New Developments in CRISPR/Cas-based Functional Genomics and their Implications for Research Using Zebrafish
Bibliographic record
Abstract
INTRODUCTION: Genome editing using CRISPR/Cas9 has advanced very rapidly in its scope, versatility and ease of use. Zebrafish (Danio rerio) has been one of the vertebrate model species where CRISPR/Cas9 has been applied very extensively for many different purposes and with great success. In particular, disease modeling in zebrafish is useful for testing specific gene variants for pathogenicity in a preclinical setting. Here we describe multiple advances in diverse species and systems that can improve genome editing in zebrafish. OBJECTIVE: To achieve temporal and spatial precision of genome editing, many new technologies can be applied in zebrafish such as artificial transcription factors, drug-inducible or optogenetically-driven expression of Cas9, or chemically-inducible activation of Cas9. Moreover, chemically- or optogenetically- inducible reconstitution of dead Cas9 (catalytically inactive, dCas9) can enable spatiotemporal control of gene regulation. In addition to controlling where and when genome editing occurs, using oligonucleotides allows for the introduction (knock-in) of precise modifications of the genome. CONCLUSION: We review recent trends to improve the precision and efficiency of oligo-based point mutation knock-ins and discuss how these improvements can apply to work in zebrafish. Similarly to how chemical mutagenesis enabled the first genetic screens in zebrafish, multiplexed sgRNA libraries and Cas9 can enable the next revolutionary transition in how genetic screens are performed in this species. We discuss the first examples and prospects of approaches using sgRNAs as specific and effective mutagens. Moreover, we have reviewed methods aimed at measuring the phenotypes of single cells after their mutagenic perturbation with vectors encoding individual sgRNAs. These methods can range from different cell-based reporters to single-cell RNA sequencing and can serve as great tools for high-throughput genetic screens.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".