RNA Mango Aptamer-Fluorophore: A Bright, High-Affinity Complex for RNA Labeling and Tracking
Bibliographic record
Abstract
Because RNA lacks strong intrinsic fluorescence, it has proven challenging to track RNA molecules in real time. To address this problem and to allow the purification of fluorescently tagged RNA complexes, we have selected a high affinity RNA aptamer called RNA Mango. This aptamer binds a series of thiazole orange (fluorophore) derivatives with nanomolar affinity, while increasing fluorophore fluorescence by up to 1,100-fold. Visualization of RNA Mango by single-molecule fluorescence microscopy, together with injection and imaging of RNA Mango/fluorophore complex in C. elegans gonads demonstrates the potential for live-cell RNA imaging with this system. By inserting RNA Mango into a stem loop of the bacterial 6S RNA and biotinylating the fluorophore, we demonstrate that the aptamer can be used to simultaneously fluorescently label and purify biologically important RNAs. The high affinity and fluorescent properties of RNA Mango are therefore expected to simplify the study of RNA complexes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".