Comparison of extracellular vesicle elution methods from aptamer-conjugated magnetic beads for downstream physical characterization and immunoassays
Bibliographic record
Abstract
Aptamer-based affinity chromatography is a promising approach for the efficient capture and gentle release of extracellular vesicles (EVs) with preserved EV integrity; however, to date, there is no direct comparison of EV elution methods from the aptamers and evaluation of their effects on downstream EV analysis. This study compares the efficacy of two simple-to-implement elution methods using deoxyribonuclease (DNase) I and NaCl in releasing EVs from anti-CD63-aptamer-conjugated magnetic beads, focusing on downstream physical and protein analysis of the EVs. We used anti-CD63-aptamer-functionalized magnetic beads to capture EVs from ovarian cancer cell line OVCAR-3 culture media, subsequently eluting them using NaCl (0.5 or 1.0 M) or DNase I (50 or 100 U/mL). Dynamic light scattering and transmission electron microscopy (TEM) were used to assess elution efficiency and EV morphology, respectively, and showed intact EVs after elution. Enzyme-linked immunosorbent assay (ELISA) quantified CD9 and EGFR protein expression in the eluted EVs. Our results indicated that NaCl and DNase I can effectively elute EVs, maintaining their structural integrity as observed by TEM. 100 U/mL DNase I provided the highest elution efficiency of 88%, followed closely by 1.0 M NaCl with a comparable efficiency of 87%. With ELISA targeting two EV surface proteins, CD9 and EGFR, EVs eluted by 1.0 M NaCl demonstrated the highest signals for both proteins. Due to the high elution efficiency and low cost of NaCl, it is more suitable for immunoassay-based downstream analysis. This comparative study demonstrated the importance of selecting an appropriate elution method to optimize EV yield and maintain protein activity for downstream applications.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".