Engineering Base Changes and Epitope‐Tagged Alleles in Mice Using Cas9 RNA‐Guided Nuclease
Bibliographic record
Abstract
Mice carrying patient-associated base changes are powerful tools to define the causality of single-nucleotide variants to disease states. Epitope tags enable immuno-based studies of genes for which no antibodies are available. These alleles enable detailed and precise developmental, mechanistic, and translational research. The first step in generating these alleles is to identify within the target sequence-the orthologous sequence for base changes or the N or C terminus for epitope tags-appropriate Cas9 protospacer sequences. Subsequent steps include design and acquisition of a single-stranded oligonucleotide repair template, synthesis of a single guide RNA (sgRNA), collection of zygotes, and microinjection or electroporation of zygotes with Cas9 mRNA or protein, sgRNA, and repair template followed by screening born mice for the presence of the desired sequence change. Quality control of mouse lines includes screening for random or multicopy insertions of the repair template and, depending on sgRNA sequence, off-target sequence variation introduced by Cas9. © 2025 The Author(s). Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Single guide RNA design and synthesis Alternate Protocol 1: Single guide RNA synthesis by primer extension and in vitro transcription Basic Protocol 2: Design of oligonucleotide repair template Basic Protocol 3: Preparation of RNA mixture for microinjection Support Protocol 1: Preparation of microinjection buffer Alternate Protocol 2: Preparation of RNP complexes for electroporation Basic Protocol 4: Collection and preparation of mouse zygotes for microinjection or electroporation Basic Protocol 5: Electroporation of Cas9 RNP into zygotes using cuvettes Alternate Protocol 3: Electroporation of Cas9 RNP into zygotes using electrode slides Basic Protocol 6: Screening and quality control of derived mice Support Protocol 2: Deconvoluting multiple sequence chromatograms with DECODR.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.008 |
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".