Free flow electrophoresis allows quick and reproducible preparation of extracellular vesicles from conditioned cell culture media
Bibliographic record
Abstract
Aim: Despite intensive research during the last decade, it remains challenging to prepare extracellular vesicles (EVs) of high purity, especially from primary body liquids or protein-rich conditioned media. For now, time-consuming combinations of at least two orthogonal methods, e.g., density and size separation, are required to enrich EVs to high purity, often at the expense of processing time. Therefore, novel technologies are required that allow EV preparation in acceptable time intervals and to fair purities. Free-flow electrophoresis (FFE) constitutes a well-established semi-preparative method to separate and prepare analytes, e.g., by inherent differences in their electric charges. FFE combines a flow-driven longitudinal transport of sample material with vertical electrophoresis and allows the separation of sample components into up to 96 different fractions. It was our aim to evaluate the potential of FFE for the separation of EVs from other sample components of EV-containing protein-rich conditioned cell culture media. Methods: Exemplarily, conditioned media of mesenchymal stem/stromal cells raised in the presence of EV-containing 10% human platelet lysate were processed. We analyzed the obtained fractions by different technologies, including imaging flow cytometry, western blot and nanoparticle tracking analysis. Results: We demonstrate that FFE quickly and reproducibly separates EVs from a huge proportion of molecules included in the original sample. Conclusion: EVs.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".