Tissue-specific murine neutrophil activation states in health and inflammation
Bibliographic record
Abstract
Neutrophils are quickly recruited to tissues in response to proinflammatory cues; however, little is known about tissue neutrophil phenotypes in health. We employ a multicolor flow cytometric approach to assess surface markers of activation on neutrophils from the bone marrow, blood, peritoneum, spleen, liver, fat, colon, and oral cavity of healthy mice. Cell preparations were promptly fixed to preserve native surface marker expression levels. Peritoneal, colonic, and oral neutrophils were also assessed in the setting of pHrodo-induced peritonitis, dextran sodium sulfate-induced colitis, and ligature-induced periodontal disease, respectively. Our results demonstrate consistent detectable neutrophil populations in various sterile and nonsterile tissues of healthy mice, and these cells had tissue-specific neutrophil immunophenotypes. Neutrophils derived from biofilm-associated mucosal tissues had 2- to 3-fold higher expression of surface markers of activation, including CD66a, CD11b, and CD62L, compared to neutrophils derived from both sterile healthy tissues as well as tissues in animals treated with broad-spectrum antibiotics. Furthermore, the unique cluster of differentiation (CD) marker activation signatures of tissue-specific neutrophils from the peritoneum, colon, and oral cavity were altered to a proinflammatory immunophenotype with the presence of an inflammatory stimulus. Based on our results, we propose a model whereby a hierarchy of tissue neutrophil immunophenotypes, based on the differential expression of CD markers of activation, correlates with sterile, healthy commensal biofilm-associated and inflamed tissue states.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".