An oocyte‐specific Cas9‐expressing mouse for germline <scp>CRISPR</scp>/Cas9‐mediated genome editing
Bibliographic record
Abstract
Cas9 transgenes can be employed for genome editing in mouse zygotes. However, using transgenic instead of exogenous Cas9 to produce gene-edited animals creates unique issues including ill-defined transgene integration sites, the potential for prolonged Cas9 expression in transgenic embryos, and increased genotyping burden. To overcome these issues, we generated mice harboring an oocyte-specific, Gdf9 promoter driven, Cas9 transgene (Gdf9-Cas9) targeted as a single copy into the Hprt1 locus. The X-linked Hprt1 locus was selected because it is a defined integration site that does not influence transgene expression, and breeding of transgenic males generates obligate transgenic females to serve as embryo donors. Using microinjections and electroporation to introduce sgRNAs into zygotes derived from transgenic dams, we demonstrate that Gdf9-Cas9 mediates genome editing as efficiently as exogenous Cas9 at several loci. We show that genome editing efficiency is independent of transgene inheritance, verifying that maternally derived Cas9 facilitates genome editing. We also show that paternal inheritance of Gdf9-Cas9 does not mediate genome editing, confirming that Gdf9-Cas9 is not expressed in embryos. Finally, we demonstrate that off-target mutagenesis is equally rare when using transgenic or exogenous Cas9. Together, these results show that the Gdf9-Cas9 transgene is a viable alternative to exogenous Cas9.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.005 | 0.003 |
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".