Efficient CRISPR/Cas9‐Based Genome Engineering in Human Pluripotent Stem Cells
Bibliographic record
Abstract
Human pluripotent stem cells (hPS cells) are rapidly emerging as a powerful tool for biomedical discovery. The advent of human induced pluripotent stem cells (hiPS cells) with human embryonic stem (hES)-cell-like properties has led to hPS cells with disease-specific genetic backgrounds for in vitro disease modeling and drug discovery as well as mechanistic and developmental studies. To fully realize this potential, it will be necessary to modify the genome of hPS cells with precision and flexibility. Pioneering experiments utilizing site-specific double-strand break (DSB)-mediated genome engineering tools, including zinc finger nucleases (ZFNs) and transcription activator-like effector nucleases (TALENs), have paved the way to genome engineering in previously recalcitrant systems such as hPS cells. However, these methods are technically cumbersome and require significant expertise, which has limited adoption. A major recent advance involving the clustered regularly interspaced short palindromic repeats (CRISPR) endonuclease has dramatically simplified the effort required for genome engineering and will likely be adopted widely as the most rapid and flexible system for genome editing in hPS cells. In this unit, we describe commonly practiced methods for CRISPR endonuclease genomic editing of hPS cells into cell lines containing genomes altered by insertion/deletion (indel) mutagenesis or insertion of recombinant genomic DNA.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".