Focused ultrasound and microbubble-mediated delivery of CRISPR-Cas9 ribonucleoprotein to human induced pluripotent stem cells
Bibliographic record
Abstract
CRISPR-Cas9 ribonucleoproteins (RNPs) have been heavily considered for gene therapy due to their high on-target efficiency, rapid activity, and lack of insertional mutagenesis relative to other CRISPR-Cas9 delivery formats. Genetic diseases such as hypertrophic cardiomyopathy currently lack effective treatment strategies and are prime targets for CRISPR-Cas9 gene editing technology. However, current in vivo delivery strategies for Cas9 pose risks of unwanted immunogenic responses. This proof-of-concept study aimed to demonstrate that focused ultrasound (FUS) in combination with microbubbles can be used to deliver Cas9-sgRNA (single-guide RNA) RNPs and functionally edit human induced pluripotent stem cells (hiPSCs) in vitro, a model system that can be expanded to cardiovascular research via hiPSC-derived cardiomyocytes. Here, we first determine acoustic conditions suitable for the viable delivery of large proteins to hiPSCs with clinical Definity microbubble agents using our customized experimental platform. From here, we delivered Cas9-sgRNA RNP complexes targeting the EGFP (enhanced green fluorescent protein) gene to EGFP-expressing hiPSCs for EGFP knockout. Simultaneous acoustic cavitation detection during treatment confirmed a strong correlation between microbubble disruption and viable FUS-mediated protein delivery in hiPSCs. This study shows for the first time the potential for an FUS-mediated technique for targeted and precise CRISPR-Cas9 gene editing in human stem cells.
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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 | 0.001 | 0.000 |
| 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".