Quantitative proteomic analysis of trypsin‐treated extracellular vesicles to identify the real‐vesicular proteins
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it