Lysis of Extracellular Vesicles and Multiplexed Protein Detection via a Reverse Phase Immunoassay Using a Gold-Nanoparticle-Embedded Membrane Platform
Bibliographic record
Abstract
Extracellular vesicles (EVs) are cell-derived membrane-bound particles with molecular cargo reflective of their cell of origin. Analysis of disease-related EVs and associated cargo from biofluids is a promising tool for disease management. To facilitate the analysis of intravesicular molecules, EV lysis is needed. Moreover, highly sensitive and multiplexed detection methods are required to achieve early diagnostics. While cell lysis approaches have been well studied, the analysis of EV lysis methods and their effects on downstream molecular detection is lacking. In this work, we analyzed chemical, thermal, and mechanical EV lysis methods and determined their efficiency based on EV particle concentration and immunoassay activity. We, for the first time, discovered that vortex was an efficient EV lysis method and used it for detection of surface and intravesicular markers in a highly sensitive multiplexed reverse phase immunoassay on a gold-nanoparticle-embedded membrane. In phosphate-buffered saline, detection limits up to 3 orders of magnitude lower than enzyme-linked immunosorbent assay were achieved. In spiked human plasma, detection limits as low as 7.27 × 10 4 EVs/mL were achieved, making it suitable for early diagnostics. These results demonstrated an effective pipeline for lysing and molecular analysis of EVs from complex biofluids, paving the way for their broad applications in biomedicine.
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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) | 0.001 | 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) | 0.000 | 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".