Comparative Analysis of Plasma Extracellular Vesicle Isolation Methods for Purity Assessment and Biomarker Discovery
Bibliographic record
Abstract
BACKGROUND: Extracellular vesicles (EVs) are an important source of blood biomarkers and are emerging as next-generation therapeutics. Demonstrating the purity of isolated EVs is essential for applications ranging from proteomics-based biomarker discovery to biomanufacturing. In this study, we systematically evaluated multiple EV isolation methods for plasma and developed a scoring method to identify the approach best suited for proteomics. METHODS: Commonly used enrichment techniques, including size-exclusion chromatography (SEC) and precipitation-based methods, were compared against the starting plasma in terms of particle yield and size, proteomic overlap, depletion of abundant plasma proteins, and enrichment of EV markers and unique proteins. To enable rigorous purity assessment, we established a targeted parallel reaction monitoring (PRM) mass spectrometry assay that quantified key EV markers and contaminant proteins across preparations. RESULTS: Among the methods tested, SEC showed the greatest enrichment of EV markers and unique proteins, with the lowest level of contaminants, resulting in the highest overall purity scores. SEC also allowed for the detection of EV-free proteins. Other methods, by contrast, performed sub-optimally and were less reliable for proteomics-driven biomarker discovery. CONCLUSIONS: SEC provides the most EV-enriched plasma isolates for proteomics information, with minimal contamination from plasma proteins. The PRM-based purity scoring offers an objective means of benchmarking EV preparations and may help standardize EV isolation quality for both biomarker discovery and therapeutic manufacturing.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".