Exploiting Conjugated Polyelectrolyte Photophysics toward Monitoring Real-Time Lipid Membrane-Surface Interaction Dynamics at the Single-Particle Level
Bibliographic record
Abstract
Herein we report the real-time observation of the interaction dynamics between cationic liposomes flowing in solution and a surface-immobilized charged scaffolding formed by the deposition of conjugated polyanion poly[5-methoxy-2-(3-sulfopropoxy)-1,4-phenylenevinylene (MPS-PPV) onto 100-nm-diameter SiO2 nanoparticles (NPs). Contact of the freely floating liposomes with the polymer-coated surfaces led to the formation of supported lipid bilayers (SLBs). The interaction of the incoming liposomes with MPS-PPV adsorbed on individual SiO2 nanoparticles promoted the deaggregation of the polymer conformation and led to large emission intensity enhancements. Single-particle total internal reflection fluorescence microscopy studies exploited this phenomenon as a way to monitor the deformation dynamics of liposomes on surface-immobilized NPs. The MPS-PPV emission enhancement (up to 25-fold) reflected on the extent of membrane contact with the surface of the NP and was correlated with the size of the incoming liposome. The time required for the MPS-PPV emission to reach a maximum (ranging from 400 to 1000 ms) revealed the dynamics of membrane deformation and was also correlated with the liposome size. Cryo-TEM experiments complemented these results by yielding a structural view of the process. Immediately following the mixing of liposomes and NPs the majority of NPs had one or more adsorbed liposomes, yet the presence of a fully formed SLB was rare. Prolonged incubation of liposomes and NPs showed completely formed SLBs on all of the NPs, confirming that the liposomes eventually ruptured to form SLBs. We foresee that the single-particle studies we report herein may be readily extended to study membrane dynamics of other lipids including cellular membranes in live cell studies and to monitor the formation of polymer-cushioned SLBs.
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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 | 0.001 |
| 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".