A Cell-Based Optimised Approach for Rapid and Efficient Gene Editing of Human Pluripotent Stem Cells
Bibliographic record
Abstract
Introducing or correcting disease-causing mutations through genome editing in human pluripotent stem cells (hPSCs) followed by tissue-specific differentiation provide sustainable models of multiorgan diseases, such as cystic fibrosis (CF). However, low editing efficiency resulting in extended cell culture periods and the use of specialised equipment for fluorescence activated cell sorting (FACS) make hPSC genome editing still challenging. We aimed to investigate whether a combination of cell cycle synchronisation, single-stranded oligodeoxyribonucleotides, transient selection, manual clonal isolation, and rapid screening can improve the generation of correctly modified hPSCs. Here, we introduced the most common CF mutation, ΔF508, into the CFTR gene, using TALENs into hPSCs, and corrected the W1282X mutation using CRISPR-Cas9, in human-induced PSCs. This relatively simple method achieved up to 10% efficiency without the need for FACS, generating heterozygous and homozygous gene edited hPSCs within 3–6 weeks in order to understand genetic determinants of disease and precision medicine.
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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 | 0.001 |
| 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".