Myeloid Plasticity of Neutrophil Precursors Contributes to Eosinophil Emergency Hematopoiesis
Bibliographic record
Abstract
Abstract Eosinophils and neutrophils are heterogenous cells that play diverse roles in homeostasis and inflammation. Tissue eosinophil origins are poorly understood, posing challenges in the functional interpretation of heterogeneous phenotypes. New evidence from our lab shows unexpected hematopoietic plasticity of Ly6G(+)IL-5Rα(+) band-stage neutrophils with potential to contribute to eosinophil lineage. Using flow cytometry and multi-omics approaches in mouse and human tissue samples derived from healthy and allergic conditions, we tested the hypothesis that neutrophil plasticity contributes to eosinophil diversity in inflammation. We found at least two distinct populations of neutrophils at a healthy baseline, one of which expressed classical neutrophils markers (S100A8/9) while the other expressed both neutrophil (MPO, ELANE) and eosinophil-specific (EPX, PRG2) granule proteins, indicating an intermediate hematopoietic state. In human and mouse neutrophils treated with IL-5/G-CSF in vitro, a subset with mixed characteristics acquired eosinophil-specific granular proteins and eosinophil cell surface markers (Siglec8, CCR3). In a mouse asthma model, using scRNA-seq, we found overlap in neutrophils and eosinophils with tissue remodeling functions. We also identified activated neutrophils and eosinophils playing unique roles in inflammation. Our findings implicate eosinophil emergency hematopoiesis driven by neutrophil plasticity as an overlooked process driving persistence of tissue eosinophils in inflammation. Supported by grants from GSK ISS and NIH R01
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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".