Quantifying extracellular vesicle heterogeneity: the effect of process conditions on protein cargo for skin therapy
Bibliographic record
Abstract
Extracellular vesicles (EVs) contain a variety of proteins with anti-inflammatory and immunomodulatory properties that offer promising benefits in skin therapy applications. An influx of EV proteomic studies in recent years has created the opportunity for a detailed comparison of EV heterogeneity between studies in the context of therapeutic applications. Although several process conditions are known to cause variability in EVs, little has been done to quantify the impact of these factors on the nature of EV protein cargo. This review aims to both compile publicly available EV proteomics data and quantitatively estimate. the impact of process conditions on protein cargo-particularly in the context of skin therapy applications. Of roughly 400 articles, 52 relevant proteomic studies were identified within the last 15 years. Across studies, 40% of the 13,000 observed proteins were identified in only a single study. EVs in general were found to be highly variable, with mixed effects models only able to account for 25-60% of variance when considering factors such as EV source, medium, isolation method, LC-MS ionization, and protein search algorithm. Overall, MSC-derived EVs contained a greater fraction of proteins within pathways associated with wound healing and skin therapy (immune system, hemostasis, extracellular matrix organization, and cellular response to stress) as well as the most number of unique proteins when compared to all other analysed EVs. Although EVs are a promising tool within skin therapeutics, the overall variability in protein cargo underscores the need for standardized methodologies to fully elucidate the impact of process conditions on EV cargo.
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How this classification was reachedexpand
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 teacher head, 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".