Profiling transcriptome composition and dynamics within nuclear compartments using SLAM-RT&Tag
Bibliographic record
Abstract
Nuclear compartments are membrane-less regions enriched in functionally related molecules. RNA is a major component of many nuclear compartments, but the identity and dynamics of transcripts within nuclear compartments are poorly understood. Here, we applied reverse transcribe and tagment (RT&Tag) to human cell lines to identify the transcript populations of Polycomb domains and nuclear speckles. We also developed SLAM-RT&Tag, which combines RNA metabolic labeling with RT&Tag, to quantify transcript dynamics within nuclear compartments. We observed unique transcript populations with differing structures and dynamics within each compartment. Intriguingly, exceptionally long genes are transcribed adjacent to Polycomb domains and are transiently associated with chromatin. By contrast, nuclear speckles act as quality control checkpoints that transiently confine incompletely spliced polyadenylated transcripts and facilitate their post-transcriptional splicing. In summary, we demonstrate that transcripts at Polycomb domains and nuclear speckles undergo distinct RNA processing mechanisms, highlighting the pivotal role of compartmentalization in RNA maturation. • Developed SLAM-RT&Tag to profile RNA dynamics within nuclear compartments • RNAs persistently associated with compartments, like XIST and MALAT1, are rare • Exceptionally long genes are transcribed adjacent to Polycomb domains • Incompletely spliced mature mRNAs transiently pass through nuclear speckles Khyzha et al. developed SLAM-RT&Tag, a kinetic labeling strategy for profiling RNA dynamics within nuclear compartments. Applied to Polycomb domains and nuclear speckles, they identified compartment-specific transcript populations with distinct structural features and demonstrated that most transcripts exhibit transient associations with these compartments.
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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 | 0.000 | 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".