Efficient Generation of Large‐Fragment Knock‐In Mouse Models Using 2‐Cell (2C)‐Homologous Recombination (HR)‐CRISPR
Bibliographic record
Abstract
Abstract Generating large‐fragment knock‐ins, such as reporters, conditional alleles, or humanized alleles, directly in mouse embryos is still a challenging feat. We have developed 2C‐HR‐CRISPR, a technology that allows highly efficient (10‐50%) and rapid (generating founders in 2 months) targeting of large DNA fragments. Key to this strategy is the delivery of CRISPR reagents into 2‐cell‐stage mouse embryos, taking advantage of the high homologous recombination activity during the long G2 cell cycle phase at this stage. Furthermore, by exploiting a Cas9–monomeric streptavidin (Cas‐mSA) and biotinylated PCR template (BioPCR) system to localize the repair template to specific double strand breaks, the efficiency can be further improved to up to 95%. Here we provide a procedure to generate large‐fragment knock‐in mouse models using 2C‐HR‐CRISPR. We first describe the principles for designing single guide RNAs and repair templates but refer to published manuscripts and protocols for molecular cloning methods or commercial sources for these reagents. We then describe two unique aspects of 2C‐HR‐CRISPR that are critical for success: (1) production of the CRISPR reagents for 2C‐HR‐CRISPR, particularly for applying the Cas9‐mSA/BioPCR method, and (2) microinjection of mouse embryos at the 2‐cell stage. © 2020 by John Wiley & Sons, Inc. Basic Protocol 1: Single guide RNA and repair template design Basic Protocol 2: Preparing reagents for 2C‐HR‐CRISPR Basic Protocol 3: Microinjecting 2‐cell‐stage mouse embryos
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".