Lung tissue-optimized gene editing in human cystic fibrosis models following topical application of lipid nanoparticles
Bibliographic record
Abstract
Cystic fibrosis (CF) is a severe monogenic disease characterized by debilitating lung dysfunction caused by loss-of-function mutations in the CFTR gene. While CRISPR-based gene editing holds promise for correcting these mutations and potentially curing CF, efficient delivery of gene editors to the lung epithelium through the mucosal barrier remains a major challenge. In this study, we developed a lung-optimized gene editing strategy using lipid nanoparticles (LNPs) and evaluated it in increasingly complex, biomimetic human-based and patient-derived models. Systematic optimization of helper lipids, genetic cargo, guide RNA modifications, and gene editor ratios, alongside analysis of innate immune responses, achieved ∼50 % editing efficiency in the model gene HPRT in two-dimensional models. Editing efficiency significantly dropped to ∼5 % in biomimetic three-dimensional CF bronchial epithelial tissue models following topical LNP application. Pretreatment with the approved mucolytic agent dornase alpha increased editing efficiency to ∼12.7 %. Finally, in CF patient-derived cells harboring the CFTR R1162X mutation, our optimized LNP formulation achieved ∼12 % correction on gene level, offering a potential treatment avenue for this yet untreatable mutation. Taken together, this study demonstrates that optimizing the genetic cargo as well as the delivery vehicle is key when striving for clinically applicable treatment approaches. It further provides insights into gene editing rates in human-based normal and CF patient-derived bronchial tissue models which express all relevant biological barriers and, thus, can pave the way for topically applicable treatment options for patients with CF and other genetic lung diseases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".