A novel single-cell method provides direct evidence of persistent DNA damage in senescent cells and aged mammalian tissues
Bibliographic record
Abstract
The DNA damage response (DDR) arrests cell cycle progression until DNA lesions, like DNA double-strand breaks (DSBs), are repaired. The presence of DSBs in cells is usually detected by indirect techniques that rely on the accumulation of proteins at DSBs, as part of the DDR. Such detection may be biased, as some factors and their modifications may not reflect physical DNA damage. The dependency on DDR markers of DSB detection tools has left questions unanswered. In particular, it is known that senescent cells display persistent DDR foci, that we and others have proposed to be persistent DSBs, resistant to endogenous DNA repair activities. Others have proposed that these peculiar DDR foci might not be sites of damaged DNA per se but instead stable chromatin modifications, termed DNA-SCARS. Here, we developed a method, named 'DNA damage in situ ligation followed by proximity ligation assay' (DI-PLA) for the detection and imaging of DSBs in cells. DI-PLA is based on the capture of free DNA ends in fixed cells in situ, by ligation to biotinylated double-stranded DNA oligonucleotides, which are next recognized by antibiotin anti-bodies. Detection is enhanced by PLA with a partner DDR marker at the DSB. We validated DI-PLA by demonstrating its ability to detect DSBs induced by various genotoxic insults in cultured cells and tissues. Most importantly, by DI-PLA, we demonstrated that both senescent cells in culture and tissues from aged mammals retain true unrepaired DSBs associated with DDR markers.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".