Transcription-dependent mobility of single genes and genome-wide motions in live human cells
Bibliographic record
Abstract
The human genome is highly dynamic across all scales. At the gene level, chromatin is persistently remodeled and rearranged during active processes such as transcription, replication and DNA repair. At the genome level, chromatin moves in micron-scale domains that break up and re-form over seconds, but the origin of these coherent motions is unknown. Here, we investigate the connection between genomic motions and gene-level activity. Simultaneous mapping of single-gene and genome-wide motions shows that the coupling of gene transcriptional activity to flows of the nearby genome is modulated by chromatin compaction. A motion correlation analysis suggests that a single active gene drives larger-scale motions in low-compaction regions, but high-compaction chromatin drives gene motion regardless of its activity state. By revealing unexpected connections among gene activity, spatial heterogeneities of chromatin and its emergent genome-wide motions, these findings uncover aspects of the genome’s spatiotemporal organization that directly impact gene regulation and expression. The human genome is dynamic across all scales, from motions of single genes to the entire genome. Here, using live cell imaging, the authors study the connection between large-scale genomic motions and the transcriptional activity of single genes.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".