Differential responses to aging amongst the transcriptome and proteome of mesenchymal progenitor populations
Bibliographic record
Abstract
The biological aging of mesenchymal stem cells is proposed to contribute to the development of a range of musculoskeletal and systemic diseases associated with older adults, such as osteoporosis, sarcopenia, and frailty. Despite this, little is understood about the specific mechanisms which drive this stem cell exhaustion, with most studies evaluating indirect effects of other aging changes, such as DNA damage, senescence, and inflammaging. In this study, we assess the transcriptomic and proteomic changes in three different populations of mesenchymal progenitor cells from older (50-70 years) and younger (20-40 years) individuals to uncover potential mechanisms driving stem cell exhaustion in mesenchymal tissues. To do this, we harvested primary bone marrow mesenchymal stem and progenitor cells (MPCs), circulating osteoprogenitors (COP), and adipose-derived stem cells (ADSCs) from younger and older donors, with an equal number of samples from males and females. These samples underwent RNA sequencing and label-free proteomic analysis, comparing the younger samples to the older ones. There was a distinct transcriptomic phenotype associated with the pooled older stem cells, indicative of suppressed proliferation and differentiation; however, there was no consistent change in the proteome of the cells. Older MPCs had a distinct phenotype in both the transcriptome and proteome, again consistent with altered differentiation and proliferation, but also a pro-inflammatory immune shift in older adults. COP cells showed a strong transcriptomic shift to pro-inflammatory signaling but no consistent proteomic phenotype. Similarly, ADSCs displayed transcriptomic shift in physiologies associated with cell migration, adherence, and immune activation, but no consistent proteomic change with age. These results show that there are underlying transcriptomic changes with stem cell aging that likely contribute to a decline in tissue regeneration; however, contextual factors such as the microenvironment and general health status also have a strong role in this.
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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) | 0.001 | 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".