Fluorescence Resonance Energy Transfer to Detect Plasma Membrane Perturbations in Giant Plasma Membrane Vesicles
Bibliographic record
Abstract
Disruptions and perturbations of the cellular plasma membrane by peptides have garnered significant interest in the elucidation of biological phenomena. Typically, these complex processes are studied using liposomes as model membranes-either by encapsulating a fluorescent dye or by other spectroscopic approaches, such as nuclear magnetic resonance. Despite incorporating physiologically relevant lipids, no synthetic model truly recapitulates the full complexity and molecular diversity of the plasma membrane. Here, biologically representative membrane models, giant plasma membrane vesicles (GPMVs), are prepared from eukaryotic cells by inducing a budding event with a chemical stressor. The GPMVs are then isolated, and bilayers are labelled with fluorescent lipophilic tracers and incubated in a microplate with a membrane-active peptide. As the membranes become damaged and/or aggregate, the resulting fluorescence resonance energy transfer (FRET) between the two tracers increases and is measured periodically in a microplate. This approach offers a particularly useful way to detect perturbations when the membrane complexity is an important variable to consider. Additionally, it provides a way to kinetically detect damage to the plasma membrane, which can be correlated with the kinetics of peptide self-assembly or structural rearrangements. Key features • Allows testing of various peptide-membrane interaction conditions (peptide:phospholipid ratio, ionic strength, buffer, etc.) at once. • Uses intact plasma membrane vesicles that can be prepared from a variety of cell lines. • Can offer comparable throughput as with traditional synthetic lipid models (e.g., dye-encapsulated liposomes).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".