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Review
. 2021 Aug 25;11(9):295.
doi: 10.3390/bios11090295.

Recent Advances in Novel Lateral Flow Technologies for Detection of COVID-19

Wesley Wei-Wen Hsiao  1 , Trong-Nghia Le  1 , Dinh Minh Pham  2   3 , Hui-Hsin Ko  4 , Huan-Cheng Chang  1   5 , Cheng-Chung Lee  4 , Neha Sharma  1 , Cheng-Kang Lee  1 , Wei-Hung Chiang  1
Affiliations
Review

Recent Advances in Novel Lateral Flow Technologies for Detection of COVID-19

Wesley Wei-Wen Hsiao et al. Biosensors (Basel). .

Abstract

The development of reliable and robust diagnostic tests is one of the most efficient methods to limit the spread of coronavirus disease 2019 (COVID-19), which is caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). However, most laboratory diagnostics for COVID-19, such as enzyme-linked immunosorbent assay (ELISA) and reverse transcriptase-polymerase chain reaction (RT-PCR), are expensive, time-consuming, and require highly trained professional operators. On the other hand, the lateral flow immunoassay (LFIA) is a simpler, cheaper device that can be operated by unskilled personnel easily. Unfortunately, the current technique has some limitations, mainly inaccuracy in detection. This review article aims to highlight recent advances in novel lateral flow technologies for detecting SARS-CoV-2 as well as innovative approaches to achieve highly sensitive and specific point-of-care testing. Lastly, we discuss future perspectives on how smartphones and Artificial Intelligence (AI) can be integrated to revolutionize disease detection as well as disease control and surveillance.

Keywords: COVID-19; SARS-CoV-2; artificial intelligence; lateral flow assay; point-of-care testing.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Schematic illustration of an antigen detection-based LFIA test.
Figure 2
Figure 2
The competitive assay and sandwich assay models.
Figure 3
Figure 3
Levels of antibody and antigen at different clinical stage of COVID-19 disease.
Figure 4
Figure 4
IgM–IgG combined antibody test for SARS-CoV-2 detection. (A) Schematic illustration of the LFIA device; (B) Results generated from the LFIA test. C: control line, G: IgG line, M: IgM line. Reprinted from [53].
Figure 5
Figure 5
LFIA device for the rapid serological IgG, IgM, and IgA detection of SARS-CoV-2. (a) Protein A (SpA), SARS-CoV-2 N protein, and avidin were printed on the membrane for the T1 test line, T2 test line, and control line, respectively. N protein-labeled AuNPs and biotin-labeled AuNPs were used as reporters. (b) Negative test results consist of a single visible control line. (c) Positive test results showed three visible lines, indicating the simultaneous binding of antibodies (IgG, IgM, and IgA) to the T1 and T2 test line. Reprinted from with permission from [55]. Copyright 2020, Elsevier.
Figure 6
Figure 6
(a) AuNPs-based LFIA device for the detection of SARS-CoV-2 antibodies. (b) IgG concentrations on LFIA test strip from 1000 to 0.1 ng/mL, bottom to top. Dips at ≈−5 mm correspond to the IgG test lines and at ≈0 mm correspond to the control lines. (c,d) expansion spectra of 0.1 ng/mL and 1 ng/mL and Gaussian fittings (solid line). Reprinted from [58], with the permission of AIP Publishing.
Figure 7
Figure 7
(a) A dual-mode SiO2@Au@QD-based LFIA biosensor for SARS-CoV-2 detection. (b) S protein-conjugated SiO2@Au@QDs were prepared by EDC/NHS coupling. (c) Optimization of LFIA NC membrane. (d) Photographs (i) and fluorescence images (ii) of the dual-mode LFIA for SARS-CoV-2-positive serum samples with different dilutions. (e) Corresponding fluorescence intensities of two test lines of the dual-mode LFIA. (f) Relationship of fluorescence intensity of test lines for three different positive serum samples with different dilutions. Preprinted with permission from [63]. Copyright © 2020, American Chemical Society.
Figure 8
Figure 8
(a) Spin-enhanced lateral flow immunoassay (SELFIA) device. (b) Emission spectra of NC membrane and FNDs excited by a 532 nm laser. The asterisk (*) denotes unfiltered scattered laser light. (c) B-BSA-FNDs captured by the neutravidin bands formed on NC strips. The B-BSA-FND solution flowed from right to left. Inset: photo of a 1.5 μL trypan blue solution deposited on an NC strip to assist the assessment of the spot size of neutravidin. (d) Measured fluorescence intensities of B-BSA-FNDs captured by NC-bound neutravidin as a function of the B-BSA-FND concentration. (e) Sandwich SELFIA of hCG with anti-β hCG-coated FNDs and anti-β hCG deposited on NC strips. Reprinted with permission from [96]. Copyright © 2020, American Chemical Society.
Figure 9
Figure 9
Production of anti-SARS-CoV-2 monoclonal scFv IgY antibodies against the spike protein (S) of SARS-CoV-2 antigen using phage display technology. Reprinted from [127]. Copyright 2020, with permission from Elsevier.
Figure 10
Figure 10
Schematic diagram of a portable AI-aided smartphone LFIA system for COVID-19 detection [61,133,134].
Figure 11
Figure 11
Comparison of current COVID-19 detection methods and advantages, limitations, and opportunities of lateral flow technologies.
See this image and copyright information in PMC

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