Chemically-inducible CRISPR/Cas9 circuits for ultra-high dynamic range gene perturbation
Bibliographic record
Abstract
CRISPR/Cas9 technologies provide unique capabilities for modeling disease and understanding gene-to-phenotype connections. In cultured cells, chemical-mediated control of Cas9 activity can limit off-target effects and enable mechanistic study of essential genes. However, widely-used Tet-On systems often show leaky Cas9 expression, leading to unintended edits, as well as weak activity upon induction. Leakiness can be problematic in the context of Cas9 nuclease activity, which may result in cumulative DNA damage and degradation of the target cell genome over time. To overcome these deficiencies, we have established transgenic platforms that minimize Cas9 functionality in the OFF-state along with maximized and uncompromised ON-state gene editing efficiency. By combining conditional destabilization and inhibition of Cas9, we have developed an all-in-one (one or multiple guide RNAs and Cas9) ultra-tight, Tet-inducible system with exceptional dynamic range (ON vs. OFF-state) across various cell lines and targets. As an alternative to Tet-mediated induction, we have created a Branaplam-regulated splice switch module for low-baseline and robust Cas9 activity control. Lastly, for circumstances where DNA damage needs to be avoided, we have constructed a dual-control, Tet-inducible CRISPRi module for tight and potent transcriptional silencing. This upgraded suite of inducible CRISPR systems has broad applications for numerous cell types and experimental conditions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".