Measurement of protein transport and confinement in heterogeneous membranes by k-space image correlation spectroscopy
Bibliographic record
Abstract
This thesis presents the application of k-space Image Correlation Spec- troscopy (kICS) to the analysis of fluorescence microscopy image time series for the measurement of particle diffusion in heterogeneous membranes, composed of micro- domains. The extension, testing and application of kICS for such measurements is developed both in silico with simulation and with in vivo cellular experiments.Connections between kICS analysis and other existing fluorescent microscopy techniques used in the study of heterogeneous membranes, such as single particle tracking (SPT) and spot vary Fluorescence Correlation Spectroscopy (FCS) are introduced. This is followed by the development of kICS theory of fluorescent particle diffusion within a heterogeneous two dimensional (2D) environment. Two possible membrane heterogeneities, isolated lipid micro-domains and actin proximal meshwork, are considered separately. The emergent models suggest that the kICS correlation function (CF) can be fit by a sum of two Gaussians in the case of particle diffusion in the presence of isolated micro-domains. These two fit components, called 'fast' and 'slow', with the fast associated with the rapid decay of the kICS CF at small spatial frequencies due to particle motion on large spatial scales outside domains while the slow component refers to the confined particle motion on large spatial frequencies or small spatial scales in domains. On the other hand, the meshwork confinement is well fit with a single Gaussian model for the analysis of kICS CF. These models suggest that the exponents and amplitudes of the fits embed the characteristic system parameters such as diffusion coefficients outside and inside domains, the partitioning rates, micro-domains radii and mesh pore size.Furthermore, systematic simulations to study different confinement scenarios were conducted and the calculated kICS correlation functions were fit and the output interpreted for recovery of self system parameters. The characterization of the simulated data suggests that kICS CFs exhibit various confinement dependent features, such as decays due to effective slow and fast dynamics populations and effective domain sizes. The in silico characterization of different confinement scenarios, suggests a connection between the apparent measured confinement properties, and the set system defining parameters. We explore the range and limits where confinement effects can be detected and accurately measured by kICS analysis. Possible systematic errors in the values of the fit extracted parameters due to background noise is discussed with possible alternative solutions.Finally, we apply this extension of kICS to the heterogeneous membrane en- vironment to explore the confinement dynamics of GPI-GFP anchored proteins in the basal plasma membrane of COS-7 cells. We employ a novel labelling approach of GPI-GFP using anti-GFP-Alexa594 and image the protein in COS-7 cell mem- branes with TIRF microscopy. Cells were exposed to enzymatic treatments, using the Cholesterol Oxidase (COase) and Sphingomyelinase (SMase), in order to dis- rupt membrane domains and change GPI-GFP confinement dynamics. We observe that GPI-GFP mobility and the effective domain size measured correlates with the enzymatic exposure time. We attribute it to the conversion of the membrane domain constituents, cholesterol and sphingomyelin, upon the enzymatic reactions, leading to membrane domain that are effectively larger and leakier. Finally, we conclude with possible improvements and future directions.
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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 | 0.001 |
| 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".