Treatment of MCPT8<sup>DTR</sup> mice with high‐ or low‐dose diphtheria toxin leads to differential depletion of basophils and granulocyte‐macrophage progenitors
Bibliographic record
Abstract
Abstract Basophils have been recently recognized to play important roles in type 2 immune responses during allergies and parasitic infection, largely due to the development of novel tools for the in vivo study of these cells. As such, the genetically‐engineered MCPT8DTR mouse line has been used to specifically deplete basophils following treatment with diphtheria toxin (DT). In this study, we showed that DT‐injected MCPT8DTR mice exhibited a striking decrease of eosinophils and neutrophils in skin when subjected to a hapten fluorescein isothiocyanate (FITC)‐induced allergic contact dermatitis (ACD) experimental protocol. Unexpectedly, we found that loss of skin eosinophils and neutrophils was not due to a lack of basophil‐mediated recruitment, as DT injection caused a systemic reduction of eosinophils and neutrophils in MCPT8DTR mice in a time‐dependent manner. Furthermore, we found that hematopoietic stem‐cell‐derived granulocyte‐macrophage progenitors (GMPs) expressed MCPT8 gene, and that these cells were depleted upon DT injection. Finally, we optimized a protocol in which a low‐dose DT achieved a better specificity for depleting basophils, but not GMPs, in MCPT8DTR mice, and demonstrate that basophils do not play a major role in recruiting eosinophils and neutrophils to ACD skin. These data provide new and valuable information about functional studies of basophils.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".