Interaction toxine-cellule étudiée par imagerie de nanoémetteurs individuels

The cellular membrane is a vital part of the cell, which plays a crucial role in many cellular processes, such as, signaling and trafficking, and pathologies. This thesis aims to investigate the architecture of the cell membrane. The study uses the motion of two membrane receptors that are exploited by bacterial toxins to probe the architecture. Advances in light microscopy techniques have shown that many membrane receptors do not diffuse freely in the membrane, but undergo confined or anomalous diffusion. Currently a few models compete to explain the confinement of the receptors, such as the Picket-Fence model, lipid rafts and protein aggregates. To investigate the membrane, lanthanide doped nanoparticles (Y0.6Eu0.4VO4) are coupled to two different peptidic pore-forming toxins, the α-toxin of C. septicum and the ǫ-toxin of C. perfingens. Single molecule tracking of receptor bound labeled toxins in the apical membrane of MDCK cells in a wide-field microscope reveals the receptor motion with sub-diffraction resolution of down to 10 nm. The α & ǫ-toxin receptors both undergo confined diffusion with similar diffusion coefficients of 0.16 ± 0.14 µm2/s in temporaly stable domains of 0.5 µm2. To analyze the receptor trajectories, we intro- duced a novel approach based on an inference method. Our only assumption is that the receptor moves according to the Langevin equation of motion. This method exploits the information of the ensemble of the trajectory and the quality of the extracted values is verified through simulations. Both receptors are confined in a spring-like potential with a spring constant of 0.45 pN/µm. Tracking after cholesterol depletion by cholesterol ox- idase and cytoskeleton depolymerization by Latrunculin B, shows that confinement of single receptors is cholesterol dependent and actin depolymerization does not influence the confinement. Using the nanoparticle labels as a hydrodynamic force amplifier in a liquid flow, tests the response of the receptor to an external force and indicates attach- ment of the confining domains to the cytoskeleton. Finally, a model for the confinement of the receptor is proposed, based on the hydrophobic coupling of the receptor and the surrounding bilayer which can explain the spring-like potential of the confining domain.La membrane cellulaire est une partie vitale de la cellule dont l'architecture joue un rˆole crucial dans de nombreux processus cellulaires, comme la signalisation et le trafic, et dans diverses pathologies. Cette th'ese vise 'a sonder l'architecture membranaire via le mouvement de deux r'ecepteurs membranaires qui sont exploit'es par des toxines bact'eriennes. Les progr'es r'ecents des techniques de microscopie optique ont montr'e que certains r'ecepteurs membranaires ne diffusent pas librement dans la membrane, mais sont confin'es ou diffusent de fa¸con anomale. Actuellement, plusieurs mod'eles con- courent pour expliquer le confinement des r'ecepteurs, tel que le mod'e le Picket-Fence, les radeaux lipidiques et les agr'egats de prot'eines. Pour sonder la membrane, des nanoparticules (Y0.6Eu0.4VO4) dop'ees avec des lan- thanides sont coupl'ees 'a deux toxines peptidiques diff'erentes formant des pores dans la membrane, la toxine α de C. septicum et la toxine ǫ de C. perfingens. Le suivi de r'ecepteurs individuels sur lesquels sont fix'ees des toxines marqu'ees dans la mem- brane apicale de cellules MDCK avec un microscope 'a champ large permet de d'etecter le mouvement du r'ecepteur avec une r'esolution meilleure que la limite de diffrac- tion. Les r'ecepteurs de la toxine α et ǫ montrent une diffusion confin'ee avec des coefficients de diffusion similaires de 0.16 ± 0.14 µm2/s dans des domaines stables de 0.5 µm2. Pour analyser les trajectoires des r'ecepteurs, nous avons mis en oeuvre une nouvelle technique bas'ee sur une m'ethode d'inf'erence. Notre seule hypoth'ese est que le r'ecepteur se d'eplace selon l''equation de Langevin. Cette m'ethode exploite l'ensemble de l'information stock'ee dans la trajectoire et la qualit'e des valeurs extraites est v'erifi'ee par des simulations. Les deux r'ecepteurs sont confin'es dans un potentiel de type ressort avec une raideur de 0.45 pN/µm. Des exp'eriences apr'es d'epl'etion du cholest'erol par la cholest'erol oxydase et apr'es la d'epolym'erisation du cytosquelette par latrunculin B montrent que le confinement des r'ecepteurs individuels d'epend du taux de cholest'erol et que la d'epolym'erisation de l'actine n'influence pas le confinement. En utilisant la nanoparticule coupl'ee aux toxines comme un amplificateur de la force hydrodynamique applique'e par un flux liquide, nous avons mesur'e la r'eponse du r'ecepteur 'a une force ext'erieure. Les r'esultats indiquent une fixation des domaines de confinement sur le cy- tosquelette. Enfin, un mod'ele pour le confinement du r'ecepteur est propos'e, bas'e sur le couplage hydrophobe entre le r'ecepteur et la bicouche lipidique qui l'entoure. Ce mod'ele permet d'expliquer le potentiel de type ressort 'a l'int'erieur du domaine de confinement

