - Journal URLs:
- http://pubs.rsc.org/en/Journals/JournalIssues/SC ↗
http://www.rsc.org/ ↗ - DOI:
- 10.1039/d1sc05852e ↗
- Languages:
- English
- ISSNs:
- 2041-6520
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3151.490000
British Library DSC - BLDSS-3PM
British Library STI - ELD Digital store - Ingest File:
- 21189.xml