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 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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.047 | 0.044 |
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".