Bayesian decision tree for the classification of the mode of motion in single-molecule trajectories.

Membrane proteins move in heterogeneous environments with spatially (sometimes temporally) varying friction and with biochemical interactions with various partners. It is important to reliably distinguish different modes of motion to improve our knowledge of the membrane architecture and to understand the nature of interactions between membrane proteins and their environments. Here, we present an analysis technique for single molecule tracking (SMT) trajectories that can determine the preferred model of motion that best matches observed trajectories. The method is based on Bayesian inference to calculate the posteriori probability of an observed trajectory according to a certain model. Information theory criteria, such as the Bayesian information criterion (BIC), the Akaike information criterion (AIC), and modified AIC (AICc), are used to select the preferred model. The considered group of models includes free Brownian motion, and confined motion in 2nd or 4th order potentials. We determine the best information criteria for classifying trajectories. We tested its limits through simulations matching large sets of experimental conditions and we built a decision tree. This decision tree first uses the BIC to distinguish between free Brownian motion and confined motion. In a second step, it classifies the confining potential further using the AIC. We apply the method to experimental Clostridium Perfingens [Formula: see text]-toxin (CP[Formula: see text]T) receptor trajectories to show that these receptors are confined by a spring-like potential. An adaptation of this technique was applied on a sliding window in the temporal dimension along the trajectory. We applied this adaptation to experimental CP[Formula: see text]T trajectories that lose confinement due to disaggregation of confining domains. This new technique adds another dimension to the discussion of SMT data. The mode of motion of a receptor might hold more biologically relevant information than the diffusion coefficient or domain size and may be a better tool to classify and compare different SMT experiments

Procédé et dispositif d'analyse d'interactions moléculaires et utilisations

L'invention se rapporte à un procédé d'analyse d'une interaction entre une première molécule et une deuxième molécule liée à une particule, comprenant les étapes suivantes : - mettre en contact la première molécule et la deuxième molécule liée à la particule dans des conditions rendant possible leur interaction, - appliquer un flux liquide déterminé sur la particule liée à la deuxième molécule, - observer un déplacement de la particule liée à la deuxième molécule sous l'action du flux appliqué, - analyser l'interaction en fonction du déplacement observé et du flux appliqué, la particule ayant une résistance hydrodynamique supérieure à celle de la première et/ou de la deuxième molécule, et un nombre de Péclet massique supérieur à 1 L'invention se rapporte également à un dispositif d'analyse d'une interaction entre une première molécule et au moins une deuxième molécule, ainsi qu'à l'utilisation du procédé ou du dispositif dans un criblage d'une molécule candidate pour le développement d'un médicament

Classification of the mode of motion along a trajectory.

