Quality control and validation of extracellular vesicles isolated from cultured human breast cancer cells
Bibliographic record
Abstract
OBJECTIVE: Extracellular vesicles (EVs) have been shown to play a critical role in promoting tumorigenesis. As EV research grows, it is of importance to have standardization of isolation, quality control, characterization and validation methods across studies along with reliable references to explore troubleshooting solutions. Therefore, our objective with this Research Note was to isolate EVs from multiple breast cancer cell lines and to describe and perform protocols for validation as outlined by the list of minimal information for studies of EVs (MISEV) from the International Society for Extracellular Vesicles. RESULTS: To isolate EVs, two techniques were employed: ultracentrifugation and size exclusion chromatography. Ultracentrifugation yielded better recovery of EVs in our hands and was therefore used for further validation. In order to satisfy the MISEV requirements, protein quantification, immunoblotting of positive (CD9, CD63, TSG101) and negative (TGFβ1, β-tubulin) markers, nanoflow cytometry and electron microscopy was performed. With these experiments, we demonstrate that yield of validated EVs varied between different breast cancer cell lines. Protocols were optimized to accommodate for low levels of EVs, and various technical and troubleshooting suggestions are included for potential application to other cell types that may provide benefit to investigators interested in future EV studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".