Oxygenation as a driving factor in epithelial differentiation at the air–liquid interface
Bibliographic record
Abstract
Culture at the air-liquid interface is broadly accepted as necessary for differentiation of cultured epithelial cells towards an in vivo-like phenotype. However, air-liquid interface cultures are expensive, laborious and challenging to scale for increased throughput applications. Deconstructing the microenvironmental parameters that drive these differentiation processes could circumvent these limitations, and here we hypothesize that reduced oxygenation due to diffusion limitations in liquid media limits differentiation in submerged cultures; and that this phenotype can be rescued by recreating normoxic conditions at the epithelial monolayer, even under submerged conditions. Guided by computational models, hyperoxygenation of atmospheric conditions was applied to manipulate oxygenation at the monolayer surface. The impact of this rescue condition was confirmed by assessing protein expression of hypoxia-sensitive markers. Differentiation of primary human bronchial epithelial cells isolated from healthy patients was then assessed in air-liquid interface, submerged and hyperoxygenated submerged culture conditions. Markers of differentiation, including epithelial layer thickness, tight junction formation, ciliated surface area and functional capacity for mucociliary clearance, were assessed and found to improve significantly in hyperoxygenated submerged cultures, beyond standard air-liquid interface or submerged culture conditions. These results demonstrate that an air-liquid interface is not necessary to produce highly differentiated epithelial structures, and that increased availability of oxygen and nutrient media can be leveraged as important strategies to improve epithelial differentiation for applications in respiratory toxicology and therapeutic development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".