Abstract

The current COVID-19 pandemic has increased the demand for pathogen detection methods that combine low detection limits with rapid results. Despite the significant progress in methods and devices for nucleic acid amplification, immunochemical methods are still preferred for mass testing without specialized laboratories and highly qualified personnel. The most widely used immunoassays are microplate enzyme-linked immunosorbent assay (ELISA) with photometric detection and lateral flow immunoassay (LFIA) with visual results assessment. However, the disadvantage of ELISA is its considerable duration, and that of LFIA is its low sensitivity. In this study, the modified LFIA of a specific antigen of the causative agent of COVID-19, spike receptor-binding domain, was developed and characterized. This modified LFIA includes the use of gold nanoparticles with immobilized antibodies and 4-mercaptobenzoic acid as surface-enhanced Raman scattering (SERS) nanotag and registration of the nanotag binding by SERS spectrometry. To enhance the sensitivity of LFIA-SERS analysis, we determined the optimal compositions of SERS nanotags and membranes used in LFIA. For benchmark comparison, ELISA and conventional colorimetric LFIA were used with the same immune reagents. The proposed method combines a low detection limit of 0.1 ng/mL (at 0.4 ng/mL for ELISA and 1 ng/mL for qualitative LFIA) with a short assay time equal to 20 min (at 3.5 h for ELISA and 15 min for LFIA). The results obtained demonstrate the promise of using the SERS effects in membrane immuno-analytical systems.

Highlights

  • The current COVID-19 pandemic has challenged the global healthcare system [1,2]

  • We developed a novel surface-enhanced Raman scattering (SERS)-based lateral flow immunoassay (LFIA) for the detection of the SARSCoV-2 spike receptor-binding domain (RBD)

  • Since 4-mercaptobenzoic acid molecules are adsorbed on the surface of AuNPs through the S-atom, and the binding of antibodies to AuNPs is dominated by electrostatic interactions between the negatively charged surface of nanoparticles promoted by carboxylic groups of MBA and antibodies containing a positive charge, it is assumed that the reporter does not significantly affect the adsorption of antibodies [31,32]

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Summary

Introduction

The current COVID-19 pandemic has challenged the global healthcare system [1,2]. The SARS-CoV-2 virus causing this disease has four main structural proteins (spike, envelope, membrane, and nucleocapsid), which contribute to the assembly of the virus and its penetration into target cells (in the case of spike protein) [3,4]. Accurate point-of-care COVID-19 tests are attracting a lot of attention thanks to their speed, ease of use, and potential for diagnostic implementation Immunoassay techniques, such as microplate enzyme-linked immunosorbent assay (ELISA) and lateral flow immunoassay (LFIA), provide more simple testing than the amplification techniques. A conjugate of anti-SARS-CoV-2 spike RBD antibodies with 4-mercaptobenzoic acid (MBA)-modified spherical gold nanoparticles (AuNPs) was synthesized and used as a SERS nanotag Parameters such as the loading of the Raman reporter molecule and concentration of specific antibodies in the SERS nanotag as well as the choice of the analytical membrane for the SERS-based LFIA were optimized. Compared to the previously mentioned LFIAs for anti-SARSCoV-2 IgM/IgG detection, which are not recommended for use as the only diagnostic tool as well as for controlling the spread of the virus, the proposed test for determining SARS-CoV-2 Spike RBD, on the contrary, will allow for detecting the virus in the first days following infection

Materials and Methods
Biotinylation of Antibody RBDF5
Sandwich ELISA of RBD
Preparation of AuNP
Preparation of Antibody RBDF5 Conjugate with AuNP
Manufacturing of Tests Strips for LFIA
LFIA and Data Processing
SERS-Based LFIA and Data Processing
2.10. Spike RBD Protein Detection in the Lysate of SARS-CoV-2 Infected Vero Cell
The Optimization of Experimental Conditions of SERS-Based LFIA
SERS-Based LFIA for RBD Detection
Conclusions
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