Differential Signalling and Kinetics of Neutrophil Extracellular Trap Release Revealed by Quantitative Live Imaging
Bibliographic record
Abstract
A wide variety of microbial and inflammatory factors induce DNA release from neutrophils as neutrophil extracellular traps (NETs). Consensus on the kinetics and mechanism of NET release has been hindered by the lack of distinctive methods to specifically quantify NET release in time. Here, we validate and refine a semi-automatic live imaging approach for quantification of NET release. Importantly, our approach is able to correct for neutrophil input and distinguishes NET release from neutrophil death by other means, aspects that are lacking in many NET quantification methods. Real time visualization shows that opsonized S. aureus rapidly induces cell death by toxins, while actual NET formation occurs after 90 minutes, similar to the kinetics of NET release by immune complexes and PMA. Inhibition of SYK, PI3K and mTORC2 attenuates NET release upon challenge with physiological stimuli but not with PMA. In contrast, neutrophils from chronic granulomatous disease patients show decreased NET release only in response to PMA. With this refined method, we conclude that NET release in primary human neutrophils is dependent on the SYK-PI3K-mTORC2 pathway and that PMA stimulation should be regarded as mechanistically distinct from NET formation induced by natural triggers.
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".