ssDNA is not superior to dsDNA as long HDR donors for CRISPR-mediated endogenous gene tagging in human diploid cells
Bibliographic record
Abstract
Abstract Recent advances in CRISPR technology have enabled us to perform gene knock-in in various species and cell lines. CRISPR-mediated knock-in requires donor DNA which serves as a template for homology-directed repair (HDR). For knock-in of short sequences or base substitutions, ssDNA donors are frequently used among various other forms of HDR donors, such as linear dsDNA. However, for insertion of long transgenes such as fluorescent reporters in human cells, the optimal type of HDR donors remains unclear. In this study, we established a simple and efficient CRISPR-mediated knock-in method for long transgenes using linear dsDNA and ssDNA donors, and systematically compared the performance of these two donors for endogenous gene tagging in human non-transformed diploid cells. Quantification using flow cytometry revealed higher efficiency of fluorescent tagging with dsDNA donors than with ssDNA. By analyzing knock-in outcomes using long-read amplicon sequencing and a classification framework, a variety of mis-integration events were detected regardless of the donor type. Importantly, the ratio of precise insertion was higher with dsDNA donors than with ssDNA. Moreover, in off-target integration analyses, dsDNA and ssDNA were comparably prone to non-homologous integration. These results indicate that ssDNA is not superior to dsDNA as long HDR donors for gene knock-in in human cells.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".