The Serological Sciences Network (SeroNet) for COVID-19: Depth and Breadth of Serology Assays and Plans for Assay Harmonization (preprint)

Background: In October 2020, the National Cancer Institute (NCI) Serological Sciences Network (SeroNet) was established to study the immune response to COVID-19, and to develop, validate, improve, and implement serological testing and associated technologies. SeroNet is comprised of 25 participating research institutions partnering with the Frederick National Laboratory for Cancer Research (FNLCR) and the SeroNet Coordinating Center. Since its inception, SeroNet has supported collaborative development and sharing of COVID-19 serological assay procedures and has set forth plans for assay harmonization. Methods: To facilitate collaboration and procedure sharing, a detailed survey was sent to collate comprehensive assay details and performance metrics on COVID-19 serological assays within SeroNet. In addition, FNLCR established a protocol to calibrate SeroNet serological assays to reference standards, such as the U.S. SARS-CoV-2 serology standard reference material and First WHO International Standard (IS) for anti-SARS-CoV-2 immunoglobulin (20/136), to facilitate harmonization of assay reporting units and cross-comparison of study data. Results: SeroNet institutions reported development of a total of 27 ELISA methods, 13 multiplex assays, 9 neutralization assays, and use of 12 different commercial serological methods. FNLCR developed a standardized protocol for SeroNet institutions to calibrate these diverse serological assays to reference standards. Conclusions: SeroNet institutions have established a diverse array of COVID-19 serological assays to study the immune response to SARS-CoV-2 virus and vaccines. Calibration of SeroNet serological assays to harmonize results reporting will facilitate future pooled data analyses and study cross-comparisons.

Serological Analysis Reveals an Imbalanced IgG Subclass Composition Associated with COVID-19 Disease Severity (preprint)

COVID-19 is associated with a wide spectrum of disease severity, ranging from asymptomatic to acute respiratory distress syndrome (ARDS). Paradoxically, a direct relationship has been suggested between COVID-19 disease severity, and the levels of circulating SARS-CoV-2-specific antibodies, including virus neutralizing titers. Through a serological analysis of serum samples from 536 convalescent healthcare workers, we found that SARS-CoV-2-specific and virus-neutralizing antibody levels were indeed elevated in individuals that experienced severe disease. The severity-associated increase in SARS-CoV-2-specific antibody was dominated by IgG, with an IgG subclass ratio skewed towards elevated receptor binding domain (RBD)- and S1-specific IgG3. However, RBD- and S1-specific IgG1, rather than IgG3 were best correlated with virus-neutralizing titers. We propose that Spike-specific IgG3 subclass utilization contributes to COVID-19 disease severity through potent Fc-mediated effector functions. These results have significant implications for SARS-CoV-2 vaccine design, and convalescent plasma therapy.

Multiplexed Detection and Quantification of Human Antibody Response to COVID-19 Infection Using a Plasmon Enhanced Biosensor Platform (preprint)

The 2019 SARS CoV-2 (COVID-19) pandemic has highlighted the need for rapid and accurate tests to diagnose acute infection and determine immune response to infection. In this work, a multiplexed grating-coupled fluorescent plasmonics (GC-FP) biosensing approach was shown to have 100% selectivity and sensitivity (n = 23) when measuring serum IgG levels against three COVID-19 antigens (spike S1, spike S1S2, and the nucleocapsid protein). The entire biosensing procedure takes less than 30 min, making it highly competitive with well-established ELISA and immunofluorescence assays. GC-FP is quantitative over a large dynamic range, providing a linear response for serum titers ranging from 1:25 to 1:1,600, and shows high correlation with both ELISA and a Luminex-based microsphere immunoassay (MIA) (Pearson r > 0.9). Compatibility testing with dried blood spot samples (n = 63) demonstrated 100% selectivity and 86.7% sensitivity. A machine learning (ML) model was trained to classify dried blood spot samples for prior COVID-19 infection status, based on the combined antibody response to S1, S1S2, and Nuc antigens. The ML model yielded 100% selectivity and 80% sensitivity and demonstrated a higher stringency than a single antibody-antigen response. The biosensor platform is flexible and will readily accommodate detection of multiple immunoglobulin isotypes. Further, it uses sub-nanogram quantities of capture ligand and is thus readily modified to include additional antigens, which is shown by the addition of RBD in later iterations of the test. The combination of rapid, multiplexed, and quantitative detection for both blood serum and dried blood spot samples makes GC-FP an attractive biosensor platform for COVID-19 antibody testing.

Neutralizing Antibody Responses in COVID-19 Convalescent Sera (preprint)

Passive transfer of antibodies from COVID-19 convalescent patients is being used as an experimental treatment for eligible patients with SARS-CoV-2 infections. The United States Food and Drug Administrations (FDA) guidelines for convalescent plasma recommends target antibody titers of 160. We evaluated SARS-CoV-2 neutralizing antibodies in sera from recovered COVID-19 patients using plaque reduction neutralization tests (PRNT) at low (PRNT50) and high (PRNT90) stringency thresholds. We found that neutralizing activity increased with time post symptom onset (PSO), reaching a peak at 31-35 days PSO. At this point, the number of sera having neutralizing titers of at least 160 was [~]93% (PRNT50) and [~]54% (PRNT90). Sera with high SARS-CoV-2 antibody levels (>960 ELISA titers) showed maximal activity, but not all high titer sera contained neutralizing antibody at FDA recommended levels, particularly at high stringency. These results underscore the value of serum characterization for neutralization activity.