Review of Clinical Performance of Serology Based Commercial Diagnostic Assays for Detection of Severe Acute Respiratory Syndrome Coronavirus 2 Antibodies

An update on lateral flow immunoassay for the rapid detection of SARS-CoV-2 antibodies

Over the last three years, after the outbreak of the COVID-19 pandemic, an unprecedented number of novel diagnostic tests have been developed. Assays to evaluate the immune response to SARS-CoV-2 have been widely considered as part of the control strategy. The lateral flow immunoassay (LFIA), to detect both IgM and IgG against SARS-CoV-2, has been widely studied as a point-of-care (POC) test. Compared to laboratory tests, LFIAs are faster, cheaper and user-friendly, thus available also in areas with low economic resources. Soon after the onset of the pandemic, numerous kits for rapid antibody detection were put on the market with an emergency use authorization. However, since then, scientists have tried to better define the accuracy of these tests and their usefulness in different contexts. In fact, while during the first phase of the pandemic LFIAs for antibody detection were auxiliary to molecular tests for the diagnosis of COVID-19, successively these tests became a tool of seroprevalence surveillance to address infection control policies. When in 2021 a massive vaccination campaign was implemented worldwide, the interest in LFIA reemerged due to the need to establish the extent and the longevity of immunization in the vaccinated population and to establish priorities to guide health policies in low-income countries with limited access to vaccines. Here, we summarize the accuracy, the advantages and limits of LFIAs as POC tests for antibody detection, highlighting the efforts that have been made to improve this technology over the last few years.

A comparative study of eight serological methods shows that spike protein-based ELISAs are the most accurate tests for serodiagnosing SARS-CoV-2 infections in cats and dogs

Introduction

Coronavirus disease 2019 (COVID-19) is an infectious zoonotic disease caused by SARS-CoV-2. Monitoring the infection in pets is recommended for human disease surveillance, prevention, and control since the virus can spread from people to animals during close contact. Several diagnostic tests have been adapted from humans to animals, but limited data on the validation process are available.

Methods

Herein, the first comparative study of six "in house" and two commercial serological tests developed to monitor SARS-CoV-2 infection in pets was performed with a well-coded panel of sera (61 cat sera and 74 dog sera) with a conservative criterion (viral seroneutralisation and/or RT-qPCR results) as a reference. Four "in house" tests based on either the RBD fragment of the spike protein (RBD-S) or the N-terminal fragment of the nucleoprotein (N) were developed for the first time. The analytical specificity (ASp) of those tests that showed the best diagnostic performance was assessed. The validation included the analysis of a panel of sera obtained pre-pandemic from cats and dogs infected with other coronaviruses to determine the analytical Sp (17 cat sera and 41 dog sera).

Results and discussion

ELISAS based on the S protein are recommended in serosurveillance studies for cats (RBD-S SALUVET ELISA, ELISA COVID UNIZAR and INgezim® COVID 19 S VET) and dogs (INgezim® COVID 19 S VET and RBD-S SALUVET ELISA). These tests showed higher diagnostic sensitivity (Se) and DSp in cats (>90%) than in dogs. When sera obtained prior to the pandemic and from animals infected with other coronaviruses were analyzed by RBD-S and N SALUVET ELISAs and INgezim® COVID 19 S VET, a few cross reactors or no cross reactions were detected when dog and cat sera were analyzed by tests based on the S protein, respectively. In contrast, the number of cross reactions increased when the test was based on the N protein. Thus, the use of tests based on the N protein was discarded for serodiagnosis purposes. The results obtained revealed the most accurate serological tests for each species. Further studies should attempt to improve the diagnostic performance of serological tests developed for dogs.

Current immunoassays and detection of antibodies elicited by Omicron SARS-CoV-2 infection

The present study aimed to determine whether current commercial immunoassays are adequate for detecting anti-Omicron antibodies. We analyzed the anti-SARS-CoV-2 antibody response of 23 unvaccinated individuals 1-2 months after an Omicron infection. All blood samples were tested with a live virus neutralization assay using a clinical Omicron BA.1 strain and four commercial SARS-CoV-2 immunoassays. We assessed three anti-Spike immunoassays (SARS-CoV-2 IgG II Quant [Abbott S], Wantaï anti-SARS-CoV-2 antibody ELISA [Wantaï], Elecsys Anti-SARS-CoV-2 S assay [Roche]) and one anti-Nucleocapsid immunoassay (Abbott SARS-CoV-2 IgG assay [Abbott N]). Omicron neutralizing antibodies were detected in all samples with the live virus neutralization assay. The detection rate of the Abbott S, Wantai, Roche, and Abbott N immunoassays were 65.2%, 69.6%, 86.9%, and 91.3%, respectively. The sensitivities of Abbott S and Wantai immunoassays were significantly lower than that of the live virus neutralization assay (p = 0.004, p = 0.009; Fisher's exact test). Antibody concentrations obtained with anti-S immunoassays were correlated with Omicron neutralizing antibody concentrations. These data provide clinical evidence of the loss of performance of some commercial immunoassays to detect antibodies elicited by Omicron infections. It highlights the need to optimize these assays by adapting antigens to the circulating SARS-CoV-2 strains.

Rapid Quantification of SARS-Cov-2 Spike Protein Enhanced with a Machine Learning Technique Integrated in a Smart and Portable Immunosensor

An IoT-WiFi smart and portable electrochemical immunosensor for the quantification of SARS-CoV-2 spike protein was developed with integrated machine learning features. The immunoenzymatic sensor is based on the immobilization of monoclonal antibodies directed at the SARS-CoV-2 S1 subunit on Screen-Printed Electrodes functionalized with gold nanoparticles. The analytical protocol involves a single-step sample incubation. Immunosensor performance was validated in a viral transfer medium which is commonly used for the desorption of nasopharyngeal swabs. Remarkable specificity of the response was demonstrated by testing H1N1 Hemagglutinin from swine-origin influenza A virus and Spike Protein S1 from Middle East respiratory syndrome coronavirus. Machine learning was successfully used for data processing and analysis. Different support vector machine classifiers were evaluated, proving that algorithms affect the classifier accuracy. The test accuracy of the best classification model in terms of true positive/true negative sample classification was 97.3%. In addition, the ML algorithm can be easily integrated into cloud-based portable Wi-Fi devices. Finally, the immunosensor was successfully tested using a third generation replicating incompetent lentiviral vector pseudotyped with SARS-CoV-2 spike glycoprotein, thus proving the applicability of the immunosensor to whole virus detection.