In-Cell Labeling Coupled to Direct Analysis of Extracellular Vesicles in the Conditioned Medium to Study Extracellular Vesicles Secretion with Minimum Sample Processing and Particle Loss
Bibliographic record
Abstract
Extracellular vesicles (EVs) are involved in a multitude of physiological functions and play important roles in health and disease. The largest proportion of studies on EVs is based on the analysis and characterization of EVs secreted in the cell culture medium. These studies remain challenging due to the small size of the EV particles, a lack of universal EV markers, and sample loss or technical artifacts that are often associated with EV labeling for single particle tracking and/or separation techniques. To address these problems, we characterized and validated a method for in-cell EV labeling with fluorescent lipids coupled with direct analysis of lipid-labeled EVs in the conditioned medium by imaging flow cytometry (IFC). This approach significantly reduces sample processing and loss compared to established methods for EV separation and labeling in vitro, resulting in improved detection of quantitative changes in EV secretion and subpopulations compared to protocols that rely on EV separation by size-exclusion chromatography and ultracentrifugation. Our optimized protocol for in-cell EV labeling and analysis of the conditioned medium reduces EV sample processing and loss, and is well-suited for cell biology studies that focus on modulation of EV secretion by cells in culture.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".