Fast spatiotemporal correlation spectroscopy to determine protein lateral diffusion laws in live cell membranes

Abstract

Spatial distribution and dynamics of plasma-membrane proteins are thought to be modulated by lipid composition and by the underlying cytoskeleton, which forms transient barriers to diffusion. So far this idea was probed by single-particle tracking of membrane components in which gold particles or antibodies were used to individually monitor the molecules of interest. Unfortunately, the relatively large particles needed for single-particle tracking can in principle alter the very dynamics under study. Here, we use a method that makes it possible to investigate plasmamembrane proteins by means of small molecular labels, specifically single GFP constructs. First, fast imaging of the region of interest on the membrane is performed. For each time delay in the resulting stack of images the average spatial correlation function is calculated. We show that by fitting the series of correlation functions, the actual protein "diffusion law" can be obtained directly from imaging, in the form of a mean-square displacement vs. time-delay plot, with no need for interpretative models. This approach is tested with several simulated 2D diffusion conditions and in live Chinese hamster ovary cells with a GFP-tagged transmembrane transferrin receptor, a well-known benchmark of membrane-skeleton-dependent transiently confined diffusion. This approach does not require extraction of the individual trajectories and can be used also with dim and dense molecules. We argue that it represents a powerful tool for the determination of kinetic and thermodynamic parameters over very wide spatial and temporal scales.fluorescence | protein dynamics | membrane heterogeneity | transient confinement | single molecule T he plasma-membrane "fluid mosaic" model was proposed in a seminal work by Singer and Nicholson in 1972 (1). Since then, an intense research effort has led to significant advancements; current models include the notion that membranes are crowded environments (2) with a complex topology and that they interact with the cytoskeleton and contain microdomains of different size and lipid/protein composition (3-5). Using electron tomography, the structure of the membrane skeleton on the cytoplasmic face of the plasma membrane was clarified (6). It was found that virtually all of the cytoplasmic surface is covered by the meshwork of the actin-based membrane skeleton, and that the latter is closely associated with the membrane (within 0.83 nm). Because transmembrane proteins protrude into the cytoplasm, their intracellular domains may collide with these actin filaments that can act as "fences," inducing temporary confinement of the protein within mesh domains. Transmembrane proteins are assumed to hop between these domains whenever there is space between the membrane and the actin filament owing to membrane structural fluctuations and/or when the actin filament temporarily dissociates (for further details see ref. 7). Overall, these phenomena are master regulators of specific molecular interactions involved, for instance, in cellular signaling (...

Segmentation of 3D Trajectories Acquired by TSUNAMI Microscope: An Application to EGFR Trafficking

“…S8 for discussion on various types of confined diffusion). Although the exact expression of MSD depends on the shape of the confining region and on spatial dimensionality, the two-dimensional (2D) MSD of confined diffusion can be approximated by (39,69) MSD 2D ðtÞy L 2 3…”

“…where D micro is the short-term ''microscopic'' diffusion coefficient (39,69). This intradomain diffusivity D micro is measured using the same method as described in Brownian diffusion section.…”

“…Whereas different confinement models and simulations have been carried out by other groups (34,37,39,100) and us (Fig. S8), it is currently difficult to rule out one model (e.g., cage diffusion (69)) from other models (e.g., hop diffusion (100)) when explaining the mechanisms behind the observed confined diffusion.…”

“…As a wealth of information about membrane structure, interior organization, and receptor biology can be derived from the long 3D trajectories acquired by TSUNAMI, a sophisticated tool is needed to segment and classify these trajectories according to their motional modes (34)(35)(36)(37), extract physical parameters of the motion (30,38), and correlate that motion to the surrounding environment (39), all with the goal of understanding the physical scenarios behind the observed motion (40,41). Considerable effort has been devoted to the identification of change points in motion (36) or diffusivity (38) along the same trajectory and to the visualization of spatial regions with different dynamic behaviors (34,35,38,42).…”

“…In this study, we demonstrate an integrated trajectory analysis tool that adopts a combination of analytical strategies (34)(35)(36)(37)39) and classification parameters (MSD scaling exponent (53)(54)(55)(56), directional persistence (36,57), and confinement index (37,58)) in achieving effective segmentation and classification of both simulated and real-world trajectories. Our algorithm has achieved 81% accuracy in classifying segments along simulated 3D trajectories.…”