Measuring Diffusion of Lipid-like Probes in Artificial and Natural Membranes by Raster Image Correlation Spectroscopy (RICS): Use of a Commercial Laser-Scanning Microscope with Analog Detection
Bibliographic record
Abstract
The heterogeneity in composition and interaction within the cellular membrane translates into a wide range of diffusion coefficients of its constituents. Therefore, several complementary microfluorimetric techniques such as fluorescence correlation spectroscopy (FCS), fluorescence recovery after photobleaching (FRAP) and single-particle tracking (SPT) have to be applied to explore the dynamics of membrane components. The recently introduced raster image correlation spectroscopy (RICS) offers a much wider dynamic range than each of these methods separately and allows for spatial mapping of the dynamic properties. RICS is implemented on a confocal laser-scanning microscope (CLSM), and the wide dynamic range is achieved by exploiting the inherent time information carried by the scanning laser beam in the generation of the confocal images. The original introduction of RICS used two-photon excitation and photon counting detection. However, most CLSM systems are based on one-photon excitation with analog detection. Here we report on the performance of such a commercial CLSM (Zeiss LSM 510 META) in the study of the diffusion of the fluorescent lipid analog 1,1'-dioctadecyl-3,3,3',3'-tetramethyl-indodicarbocyanine perchlorate (DiI-C(18)(5)) both in giant unilamellar vesicles and in the plasma membrane of living oligodendrocytes, i.e., the myelin-producing cells of the central nervous system. It is shown that RICS on a commercial CLSM with analog detection allows for reliable results in the study of membrane diffusion by removal of unwanted correlations introduced by the analog detection system. The results obtained compare well with those collected by FRAP and FCS.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 teacher head, 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".