Interphase Chromosomes in Replicative Senescence: Chromosome Positioning as a Senescence Biomarker and the Lack of Nuclear Motor-Driven Chromosome Repositioning in Senescent Cells
Bibliographic record
Abstract
This study demonstrates, and confirms, that chromosome territory positioning is altered in primary senescent human dermal fibroblasts (HDFs). The chromosome territory positioning pattern is very similar to that found in HDFs made quiescent either by serum starvation or confluence; but not completely. A few chromosomes are found in different locations. One chromosome in particular stands out, chromosome 10, which is located in an intermediate location in young proliferating HDFs, but is found at the nuclear periphery in quiescent cells and in an opposing location of the nuclear interior in senescent HDFs. We have previously demonstrated that individual chromosome territories can be actively and rapidly relocated, with 15 min, after removal of serum from the culture media. These chromosome relocations require nuclear motor activity through the presence of nuclear myosin 1β (NM1β). We now also demonstrate rapid chromosome movement in HDFs after heat-shock at 42°C. Others have shown that heat shock genes are actively relocated using nuclear motor protein activity via actin or NM1β (Khanna et al., 2014; Pradhan et al., 2020). However, this current study reveals, that in senescent HDFs, chromosomes can no longer be relocated to expected nuclear locations upon these two types of stimuli. This coincides with a entirely different organisation and distribution of NM1β within senescent HDFs.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".