A general genome editing strategy using CRISPR lipid nanoparticle spherical nucleic acids
Bibliographic record
Abstract
Genome editing with CRISPR-Cas systems hold promise for treating a wide range of genetic disorders and cancers. However, efficient delivery of genome editors remains challenging due to the requirement for the simultaneous delivery or intracellular generation of Cas proteins, guide RNAs, and, in some applications, donor DNAs. Furthermore, the immunogenicity and toxicity of delivery vehicles can limit the safety and efficacy of genetic medicines. Here, we combine two nucleic acid delivery approaches to create CRISPR lipid nanoparticle-spherical nucleic acids (LNP-SNAs) that are both efficient and biocompatible. Compared to lipid nanoparticles (LNPs) lacking a surface-bound DNA shell, CRISPR LNP-SNAs exhibit two- to three-fold higher cellular uptake, reduced cytotoxicity, and improved gene transfection efficiency. Across multiple cell lines and genomic loci, CRISPR LNP-SNAs induce insertion-deletion mutations at average frequencies two- to three-fold higher than those observed with LNPs. When codelivered with donor templates, CRISPR LNP-SNAs enable homology-directed repair at an average efficiency of 21 ± 7%, a 2.5-fold improvement over LNPs (8 ± 4%). The ease of synthesis and biocompatibility of CRISPR LNP-SNAs highlight their potential as a versatile delivery platform for CRISPR-Cas and other gene therapies.
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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".