CRISPR/Cas9-mediated base-editing enables a chain reaction through sequential repair of sgRNA scaffold mutations
Bibliographic record
Abstract
Cell behavior is controlled by complex gene regulatory networks. Although studies have uncovered diverse roles of individual genes, it has been challenging to record or control sequential genetic events in living cells. In this study, we designed two cellular chain reaction systems that enable sequential sgRNA activation in mammalian cells using a nickase Cas9 tethering of a cytosine nucleotide deaminase (nCas9-CDA). In these systems, thymidine (T)-to-cytosine (C) substitutions in the scaffold region of the sgRNA or the TATA box-containing loxP sequence (TATAloxP) are corrected by the nCas9-CDA, leading to activation of the next sgRNA. These reactions can occur multiple times, resulting in cellular chain reactions. As a proof of concept, we established a chain reaction by repairing sgRNA scaffold mutations in 293 T cells. Importantly, the results obtained in yeast or in vitro did not match those obtained in mammalian cells, suggesting that in vivo chain reactions need to be optimized in appropriate cellular contexts. Our system may lay the foundation for building cellular chain reaction systems that have a broad utility in the future biomedical research.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".