Human multipotent mesenchymal stromal cells cytokine priming promotes RAB27B-regulated secretion of small extracellular vesicles with immunomodulatory cargo
Bibliographic record
Abstract
BACKGROUND: The paracrine effects of multipotent mesenchymal stromal cells (MSCs) are mediated by their secretome composed by soluble factors (i.e., cytokines, growth factors, hormones) and extracellular vesicles (EVs). EVs promote intercellular communication, and the EV cargoes [e.g., proteins, soluble factors, microRNAs (miRNAs), messenger RNA (mRNA), DNA] reflect the molecular and functional characteristics of their parental cells. MSC-derived EVs (MSC-EVs) are currently evaluated as subcellular therapeutics. A key function of the MSC secretome is its ability to promote immune tolerance (i.e., immunopotency), a property that is enhanced by priming approaches (e.g., cytokines, hypoxia, chemicals) and inversely correlates with the age of the MSC donors. We evaluated mechanisms underlying MSC vesiculation and the effects of inflammation and aging on this process. METHODS: Profiler PCR Array), (b) EV profiles (Nanoparticle Tracking Analysis and Nanoparticle Flow Cytometry), (c) EV cargo (proteomic analysis and Western blot analysis), and (d) immunopotency (standard MSC:CD4 T cell proliferation inhibition assay). We confirmed the role of RAB27B on MSC vesiculation (RAB27B siRNA) and assessed its differential contribution to vesiculation in adult and pediatric MSCs (qPCR). RESULTS: Cytokine priming upregulated RAB27B in adipose-derived MSCs increasing their secretion of exosome-like small EVs (sEVs; < 200 nm) containing two key mediators of immunopotency: A20 and TSG-6. These EVs inhibited T cell proliferation in a dose-dependent manner. RAB27B siRNA inhibited MSC vesiculation. Adipose-derived MSCs isolated from pediatric donors exhibited higher RAB27B expression and secreted more sEVs than adult MSCs. CONCLUSIONS: Cytokine priming is a useful strategy to harvest anti-inflammatory MSC-sEVs for clinical applications. Of relevance, donor age should be considered in the selection of MSC-sEVs for clinical applications.
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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 | 0.001 |
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".