Characterization of fluorescent probe partitioning in giant unilamellar vesicles of “lipid raft” mixtures using confocal fluorescence microscopy
Bibliographic record
Abstract
Direct visualization of raft‐like liquid ordered (l o ) domains requires fluorescence probes with known partitioning preference for a specific lipid phase. Therefore, a detailed understanding of the behaviour of commonly used fluorescent probes in defined lipid bilayer systems is needed before they can be used to draw conclusions about the physical state of the membrane. Using giant unilamellar vesicles composed of a ternary “lipid raft” mixture (DOPC/DPPC/cholesterol) for which the phase behaviour is known, we examined nine commonly used fluorescent probes using confocal fluorescence microscopy. The partitioning preference of each probe was assigned using either a well‐characterized l d phase marker, or by quantitation of the domain area fraction. Fluorescent molecules were examined individually, in pairs, and in threes; most of them partitioned into the l d phase, while two probes preferred the l o phase or gel phase. Interestingly, the partitioning of DiIC 18 was influenced by Bodipy‐PC, and Lissamine rhodamine B‐DPPE affected the partitioning preference of other fluorescence probes when used with them. We compare and contrast the different fluorescence probes in terms of their partitioning preference, ability to detect phase separations, photostability, and any induced change in lipid miscibility transition temperature. Supported by the Natural Sciences and Engineering Research Council of Canada
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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 | 0.001 |
| 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) | 0.001 | 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".