A Human Single-Nuclei Atlas Reveals Novel Cell States during the Pseudoglandular-to-Canalicular Transition
Bibliographic record
Abstract
Most of our knowledge of human lung development is derived from morphologic studies and extrapolations of the underlying molecular mechanisms from animal models. Here we describe developmental changes in human fetal lungs during the pseudoglandular and early canalicular period, detailing this critical but previously poorly described transition period. We report the cellular composition and cell-to-cell communication in a single-nuclei dataset from nine human fetal lungs between 14 and 19 weeks of gestation. We identified 9 main populations and 19 subpopulations, including the rare pulmonary neuroendocrine cells. For each population, marker genes were reported, and selected markers were validated. Enrichment analysis were performed to explore the potential molecular mechanisms and pathways within individual populations according to gestational age. Finally, cell-to-cell communication was studied using ligand-receptor analysis among the different cell types. General developmental pathways, as well as pathways involved in vasculogenesis, neurogenesis, and immune regulation, were identified. This study provides an important background to generate research hypotheses in projects studying normal or impaired lung development and help to validate surrogate models (e.g., lung organoids) to study human lung development.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".