Modeling lung cell development using human pluripotent stem cells
Bibliographic record
Abstract
ABSTRACT Human pluripotent stem cells (hPSC) differentiations can capture developmental phenotypes and processes. They are useful for studying fundamental biological mechanisms driving tissue morphogenesis and cell lineage development. Here, we show temporal development of lung cell lineages using hPSC that recapitulate developmental milestones observed in primary tissue, the generation of renewable fetal lung epithelial spheroids, and the functional utility of the lung models at different differentiation stages for cystic fibrosis disease modeling. We first show the presence of hPSC-derived lung progenitor cells reminiscent of early trimester lung development and containing basal stem cells that generate renewable airway spheroids. Maturation and polarization in air liquid interface (ALI) generates additional epithelial cell lineages found in adult airways, including pulmonary neuroendocrine, brush, mature basal, ciliated and secretory cell types. Finally, pseudotime and RNA velocity analyses of the integrated datasets from fetal and ALI stages reveal both previously identified and new cell lineage relationships. Overall, hPSC differentiation can capture aspects of human lung development and potentially provide important insight into congenital causes of diseases.
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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".