Senescence during early differentiation reduced the chondrogenic differentiation capacity of mesenchymal progenitor cells
Bibliographic record
Abstract
Introduction: Mesenchymal stromal/progenitor cells (MSCs) are promising for cartilage cell-based therapies due to their chondrogenic differentiation capacity. However, MSCs can become senescent during in vitro expansion, a state characterized by stable cell cycle arrest, metabolic alterations, and substantial changes in the gene expression and secretory profile of the cell. In this study, we aimed to investigate how senescence and the senescence-associated secretory phenotype (SASP) affect chondrogenic differentiation of MSCs. Methods: To study the effect of senescence, we exposed MSCs to gamma irradiation during expansion or during chondrogenic differentiation (the pellet culture). Western blot analysis was used to evaluate MSCs response to the chondrogenic inductor TGF-β. Results: When senescence was induced during expansion or at day 7 of chondrogenic differentiation, we observed a significant reduction in the cartilage matrix. Interestingly, when senescence was induced at day 14 of differentiation, chondrogenesis was not significantly altered. Moreover, exposing chondrogenic pellets to the medium conditioned by senescent pellets had no significant effect on the expression of anabolic or catabolic cartilage markers, suggesting a neglectable paracrine effect of senescence on cartilage generation in our model. Finally, we show that senescent MSCs showed lower phosphorylated SMAD2 levels after TGFβ1 stimulation than control MSCs. Conclusion: Overall, these results suggest that the occurrence of senescence in MSCs during expansion or early differentiation could be detrimental for cartilage tissue engineering.
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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 | 0.001 |
| 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".