Development of a silver nanoparticle embedded membrane platform for highly sensitive and multiplexed detection of extracellular vesicle proteins
Bibliographic record
Abstract
Abstract Extracellular vesicles (EVs) are membrane bound nanoscale particles released by cells that contain molecular cargo reflective of their parental cell and can be found in many biofluids. The overexpression of EVs and EV-related protein markers has been linked to various diseased states, making them a promising tool for liquid biopsy-based disease diagnostics. Many complex diseases, like cancer, impact multiple markers simultaneously, and during early stages, are present at low concentrations. Current EV analysis technology is limited in sensitivity, multiplexing, and ease of use. We have developed a silver nanoparticle embedded membrane (sNEM) platform that utilizes the 3D structure of nitrocellulose membrane, metal-enhanced fluorescence (MEF)-based detection and a novel wax-based compartmentalization technique for highly sensitive multiplex EV protein detection from minimal sample volume. We compared various nanoparticle shapes, sizes, and metal types with fluorophores of different wavelengths to determine which provided optimal MEF-based detection with high sensitivity. Fluorescence intensity from FITC was much lower than that from Cy5 and was found to pronounce the effects of autofluorescence by 2 times. After selecting 30 nm silver nanoparticles at a concentration of 10 9 particles ml −1 and the Cy5 fluorophore based on greatest fluorescence enhancement, we then demonstrated its application for multiplexed detection of surface and intravesicular proteins directly from lysed EVs in both buffer and human plasma. In PBS, detection limits of 2–3 orders of magnitude lower than traditional ELISA were achieved. Directly from human plasma, detection limits of 1.97 × 10 5 EVs ml −1 , 1.94 × 10 6 EVs ml −1 , and 2.17 × 10 4 EVs ml −1 for TGF- β 1, AKT1, and TSG101 were achieved. These results demonstrate the suitability of sNEM for highly sensitive, multiplexed detection of EV markers from complex biofluids for early diagnostics while offering advantages such as low reagent/sample consumption, scalability, reduced sample preparation, and ease of use.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".