Charged giant unilamellar vesicles prepared by electroformation exhibit nanotubes and transbilayer lipid asymmetry
Bibliographic record
Abstract
Giant unilamellar vesicles (GUVs) are increasingly used as a versatile research tool to investigate membrane structure, morphology and phase state. In these studies, GUV preparation is typically enhanced by an externally applied electric field, a process called electroformation. We find that upon osmotic deflation, GUVs electroformed from charged and neutral lipids exhibit inward pointing lipid nanotubes, suggesting negative spontaneous curvature of the membrane. By quenching a fluorescent analog of the charged lipid, zeta potential measurements and experiments with the lipid marker annexin A5, we show that electroformed GUVs exhibit an asymmetric lipid distribution across the bilayer leaflets. The asymmetry is lost either after storing electroformed GUVs at room temperature for one day or by applying higher voltages and temperatures during electroformation. GUVs having the same lipid composition but grown via gel-assisted swelling do not show asymmetric lipid distribution. We discuss possible mechanisms for the generation and relaxation of lipid asymmetry, as well as implications for studies using electroformed vesicles. The observed effects allow to control the molecular assembly of lipid bilayer leaflets. Vesicle tubulation as reported here is an example of protein-free reshaping of membranes and is caused by compositional lipid asymmetry between leaflets.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".