A protocol for the detection of precision genome editing in human cells using One-pot DTECT
Bibliographic record
Abstract
Prime editing is a highly versatile CRISPR-based genome editing technology that allows for the precise installation of desired genetic variants. This protocol describes how to use One-pot DTECT to assess prime editing efficiency in human cells. Key steps include conducting prime editing, extracting genomic DNA, performing AcuI-tagging PCR, capturing genetic signatures, and detecting captured signatures through qualitative, quantitative, and visual methods. One-pot DTECT enables same-day detection of targeted genetic signatures introduced by precision genome editing technologies using off-the-shelf reagents. For complete details on the use and execution of this protocol, please refer to Baudrier et al. 1 • Perform prime editing in human cells • Prepare a homemade One-pot DTECT kit combining multiple enzymes • Conduct AcuI-tagging PCRs to amplify and tag targeted genetic signatures • Detect prime editing signatures using quantitative, qualitative, and visual detection Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. Prime editing is a highly versatile CRISPR-based genome editing technology that allows for the precise installation of desired genetic variants. This protocol describes how to use One-pot DTECT to assess prime editing efficiency in human cells. Key steps include conducting prime editing, extracting genomic DNA, performing AcuI-tagging PCR, capturing genetic signatures, and detecting captured signatures through qualitative, quantitative, and visual methods. One-pot DTECT enables same-day detection of targeted genetic signatures introduced by precision genome editing technologies using off-the-shelf reagents.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".