A flow cytometry-based neutralization assay for simultaneous evaluation of blocking antibodies against SARS-CoV-2 variants
Bibliographic record
Abstract
Vaccines against SARS-CoV-2 have alleviated infection rates, hospitalization and deaths associated with COVID-19. In order to monitor humoral immunity, several serology tests have been developed, but the recent emergence of variants of concern has revealed the need for assays that predict the neutralizing capacity of antibodies in a fast and adaptable manner. Sensitive and fast neutralization assays would allow a timely evaluation of immunity against emerging variants and support drug and vaccine discovery efforts. Here we describe a simple, fast, and cell-free multiplexed flow cytometry assay to interrogate the ability of antibodies to prevent the interaction of Angiotensin-converting enzyme 2 (ACE2) and the receptor binding domain (RBD) of the original Wuhan-1 SARS-CoV-2 strain and emerging variants simultaneously, as a surrogate neutralization assay. Using this method, we demonstrate that serum antibodies collected from representative individuals at different time-points during the pandemic present variable neutralizing activity against emerging variants, such as Omicron BA.1 and South African B.1.351. Importantly, antibodies present in samples collected during 2021, before the third dose of the vaccine was administered, do not confer complete neutralization against Omicron BA.1, as opposed to samples collected in 2022 which show significant neutralizing activity. The proposed approach has a comparable performance to other established surrogate methods such as cell-based assays using pseudotyped lentiviral particles expressing the spike of SARS-CoV-2, as demonstrated by the assessment of the blocking activity of therapeutic antibodies (i.e. Imdevimab) and serum samples. This method offers a scalable, cost effective and adaptable platform for the dynamic evaluation of antibody protection in affected populations against variants of SARS-CoV-2.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".