Donor Age of Human Platelet Lysate Affects Proliferation and Differentiation of Mesenchymal Stem Cells
Bibliographic record
Abstract
The regenerative potential declines upon aging. This might be due to cell-intrinsic changes in stem and progenitor cells or to influences by the microenvironment. Mesenchymal stem cells (MSC) raise high hopes in regenerative medicine. They are usually culture expanded in media with fetal calf serum (FCS) or other serum supplements such as human platelet lysate (HPL). In this study, we have analyzed the impact of HPL-donor age on culture expansion. 31 single donor derived HPLs (25 to 57 years old) were simultaneously compared for culture of MSC. Proliferation of MSC did not reveal a clear association with platelet counts of HPL donors or growth factors concentrations (PDGF-AB, TGF-β1, bFGF, or IGF-1), but it was significantly higher with HPLs from younger donors (<35 years) as compared to older donors (>45 years). Furthermore, HPLs from older donors increased activity of senescence-associated beta-galactosidase (SA-βgal). HPL-donor age did not affect the fibroblastoid colony-forming unit (CFU-f) frequency, immunophenotype or induction of adipogenic differentiation, whereas osteogenic differentiation was significantly lower with HPLs from older donors. Concentrations of various growth factors (PDGF-AB, TGF-β1, bFGF, IGF-1) or hormones (estradiol, parathormone, leptin, 1,25 vitamin D3) were not associated with HPL-donor age or MSC growth. Taken together, our data support the notion that aging is associated with systemic feedback mechanisms acting on stem and progenitor cells, and this is also relevant for serum supplements in cell culture: HPLs derived from younger donors facilitate enhanced expansion and more pronounced osteogenic differentiation.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".