Forming giant vesicles with controlled membrane composition, asymmetry, and contents
Bibliographic record
Abstract
Growing knowledge of the key molecular components involved in biological processes such as endocytosis, exocytosis, and motility has enabled direct testing of proposed mechanistic models by reconstitution. However, current techniques for building increasingly complex cellular structures and functions from purified components are limited in their ability to create conditions that emulate the physical and biochemical constraints of real cells. Here we present an integrated method for forming giant unilamellar vesicles with simultaneous control over (i) lipid composition and asymmetry, (ii) oriented membrane protein incorporation, and (iii) internal contents. As an application of this method, we constructed a synthetic system in which membrane proteins were delivered to the outside of giant vesicles, mimicking aspects of exocytosis. Using confocal fluorescence microscopy, we visualized small encapsulated vesicles docking and mixing membrane components with the giant vesicle membrane, resulting in exposure of previously encapsulated membrane proteins to the external environment. This method for creating giant vesicles can be used to test models of biological processes that depend on confined volume and complex membrane composition, and it may be useful in constructing functional systems for therapeutic and biomaterials applications.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".