Bridging the Gap between Single Molecule and Ensemble Methods for Measuring Lateral Dynamics in the Plasma Membrane
Bibliographic record
Abstract
The lateral dynamics of proteins and lipids in the mammalian plasma membrane are heterogeneous likely reflecting both a complex molecular organization and interactions with other macromolecules that reside outside the plane of the membrane. Several methods are commonly used for characterizing the lateral dynamics of lipids and proteins. These experimental and data analysis methods differ in equipment requirements, labeling complexities, and further oftentimes give different results. It would therefore be very convenient to have a single method that is flexible in the choice of fluorescent label and labeling densities from single molecules to ensemble measurements, that can be performed on a conventional wide-field microscope, and that is suitable for fast and accurate analysis. In this work we show that k-space image correlation spectroscopy (kICS) analysis, a technique which was originally developed for analyzing lateral dynamics in samples that are labeled at high densities, can also be used for fast and accurate analysis of single molecule density data of lipids and proteins labeled with quantum dots (QDs). We have further used kICS to investigate the effect of the label size and by comparing the results for a biotinylated lipid labeled at high densities with Atto647N-strepatvidin (sAv) or sparse densities with sAv-QDs. In this latter case, we see that the recovered diffusion rate is two-fold greater for the same lipid and in the same cell-type when labeled with Atto647N-sAv as compared to sAv-QDs. This data demonstrates that kICS can be used for analysis of single molecule data and furthermore can bridge between samples with a labeling densities ranging from single molecule to ensemble level measurements.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".