Extracellular vesicle depletion and UGCG overexpression mitigate the cell density effect in HEK293 cell culture transfection
Bibliographic record
Abstract
The hitherto unexplained reduction of cell-specific productivity in transient gene expression (TGE) at high cell density (HCD) is known as the cell density effect (CDE). It currently represents a major challenge in TGE-based bioprocess intensification. This phenomenon has been largely reported, but the molecular principles governing it are still unclear. The CDE is currently understood to be caused by the combination of an unknown inhibitory compound in the extracellular medium and an uncharacterized cellular change at HCD. This study investigates the role of extracellular vesicles (EVs) as extracellular inhibitors for transfection through the production of HIV-1 Gag virus-like particles (VLPs) via transient transfection in HEK293 cells. EV depletion from the extracellular medium restored transfection efficiency in conditions that suffer from the CDE, also enhancing VLP budding and improving production by 60%. Moreover, an alteration in endosomal formation was observed at HCD, sequestering polyplexes and preventing transfection. Overexpression of UDP-glucose ceramide glucosyltransferase (UGCG) enzyme removed intracellular polyplex sequestration, improving transfection efficiency. Combining EV depletion and UGCG overexpression improved transfection efficiency by ∼45% at 12 × 10 6 cells/mL. These results suggest that the interaction between polyplexes and extracellular and intracellular vesicles plays a crucial role in the CDE, providing insights for the development of strategies to mitigate its impact.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".