The neutrotime transcriptional signature defines a single continuum of neutrophils across biological compartments
Bibliographic record
Abstract
Neutrophils are implicated in multiple homeostatic and pathological processes, but whether functional diversity requires discrete neutrophil subsets is not known. Here, we apply single-cell RNA sequencing to neutrophils from normal and inflamed mouse tissues. Whereas conventional clustering yields multiple alternative organizational structures, diffusion mapping plus RNA velocity discloses a single developmental spectrum, ordered chronologically. Termed here neutrotime, this spectrum extends from immature pre-neutrophils, largely in bone marrow, to mature neutrophils predominantly in blood and spleen. The sharpest increments in neutrotime occur during the transitions from pre-neutrophils to immature neutrophils and from mature marrow neutrophils to those in blood. Human neutrophils exhibit a similar transcriptomic pattern. Neutrophils migrating into inflamed mouse lung, peritoneum and joint maintain the core mature neutrotime signature together with new transcriptional activity that varies with site and stimulus. Together, these data identify a single developmental spectrum as the dominant organizational theme of neutrophil heterogeneity.
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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) | 0.000 | 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".