A Safe and Accessible Cell-Based Spike–ACE2 Binding Assay for Evaluating SARS-CoV-2 Neutralization Activity in Biological Samples Using Flow Cytometry
Bibliographic record
Abstract
SARS-CoV-2, the agent responsible for coronavirus disease in 2019 (COVID-19), has caused extensive global health and socioeconomic impact due to its transmissibility and pathology. As a result, it was classified as a Risk Group 3 human pathogen, and handling samples containing live virus requires enhanced biological containment facilities (i.e., CL3) to reduce the potential of laboratory infection to personnel and the spread of the virus into the community. While the use of an authentic live virus remains the gold standard for biological assays, alternative methods have been developed to effectively evaluate neutralization activity in the absence of a replicating viral agent. Here, we describe a cell-based spike-ACE2 binding assay as a surrogate for neutralization of SARS-CoV-2 spike to identify potential neutralizing antibodies. A main advantage of this approach is the exclusion of infectious viral particles, increasing biosafety for laboratory personnel. The interaction of recombinant SARS-CoV-2 trimeric spike protein with ACE2 is monitored and quantified by flow cytometry. Notably, our previous studies have demonstrated the utility of this assay for other viruses, beyond SARS-CoV-2. The methodology presented here has exhibited a strong correlation to other widely accepted methods, such as pseudotyped lentiviral and live virus neutralization assays, in identifying neutralizing antibodies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".