Three-Dimensional Culture and FGF Signaling Drive Differentiation of Murine Pluripotent Cells to Distal Lung Epithelial Cells
Bibliographic record
Abstract
Reciprocal signaling between the lung mesenchyme and epithelium is crucial for differentiation and branching morphogenesis. We hypothesized that the combination of signaling pathways comprising early epithelial-mesenchymal interactions and a 3D spatial environment are necessary for an efficient induction of embryonic and induced pluripotent stem cells (ESCs and iPSCs) into a lung cell phenotype with hallmarks of the distal niche. Aggregating early, but not late, embryonic lung mesenchyme with endoderm-induced mouse ESCs and iPSCs for 6 days resulted in organization into tubular structures and differentiation of the tubular lining cells to an NKX2-1(+)/SOX2(-)/SOX9(+)/proSFTPC(+) lineage. Over 80% of the endoderm-induced cells committed to an NKX2-1(+) lineage. Electron microscopy analysis demonstrated numerous multivesicular bodies and glycogen deposits in the tubular lining cells, characteristic features of type II epithelial cell progenitors. Using soluble FGFR2 receptor antagonists, we demonstrate that reciprocal fibroblast growth factor (FGF) 2, 7, and 10 signaling is essential for differentiation of endoderm-induced cells to an NKX2-1(+)/proSFTPC(+) phenotype within 3D aggregates. Only FGF2 was able to commit endoderm-induced cells in monolayer cultures to an NKX2-1(+) lineage, however with a significant lower efficiency (∼16%) than seen with mesenchyme. Thus, while FGF2 signaling alone can induce a primed population of ESCs and iPSCs, the cells do not differentiate to distal lung epithelial progenitors with the same efficiency and level of maturity that is achieved when the complex tissue and 3D environment of the developing lung is more accurately recapitulated.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".