Stage‐Specific Generation of Human Pluripotent Stem Cell Derived Lung Models to Measure CFTR Function
Bibliographic record
Abstract
Human embryonic stem cells (ES) and induced pluripotent stem cells (iPSC) are powerful tools that have the potential to generate in vitro human lung epithelial cells. However, challenges in efficiency and reproducibility remain in utilizing the cells for therapy discovery platforms. Here, we optimize our previously published protocols to efficiently generate three developmental stages of the lung model (fetal lung epithelial progenitors, fLEP; immature airway epithelial spheroid, AES; air-liquid interface culture, ALI), and demonstrate its potential for cystic fibrosis (CF) drug discovery platforms. The stepwise approach directs differentiation from hPSC to definitive endoderm, anterior ventral foregut endoderm, and fetal lung progenitor cells. The article also describes the generation of immature airway epithelial spheroids in Matrigel with epithelial cells sorted by a magnetic-activated cell sorting system, and the generation of adult-like airway epithelia through air-liquid interface conditions. We demonstrate that this optimized procedure generates remarkably higher cystic fibrosis transmembrane conductance regulator (CFTR) expression and function than our previous method, and thus is uniquely suitable for CF research applications. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: hESC/hiPSC differentiation to fetal lung progenitors Basic Protocol 2: Formation of airway epithelial spheroids Alternate Protocol 1: Cryopreservation of airway epithelial spheroids Basic Protocol 3: Differentiation and maturation in air-liquid interface culture Alternate Protocol 2: Differentiation and maturation of epithelial progenitors from airway epithelial spheroids in ALI culture.
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 | 0.001 | 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) | 0.003 | 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".