High-throughput DNA Extraction and Genotyping of 3dpf Zebrafish Larvae by Fin Clipping
Bibliographic record
Abstract
Zebrafish (Danio rerio) possess orthologues for 84% of the genes known to be associated with human diseases. In addition, these animals have a short generation time, are easy to handle, display a high reproductive rate, low cost, and are easily amenable to genetic manipulations by microinjection of DNA in embryos. Recent advances in gene editing tools are enabling precise introduction of mutations and transgenes in zebrafish. Disease modeling in zebrafish often leads to larval phenotypes and early death which can be challenging to interpret if genotypes are unknown. This early identification of genotypes is also needed in experiments requiring sample pooling, such as in gene expression or mass spectrometry studies. However, extensive genotypic screening is limited by traditional methods, which in most labs are performed only on adult zebrafish or in postmortem larvae. We addressed this problem by adapting a method for the isolation of PCR-ready genomic DNA from live zebrafish larvae that can be achieved as early as 72 h post-fertilization (hpf). This time and cost-effective technique, improved from a previously published genotyping protocol, allows the identification of genotypes from microscopic fin biopsies. The fins quickly regenerate as the larvae develop. Researchers are then able to select and raise the desired genotypes to adulthood by utilizing this high-throughput PCR-based genotyping procedure.
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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 | 0.002 |
| 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) | 0.004 | 0.005 |
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".