Probing Intranuclear Environments at the Single-Molecule Level
Bibliographic record
Abstract
Genome activity and nuclear metabolism clearly depend on accessibility, but it is not known whether and to what extent nuclear structures limit the mobility and access of individual molecules. We used fluorescently labeled streptavidin with a nuclear localization signal as an average-sized, inert protein to probe the nuclear environment. The protein was injected into the cytoplasm of mouse cells, and single molecules were tracked in the nucleus with high-speed fluorescence microscopy. We analyzed and compared the mobility of single streptavidin molecules in structurally and functionally distinct nuclear compartments of living cells. Our results indicated that all nuclear subcompartments were easily and similarly accessible for such an average-sized protein, and even condensed heterochromatin neither excluded single molecules nor impeded their passage. The only significant difference was a higher frequency of transient trappings in heterochromatin, which lasted only tens of milliseconds. The streptavidin molecules, however, did not accumulate in heterochromatin, suggesting comparatively less free volume. Interestingly, the nucleolus seemed to exclude streptavidin, as it did many other nuclear proteins, when visualized by conventional fluorescence microscopy. The tracking of single molecules, nonetheless, showed no evidence for repulsion at the border but relatively unimpeded passage through the nucleolus. These results clearly show that single-molecule tracking can provide novel insights into mobility of proteins in the nucleus that cannot be obtained by conventional fluorescence microscopy. Our results suggest that nuclear processes may not be regulated at the level of physical accessibility but rather by local concentration of reactants and availability of binding sites.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".