» Articles » PMID: 36773929

Methodological Approaches to Optimize Multiplex Oral Fluid SARS-CoV-2 IgG Assay Performance and Correlation with Serologic and Neutralizing Antibody Responses

Abstract

Background: Oral fluid (hereafter, saliva) is a non-invasive and attractive alternative to blood for SARS-CoV-2 IgG testing; however, the heterogeneity of saliva as a matrix poses challenges for immunoassay performance.

Objectives: To optimize performance of a magnetic microparticle-based multiplex immunoassay (MIA) for SARS-CoV-2 IgG measurement in saliva, with consideration of: i) threshold setting and validation across different MIA bead batches; ii) sample qualification based on salivary total IgG concentration; iii) calibration to U.S. SARS-CoV-2 serological standard binding antibody units (BAU); and iv) correlations with blood-based SARS-CoV-2 serological and neutralizing antibody (nAb) assays.

Methods: The salivary SARS-CoV-2 IgG MIA included 2 nucleocapsid (N), 3 receptor-binding domain (RBD), and 2 spike protein (S) antigens. Gingival crevicular fluid (GCF) swab saliva samples were collected before December 2019 (n = 555) and after molecular test-confirmed SARS-CoV-2 infection from 113 individuals (providing up to 5 repeated-measures; n = 398) and used to optimize and validate MIA performance (total n = 953). Combinations of IgG responses to N, RBD and S and total salivary IgG concentration (μg/mL) as a qualifier of nonreactive samples were optimized and validated, calibrated to the U.S. SARS-CoV-2 serological standard, and correlated with blood-based SARS-CoV-2 IgG ELISA and nAb assays.

Results: The sum of signal to cutoff (S/Co) to all seven MIA SARS-CoV-2 antigens and disqualification of nonreactive saliva samples with ≤15 μg/mL total IgG led to correct classification of 62/62 positives (sensitivity [Se] = 100.0%; 95% confidence interval [CI] = 94.8%, 100.0%) and 108/109 negatives (specificity [Sp] = 99.1%; 95% CI = 97.3%, 100.0%) at 8-million beads coupling scale and 80/81 positives (Se = 98.8%; 95% CI = 93.3%, 100.0%] and 127/127 negatives (Sp = 100%; 95% CI = 97.1%, 100.0%) at 20-million beads coupling scale. Salivary SARS-CoV-2 IgG crossed the MIA cutoff of 0.1 BAU/mL on average 9 days post-COVID-19 symptom onset and peaked around day 30. Among n = 30 matched saliva and plasma samples, salivary SARS-CoV-2 MIA IgG levels correlated with corresponding-antigen plasma ELISA IgG (N: ρ = 0.76, RBD: ρ = 0.83, S: ρ = 0.82; all p < 0.001). Correlations of plasma SARS-CoV-2 nAb assay area under the curve (AUC) with salivary MIA IgG (N: ρ = 0.68, RBD: ρ = 0.78, S: ρ = 0.79; all p < 0.001) and with plasma ELISA IgG (N: ρ = 0.76, RBD: ρ = 0.79, S: ρ = 0.76; p < 0.001) were similar.

Conclusions: A salivary SARS-CoV-2 IgG MIA produced consistently high Se (> 98.8%) and Sp (> 99.1%) across two bead coupling scales and correlations with nAb responses that were similar to blood-based SARS-CoV-2 IgG ELISA data. This non-invasive salivary SARS-CoV-2 IgG MIA could increase engagement of vulnerable populations and improve broad understanding of humoral immunity (kinetics and gaps) within the evolving context of booster vaccination, viral variants and waning immunity.

Citing Articles

Application of machine learning algorithms to identify serological predictors of COVID-19 severity and outcomes.

Dhakal S, Yin A, Escarra-Senmarti M, Demko Z, Pisanic N, Johnston T Commun Med (Lond). 2024; 4(1):249.

PMID: 39592832 PMC: 11599591. DOI: 10.1038/s43856-024-00658-w.


Early, Robust Mucosal Secretory Immunoglobulin A but not Immunoglobulin G Response to Severe Acute Respiratory Syndrome Coronavirus 2 Spike in Oral Fluid Is Associated With Faster Viral Clearance and Coronavirus Disease 2019 Symptom Resolution.

Pisanic N, Antar A, Hetrich M, Demko Z, Zhang X, Spicer K J Infect Dis. 2024; 231(1):121-130.

PMID: 39269503 PMC: 11793072. DOI: 10.1093/infdis/jiae447.


SARS-CoV-2 antibody prevalence by industry, workplace characteristics, and workplace infection prevention and control measures, North Carolina, USA, 2021 to 2022.

Gigot C, Pisanic N, Spicer K, Davis M, Kruczynski K, Rivera M Ann Work Expo Health. 2024; 68(8):881-889.

PMID: 39102901 PMC: 11427537. DOI: 10.1093/annweh/wxae067.


COVID-19 point-of-care tests can identify low-antibody individuals: In-depth immunoanalysis of boosting benefits in a healthy cohort.

Mallory M, Munt J, Narowski T, Castillo I, Cuadra E, Pisanic N Sci Adv. 2024; 10(24):eadi1379.

PMID: 38865463 PMC: 11168476. DOI: 10.1126/sciadv.adi1379.


Application of machine learning models to identify serological predictors of COVID-19 severity and outcomes.

Klein S, Dhakal S, Yin A, Escarra-Senmarti M, Demko Z, Pisanic N Res Sq. 2023; .

PMID: 38014049 PMC: 10680931. DOI: 10.21203/rs.3.rs-3463155/v1.


References
1.
Pond E, Rutkow L, Blauer B, Aliseda Alonso A, Lis S, Nuzzo J . Disparities in SARS-CoV-2 Testing for Hispanic/Latino Populations: An Analysis of State-Published Demographic Data. J Public Health Manag Pract. 2022; 28(4):330-333. PMC: 9112961. DOI: 10.1097/PHH.0000000000001510. View

2.
Patel E, Bloch E, Clarke W, Hsieh Y, Boon D, Eby Y . Comparative Performance of Five Commercially Available Serologic Assays To Detect Antibodies to SARS-CoV-2 and Identify Individuals with High Neutralizing Titers. J Clin Microbiol. 2020; 59(2). PMC: 8111143. DOI: 10.1128/JCM.02257-20. View

3.
Keuning M, Grobben M, de Groen A, Berman-de Jong E, Bijlsma M, Cohen S . Saliva SARS-CoV-2 Antibody Prevalence in Children. Microbiol Spectr. 2021; 9(2):e0073121. PMC: 8557814. DOI: 10.1128/Spectrum.00731-21. View

4.
Long Q, Liu B, Deng H, Wu G, Deng K, Chen Y . Antibody responses to SARS-CoV-2 in patients with COVID-19. Nat Med. 2020; 26(6):845-848. DOI: 10.1038/s41591-020-0897-1. View

5.
Chen X, Chen Z, Azman A, Deng X, Sun R, Zhao Z . Serological evidence of human infection with SARS-CoV-2: a systematic review and meta-analysis. Lancet Glob Health. 2021; 9(5):e598-e609. PMC: 8049592. DOI: 10.1016/S2214-109X(21)00026-7. View