A comprehensive proteomics profiling identifies NRP1 as a novel identity marker of human bone marrow mesenchymal stromal cell-derived small extracellular vesicles
Bibliographic record
Abstract
BACKGROUND: Clinical applications have shown extracellular vesicles (EVs) to be a major paracrine effector in therapeutic responses produced by human mesenchymal stromal/stem cells (hMSCs). As the regenerative capacity of EVs is mainly ascribed to the transfer of proteins and RNA composing its cargo, and to the activity attributed by the protein surface markers, we sought to profile the protein composition of small EVs released from hMSCs to identify hMSC-EV biomarkers with potential clinical relevance. METHODS: Small EVs were produced and qualified from five human bone marrow MSC donors at low passage following a 48-h culture in exosome-depleted medium further processed by steps of centrifugation, filtration, and precipitation. Quantitative proteomic analysis comparing the protein profile of the EVs released from hMSCs and their parental cell was conducted using tandem mass tag labeling combined to mass spectrometry (LC-MS/MS) to identify enriched EV protein markers. RESULTS: /mL), with the mode particle size measuring at 109.3 ± 5.7 nm. Transmission electron microscopy confirmed the presence of nanovesicles with bilayer membranes. Flow cytometric analysis identified commonly found exosomal (CD63/CD81) and hMSC (CD105/CD44/CD146) markers from released EVs in addition to surface mediators of migration (CD29 and MCSP). Quantitative proteomic identified 270 proteins significantly enriched by at least twofold in EVs released from hMSCs as compared to parental hMSCs, where neuropilin 1 (NRP1) was identified among 21 membrane-bound proteins regulating the migration and invasion of cells, as well as chemotaxis and vasculogenesis. Validation by western blot of multiple batches of EVs confirmed consistent enrichment of NRP1 in the nanovesicles released from all five hMSC donors. CONCLUSION: The identification and verification of NRP1 as a novel enriched surface marker from multiple batches of EVs derived from multiple hMSC donors may serve as a biomarker for the assessment and measurement of EVs for therapeutic uses.
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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 | 0.001 | 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".