A Universal Surrogate Reporter for Efficient Enrichment of CRISPR/Cas9-Mediated Homology-Directed Repair in Mammalian Cells
Bibliographic record
Abstract
CRISPR/Cas9-mediated homology-directed repair (HDR) can be leveraged to precisely engineer mammalian genomes. However, the inherently low efficiency of HDR often hampers to identify the desired modified cells. Here, we developed a novel universal surrogate reporter system that efficiently enriches for genetically modified cells arising from CRISPR/Cas9-induced HDR events (namely, the "HDR-USR" system). This episomally based reporter can be self-cleaved and self-repaired via HDR to create a functional puromycin selection cassette without compromising genome integrity. Co-transfection of the HDR-USR system into host cells and transient puromycin selection efficiently achieves enrichment of HDR-modified cells. We tested the system for precision point mutation at 16 loci in different human cell lines and one locus in two rodent cell lines. This system exhibited dramatic improvements in HDR efficiency at a single locus (up to 20.7-fold) and two loci at once (42% editing efficiency compared to zero in the control), as well as greatly improved knockin efficiency (8.9-fold) and biallelic deletion (35.9-fold) at test loci. Further increases were achieved by co-expression of yeast Rad52 and linear single-/double-stranded DNA donors. Taken together, our HDR-USR system provides a simple, robust and efficient surrogate reporter for the enrichment of CRISPR/Cas9-induced HDR-based precision genome editing across various targeting loci in different cell lines.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".