The proteome of extracellular vesicles released by clastic cells differs based on their substrate
Bibliographic record
Abstract
Extracellular vesicles (EVs) from osteoclasts are important regulators in intercellular communication. Here, we investigated the proteome of EVs from clastic cells plated on plastic (clasts), bone (osteoclasts) and dentin (odontoclasts) by two-dimensional high performance liquid chromatography mass spectrometry seeking differences attributable to distinct mineralized matrices. A total of 1,952 proteins were identified. Of the 500 most abundant proteins in EVs, osteoclast and odontoclast EVs were 83.3% identical, while clasts shared 70.7% of the proteins with osteoclasts and 74.2% of proteins with odontoclasts. For each protein, the differences between the total ion count values were mapped to an expression ratio histogram (Z-score) in order to detect proteins differentially expressed. Stabilin-1 and macrophage mannose receptor-1 were significantly-enriched in EVs from odontoclasts compared with osteoclasts (Z = 2.45, Z = 3.34) and clasts (Z = 13.86, Z = 1.81) and were abundant in odontoclast EVs. Numerous less abundant proteins were differentially-enriched. Subunits of known protein complexes were abundant in clastic EVs, and were present at levels consistent with them being in assembled protein complexes. These included the proteasome, COP1, COP9, the T complex and a novel sub-complex of vacuolar H+-ATPase (V-ATPase), which included the (pro) renin receptor. The (pro) renin receptor was immunoprecipitated using an anti-E-subunit antibody from detergent-solubilized EVs, supporting the idea that the V-ATPase subunits present were in the same protein complex. We conclude that the protein composition of EVs released by clastic cells changes based on the substrate. Clastic EVs are enriched in various protein complexes including a previously undescribed V-ATPase sub-complex.
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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 | 0.001 | 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".