Your browser doesn't support javascript.
Multiplex metal-detection based assay (MMDA) for COVID-19 diagnosis and identification of disease severity biomarkers.
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.
  • Zhou Y; Department of Chemistry, State Key Laboratory of Synthetic Chemistry, CAS-HKU Joint Laboratory of Metallomics on Health and Environment, The University of Hong Kong Pokfulam Hong Kong Special Administrative Region China hsun@hku.hk.
  • Yuan S; State Key Laboratory of Emerging Infectious Diseases, Carol Yu Centre for Infection, Department of Microbiology, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong Pokfulam Hong Kong Special Administrative Region China jfwchan@hku.hk.
  • To KK; Department of Clinical Microbiology, and Infection Control, The University of Hong Kong-Shenzhen Hospital Shenzhen Guangdong Province China.
  • Xu X; Centre for Virology, Vaccinology and Therapeutics, Hong Kong Science and Technology Park Hong Kong Special Administrative Region China.
  • Li H; State Key Laboratory of Emerging Infectious Diseases, Carol Yu Centre for Infection, Department of Microbiology, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong Pokfulam Hong Kong Special Administrative Region China jfwchan@hku.hk.
  • Cai JP; Department of Clinical Microbiology, and Infection Control, The University of Hong Kong-Shenzhen Hospital Shenzhen Guangdong Province China.
  • Luo C; Centre for Virology, Vaccinology and Therapeutics, Hong Kong Science and Technology Park Hong Kong Special Administrative Region China.
  • Hung IF; Department of Microbiology, Queen Mary Hospital Pokfulam Hong Kong Special Administrative Region China.
  • Chan KH; Department of Chemistry, State Key Laboratory of Synthetic Chemistry, CAS-HKU Joint Laboratory of Metallomics on Health and Environment, The University of Hong Kong Pokfulam Hong Kong Special Administrative Region China hsun@hku.hk.
  • Yuen KY; Department of Chemistry, State Key Laboratory of Synthetic Chemistry, CAS-HKU Joint Laboratory of Metallomics on Health and Environment, The University of Hong Kong Pokfulam Hong Kong Special Administrative Region China hsun@hku.hk.
  • Li YF; State Key Laboratory of Emerging Infectious Diseases, Carol Yu Centre for Infection, Department of Microbiology, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong Pokfulam Hong Kong Special Administrative Region China jfwchan@hku.hk.
  • Chan JF; Department of Clinical Microbiology, and Infection Control, The University of Hong Kong-Shenzhen Hospital Shenzhen Guangdong Province China.
  • Sun H; State Key Laboratory of Emerging Infectious Diseases, Carol Yu Centre for Infection, Department of Microbiology, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong Pokfulam Hong Kong Special Administrative Region China jfwchan@hku.hk.
Chem Sci ; 13(11): 3216-3226, 2022 Mar 16.
Article in English | MEDLINE | ID: covidwho-1764224
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.

Full text: Available Collection: International databases Database: MEDLINE Type of study: Diagnostic study / Prognostic study Language: English Journal: Chem Sci Year: 2022 Document Type: Article

Similar

MEDLINE

...
LILACS

LIS


Full text: Available Collection: International databases Database: MEDLINE Type of study: Diagnostic study / Prognostic study Language: English Journal: Chem Sci Year: 2022 Document Type: Article