Membrane protein dynamics measured by two-photon ring correlation spectroscopy: theory and application to living cells
Bibliographic record
Abstract
Many biochemical reactions and processes are regulated by proteins associated with cellular membranes. Trans-membrane proteins play an important role in many aspects of cellular development, cellular migration and signaling, and many diseases. Quantitative measurement of protein dynamics under various experimental conditions can give insights into the mechanisms of interaction and the functionality of the protein. Fluctuation techniques, such as fluorescence correlation spectroscopy (FCS) and image correlation spectroscopy (ICS), have been used for such dynamic measurements in membranes. However, FCS is limited to fast dynamics, and ICS works best on a flat 2-dimensional area. We present an alternative way to measure protein transport in spherical (non flat) living cells that combines laser scanning microscopy and image correlation methods: ring correlation spectroscopy (RCS). The RCS analysis is performed on CLSM or two-photon cross-sectional images of labeled proteins in the cell membrane, where the optical sectioning gives a “ring” of fluorescence in the images. We present computer simulations of two dimensional diffusion confined to the surface of a spherical shell, where the RCS analysis can extract the set input parameters from the simulation. As well, we present RCS analysis of two-photon microscopy images of Pre-B leukocytes cells expressing CD44 labeled with EGFP.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".