Single-cell RNA-seq of primary bone marrow neutrophils from female and male adult mice
Bibliographic record
Abstract
Widespread sex-dimorphism is observed in the mammalian immune system. Consistently, studies have reported sex differences in the transcriptome of immune cells at the bulk level, including neutrophils. Neutrophils are the most abundant cell type in human blood, and they are key components of the innate immune system as they form a first line of defense against pathogens. Neutrophils are produced in the bone marrow, and differentiation and maturation produce distinct neutrophil subpopulations. Thus, single-cell resolution studies are crucial to decipher the biological significance of neutrophil heterogeneity. However, since neutrophils are very RNA-poor, single-cell profiling of these cells has been technically challenging. Here, we generated a single-cell RNA-seq dataset of primary neutrophils from adult female and male mouse bone marrow. After stringent quality control, we found that previously characterized neutrophil subpopulations can be detected in both sexes. Additionally, we confirmed that canonical sex-linked markers are differentially expressed between female and male cells across neutrophil subpopulations. This dataset provides a groundwork for comparative studies on the lifelong transcriptional sexual dimorphism of neutrophils.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".