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 plasma-membrane 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.

Keywords: fluorescence; membrane heterogeneity; protein dynamics; single molecule; transient confinement.

Fig. 1.

From STICS to iMSD to study protein diffusion on membranes. STICS operation converts (A) a stack of intensity images into (B) a stack of images representing the spatiotemporal evolution of correlation . (C) When particle dynamics is governed only by diffusion, the maximum of correlation remains in the origin (peak projection on Cartesian axis). It is possible to approximate with a Gaussian function whose variance corresponds to the particles average iMSD. (D) Plot of iMSD vs. time may be used to distinguish, for example, between free and confined diffusion.

Fig. 2.

iMSD analysis on simulated 2D diffusion. (A) Simulated condition: 2D isotropic diffusion, with diffusivity D. (B) iMSD is linear, with a higher slope for increasing D values. (C) Accordance between the theoretical D value and that recovered from the analysis. (D) Simulated condition: 2D isotropic diffusion in a meshwork of impenetrable barriers (probability P = 0 to overcome the barrier). (E) iMSD plot starts linear and then reaches a plateau that identifies the confinement area and the corresponding linear size L. (F) Accordance between the theoretical L value and that recovered from the analysis. (G) Simulated condition: 2D isotropic diffusion in a meshwork of penetrable barriers. Particles have probability P > 0 to overcome the barrier, thus generating a hop diffusion component. (H) iMSD plot starts linear (with a slope dependent on D_micro) and then deviates toward a lower slope which depends on P. (I) Calculated S_conf as a function of the imposed P. (J) Simulated condition: dynamic partitioning. Particles diffuse freely outside and inside the domain with diffusivity D_out and D_in, respectively, and have probability P_in and P_out to enter or exit the domain, respectively. (K) Two characteristic iMSD traces in dynamic partitioning: diffusion (black) and trapping (gray). Diffusion shows linear iMSD, whereas trapping yields a constant iMSD. (L) Calculated τ_T as a function of the imposed P_out (red dots) with respect to the trapping time directly calculated from molecular trajectories (black line).

Fig. 3.

Effect of particle size on . (A) iMSD is linear with increasing values for increasing particle sizes. PSF on ordinate indicates the contribution of the instrumental waist to σ₀ value. (B) Accordance between the theoretical R value (imposed in the simulation) and that recovered from the analysis.

Fig. 4.

Analysis of TfR dynamics in living cells. (A) Schematic representation of a GFP-tagged TfR diffusing in the cytoskeleton hierarchical meshwork, with particular emphasis on the spatial size accessible in the tens of milliseconds timescale. (B) Total internal reflection fluorescence microscopy image of a CHO cell expressing GFP-tagged TfR (Left) and the detail of the membrane patch used for the measurement (Right). (C) Correlation function temporal evolution, Gaussian fit, and residues. (D) Average temporal autocorrelation [G(0,0,τ), as defined in Eq. S1] of GFP-TfR shows that the characteristic time of the fluctuations is shorter than the total length of the measurement (arrow). Thus, immobile fraction removal is a safe operation (SI Text). (E) iMSD vs. time plot for GFP-TfR in physiological conditions (red curve) and after 30 min of Lat-B treatment (green curve).(Inset) iMSD trend at a short timescale. (F) Fluorescence images of cells transfected with actin-GFP, showing the effect of Lat-B on the integrity of actin filaments.

Fig. 5.

Temperature dependence of TfR dynamics. (A) iMSD for GFP-tagged TfR in CHO cells at 20 °C (red curve) and at 40 °C (green curve). An increased slope at longer times indicates that hop diffusivity increases with temperature. (B) Arrhenius plot of D_macro.

Fig. 6.

Effect of sampling frequency on : faster acquisition is needed to reveal subtle dynamics. (A) Comparison of the GFP-TfR iMSD calculated at 10-ms repetition time (light red line) with that at 200 ms (red dots). Black line represents the best linear fit of red dots. (B) Representative iMSD derived from a line-scanning measurement (125-µs repetition time).