CRISPR-Cas9-Mediated Precise Knock-In Edits in Zebrafish Hearts
Bibliographic record
Abstract
Clustered regularly interspaced short palindromic repeats (CRISPR) in animal models enable precise genetic manipulation for the study of physiological phenomena. Zebrafish have been used as an effective genetic model to study numerous questions related to heritable disease, development, and toxicology at the whole-organ and -organism level. Due to the well-annotated and mapped zebrafish genome, numerous tools for gene editing have been developed. However, the efficacy of generating and ease of detecting precise knock-in edits using CRISPR is a limiting factor. Described here is a CRISPR-Cas9-based knock-in approach with the simple detection of precise edits in a gene responsible for cardiac repolarization and associated with the electrical disorder, Long QT Syndrome (LQTS). This two-single-guide RNA (sgRNA) approach excises and replaces the target sequence and links a genetically encoded reporter gene. The utility of this approach is demonstrated by describing non-invasive phenotypic measurements of cardiac electrical function in wild-type and gene-edited zebrafish larvae. This approach enables the efficient study of disease-associated variants in a whole organism. Furthermore, this strategy offers possibilities for the insertion of exogenous sequences of choice, such as reporter genes, orthologs, or gene editors.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".