Extracellular vesicle microRNA and protein cargo profiling in three clinical-grade stem cell products reveals key functional pathways
Bibliographic record
Abstract
The cell origin-specific payloads within extracellular vesicles (EVs) mediate therapeutic bioactivity for a wide variety of stem cell types. In this study, we profiled the microRNA (miRNA) and protein cargos found within EVs produced by three clinical-grade stem cell products of different ontogenies being considered for clinical application, namely bone marrow-derived mesenchymal stromal cells (BM-MSCs), heart-derived cells (HDCs), and umbilical cord-derived MSCs (UC-MSCs). Although several miRNAs (757) and proteins (420) were found in common, each producer cell type expressed unique miRNA profiles when the most highly expressed transcripts were compared. Differential expression analysis revealed that BM-MSCs and HDCs were quite similar, while UC-MSCs had the greatest number of unique miRNAs and proteins. Despite these differences, all three EVs promoted cell adhesion/migration, immune response, platelet aggregation, protein translation/stabilization, and RNA processing. EVs from BM-MSCs were implicated in apoptosis, cell-cycle progression, collagen formation, heme pigment synthesis, and smooth muscle differentiation, while HDC and UC-MSC EVs were found to regulate complement activation, endopeptidase activity, and matrix metallopeptidases. Overall, miRNA and protein profiling reveal functional differences between three leading stem cell products. These findings provide a framework for mechanistic exploration of candidate therapeutic molecules driving the salutary effects of EVs.
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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".