Trapping Single Molecules in Liposomes: Surface Interactions and Freeze−Thaw Effects
Bibliographic record
Abstract
We report on an improved method to encapsulate proteins and other macromolecules inside surface-tethered liposomes to reduce or eliminate environmental interference for single-molecule investigations. These lipid vesicles are large enough for the molecule to experience free diffusion but sufficiently small so that the molecule appears effectively immobile under the fluorescence microscope. Single-molecule fluorescence experiments were used to characterize this anchoring method relative to direct immobilization via biotin-streptavidin linkers. Multidimensional histograms of intensity, polarization, and lifetime revealed that molecules trapped in liposomes display a narrow distribution around a single peak, while the molecules directly immobilized on surface show highly dispersed values for all parameters. By hydrating the lipid film at low volumes, high encapsulation efficiencies can be achieved with ~10 times less biological material than previous protocols. We measured vesicle size distributions and found no significant advantage for using freeze-thaw cycles during vesicle preparation. On the contrary, the temperature jump can induce irreversible damage of fluorophores and it reduces significantly the functionality of proteins, as demonstrated on single-molecule binding experiments on STAT3. Our improved and biologically gentle molecule encapsulation protocol has a great potential for widespread applications in single-molecule fluorescence spectroscopy.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".