RNA programmable cell targeting and manipulation with CellREADR
Bibliographic record
Abstract
Methods that provide specific, easy, and scalable experimental access to animal cell types and cell states will have broad applications in biology and medicine. CellREADR - Cell access through RNA sensing by Endogenous ADAR (adenosine deaminase acting on RNA), is a programmable RNA sensor-actuator technology that couples the detection of a cell-defining RNA to the translation of an effector protein to monitor and manipulate the cell. The CellREADR RNA device consists of a 5' sensor region complementary to a cellular RNA and a 3' payload coding region; payload translation is gated by the removal of a STOP codon in the sensor region upon base pairing with the cognate cellular RNA through an ADAR-mediated A- to-I editing mechanism ubiquitous to metazoan cells. CellREADR thus highlights the potential for RNA-based monitoring and manipulation of animal cells in ways that are simple, versatile, and generalizable across tissues and species. Here, we describe a detailed protocol for implementing CellREADR experiments in cell cultures and in animals. The procedure includes sensor and payload design, cloning, validation and characterization in mammalian cell cultures. The in vivo animal protocol focuses on AAV-based delivery of CellREADR using brain tissue as examples. We describe current best practices, various experimental controls and trouble-shooting steps. Beginning from sensor design, the validation of RNA sensor-actuators in cell cultures can be completed in 2-3 weeks. The construction of AAV vectors and their in vivo validation and characterization will take additional 6-8 weeks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".