Measurement of protein transport and confinement in heterogeneous membranes by k-space image correlation spectroscopy
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».