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Microfluidic particle dam for direct visualization of SARS-CoV-2 antibody levels in COVID-19 vaccinees

Minghui Wu (Co-first Author), Siying Wu (Co-first Author), Gaobo Wang, Wengang Liu, Lok Ting Chu, Tianyi Jiang, Hoi Kwan Kwong, Hiu Lam Chow, Iris Wai Sum Li, Ting-Hsuan Chen*

*Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

75 Downloads (CityUHK Scholars)

Abstract

Various COVID-19 vaccines are currently deployed, but their immunization varies and decays with time. Antibody level is a potent correlate to immune protection, but its quantitation relies on intensive laboratory techniques. Here, we report a decentralized, instrument-free microfluidic device that directly visualizes SARS-CoV-2 antibody levels. Magnetic microparticles (MMPs) and polystyrene microparticles (PMPs) can bind to SARS- CoV-2 antibodies simultaneously. In a microfluidic chip, this binding reduces the incidence of free PMPs escaping from magnetic separation and shortens PMP accumulation length at a particle dam. This visual quantitative result enables use in either sensitive mode [limit of detection (LOD): 13.3 ng/ml; sample-to-answer time: 70 min] or rapid mode (LOD: 57.8 ng/ml; sample-to-answer time: 20 min) and closely agrees with the gold standard enzyme-linked immunosorbent assay. Trials on 91 vaccinees revealed higher antibody levels in mRNA vaccinees than in inactivated vaccinees and their decay in 45 days, demonstrating the need for point-of-care devices to monitor immune protection.
Original languageEnglish
Article numbereabn6064
Number of pages11
JournalScience Advances
Volume8
Issue number22
Online published3 Jun 2022
DOIs
Publication statusPublished - 3 Jun 2022

Funding

This study was supported by the Hong Kong Research Grant Council (11217820 and N_CityU119/19), Innovation and Technology Commission (ITS/098/20), The Science, Technology and Innovation Commission of Shenzhen Municipality (JCYJ20210324134006017), and City University of Hong Kong (9678242 and 6430620).

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Research Keywords

  • DETECTION LIMITS
  • QUANTITATIVE DETECTION

Publisher's Copyright Statement

  • This full text is made available under CC-BY 4.0. https://creativecommons.org/licenses/by/4.0/

RGC Funding Information

  • RGC-funded

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