Influence of mesenchymal and biophysical components on distal lung organoid differentiation
Bibliographic record
Abstract
BACKGROUND: Chronic lung disease of prematurity, called bronchopulmonary dysplasia (BPD), lacks effective therapies, stressing the need for preclinical testing systems that reflect human pathology for identifying causal pathways and testing novel compounds. Alveolar organoids derived from human pluripotent stem cells (hPSC) are promising test platforms for studying distal airway diseases like BPD, but current protocols do not accurately replicate the distal niche environment of the native lung. Herein, we investigated the contributions of cellular constituents of the alveolus and fetal respiratory movements on hPSC-derived alveolar organoid formation. METHODS: Human PSCs were differentiated in 2D culture into lung progenitor cells (LPC) which were then further differentiated into alveolar organoids before and after removal of co-developing mesodermal cells. LPCs were also differentiated in Transwell® co-cultures with and without human fetal lung fibroblast. Forming organoids were subjected to phasic mechanical strain using a Flexcell® system. Differentiation within organoids and Transwell® cultures was assessed by flow cytometry, immunofluorescence, and qPCR for lung epithelial and alveolar markers of differentiation including GATA binding protein 6 (GATA 6), E-cadherin (CDH1), NK2 Homeobox 1 (NKX2-1), HT2-280, surfactant proteins B (SFTPB) and C (SFTPC). RESULTS: lung progenitors. Additionally, we demonstrate that episodic mechanical deformation of hPSC-derived lung organoids, mimicking in situ fetal respiratory movements, increased AEC2 differentiation without affecting proximal epithelial differentiation. CONCLUSION: Our data suggest that biophysical and mesenchymal components promote AEC2 differentiation within hPSC-derived distal organoids in vitro.
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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) | 0.001 | 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".