Sequence-Independent RNA Sensing in Living Mammalian Cells
Bibliographic record
Abstract
Recently, several groups described sensors in living cells that take advantage of adenosine deaminases acting on RNA (ADARs) to link the presence of an RNA (a "target transcript") to the translation of a payload from a second, exogenously introduced mRNA. These sensors share the key mechanism of editing a stop codon opposite a specific sequence motif in the target transcript, where this motif requirement is dictated by ADAR's strong sequence preference. This constrains sensor design and precludes the sensing of short sequences that lack such motifs, often essential for key applications such as sensing viral RNAs and differentiating splice isoforms. Here we address this limitation with modular RNA sensors using adenosine deaminases acting on RNA ("modulADAR"). ModulADAR features two key elements that mirror the modularity of ADARs: regions that hybridize with the target transcript to recruit ADAR's dsRNA-binding domains, and a stem-loop for stop-codon editing by ADAR's catalytic domain. We optimize modulADAR and apply it to detect short subsequences that cannot be sensed by prior-generation sensors. We anticipate that modulADAR will empower broader basic science and therapeutic applications, especially those that will uniquely benefit from programmable RNA detection in living cells.
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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 | 0.000 |
| Research integrity | 0.001 | 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".