Unveiling the cut-and-repair cycle of designer nucleases in human stem and T cells via CLEAR-time dPCR
Bibliographic record
Abstract
DNA repair mechanisms in human primary cells, including error-free repair, and, recurrent nuclease cleavage events, remain largely uncharacterised. We elucidate gene-editing related repair processes using Cleavage and Lesion Evaluation via Absolute Real-time dPCR (CLEAR-time dPCR), an ensemble of multiplexed dPCR assays that quantifies genome integrity at targeted sites. Utilising CLEAR-time dPCR we track active DSBs, small indels, large deletions, and other aberrations in absolute terms in clinically relevant edited cells, including HSPCs, iPSCs, and T-cells. By quantifying up to 90% of loci with unresolved DSBs, CLEAR-time dPCR reveals biases inherent to conventional mutation screening assays. Furthermore, we accurately quantify DNA repair precision, revealing prevalent scarless repair after blunt and staggered end DSBs and recurrent nucleases cleavage. This work provides one of the most precise analyses of DNA repair and mutation dynamics, paving the way for mechanistic studies to advance gene therapy, designer editors, and small molecule discovery.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".