Epigenetic Biomarker to Determine Replicative Senescence of Cultured Cells
Bibliographic record
Abstract
Somatic cells change continuously during culture expansion-long-term culture evokes increasing cell size, declining differentiation potential, and ultimate cell cycle arrest upon senescence. These changes are of particular relevance for cellular therapy which necessitates standardized products and reliable quality control. Recently, replicative senescence has been shown to be associated with highly reproducible epigenetic modifications. Here, we describe a simple method to track the state of senescence in mesenchymal stromal cells (MSCs) or fibroblasts by monitoring continuous DNA methylation (DNAm) changes at specific sites in the genome. Six CpG sites have been identified which reveal either linear hypermethylation or hypomethylation with respect to the number of cumulative population doublings (cPDs). Conversely, the DNAm level at these CpG sites can be analyzed-for example, by pyrosequencing of bisulfite-converted DNA-and then used for linear regression models to predict cPDs. Our method provides an epigenetic biomarker to determine the state of senescence in cell preparations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| 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".