Protocol for the spatiotemporal profiling of RNA within nuclear compartments in human cell lines using SLAM-RT&Tag
Bibliographic record
Abstract
Measuring RNA residence time within nuclear compartments provides insight into their roles as either storage sites or transient processing hubs. This protocol describes SLAM-RT&Tag, a genomic technique that integrates RNA metabolic labeling with RT&Tag, an RNA proximity labeling method. We detail steps for RNA labeling, library preparation, and computational quantification of T-to-C conversion events to infer RNA dynamics within nuclear compartments in human cell lines. For complete details on the use and execution of this protocol, please refer to Khyzha et al. 1 • Steps for quantifying localized RNA dynamics using SLAM-RT&Tag • Instructions for preparing SLAM-RT&Tag Illumina sequencing libraries • Procedures for differential kinetic analysis of SLAM-RT&Tag data Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. Measuring RNA residence time within nuclear compartments provides insight into their roles as either storage sites or transient processing hubs. This protocol describes SLAM-RT&Tag, a genomic technique that integrates RNA metabolic labeling with RT&Tag, an RNA proximity labeling method. We detail steps for RNA labeling, library preparation, and computational quantification of T-to-C conversion events to infer RNA dynamics within nuclear compartments in human cell lines.
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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 | 0.001 | 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".