<p>(A) We apply the first part of the decision tree in Fig. 1 to single numerical trajectories, which switch from being confined by a spring potential (red) to free Brownian motion (blue). (Parameters: , , , ). (B) shows the result of using the BIC criterion along the numerical trajectory shown in (A). We use a window of 51 frames that slides along the trajectory, and a classification is made for each central frame of the window. The method can correctly identify confinement (red). (C) Low-pass filtering the classifications gives a very robust method to determine the mode of motion of a trajectory that changes. (D) shows the performance of the BIC along the 500 frames of 50 numeric trajectories. The input mode is shown by the black dotted line, which is at first confined and switches to free Brownian motion at frame 250. The blue histogram shows the number of free Brownian classifications at a certain central frame. The red histogram shows the number of spring-potential confined classifications. (E) Classification along a CPT receptor trajectory, while confinement is reduced due to a modification of the cell membrane.</p

Building the decision tree using information criteria from simulated trajectories.

<p>The 2D plots show the heat map of the percentage of correct decisions out of 300 simulated trajectories per square for the BIC (first row), AIC (middle row), and AICc (bottom row). The input trajectories were free Brownian (left column), Brownian confined in a 2nd order spring potential (middle column), and Brownian confined in a 4th order potential (right column). The heat map has a threshold of 0.5, which means that only cases where the information criterion works correctly more than half of the time are non-black as indicated by the color scale. The BIC is the better criterion to determine if a trajectory is undergoing purely Brownian motion or if is confined by a potential (red box & red arm in decision tree in Fig. 1). The BIC is not suited to distinguish between a 2nd and 4th order potential. Here, the AIC and AICc provide a solution (blue box & blue arm in decision tree in Fig. 1).</p

Information criteria for simulated Brownian trajectories confined in a spring-like potential (<i>V</i> = 1/2<i>kr</i>²).

<p>To determine the performance of the decision criteria, we calculated the BIC (black), AIC (blue) and AICc (red) for trajectories under various conditions. (A) Percentage of correct decisions (300 trajectories per point) versus the length of the trajectory (Parameters: The BIC outperforms the AIC and AICc. (B) Percentage of correct decisions versus the input diffusion coefficient (Parameters: points, The BIC outperforms the AIC and AICc and works down to a diffusion coefficient of (C) Percentage of correct decisions versus acquisition time (Parameters: points, ). The BIC outperforms the AIC and AICc and works for acquisition times between 1 ms and 1000 ms. (D) Percentage of correct decisions versus input spring constant (Parameters: points, ). The BIC outperforms the AIC and AICc.</p

Comparison to the residence time method.

<p>We apply the first part of the decision tree in Fig. 1 to single numerical trajectories, which cycle between confinement by a spring potential and free Brownian motion. (Parameters: , , , & ). Receptors are confined three times for 200 frames (10 s). (A) Histogram of the correctly found confinement zones out of 15 zones for the Bayesian decision tree method (blue) and the residence time method (red) for two spring constants. We use a window of 51 frames that slides along the trajectory, and a classification is made for each central frame of the window. (B) shows a histogram of non-existing found confinement zones for the Bayesian decision tree method (blue) and the residence time method (red) for two spring constants. (C & F) One of the five input trajectories with three confining zones (red) with a spring constant of and , respectively. (D & G) Result using the Bayesian decision tree method with free brownian motion in blue and confined motion in red. (E & H) Result using the residence time method with free brownian motion in blue and confined motion in red.</p

Classification of experimental Clostridium Perfingens -toxin (CPT) receptor trajectories.

<p>We apply the decision tree in Fig. 1 to 60 experimental trajectories with a length of 500 frames. First we use the BIC to determine that the trajectories are confined (red insert). 59 trajectories were found to be confined while one trajectory was attributed to free Brownian motion. The AICc shows that the CPT receptors are confined in a 2nd order potential , which is in agreement with previous results <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0082799#pone.0082799-Trkcan2" target="_blank">[11]</a>.</p