Label‐Free Measurements of the Diffusivity of Molecules in Lipid Membranes
Bibliographic record
Abstract
An important and characteristic property of a cell membrane is the lateral mobility of protein molecules in the lipid bilayer. This has conventionally been measured by labeling the molecules with fluorescent markers and monitoring their mobility by different fluorescence-based techniques. However, adding the label to the studied molecule may affect the system, so it is an assumption in almost all experiments that the measured mobility of the biomolecule with its label is the same as that of the unlabeled molecule. However, this assumption is rarely tested due to a lack of suitable methods. In this work, a new technique to perform label-free diffusivity measurements is developed and used to measure the effect of the label for two common protein-lipid systems: 1) streptavidin (SA) coupled to a supported lipid bilayer (SLB) through biotinylated lipids and 2) the extracellular part of the T-cell adhesion protein CD2, coupled to an SLB through histidine tags to nickel-chelating lipids. A measurable (≈12%) decrease in diffusivity is found for both labeled proteins, even though the molecular mass of the label is almost 100 times smaller than those of the proteins (≈50 kDa). The results illustrate the importance of being able to study different biophysical properties of cell membranes and their mimics without relying on fluorescent labels, especially if fluorescent labeling is difficult or is expected to affect the nature of the intermolecular interactions being studied.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".