A single-cell atlas of human fetal lung development between 14 and 19 weeks of gestation
Bibliographic record
Abstract
ABSTRACT Rationale Human lung development has been mainly described in morphologic studies and the potential underlying molecular mechanisms were extrapolated from animal models. Therefore, there is a need to gather knowledge from native human lung tissue. In this study we describe changes at a single-cell level in human fetal lungs during the pseudoglandular stage. Methods We report the cellular composition, cell trajectories and cell-to-cell communication in developing human lungs with single-nuclei RNA sequencing (snRNA-seq) on 23,251 nuclei isolated from nine human fetuses with gestational ages between 14 to 19 weeks of gestation. Results We identified nine different cell types, including a rare pulmonary neuroendocrine cells population. For each cell type, marker genes are reported, and selected marker genes are used for spatial validation with fluorescent RNA in situ hybridization. Enrichment and developmental trajectory analysis provide insight into molecular mechanisms and signaling pathways within individual cell clusters according to gestational age. Lastly, ligand-receptor analysis highlights determinants of cell-to-cell communication among the different cell types through the pseudoglandular stage, including general developmental pathways (NOTCH and TGFB), as well as more specific pathways involved in vasculogenesis, neurogenesis, and immune system regulation. Conclusion These findings provide a clinically relevant background for research hypotheses generation in projects studying normal or impaired lung development and help to develop and validate surrogate models to study human lung development, such as human lung organoids. TAKE HOME MESSAGE Using a single-cell transcriptomic approach (single-nuclei RNA sequencing), we describe here, for the first time, the cellular landscape, cell developmental trajectories, and cell-to-cell communication in the developing human lung during the pseudoglandular stage.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".