Contaminating transfection complexes can masquerade as small extracellular vesicles and impair their delivery of RNA
Bibliographic record
Abstract
One of the functions of small extracellular vesicles (sEVs) which has received the most attention is their capacity to deliver RNA into the cytoplasm of target cells. These studies have often been performed by transfecting RNAs into sEV-producing cells, to later purify and study sEV delivery of RNA. Transfection complexes and other delivery vehicles accumulate in late endosomes where sEV are formed and over 50% of transfection complexes or delivery vehicles administered to cells are released again to the extracellular space by exocytosis. This raises the possibility that transfection complexes could alter sEVs and contaminate sEV preparations. We found that widely used transfection reagents including RNAiMax and INTERFERin accumulated in late endosomes. These transfection complexes had a size similar to sEV and were purified by ultracentrifugation like sEV. Focusing on the lipid-based transfection reagent RNAiMax, we found that preparations of sEV from transfected cells contained lipids from transfection complexes and transfected siRNA was predominantly in particles with the density of transfection complexes, rather than sEV. This suggests that transfection complexes, such as lipid-based RNAiMax, may frequently contaminate sEV preparations and could account for some reports of sEV-mediated delivery of nucleic acids. Transfection of cells also impaired the capacity of sEVs to deliver stably-expressed siRNAs, suggesting that transfection of cells may alter sEVs and prevent the study of their endogenous capacity to deliver RNA to target cells.
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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 | 0.002 |
| 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 | 0.000 |
| 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".