Diversity of immunization strongly impacts SARS-CoV-2 antibody function surrogates

Abstract

System serology offers a comprehensive approach to evaluate the humoral immune response by evaluating multiple parameters. In the present study, based on four groups of individuals with a different history of SARS-CoV-2 immunization, we analyzed the serum of 180 individuals based on six serological methods to better decipher their immunity. Through our analysis, against different SARS-CoV-2 antigens or variants, we report the importance of system serology to better decipher population immunity. Fc-dependent parameters are key factors underlying the variability of humoral immune response triggered by different schemes of SARS-CoV-2 immunization. With an evolving exposure to new variants, the acquisition of robust cross-reactive Fc-dependent effector functions are likely to be key to control viral replication when neutralizing antibodies are poorly cross-reactive. As booster vaccination remains a useful tool in periodically bolstering humoral immunity, particularly in vulnerable populations, studies should continue to evaluate the humoral immune response using system serology approach.

Fig. 1. Study analysis workflow.

Figure 1 illustrates the definition of the 9 groups of individuals (grey square) and the workflow of their samples after selection. A total of 180 individuals were selected from the 3 cohorts and subdivided into 9 different sub-groups according to immunization status, each composed of 20 participants: i) convalescent patients after severe (severe WT, n = 20) or mild (mild WT, n = 20) COVID-19 during the first wave of the pandemic; ii) vaccinated convalescent individuals (after mild COVID-19 during the first wave of the pandemic) who received either one (WT/BNT, n = 20) or two (WT/BNT (2), n = 20) doses of the Pfizer BNT162b2 mRNA vaccine or one dose of the adenoviral-based vaccine ChadOx1 (WT/ChAdOx, n = 20; this group is designed as “hybrid immunity”); iii) COVID-19 naïve individuals fully vaccinated with two (BNT (2), n = 20) or three (BNT (3), n = 20) doses of BNT162b2 or one dose of ChadOx1 followed by one dose of BNT162b2 (ChAdOx/BNT, n = 20), and iv) individuals vaccinated with two or three doses of the BNT162b2 vaccine followed by a breakthrough infection during the Omicron BA.1 wave (BNT/WT, n = 20). System serology (green central square) approach was to analyze 6 different technologies. Database was managed by BIOASTER to generate results, graph, ease scientific communication and potentially inform public health offices and standards of care. WT: Wild type (Lineage B) variant of SARS-CoV-2. IgG levels include anti-NTD and anti-RBD (WT, BA.1). Created in BioRender. Reynaud, K. (2025) https://BioRender.com/ak4i172.

Fig. 2. Principal component analysis on nine distinct clinical groups profiled with six system serology technologies.

Sample projections onto PC1-2 (a) and PC3-4 (b), (c) heatmap of PCA loadings representing the contribution of each variable to each principal component.

Fig. 3. Analysis of the groups of individuals with different patterns of immunization.

Radar plot showing the 20 features with highest variability (ANOVA), in early (a) and late (b) variants. Because ranges vary across platforms, the per-group medians are shown as percentage of the maximum value. c Breadth-potency curves representing the percentage of individuals with a response exceeding a given log₁₀(intensity) level (ADCD activity, FcγR binding ability and sero-neutralization (Neut), see Methods section). d Boxplots of system serology activity (ADCD, FcγR binding ability and sero-neutralization against RBD, S1 and S2 subunits) measured in five variants across the four meta clinical groups.

Fig. 4. Boxplots of glycosylation (percentage of Bisection, Fucosylation, Galactosylation, Sialylation) in bulk (first row) and anti-RBD (second row) IgG1 measured in serum.

Pairwise Student's t-test p-values across meta-groups, categorized into significance bins in bulk (third row) and anti-RBD (fourth row) IgG1.