No evidence of direct activation of human neutrophil responses by multivalent prefusion trimeric SARS-CoV-2 Spike protein ex vivo
Bibliographic record
Abstract
The SARS-CoV-2 Spike (S) protein is essential for viral entry and serves as the primary immunogen in most COVID-19 vaccines. While its role in adaptive immunity is well defined, its potential to contribute directly to innate immune activation remains incompletely understood. Neutrophils, in particular, are prominent effectors in COVID-19 severity, yet how they respond directly to the S protein presented in a multivalent format is unclear. Here, we investigated whether the S protein can directly activate human neutrophils ex vivo using two biologically relevant models: nanoparticles displaying multivalent stabilized prefusion trimeric S glycoprotein, and purified β-propiolactone-inactivated SARS-CoV-2 virions. Neutrophils were exposed to nanoparticles or inactivated virus, either alone or pre-coated with monoclonal or polyclonal anti-S antibodies. Nanoparticles displaying Respiratory Syncytial Virus (RSV) Fusion (F) protein and purified β-propiolactone-inactivated RSV served as comparators. Across all models and conditions tested, the S protein did not induce significant neutrophil responses. No consistent effects were observed on cell viability, surface marker expression, reactive oxygen species production, neutrophil extracellular trap formation, cytokine release, or inflammatory gene expression-even in the presence of anti-S antibodies mimicking immune complexes. Results with F-nanoparticles and inactivated RSV were similarly modest. These findings indicate that the trimeric prefusion S protein, whether displayed multivalently on nanoparticles or in the context of inactivated viral particles, is insufficient to trigger robust neutrophil activation. This work provides insight into the innate immune profile of the S protein and suggests that its use in vaccine platforms is unlikely to directly provoke neutrophil-mediated inflammatory responses.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".