Generation of apical-out airway organoids from human primary airway epithelial cells
Bibliographic record
Abstract
Airway organoids can be successfully generated from primary airway epithelial cells, recapitulating the architecture, polar organization, and key functions of the in vivo tissue. However, the closed organoid structure limits the accessibility to the luminal apical cell surface, which is exposed to the environment in vivo. This inaccessibility hinders the utilization of airway organoids in assays examining host-pathogen interactions and responses to environmental stimuli, both of which occur primarily at the apical surface. To address this limitation, we have developed PneumaCult™ Apical-Out Airway Organoid (AOAO) Medium, which supports the fast and efficient generation of airway organoids exposing their apical side to the environment in the absence of an extracellular matrix hydrogel. To generate AOAOs, two-dimensional expanded human bronchial epithelial cells (HBECs) were seeded in PneumaCult™ AOAO Medium in micropatterned [AggreWell™400] plates to promote cell aggregation. The aggregates were then transferred to suspension culture in fresh PneumaCult™ AOAO, and incubated at 37°C until they differentiated into AOAOs. These organoids can be generated from HBECs from a multitude of different passages and display high culture homogeneity, with low inter-donor size variability (average diameter 74 ± 4 μm at p3, n=3). The organoids are mainly composed of ciliated cells (61 ± 16% of all cells at p3) that display outward-facing, beating cilia and KRT5-positive basal cells (n=3). The easy access to the apical surface of the epithelium and scalability of this culture system offer a powerful in vitro model suitable for studying host-pathogen interactions and high-throughput antiviral drug screening.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".