Successful optimization of CRISPR/Cas9-mediated defined point mutation knock-in using allele-specific PCR assays in zebrafish
Bibliographic record
Abstract
Abstract Single-stranded oligodeoxynucleotides (ssODN) are donor templates for homology-directed repair-based knock-in of point mutations using CRISPR/Cas9. To optimize the efficiency of ssODN-based knock-ins in zebrafish, we developed allele-specific PCR (AS-PCR) assays for introducing point mutations in tp53 , cdh5 and lmna as case studies. In these point mutation strategies we introduced the codon mutations, sgRNA site mutations and restriction sites which can be detected by AS-PCR with the primers matching their respective alleles in combination with a common primer. We employed the anti-sense asymmetric oligo design as the main optimization as well as phosphorothioate oligo modification and also observed that proximity of the mutation site to the Cas9 cut site improves the efficiency when knock-ins into different genes were compared. We improved the efficiencies of two tp53 knock-ins using anti-sense asymmetric ultramer oligos (126-nt in length with homology arms of 36 and 90 nucleotides, anti-sense to the sgRNA) by 3-10 fold, the optimizations which resulted in successful founders for both tp53 knock-ins with transmission rates of 20-40 %. The initially low knock-in efficiency for tp53 mutants was likely due to the distance between the Cas9 cut site and mutations since cdh5 G767S knock-in located at the cut site had much higher founder identification and germline transmission rates. The phosphorothioate oligo modifications was used for a lamin A/C ( lmna ) knock-in strategy and it resulted in 40 % overall improvement in knock-in efficiency and greater knock-in consistency. We also determined that AS-PCR detected false-positive knock-ins which constituted 25-80 % of total in different strategies and developed a workflow to screen out the founders and F1 zebrafish carrying these undesirable modifications. In summary, we provide a complementary set of optimizations for CRISPR/Cas9-based ssODN knock-ins in zebrafish using a novel combination of methods.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".