The proteomic landscape of extracellular vesicles derived from human intervertebral disc cells
Bibliographic record
Abstract
Background: Extracellular vesicles (EVs) function as biomarkers and are crucial in cell communication and regulation, with therapeutic potential for intervertebral disc (IVD)-related low back pain (LBP). EV cargo is often affected by tissue health, which may affect the therapeutic potential. There is currently limited knowledge of how the cargo of IVD cell-derived EVs varies with tissue health and how differences in proteomic profile affect the predicted biological functions. Methods: Our study purified EVs from human IVD cell conditioned media by size-exclusion chromatography. Nanoparticle tracking analysis was conducted to measure EV size and concentration. Transmission electron microscopy and Western blot were performed to examine EV structure and markers. Tandem mass tag-mass spectrometry was conducted to determine protein cargo. Results: Most EVs were exosomes and intermediate microvesicles with an increasing amount linked to disease progression. Of the proteins detected, 88.6% were shared across the non-degenerate, mildly-degenerate, and degenerate samples. GO and KEGG analyses revealed that cargo from the mildly-degenerate samples was the most distinct, with the proteins in high abundance strongly associated with extracellular matrix (ECM) organization and structure. Shared proteins, highly expressed in the non-degenerate and degenerate samples, showed strong associations with cell adhesion, ECM-receptor interaction, and vesicle-mediated transport, respectively. Conclusions: Our findings indicate that EVs from IVD cells from tissue with different degrees of degeneration share a majority of the cargo proteins. However, the level of expression differs with degeneration grade. Cargo from the mildly-degenerate samples exhibits the most differences. A better understanding of changes in EV cargo in the degenerative process may provide novel information related to molecular mechanisms underlying IVD degeneration and suggest new potential treatment modalities for IVD-related LBP.
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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 | 0.001 | 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".