Issue 12, 2021

Development of spike protein-based fluorescence lateral flow assay for the simultaneous detection of SARS-CoV-2 specific IgM and IgG

Abstract

The pandemic outbreak of the 2019 coronavirus disease (COVID-19), which is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is still spreading rapidly and poses a great threat to human health. As such, developing rapid and accurate immunodiagnostic methods for the identification of infected persons is needed. Here, we proposed a simple but sensitive on-site testing method based on spike protein-conjugated quantum dot (QD) nanotag-integrated lateral flow immunoassay (LFA) to simultaneously detect SARS-CoV-2-specific IgM and IgG in human serum. Advanced silica-core@dual QD-shell nanocomposites (SiO2@DQD) with superior luminescence and stability were prepared to serve as fluorescent nanotags in the LFA strip and guarantee high sensitivity and reliability of the assay. The performance of the SiO2@DQD-strip was fully optimized and confirmed by using 10 positive serum samples from COVID-19 patients and 10 negative samples from patients with other respiratory diseases. The practical clinical value of the assay was further evaluated by testing 316 serum samples (114 positive and 202 negative samples). The overall detection sensitivity and specificity reached 97.37% (111/114) and 95.54% (193/202), respectively, indicating the huge potential of our proposed method for the rapid and accurate detection of SARS-CoV-2-infected persons and asymptomatic carriers.

Graphical abstract: Development of spike protein-based fluorescence lateral flow assay for the simultaneous detection of SARS-CoV-2 specific IgM and IgG

Supplementary files

Article information

Article type
Paper
Submitted
19 Feb 2021
Accepted
06 Apr 2021
First published
19 Apr 2021

Analyst, 2021,146, 3908-3917

Development of spike protein-based fluorescence lateral flow assay for the simultaneous detection of SARS-CoV-2 specific IgM and IgG

C. Wang, D. Shi, N. Wan, X. Yang, H. Liu, H. Gao, M. Zhang, Z. Bai, D. Li, E. Dai, Z. Rong and S. Wang, Analyst, 2021, 146, 3908 DOI: 10.1039/D1AN00304F

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