A primary ciliary dyskinesia in vitro model using CRISPR-edited clonal primary epithelial cells
Bibliographic record
Abstract
Background: Primary ciliary dyskinesia (PCD) is a rare autosomal recessive disorder. Mutations in ciliary genes lead to dyskinetic or static cilia, resulting in poor mucociliary clearance, recurrent infection, and progressive lung damage. Existing models for PCD are constrained by the scarcity of patient biopsies and the limited proliferative capacity of primary airway epithelial cells in vitro. Aim: To create an isogenic in vitro PCD model using CRISPR editing of the ciliary gene DNAI2 and clonal expansion of primary human nasal epithelial cells (HNECs). Method: CRISPR/Cas9 was delivered to HNECs as an RNP complex using a Lonza 4D Nucleofector. After a 48-hour incubation, HNECs were clonally sorted into 96-well plates with 3T3-J2 feeders in epithelial cell culture medium, with an additional small molecule inhibitor (Compound E, to be revealed at the meeting). Clones were expanded and editing was confirmed with Sanger sequencing and low-pass whole-genome sequencing. DNAI2 knockout clones were differentiated, processed for electron microscopy and functionally characterised in air-liquid interface (ALI) cultures. Results: 27% of basal cells grew as clones in 96-well plates (n=7 replicates). Clonal cultures retained their differentiation capacity, forming ciliated epithelium at ALI. Transepithelial electrical resistance (TEER) was comparable to non-clonal ALI cultures. DNAI2-knockout was present in 60% of clonal cultures, and a complete static cilia phenotype was observed in these clones using high-speed video microscopy. Conclusion: Incorporating compound E into airway basal cell culture facilitates clonal growth of HNECs, advancing in vitro modelling of rare respiratory diseases such as PCD.
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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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".