DMEM, or Opti-MEM, that is the Question: An Important Consideration for Extracellular Vesicle Isolation and their Downstream Applications
Bibliographic record
Abstract
Abstract Serum-free synthetic media are frequently used as an alternative to extracellular vesicle-depleted serum containing media (EV-DEP) for EV production. However, the impact of this medium on EV biogenesis, release, and composition remains poorly understood. Here, we comprehensively characterised EV release by MDA-MB-231 or HEK-293T cells cultured in EV-DEP DMEM versus a serum-free synthetic medium, Opti-MEM. In Opti-MEM culture, cells released significantly more CD9- and CD63-positive EVs compared to EV-DEP DMEM. Proteomic analysis revealed that EVs from EV-DEP DMEM contained more histones and bovine proteins. Cells cultured in Opti-MEM released a substantially higher proportion of small-EVs derived from the sphingomyelinase-MVB pathway (60%) compared to EV-DEP DMEM (30%). Conversely, cells cultured in EV-DEP DMEM were more reliant upon the ROCK kinase pathway to release ectosomes (45 %) compared to OptiMEM (15 %). Where cells cultured in EV-DEP DMEM employed RabGTPase-dependent mechanisms for MVB-derived EV release (Rab3d, Rab27a, and Rab27b), cells cultured in Opti-MEM did not. Given that cells cultured in Opti-MEM vs EV-DEP DMEM produce EVs with different protein signatures from distinct molecular pathways, the choice of medium should be carefully considered when designing EV studies – particularly if they are to be used in therapeutic or immunological experiments.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".