A simplified method for identifying early CRISPR-induced indels in zebrafish embryos using High Resolution Melting analysis
Bibliographic record
Abstract
BACKGROUND: The CRISPR/Cas9 system has become a regularly used tool for editing the genome of many model organisms at specific sites. However, two limiting steps arise in the process of validating guide RNA target sites in larvae and adults: the time required to identify indels and the cost associated with identifying potential mutant animals. RESULTS: Here we have combined and optimized the HotSHOT genomic DNA extraction technique with a two-steps Evagreen PCR, followed by a high-resolution melting (HRM) assay, which facilitates rapid identification of CRISPR-induced indels. With this technique, we were able to genotype adult zebrafish using genomic DNA extracted from fin-clips in less than 2 h. We were also able to obtain a reliable and early read-out of the effectiveness of guide RNAs only 4 h after the embryos were injected with the constructs for the CRISPR/Cas9 mutagenic system. Furthermore, through mutagenesis kinetic assay, we identified that the 2-cell stage is the earliest time point at which indels can be observed. CONCLUSIONS: By combining an inexpensive and rapid genomic DNA extraction method with an HRM-based assay, our approach allows for high-throughput genotyping of adult zebrafish and embryos, and is more sensitive than standard PCR approaches, permitting early identification of CRISPR-induced indels and with applications for other model organisms as well.
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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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".