Serum extracellular vesicles for delivery of CRISPR-CAS9 ribonucleoproteins to modify the dystrophin gene
Bibliographic record
Abstract
Extracellular vesicles (EVs) mediate intercellular biomolecule exchanges in the body, making them promising delivery vehicles for therapeutic cargo. Genetic engineering by the CRISPR system is an interesting therapeutic avenue for genetic diseases such as Duchenne muscular dystrophy (DMD). We developed a simple method for loading EVs with CRISPR ribonucleoproteins (RNPs) consisting of SpCas9 proteins and guide RNAs (gRNAs). EVs were first purified from human or mouse serum using ultrafiltration and size-exclusion chromatography. Using protein transfectant to load RNPs into serum EVs, we showed that EVs are good carriers of RNPs in vitro and restored the expression of the tdTomato fluorescent protein in muscle fibers of Ai9 mice. EVs carrying RNPs targeting introns 22 and 24 of the DMD gene were also injected into muscles of mdx mice having a non-sense mutation in exon 23. Up to 19% of the cDNA extracted from treated mdx mice had the intended deletion of exons 23 and 24, allowing dystrophin expression in muscle fibers. RNPs alone, without EVs, were inefficient in generating detectable deletions in mouse muscles. This method opens new opportunities for rapid and safe delivery of CRISPR components to treat DMD.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".