Reliability of SARS-CoV-2 serological testing for influencing public health policies: A reappraisal

Vaccine-induced SARS-CoV-2 antibody response: the comparability of S1-specific binding assays depends on epitope and isotype discrimination

Background

Quantification of the SARS-CoV-2-specific immune response by serological immunoassays is critical for the management of the COVID-19 pandemic. In particular, neutralizing antibody titers to the viral spike (S) protein have been proposed as a correlate of protection (CoP). The WHO established the First International Standard (WHO IS) for anti-SARS-CoV-2 immunoglobulin (Ig) (NIBSC 20/136) to harmonize binding assays with the same antigen specificity by assigning the same unitage in binding antibody units (BAU)/ml.

Method

In this study, we analyzed the S1-specific antibody response in a cohort of healthcare workers in Germany (n = 76) during a three-dose vaccination course over 8.5 months. Subjects received either heterologous or homologous prime-boost vaccination with ChAdOx1 nCoV-19 (AstraZeneca) and BNT162b2 (Pfizer-BioNTech) or three doses of BNT162b2. Antibodies were quantified using three anti-S1 binding assays (ELISA, ECLIA, and PETIA) harmonized to the WHO IS. Serum levels of neutralizing antibodies were determined using a surrogate virus neutralization test (sVNT). Binding assays were compared using Spearman's rank correlation and Passing-Bablok regression.

Findings

All assays showed good correlation and similar antibody kinetics correlating with neutralizing potential. However, the assays show large proportional differences in BAU/ml. ECLIA and PETIA, which detect total antibodies against the receptor- binding domain (RBD) within the S1 subunit, interact similarly with the convalescent plasma-derived WHO IS but differently with vaccine serum, indicating a high sensitivity to the IgG/IgM/IgA ratio.

Conclusion

All three binding assays allow monitoring of the antibody response in COVID-19-vaccinated individuals. However, the assay-specific differences hinder the definition of a common protective threshold in BAU/ml. Our results highlight the need for the thoughtful use of conversion factors and consideration of method-specific differences. To improve the management of future pandemics and harmonize total antibody assays, we should strive for reference material with a well-characterized Ig isotype composition.

Advancements in SARS-CoV-2 Testing: Enhancing Accessibility through Machine Learning-Enhanced Biosensors

The COVID-19 pandemic highlighted the importance of widespread testing for SARS-CoV-2, leading to the development of various new testing methods. However, traditional invasive sampling methods can be uncomfortable and even painful, creating barriers to testing accessibility. In this article, we explore how machine learning-enhanced biosensors can enable non-invasive sampling for SARS-CoV-2 testing, revolutionizing the way we detect and monitor the virus. By detecting and measuring specific biomarkers in body fluids or other samples, these biosensors can provide accurate and accessible testing options that do not require invasive procedures. We provide examples of how these biosensors can be used for non-invasive SARS-CoV-2 testing, such as saliva-based testing. We also discuss the potential impact of non-invasive testing on accessibility and accuracy of testing. Finally, we discuss potential limitations or biases associated with the machine learning algorithms used to improve the biosensors and explore future directions in the field of machine learning-enhanced biosensors for SARS-CoV-2 testing, considering their potential impact on global healthcare and disease control.

Detection of SARS-CoV-2 antibodies after confirmed Omicron BA.1 and presumed BA.4/5 infections using Abbott ARCHITECT and Panbio assays

Background

Commercial severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibody tests were developed before variants with spike protein mutations emerged, leading to concerns that these tests have reduced sensitivity for detecting antibody responses in individuals infected with Omicron subvariants. This study was performed to evaluate Abbott ARCHITECT serologic assays, AdviseDx SARS-CoV-2 IgG II, and SARS-CoV-2 IgG for the detection of spike (S) and nucleocapsid (N) IgG antibody increases in vaccinated healthcare workers infected with Omicron subvariants.

Methods

During the BA.1/2 and BA.4/5 waves, 171 SARS-CoV-2-infected individuals (122 in the BA.1/2 wave, 49 in the BA.4/5 wave) were tested for S and N IgG post infection. Sequencing and SARS-CoV-2 variant confirmation were performed on nasal swab samples from individuals infected during the BA.1/2 wave.

Results

Twenty-seven Omicron sequence confirmed individuals in the BA.1/2 wave and all 49 in the BA.4/5 wave had pre-infection antibody data. Compared to pre-infection levels, post-infection S IgG increased 6.6-fold from 1294 ± 302 BAU/ml (mean ± standard error measurement) to 9796 ± 1252 BAU/ml (P < 0.001) during the BA.1/2 wave, and 3.6-fold from 1771 ± 351 BAU/ml to 8224 ± 943 BAU/ml (P < 0.001) during the BA.4/5 wave. N IgG increased post infection 19.1-fold from 0.2 ± 0.1 to 3.7 ± 0.5 (P < 0.001) during the BA.1/2 wave and 13.5-fold from 0.22 ± 0.1 to 3.2 ± 0.3 (P < 0.001) during the BA.4/5 wave. Among 159 infection-naïve individuals, positive N IgG levels were detected with a sensitivity of 88% in the 87 individuals who were tested between 14 days and 60 days post infection.

Conclusions

The large increases in post-infection S IgG along with the N IgG sensitivity that was comparable to previously reported N IgG sensitivity data in unvaccinated individuals after Omicron infection, support the use of Abbott SARS-CoV-2 assays for detecting increased S IgG and seroconversion of N IgG in vaccinated individuals post Omicron infection. Given that 68% of the United States population is fully vaccinated, these results are of current relevance.

Are anti-SARS-CoV-2 S/N IgG/IgM antibodies always predictive of previous SARS-CoV-2 infection?

Objectives

We planned this study to verify whether immunoassays for quantifying anti-SARS-CoV-2 IgG/IgM antibodies against both spike (S) and nucleocapsid (N) proteins may be used for identifying previous SARS-CoV-2 infections.

Methods

The study population consisted of a cohort of fully vaccinated healthcare workers. All study subjects underwent regular medical visits and molecular testing for diagnosing SARS-CoV-2 infections every 2-4 weeks between 2020-2022. Venous blood was drawn for measuring anti-SARS-CoV-2 antibodies with MAGLUMI 2019-nCoV lgG/IgM CLIA Assays directed against both SARS-CoV-2 S and N proteins.

Results

Overall, 31/53 (58.5%) subjects had tested positive for SARS-CoV-2 by RT-PCR throughout the study (24 once, 7 twice). No positive correlation was found between anti-SARS-CoV-2 S/N IgM antibodies and molecular test positivity. In univariate regression analysis, both a molecular test positivity (r=0.33; p=0.015) and the number of positive molecular tests (r=0.43; p=0.001), but not vaccine doses (r=-0.12; p=0.392), were significantly correlated with anti-SARS-CoV-2 S/N IgG antibodies. These two associations remained significant in multiple linear regression analysis (p=0.029 and p<0.001, respectively) after adjusting for sex, age, body mass index, and vaccine doses. In ROC curve analysis, anti-SARS-CoV-2 S/N IgG antibodies significantly predicted molecular test positivity (AUC, 0.69; 95% CI; 0.55-0.84), with the best cutoff of 0.05 AU/mL displaying 67.9% accuracy, 0.97 sensitivity, and 0.27 specificity.

Conclusions

Although anti-SARS-CoV-2 S/N IgG antibodies provide helpful information for identifying previous SARS-CoV-2 infections, a lower cutoff than that of sample reactivity should be used. Anti-SARS-CoV-2 S/N IgM antibodies using conventional cutoffs seem useless for this purpose.