Proof of principle: quality control of therapeutic cell preparations using senescence-associated DNA-methylation changes
Bibliographic record
Abstract
BACKGROUND: Tracking of replicative senescence is of fundamental relevance in cellular therapy. Cell preparations - such as mesenchymal stromal cells (MSCs) - undergo continuous changes during culture expansion, which is reflected by impaired proliferation and loss of differentiation potential. This process is associated with epigenetic modifications: during in vitro culture, cells acquire senescence-associated DNA methylation (SA-DNAm) changes at specific sites in the genome. We have recently described an Epigenetic-Senescence-Signature that facilitates prediction of the state of cellular aging by analysis of DNAm at six CpG sites (associated with the genes GRM7, CASR, PRAMEF2, SELP, CASP14 and KRTAP13-3), but this has not yet been proven over subsequent passages and with MSCs isolated under good manufacturing practice (GMP) conditions. FINDINGS: MSCs were isolated from human bone marrow and GMP-conform expanded for up to 11 passages. Cumulative population doublings (cPDs) and long-term growth curves were calculated based on cell numbers at each passage. Furthermore, 32 cryopreserved aliquots of these cell preparations were retrospectively analyzed using our Epigenetic-Senescence-Signature: DNAm-level was analyzed at six specific CpGs, and the results were used to estimate cPDs, time of culture expansion, and passage numbers. Overall, predicted and real parameters revealed a good correlation, particularly in cPDs. Based on predicted cPDs we could reconstruct long-term growth curves and demonstrated the continuous increase in replicative senescence on molecular level. CONCLUSION: Epigenetic analysis of specific CpG sites in the genome can be used to estimate the state of cellular aging for quality control of therapeutic cell products.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".