Eosinophil accumulation in postnatal lung is specific to the primary septation phase of development
Bibliographic record
Abstract
Abstract Type 2 immune cells and eosinophils are transiently present in lung tissue not only in pathology but also during normal postnatal development. However, the lung developmental processes underlying postnatal airway recruitment of eosinophils after birth remain unexplored. We determined that in mice, mature eosinophils are recruited to the lung during P3–14, which corresponds to the primary septation/alveolarization phase of lung development. Developmental eosinophils peaked during P10–14 and exhibited Siglec-Fmed/highCD11c−/low phenotypes, similar to allergic asthma models. By interrogating the lung transcriptome and proteome during peak eosinophil recruitment in postnatal development, we identified markers that functionally capture mesenchymal-epithelial interface establishment (Nes, Smo, Wnt5a, Nog) and provisional extracellular matrix (ECM) deposition (Tnc, Postn, Spon2, Thbs2) as key lung morphogenetic events associating with eosinophils. We identifiedTenascin-C (TNC) as one of the key ECM markers in the lung epithelial-mesenchymal interface at the RNA and protein levels, consistently associating with eosinophils in development and disease. By RNA-seq analysis, naïve murine eosinophils cultured with TNC-enriched ECM significantly induced expression of Siglec-F, CD11c, eosinophil peroxidase, and other markers typical for activated eosinophils in development and allergic inflammatory responses. TNC knockout mice had an altered eosinophil recruitment profile in development. Collectively, our results indicate that lung morphogenetic processes associated with heightened Type 2 immunity are not merely tissue “background” but specifically guide immune cells both in development and pathology.
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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".