CRISPR-Cas tools to study gene function in cytokinesis
Bibliographic record
Abstract
Cytokinesis is the process that separates a cell into two daughter cells at the end of mitosis. Most of our knowledge of cytokinesis comes from overexpression studies, which affects our interpretation of protein function. Gene editing can circumvent this issue by introducing functional mutations or fluorescent probes directly into a gene locus. However, despite its potential, gene editing is just starting to be used in the field of cytokinesis. Here, we discuss the benefits of using gene editing tools for the study of cytokinesis and highlight recent studies that successfully used CRISPR-Cas (clustered regularly interspaced short palindromic repeats-CRISPR-associated proteins) technology to answer critical questions regarding the function of cytokinesis proteins. We also present methodologies for editing essential genes and discuss how CRISPR interference (CRISPRi) and activation (CRISPRa) can enable precise control of gene expression to answer important questions in the field. Finally, we address the need for gene editing to study cytokinesis in more physiologically relevant contexts. Therefore, this Review provides a roadmap for gene editing to be used in the study of cytokinesis and other cellular processes.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".