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Quantitative, multiplexed, targeted proteomics for ascertaining variant specific SARS-CoV-2 antibody response (preprint)
medrxiv; 2022.
Preprint
in English
| medRxiv | ID: ppzbmed-10.1101.2022.02.14.22270845
ABSTRACT
Determining the protection an individual has to SARS-CoV-2 variants of concern (VoC) will be crucial for future immune surveillance and understanding the changing immune response. As further variants emerge, current serology tests are becoming less effective in reflecting neutralising capability of the immune system. A better measure of an evolving antigen-antibody immune response is needed. We describe a multiplexed, baited, targeted-proteomic assay for direct detection of multiple proteins in the SARS-CoV-2 anti-spike antibody immunocomplex. This enables a more sophisticated and informative characterisation of the antibody response to vaccination and infection against VoC. Using this assay, we detail different and specific responses to each variant by measuring several antibody classes, isotypes and associated complement binding. Furthermore, we describe how these proteins change using serum from individuals collected after infection, first and second dose vaccination. We show complete IgG1 test concordance with gold standard ELISA (r>0.8) and live virus neutralisation against Wuhan Hu-1, Alpha B.1.1.7, Beta B.1.351, and Delta B.1.617.1 variants (r>0.79). We also describe a wide degree of heterogeneity in the immunocomplex of individuals and a greater IgA response in those patients who had a previous infection. Significantly, our test points to an important role the complement system may play particularly against VoC. Where we observe altered Complement C1q association to the Delta VoC response and a stronger overall association with neutralising antibodies than IgG1. A detailed understanding of an individuals antibody response could benefit public health immunosurveillance, vaccine design and inform vaccination dosing using a personalised medicine approach.
Full text:
Available
Collection:
Preprints
Database:
medRxiv
Language:
English
Year:
2022
Document Type:
Preprint
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