Use of inert gas jets to measure the forces required for mechanical gene transfection
Bibliographic record
Abstract
BACKGROUND: Transferring genes and drugs into cells is central to how we now study, identify and treat diseases. Several non-viral gene therapy methods that rely on the mechanical disruption of the plasma membrane have been proposed, but the success of these methods has been limited due to a lack of understanding of the mechanical parameters that lead to cell membrane permeability. METHODS: We use a simple jet of inert gas to induce local transfection of plasmid DNA both in vitro (HeLa cells) and in vivo (chicken chorioallantoic membrane). Five different capillary tube inner diameters and three different gases were used to treat the cells to understand the dependency of transfection efficiency on the dynamic parameters. RESULTS: The simple setup has the advantage of allowing us to calculate the forces acting on cells during transfection. We found permeabilization efficiency was related to the dynamic pressure of the jet. The range of dynamic pressures that led to transfection in HeLa cells was small (200 ± 20 Pa) above which cell stripping occurred. We determined that the temporary pores allow the passage of dextran up to 40 kDa and reclose in less than 5 seconds after treatment. The optimized parameters were also successfully tested in vivo using the chorioallantoic membrane of the chick embryo. CONCLUSIONS: The results show that the number of cells transfected with the plasmid scales with the dynamic pressure of the jet. Our results show that mechanical methods have a very small window in which cells are permeabilized without injury (200 to 290 Pa). This simple apparatus helps define the forces needed for physical cell transfection methods.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".