Lateral diffusion of CD14 and TLR2 in macrophage plasma membrane assessed by raster image correlation spectroscopy and single particle tracking
Bibliographic record
Abstract
The diffusion of membrane receptors is central to many biological processes, such as signal transduction, molecule translocation, and ion transport, among others; consequently, several advanced fluorescence microscopy techniques have been developed to measure membrane receptor mobility within live cells. The membrane-anchored receptor cluster of differentiation 14 (CD14) and the transmembrane toll-like receptor 2 (TLR2) are important receptors in the plasma membrane of macrophages that activate the intracellular signaling cascade in response to pathogenic stimuli. The aim of the present work was to compare the diffusion coefficients of CD14 and TLR2 on the apical and basal membranes of macrophages using two fluorescence-based methods: raster image correlation spectroscopy (RICS) and single particle tracking (SPT). In the basal membrane, the diffusion coefficients obtained from SPT and RICS were found to be comparable and revealed significantly faster diffusion of CD14 compared with TLR2. In addition, RICS showed that the diffusion of both receptors was significantly faster in the apical membrane than in the basal membrane, suggesting diffusion hindrance by the adhesion of the cells to the substrate. This finding highlights the importance of selecting the appropriate membrane (i.e., basal or apical) and corresponding method when measuring receptor diffusion in live cells. Accurately knowing the diffusion coefficient of two macrophage receptors involved in the response to pathogen insults will facilitate the study of changes that occur in signaling in these cells as a result of aging and disease.
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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) | 0.000 | 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 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".