Replicative senescence-associated gene expression changes in mesenchymal stromal cells are similar under different culture conditions
Bibliographic record
Abstract
BACKGROUND: Research on mesenchymal stromal cells has created high expectations for a variety of therapeutic applications. Extensive propagation to yield enough mesenchymal stromal cells for therapy may result in replicative senescence and thus hamper long-term functionality in vivo. Highly variable proliferation rates of mesenchymal stromal cells in the course of long-term expansions under varying culture conditions may already indicate different propensity for cellular senescence. We hypothesized that senescence-associated regulated genes differ in mesenchymal stromal cells propagated under different culture conditions. DESIGN AND METHODS: Human bone marrow-derived mesenchymal stromal cells were cultured either by serial passaging or by a two-step protocol in three different growth conditions. Culture media were supplemented with either fetal bovine serum in varying concentrations or pooled human platelet lysate. RESULTS: All mesenchymal stromal cell preparations revealed significant gene expression changes upon long-term culture. Especially genes involved in cell differentiation, apoptosis and cell death were up-regulated, whereas genes involved in mitosis and proliferation were down-regulated. Furthermore, overlapping senescence-associated gene expression changes were found in all mesenchymal stromal cell preparations. CONCLUSIONS: Long-term cell growth induced similar gene expression changes in mesenchymal stromal cells independently of isolation and expansion conditions. In advance of therapeutic application, this panel of genes might offer a feasible approach to assessing mesenchymal stromal cell quality with regard to the state of replicative senescence.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".