Single-molecule tracking of membrane molecules: plasma membrane compartmentalization and dynamic assembly of raft-philic signaling molecules

Kusumi, Akihiro; Ike, H.; Nakada, Chieko; Murase, Kotono; Fujiwara, Takeshi

doi:10.1016/j.smim.2004.09.004

Cited by 213 publications

(194 citation statements)

References 163 publications

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“…Note, however, that the solvent and lipid subsystems may move relative to one another, such that the overall CoM drift is negligible, and that this FIGURE 3 Dynamic viscosity found in simulation (red points) agrees with the theoretical prediction (blue line). SRD parameters are a ¼ 2.0 nm, t C ¼ 20 fs, and N ¼ 1/nm 3 . To see this figure in color, go online.…”

Section: Center-of-mass Driftmentioning

confidence: 99%

“…In live cells, single-particle tracking (1)(2)(3) and fluorescence correlation spectroscopy (FCS) with subdiffraction detection volumes (achieved by stimulated emission depletion microscopy)(4) have revealed the plasma membrane to be heterogeneous on tens of nanometers, with correspondingly heterogeneous dynamics. Taken together, these results suggest a hierarchical membrane organization, with the cytoskeleton influencing transport above 80 nm lengthscales, (5) and lipid-protein interactions operating below this lengthscale (6).…”

Section: Introductionmentioning

confidence: 99%

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Toward Hydrodynamics with Solvent Free Lipid Models: STRD Martini

Zgorski

Lyman

2016

Biophysical Journal

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Solvent hydrodynamics are incorporated into simulations of the solvent-free Dry Martini model. The solvent hydrodynamics are modeled with the stochastic rotation dynamics (SRD) algorithm, a particle-based method for resolving fluid hydrodynamics. SRD does not require calculation of particle-particle distances in the solvent, and so is scalable to arbitrary volumes of solvent with minimal additional computational overhead. The viscosity of the solvent is easily tuned via parameters of the algorithm to span an order of magnitude in viscosity around the viscosity of water at room temperature. The combination ''Stochastic Thermostatted Rotation Dynamics (STRD) with Martini'' was implemented in Gromacs v.5.01. Simulations of an SRD/palmitoyloleoylphosphatidylcholine membrane demonstrate that the solvent may be included without reparametrizing the lipid model, with minimal perturbation to the thermodynamics. A recent generalization of Saffman-Delbruck theory to periodic geometries by Camley and Brown indicates that lipid dynamics are contaminated by a finite-size effect in typical molecular dynamics (MD) simulations, and that very large systems are required for quantitative simulation of dynamics. Analysis of lipid translational diffusion in this work shows good agreement with the theory, and with explicitly solvated simulations. This indicates that STRD Martini is a viable approach for quantitative simulation of membrane dynamics and does not require massive computational overhead to model the solvent.

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Section: Center-of-mass Driftmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Toward Hydrodynamics with Solvent Free Lipid Models: STRD Martini

Zgorski

Lyman

2016

Biophysical Journal

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“…This implies that additional molecules enriched in GEM contribute to the association. It is known that Src family proteins are located in GEM [49] with CD44 enrichment in GEM [21] possibly being due to its constitutive association with lck [24]. CD49d ligation induces FAK phosphorylation, which creates docking sites for SH2 and SH3 domain containing proteins [50].…”

Section: Cd44 Associates With Cd49d In Gemmentioning

confidence: 99%

In vivo CD44‐CD49d complex formation in autoimmune disease has consequences on T cell activation and apoptosis resistance

2006

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CD44 is involved in leukocyte migration and activation and has recently been reported to contribute to leukocyte extravasation by associating with CD49d. We explored whether similar changes in CD44 activity are seen in vivo using murine alopecia areata (AA) as a chronic, organ‐related autoimmune disease model system. Expression of the activated, hyaluronan‐binding form of CD44, and of CD49d, was elevated in draining lymph node cells (LNC) of AA‐affected mice as compared to control mice. LNC of AA mice displayed increased motility, proliferative activity and apoptosis resistance, which were equally well inhibited by anti‐CD44 and anti‐CD49d. The latter is the sequelae of the association between CD44 and CD49d that is seen in activated lymphocytes. Significantly, due to CD44‐CD49d complex formation, CD44 gains access to focal adhesion kinase and CD49d gains access to CD44‐associated lck and ezrin, such that downstream kinases become activated via CD44 or CD49d engagement. Thus, by their association, CD44 and CD49d mutually avail themselves of the partner's signaling pathways and the ligand binding of each one triggers signaling pathways of both. This strongly influences the lymphocytes’ activation state and function.

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“…A great deal of information about motion of molecules in living cells has been obtained from intracellular measurements using different experimental techniques 13,[16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35] and from simulations 14,[36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51][52][53] . Experimental data are usually obtained by fluorescence recovery after photobleaching (FRAP) and fluorescence correlation spectroscopy (FCS) techniques.…”

Section: Introductionmentioning

confidence: 99%

New insights into diffusion in 3D crowded media by Monte Carlo simulations: effect of size, mobility and spatial distribution of obstacles

Vilaseca

Isvoran

Madurga

et al. 2011

Phys. Chem. Chem. Phys.

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Abstract. Particle diffusion in crowded media was studied through Monte Carlo simulations in 3D obstructed lattices. Three particular aspects affecting the diffusion, not extensively treated in three-dimensional geometry, were analysed: the relative particle-obstacle size, the relative particle-obstacle mobility and the way of having the obstacles distributed in the simulation space (randomly or uniformly). The results are interpreted in terms of the parameters that characterize the time dependence of the diffusion coefficient: the anomalous diffusion exponent (a), the crossover time from anomalous to normal diffusion regimes (τ) and the long time diffusion coefficient (D*). Simulation results indicate that there is a more anomalous diffusion (smaller a) and lower long time diffusion coefficient (D*) when obstacle concentration increases, and that, for a given total excluded volume and immobile obstacles, the anomalous diffusion effect is less important for bigger size obstacles. However, for the case of mobile obstacles, this size effect is inverted yielding values that are in qualitatively good agreement with in vitro experiments of protein diffusion in crowded media.These results underline that the pattern of the spatial partitioning of the obstacleexcluded volume is a factor to be considered together with the value of the excluded volume itself.

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Single-molecule tracking of membrane molecules: plasma membrane compartmentalization and dynamic assembly of raft-philic signaling molecules

Cited by 213 publications

References 163 publications

Toward Hydrodynamics with Solvent Free Lipid Models: STRD Martini

Toward Hydrodynamics with Solvent Free Lipid Models: STRD Martini

In vivo CD44‐CD49d complex formation in autoimmune disease has consequences on T cell activation and apoptosis resistance

New insights into diffusion in 3D crowded media by Monte Carlo simulations: effect of size, mobility and spatial distribution of obstacles

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