Extracellular Vesicle Antibody Microarray for Multiplexed Inner and Outer Protein Analysis
Bibliographic record
Abstract
Proteins are found both outside and inside of extracellular vesicles (EVs) and govern the properties and functions of EVs, while also constituting a signature of the cell of origin and of biological function and disease. Outer proteins on EVs can be directly bound by antibodies to either enrich EVs, or probe the expression of a protein on EVs, including in a combinatorial manner. However, co-profiling of inner proteins remains challenging. Here, we present the high-throughput, multiplexed analysis of EV inner and outer proteins (EVPio). We describe the optimization of fixation and heat-induced protein epitope retrieval for EVs, along with oligo-barcoded antibodies and branched DNA signal amplification for sensitive, multiplexed, and high-throughput assays. We captured four subpopulations of EVs from colorectal cancer (CRC) cell lines HT29 and SW403 based on EpCAM, CD9, CD63, and CD81 expression, and quantified the co-expression of eight outer [integrins (ITGs) and tetraspanins] and four inner (heat shock, endosomal, and inner leaflet) proteins. The differences in co-expression patterns were consistent with the literature and known biological function. In conclusion, EVPio analysis can simultaneously detect multiple inner and outer proteins in EVs immobilized on a surface, opening the way to extensive combinatorial protein profiles for both discovery and clinical translation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".