Quantitative proteomic analysis of trypsin‐treated extracellular vesicles to identify the real‐vesicular proteins
Bibliographic record
Abstract
Extracellular vesicles (EVs) are nano-sized vesicles surrounded by a lipid bilayer and released into the extracellular milieu by most of cells. Although various EV isolation methods have been established, most of the current methods isolate EVs with contaminated non-vesicular proteins. By applying the label-free quantitative proteomic analyses of human colon cancer cell SW480-derived EVs, we identified trypsin-sensitive and trypsin-resistant vesicular proteins. Further systems biology and protein-protein interaction network analyses based on their cellular localization, we classified the trypsin-sensitive and trypsin-resistant vesicular proteins into two subgroups: 363 candidate real-vesicular proteins and 151 contaminated non-vesicular proteins. Moreover, the protein interaction network analyses showed that candidate real-vesicular proteins are mainly derived from plasma membrane (46.8%), cytosol (36.6%), cytoskeleton (8.0%) and extracellular region (2.5%). On the other hand, most of the contaminated non-vesicular proteins derived from nucleus, Golgi apparatus, endoplasmic reticulum and mitochondria. In addition, ribosomal protein complexes and T-complex proteins were classified as the contaminated non-vesicular proteins. Taken together, our trypsin-digested proteomic approach on EVs is an important advance to identify the real-vesicular proteins that could help to understand EV biogenesis and protein cargo-sorting mechanism during EV release, to identify more reliable EV diagnostic marker proteins, and to decode pathophysiological roles 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".