STICCS reveals matrix-dependent adhesion slipping and gripping in migrating cells

Abstract

Two-color spatio-temporal image cross-correlation spectroscopy (STICCS) is a new, to our knowledge, image analysis method that calculates space-time autocorrelation and cross-correlation functions from fluorescence intensity fluctuations. STICCS generates cellular flow and diffusion maps that reveal interactions and cotransport of two distinct molecular species labeled with different fluorophores. Here we use computer simulations to map the capabilities and limitations of STICCS for measurements in complex heterogeneous environments containing micro- and macrostructures. We then use STICCS to analyze the co-flux of adhesion components in migrating cells imaged using total internal reflection fluorescence microscopy. The data reveal a robust, time-dependent co-fluxing of certain integrins and paxillin in adhesions in protrusions when they pause, and in adhesions that are sliding and disassembling, demonstrating that the molecules in these adhesions move as a complex. In these regions, both α6β1- or αLβ2-integrins, expressed in CHO.B2 cells, co-flux with paxillin; an analogous cotransport was seen for α6β1-integrin and α-actinin in U2OS. This contrasts with the behavior of the α5β1-integrin and paxillin, which do not co-flux. Our results clearly show that integrins can move in complexes with adhesion proteins in protrusions that are retracting.

Figure 1

STICCS overview. (A) Fluorescence microscopy images in two channels, red and green, of a cell edge with visible adhesions, which are part of an image time series (B). The time subset of interest (TOI) is the subset of frames analyzed and the ROIs (shown as yellow squares in panels B–D) are the analyzed subregions. (D) The emission point spread function (PSF) represents the general focal volume and is oversampled by the pixels in the imaging charge-coupled device camera to have spatial correlations between adjacent pixels. The pixilated image maps fluorescence intensities integrated from a distribution of fluorophores with a spatial resolution set by the PSF; however, macroscopic fluorescent objects will be larger than the PSF, such as a focal adhesion made up of many fluorescent proteins (E in profile (26–29)). (F and G) General schematics of the spatio-temporal correlation functions (CF: r_aa, r_bb, r_ab, or r_ba) at one ROI before and after k-space normalization, which effectively removes the PSF. Bivariate Gaussian functions are fit for each time lag (H and I), with the axes rotated by an angle θ to align with the major axis of the evolving CF (if present), yielding time-dependent radii of the major (ω_M(τ)) and minor (ω_M(τ)) axes. The CFs are fit for all ROIs (J) and all time lags. (K) Shows one time lag of a typical CF for a 16 × 16 pixel ROI from a cell. Velocity vectors are obtained by fitting the translating CF peaks (I) that are above defined thresholds.

Figure 2

Simulation results and their resulting ACFs obtained via STICCS analysis for the following particle/macroscopic adhesion models: (A) Treadmilling, where adhesions flow due to particle addition/removal at the ends but particles inside the adhesion masks are static. (B) Sliding, where particles and adhesion masks flow together. (C) Antisliding, with particles flowing in the opposite direction of the adhesion masks (so peak separation can be visualized). (D) Spreading of immobile adhesion masks with immobile particles. (E) Particles undergoing anisotropic diffusion and flow in free space. (F) Dispersed flow of four particle populations with different but narrowly distributed velocities. Each model was analyzed for lower and higher particle densities (5 particles/μm², top panels in each square; and 50 particles/μm², bottom panels in each square). Each odd-numbered panel shows the superposition of the first and 100th frames (t = 1 and 100 s) from a single image series while each even-numbered panel shows the superposition of the corresponding ACFs at two time lags (τ = 2 and 15 s). Each of these panels have markers (yellow) superimposed to symbolize the motion of the particles, indicating: (A and D) static motion (solitary yellow rings), (B and C) constant velocity (identical arrows), (E) anisotropic diffusion and flow (double arrows with the longer arrows indicating flow direction along the adhesion mask’s semimajor axis), and (F) dispersed velocities (arrows of variable length representing a distribution of particle speeds). For each panel, data (in red and cyan) represent initial and final configurations, respectively. (White) Areas of overlap between features in the superposition. Scale bars are 5 μm for the image series and 1 μm for the CFs.

Figure 3

Results from STICCS analysis of simulations of the antisliding model with two particle populations that are fractionally colocalized and imaged in two detection channels. The model was analyzed for lower and higher colocalization of particles (top two rows, 20%; bottom two rows, 80%), with the odd and even rows representing early versus later times (τ = 1 and 100 s) and time lags (τ = 5 and 10 s). The particle density was 10/μm². (First column) Composite images of the channels (red and green), with scale bar 5 μm. (Second, third, and fourth columns) r_aa, r_bb, and r_ab CFs, respectively, where r_ab = r_ba in this scenario with scale bar 1 μm. (H and P, red arrow) Adhesion peak; (blue arrow) particle peak.

Figure 4

U2OS with α6β1-GFP and paxillin-mKO on laminin. The lamellipodium is protruding toward the northeast, with its sides retracting inward. Vector and elliptical maps are shown in the first and second rows. The elliptical maps represent the radii and orientation of each CF for τ = 5 s. N_f = 20, frames 101–120, δt = 5 s, p_x,y = 0.105 μm, and 16 × 16 pixels (1.7 × 1.7 μm) ROI (yellow square). Representative of three adhesions measured in three cells.

Figure 5

ACF- and CCF-calculated velocity maps of CHO.B2 α6β1-GFP and α-actinin-mCherry, superimposed on the first image of the TOI. N_f = 10, frames 21–30, δt = 5 s, p_x,y = 0.105 μm, and 16 × 16 pixels (1.7 × 1.7 μm) ROI (yellow square). The bulk of the movement is a protrusion moving in the southwest direction with a retraction of the cell boundary in the northwest corner moving southeast. Representative of four adhesions measured in four cells.

Figure 6

(A and B) ACF- and (C and D) CCF-calculated velocity maps of paxillin-GFP and αLβ2-integrin-mCherry (superimposed on the first image from the TOI). The analyses were performed at two different TOIs, each over N_f = 10 and δt = 5 s. (Top row) Frames 1–10 (t = 1–50 s). (Bottom row) Frames 71–80 (t = 351–400 s). Cross-correlation maps in panels C and D show transient interactions occurring at different times in regions where the adhesions become active. p_x,y = 0.21 μm and 16 × 16 pixels (3.4 × 3.4 μm) ROI (yellow square). Representative of two adhesions measured in four cells.

Figure 7

ACF- and CCF-calculated velocity maps of CHO.B2 α6β1-GFP and paxillin-mKO superimposed on the first image from the TOI. N_f = 10, frames 71–80, δt = 5 s, p_x,y = 0.105 μm, and 16 × 16 pixels (1.7 × 1.7 μm) ROI (yellow square). Representative of six adhesions measured in six cells.