Lipid Nanoparticle-Mediated Hit-and-Run Approaches Yield Efficient and Safe <i>In Situ</i> Gene Editing in Human Skin
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Despite exciting advances in gene editing, the efficient delivery of genetic tools to extrahepatic tissues remains challenging. This holds particularly true for the skin, which poses a highly restrictive delivery barrier. In this study, we ran a head-to-head comparison between Cas9 mRNA or ribonucleoprotein (RNP)-loaded lipid nanoparticles (LNPs) to deliver gene editing tools into epidermal layers of human skin, aiming for in situ gene editing. We observed distinct LNP composition and cell-specific effects such as an extended presence of RNP in slow-cycling epithelial cells for up to 72 h. While obtaining similar gene editing rates using Cas9 RNP and mRNA with MC3-based LNPs (10–16%), mRNA-loaded LNPs proved to be more cytotoxic. Interestingly, ionizable lipids with a p K a ∼ 7.1 yielded superior gene editing rates (55%–72%) in two-dimensional (2D) epithelial cells while no single guide RNA-dependent off-target effects were detectable. Unexpectedly, these high 2D editing efficacies did not translate to actual skin tissue where overall gene editing rates between 5%–12% were achieved after a single application and irrespective of the LNP composition. Finally, we successfully base-corrected a disease-causing mutation with an efficacy of ∼5% in autosomal recessive congenital ichthyosis patient cells, showcasing the potential of this strategy for the treatment of monogenic skin diseases. Taken together, this study demonstrates the feasibility of an in situ correction of disease-causing mutations in the skin that could provide effective treatment and potentially even a cure for rare, monogenic, and common skin diseases.
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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 | 0.000 | 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) | 0.001 | 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".