A scalable serology solution for profiling humoral immune responses to SARS-CoV-2 infection and vaccination

Abstract

OBJECTIVES: Antibody testing against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been instrumental in detecting previous exposures and analyzing vaccine-elicited immune responses. Here, we describe a scalable solution to detect and quantify SARS-CoV-2 antibodies, discriminate between natural infection- and vaccination-induced responses, and assess antibody-mediated inhibition of the spike-angiotensin converting enzyme 2 (ACE2) interaction. METHODS: We developed methods and reagents to detect SARS-CoV-2 antibodies by enzyme-linked immunosorbent assay (ELISA). The main assays focus on the parallel detection of immunoglobulin (Ig)Gs against the spike trimer, its receptor binding domain (RBD) and nucleocapsid (N). We automated a surrogate neutralisation (sn)ELISA that measures inhibition of ACE2-spike or -RBD interactions by antibodies. The assays were calibrated to a World Health Organization reference standard. RESULTS: Our single-point IgG-based ELISAs accurately distinguished non-infected and infected individuals. For seroprevalence assessment (in a non-vaccinated cohort), classifying a sample as positive if antibodies were detected for ≥ 2 of the 3 antigens provided the highest specificity. In vaccinated cohorts, increases in anti-spike and -RBD (but not -N) antibodies are observed. We present detailed protocols for serum/plasma or dried blood spots analysis performed manually and on automated platforms. The snELISA can be performed automatically at single points, increasing its scalability. CONCLUSIONS: Measuring antibodies to three viral antigens and identify neutralising antibodies capable of disrupting spike-ACE2 interactions in high-throughput enables large-scale analyses of humoral immune responses to SARS-CoV-2 infection and vaccination. The reagents are available to enable scaling up of standardised serological assays, permitting inter-laboratory data comparison and aggregation.