License to cut: Smart RNA guides for conditional control of CRISPR-Cas9
Bibliographic record
Abstract
Abstract The Cas9 enzyme is a programmable endonuclease, whose target sequence is directed by a companion RNA guide. Cas9 and RNA guides have revolutionized biology, enabling facile editing of the genome in almost all organisms. Controlling where and when Cas9 and the guide operate is indispensable for many fields ranging from developmental biology to therapeutics, but it remains a challenge. Most methods focus on controlling Cas9 with physico-chemical means (which lack finesse, precision or multiplexing), or transcriptional tools (which are slow and difficult to design). Rather than directly engineering Cas9, engineering the RNA guide itself has emerged as a more general and potent way to manage the activity of Cas9. Here we report smart RNA guides that are conditionally activated by the presence of a specific RNA opener. Contrary to most previous approaches, the design affords ample freedom as spacer and the opener are independent. We demonstrate this flexibility by operating SmartGuides activated by a panel of miRNA relevant for human health, and by composing SmartGuides in Boolean logic circuits. Lastly, we test the SmartGuides in mammalian cells - validating the basics tenets of the design, but also highlighting the challenges that remain to be lifted for in-vivo operation.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.040 | 0.022 |
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".