Precipitation with polyethylene glycol followed by washing and pelleting by ultracentrifugation enriches extracellular vesicles from tissue culture supernatants in small and large scales
Bibliographic record
Abstract
Extracellular vesicles (EVs) provide a complex means of intercellular signalling between cells at local and distant sites, both within and between different organs. According to their cell-type specific signatures, EVs can function as a novel class of biomarkers for a variety of diseases, and can be used as drug-delivery vehicles. Furthermore, EVs from certain cell types exert beneficial effects in regenerative medicine and for immune modulation. Several techniques are available to harvest EVs from various body fluids or cell culture supernatants. Classically, differential centrifugation, density gradient centrifugation, size-exclusion chromatography and immunocapturing-based methods are used to harvest EVs from EV-containing liquids. Owing to limitations in the scalability of any of these methods, we designed and optimised a polyethylene glycol (PEG)-based precipitation method to enrich EVs from cell culture supernatants. We demonstrate the reproducibility and scalability of this method and compared its efficacy with more classical EV-harvesting methods. We show that washing of the PEG pellet and the re-precipitation by ultracentrifugation remove a huge proportion of PEG co-precipitated molecules such as bovine serum albumine (BSA). However, supported by the results of the size exclusion chromatography, which revealed a higher purity in terms of particles per milligram protein of the obtained EV samples, PEG-prepared EV samples most likely still contain a certain percentage of other non-EV associated molecules. Since PEG-enriched EVs revealed the same therapeutic activity in an ischemic stroke model than corresponding cells, it is unlikely that such co-purified molecules negatively affect the functional properties of obtained EV samples. In summary, maybe not being the purification method of choice if molecular profiling of pure EV samples is intended, the optimised PEG protocol is a scalable and reproducible method, which can easily be adopted by laboratories equipped with an ultracentrifuge to enrich for functional active 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".