Effect of sample preprocessing and extraction methods on the physical and molecular profiles of extracellular vesicles
Bibliographic record
Abstract
ABSTRACT Extracellular vesicles (EVs) are nanometric lipid vesicles that shuttle cargo between cells. Their analysis could shed light on health and disease conditions, but EVs must first be preserved, extracted and often pre-concentrated. Here we firstly compare plasma preservation agents, and secondly, using both plasma and cell supernatant, four EV-extraction methods including (i) ultracen-trifugation (UC), (ii) size exclusion chromatography (SEC), (iii) centrifugal filtration (LoDF), and (iv) accousto-sorting (AcS). We benchmarked them by characterizing integrity, size-distribution, concentration, purity and the expression profiles for nine proteins of EVs, as well as overall throughput, time-to-result and cost. We found that the difference between EDTA and citrate anticoagulants vary with the extraction method. In our hands, ultracentrifugation produced a high yield of EVs with low contamination; SEC is low-cost, fast, and easy to implement, but the purity of EVs is lower; LoDF and AcS are both compatible with process automation, small volume requirement, and rapid processing times. When using plasma, the LoDF was susceptible to clogging and sample contamination, while the AcS featured high purity but a lower yield of extraction. Analysis of protein profiles suggest that extraction methods extract different sub-population of EVs. Our study highlights the strength and weakness of sample preprocessing methods, and the variability in concentration, purity, and EV expression profiles of the extracted EVs. Pre-analytical parameters such as collection or pre-processing protocols must be considered as part of the entire process in order to address EV diversity and their use as clinically actionable indicators.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".