Focused ultrasound-guided delivery of gene editing protein in human induced pluripotent stem cells
Bibliographic record
Abstract
Focused ultrasound (FUS) in combination with microbubbles (MBs) can induce cavitation-mediated plasma membrane permeabilization in nearby cells, thus permitting entry to otherwise impermeable macromolecules. Cas9 is an endonuclease protein currently at the forefront of gene editing due to its efficiency, ease of use, and low cost. In complex with single guide RNA (sgRNA), Cas9 can target specific gene sequences to cause double-stranded breaks, interrupting gene function in the process. Cas9 is best delivered as a ribonucleoprotein (RNP) for the most effective results, however, suffers from inefficient delivery methods for in-vivo applications due to its large size (160 kDa). FUS and microbubbles can be an effective alternative to currently applied systems (e.g., adeno-associated vectors) to deliver Cas9:sgRNA RNPs for CRISPR-mediated knockout. Currently, we are exploring the use of FUS for Cas9-mediated knockout of EGFP in both EGFP-expressing human induced pluripotent stem cells (hiPSC) and human cardiomyocytes. Treatment of hiPSC under acoustic conditions (1 MHz, 1000 cycles, 5 ms intervals, >208 kPa) suitable for cavitation-mediated sonoporation led to EGFP knockout in hiPSCs. By modulating FUS parameters and setup, we can optimize delivery of Cas9 as an RNP for treatment of genetic diseases, such as hypertrophic cardiomyopathy.
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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 | 0.000 |
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".