Quantitative Capillary Electrophoresis for Analysis of Extracellular Vesicles (EVqCE)
Bibliographic record
Abstract
Extracellular Vesicles (EVs) gained significant interest within the last decade as a new source of biomarkers for the early detection of diseases and a promising tool for therapeutic applications. In this work, we present Extracellular Vesicles Quantitative Capillary Electrophoresis (EVqCE) to measure an average mass of RNA in EVs, determine EV concentrations and the degree of EV degradation after sample handling. We used EVqCE to analyze EVs isolated from conditioned media of three cancer cell lines. EVqCE employs capillary zone electrophoresis with laser-induced fluorescent detection to separate intact EVs from free nucleic acids. After lysis of EVs with a detergent, the encapsulated nucleic acids are released. Therefore, the initial concentration of intact EVs is calculated based on a nucleic acid peak gain. EVqCE works in a dynamic range of EV concentrations from 108 to 1010 particles/mL. The quantification process can be completed in less than one hour and requires minimum optimization. Furthermore, the average mass of RNA was found to be in the range of 200–400 ag per particle, noting that more aggressive cancer cells have less RNA in EVs (200 ag per particle) than non-aggressive cancer cells (350 ag per particle). EVqCE works well for the degradation analysis of EVs. Sonication for 10 min at 40 kHz caused 85% degradation of EVs, 10 freeze-thaw cycles (from −80 °C to 22 °C) produced 40%, 14-day storage at 4 °C made 32%, and vortexing for 5 min caused 5% degradation. Presently, EVqCE cannot separate and distinguish individual EV populations (exosomes, microvesicles, apoptotic bodies) from each other. Still, it is tolerant to the presence of non-EV particles, protein-lipid complexes, and protein aggregates.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".