Comparative analysis of plasma‐derived extracellular vesicles isolation methods in the context of neurodegenerative diseases
Bibliographic record
Abstract
BACKGROUND: Extracellular vesicles (EVs), particularly exosomes derived from plasma, are emerging as valuable tools in the search for non-invasive biomarkers for early detection and monitoring of neurodegenerative diseases, such as Alzheimer's disease. However, isolating EVs from blood is challenging due to its complex protein content. Optimizing isolation methods is crucial to maximize yield and purity, improving biomarker detection and clinical translation. Thus, this study aims to compare EVs isolation techniques to determine the most efficient approach for detecting neurodegenerative biomarkers using the Simoa HD-X platform. METHOD: Plasma samples were collected from 4 healthy volunteers aged 20-40 years. EVs were isolated using different methods: super centrifugation, precipitation (ExoQuick ULTRA), size-exclusion chromatography (SEC - Izon 70 nm) and SmartSEC, which combines SEC with an affinity-based mechanism to capture protein impurities. To characterize the EVs and compare the isolation techniques, nanoparticle tracking analysis (NTA) was used to evaluate the concentration and size, while transmission electron microscopy (TEM) assessed morphology. The protein content of EVs was quantified using the Bradford assay. Immunoassays for EV markers and the ultrasensitive immunoassay Simoa HD-X for neurodegenerative biomarkers in EV cargo, such as neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP), were performed. RESULT: particles/mL). TEM showed that SEC provided a high yield of particles and a greater size heterogeneity (100-1000 nm), while ExoQuick ULTRA was more pure by enriching specifically exosomes (30-150 nm). However, higher purity methods did not yield detectable biomarker levels when used with standard volumes of plasma, so additional biomarkers are being assessed. CONCLUSION: All methods are effective for isolating plasma-derived EVs, but ExoQuick ULTRA and SEC demonstrated superior performance and are now being further evaluated for biomarker detection. These findings contribute to identifying efficient EV isolation methods, supporting advances in blood biomarkers for neurodegenerative diseases.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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 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".