Extracellular Vesicle (EV) Dot Blotting for Multiplexed EV Protein Detection in Complex Biofluids
Bibliographic record
Abstract
Extracellular vesicles (EVs) are nanoscale vesicles secreted from cells, carrying biomolecular cargos similar to their cells of origin. Measuring the protein content of EVs in biofluids can offer a crucial insight into human health and disease. For example, detecting tumor-derived EVs’ protein markers can aid in early diagnosis of cancer, which is life-saving. In order to use these EV proteins for diagnosis, sensitive and multiplexed methods are required. The current methods for EV protein detection typically require large sample consumption due to challenges with sensitivity and often need an EV isolation step for complex biofluid samples such as blood plasma. In this work, we have developed a simple and sensitive method for multiplexed detection of protein markers on EV membrane surfaces, which we call “EV dot blotting”, inspired by conventional dot blotting techniques. After optimization of multiple factors such as antibody concentration, blocking reagent, type of 3D membranes, and use of gold nanoparticles for signal enhancement, cancer-cell-derived EVs were spiked in pooled normal human plasma for conducting a multiplexed assay in a microarray format. Without the need of isolating EVs from blood plasma, a limit of detection of 3.1 × 10 5 EVs/mL or 1863 EVs/sample was achieved for CD9 protein, 4.7 × 10 4 EVs/mL or 281 EVs/sample for CD24, and 9.0 × 10 4 EVs/mL or 538 EVs/sample for EpCAM, up to 4 orders of magnitude lower than those of conventional ELISA. This platform offers sensitive, multiplexed, simple, and low-cost EV protein detection directly from complex biofluids with minimal sample consumption, providing a useful tool for multiplexed EV protein quantification for a variety of 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".