Influence of passage number on the impact of the secretome of adipose tissue stem cells on neural survival, neurodifferentiation and axonal growth
Bibliographic record
Abstract
Mesenchymal stem cells (MSCs), and within them adipose tissue derived stem cells (ASCs), have been shown to have therapeutic effects on central nervous system (CNS) cell populations. Such effects have been mostly attributed to soluble factors, as well as vesicles, present in their secretome. Yet, little is known about the impact that MSC passaging might have in the secretion therapeutic profile. Our aim was to show how human ASCs (hASCs) passage number influences the effect of their secretome in neuronal survival, differentiation and axonal growth. For this purpose, post-natal rat hippocampal primary cultures, human neural progenitor cell (hNPCs) cultures and dorsal root ganglia (DRGs) explants were incubated with secretome, collected as conditioned media (CM), obtained from hASCs in P3, P6, P9 and P12. Results showed no differences when comparing percentages of MAP-2 positive cells (a mature neuronal marker) in neuronal cultures or hNPCs, after incubation with hASCs secretome from different passages. The same was observed regarding DRG neurite outgrowth. In order to characterize the secretomes obtained from different passages, a proteomic analysis was performed, revealing that its composition did not vary significantly with passage number P3 to P12. Results allowed us to identify several key proteins, such as pigment epithelium derived factor (PEDF), DJ-1, interleucin-6 (IL-6) and galectin, all of which have already proven to play neuroprotective and neurodifferentiating roles. Proteins that promote neurite outgrowth were also found present, such as semaphorin 7A and glypican-1. We conclude that cellular passaging does not influence significantly hASCs's secretome properties especially their ability to support post-natal neuronal survival, induce neurodifferentiation and promote axonal growth.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".