Flow cytometric quantitation of EpCAM‐positive extracellular vesicles by immunomagnetic separation and phospholipid staining method
Bibliographic record
Abstract
Extracellular vesicles (EV) have attracted attention as circulating biomarkers for many diseases, particularly cancer. Conventional immunofluorescence staining has been used for the detection of target antigens on EV by flow cytometry. However, the staining intensity depends on the amount of antigen expressed on the vesicles and is often only around the noise level. Instead of immunofluorescence, we combined immunomagnetic separation using nanosize MACS® MicroBeads with phospholipid staining of EV (IMS-PS method). EpCAM-positive EV were prepared from the culture supernatants of OVCAR3 (EpCAM-high), A431 (EpCAM-low) or Colon-26 (non-human control) cells as cancer models and were examined by the IMS-PS method using EpCAM mAb-coated MicroBeads. By employing Polaric-500c6F as the dye for staining EV phospholipids and using appropriate flow cytometry settings, autofluorescence was excluded, whereas pretreatment of the MicroBeads with conventional blocking agents reduced nonspecific binding to non-target vesicles. These modifications resulted in a linear relation between the number of EV detected and the sample volume, regardless of the level of EpCAM expression on the vesicles. A431 EV spiked into healthy volunteer plasma were enumerated with good accuracy. The IMS-PS method may be useful for clinical evaluation of EV with low levels of antigen expression that are difficult to detect by conventional immunofluorescence.
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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 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".