Dynamic regulation of CD45 lateral mobility by the spectrin-ankyrin cytoskeleton of T cells

Abstract

The leukocyte common antigen, CD45, is a critical immune regulator whose activity is modulated by cytoskeletal interactions. Components of the spectrin-ankyrin cytoskeleton have been implicated in the trafficking and signaling of CD45. We have examined the lateral mobility of CD45 in resting and activated T lymphocytes using single-particle tracking and found that the receptor has decreased mobility caused by increased cytoskeletal contacts in activated cells. Experiments with cells that have disrupted betaI spectrin interactions show decreased cytoskeletal contacts in resting cells and attenuation of receptor immobilization in activated cells. Applying two types of population analyses to single-particle tracking trajectories, we find good agreement between the diffusion coefficients obtained using either a mean squared displacement analysis or a hidden Markov model analysis. Hidden Markov model analysis also reveals the rate of association and dissociation of CD45-cytoskeleton contacts, demonstrating the importance of this analysis for measuring cytoskeleton binding events in live cells. Our findings are consistent with a model in which multiple cytoskeletal contacts, including those with spectrin and ankyrin, participate in the regulation of CD45 lateral mobility. These interactions are a major factor in CD45 immobilization in activated cells. Furthermore, cellular activation leads to CD45 immobilization by reduction of the CD45-cytoskeleton dissociation rate. Short peptides that mimic spectrin repeat domains alter the association rate of CD45 to the cytoskeleton and cause an apparent decrease in dissociation rates. We propose a model for CD45-cytoskeleton interactions and conclude that the spectrin-ankyrin-actin network is an essential determinant of immunoreceptor mobility.

FIGURE 1.

Single-particle tracking of CD45: population analysis of macrodiffusion (D_M) values. The diffusion of CD45 was observed using F(ab)′-coated beads and high speed microscopy. A–C, the distribution of calculated diffusion coefficients (D_M) is plotted as a histogram (gray) for each treatment: A, control (0.1% DMSO in buffer); B, cytoD (1 μm, in buffer containing 0.1% DMSO); and C, PMA (200 ng/ml, in buffer containing 0.1% DMSO). The population density is shown in black (bold solid line); the best fit of the population density is shown in black (bold dashed line) with the best fitted subpopulations of D₁ (dotted) or D₂ (dashed). D, representative trajectories are shown for each condition. Trajectories are oriented with the first time point at the bottom and the last time point at the top. Each trajectory was recorded for 2–4 s at 1000 frames per second. Representative trajectories from each subpopulation are shown in proportion to the fits (D₁, black; D₂, gray). The scale bar represents 1 μm. Each condition represents the results of two to four independent experiments. wt, wild type.

FIGURE 2.

Single-particle tracking of CD45 on stable transfectants: population analysis of macrodiffusion (D_M) values. The diffusion of CD45 on stable transfectants expressing fragments of βΙ spectrin was observed by SPT. Macrodiffusion (A–D) and trajectory data (E) are plotted as described in the legend to Fig. 1.

FIGURE 3.

Single-particle tracking of CD45: population analysis of microdiffusion (D_micro) values. SPT trajectories of CD45 on Jurkat cells are plotted using a microdiffusion analysis and labeled as described in the legend to Fig. 1. The center of the D₁ and D₂ subpopulations as determined by a HMM are indicated by black and gray triangles, respectively. wt, wild type.

FIGURE 4.

Single-particle tracking of CD45 on stable transfectants: population analysis of microdiffusion (D_micro) values. SPT trajectories of CD45 on stable transfectants expressing fragments of βΙ spectrin are plotted using a microdiffusion analysis and labeled as described in Fig. 1. The plots are shown for untreated N-5, N-5,15, and 14–15 cells (A–C) and 14–15 cells treated with PMA (D). The centers of the D₁ and D₂ subpopulations as determined by HMM are indicated by black and gray triangles, respectively.

FIGURE 5.

Fraction of bound CD45 as determined by HMM. SPT trajectories were analyzed using a two-state HMM (21). Individual trajectories were then classified according to the fraction of total steps spent in D₂ (% D₂ = π₂/(π₁ + π₂)) (A and C) or the total number of transitions between states (switching frequency [Hz]) (B and D). These data are plotted versus the effective diffusion coefficient calculated from the HMM analysis for each individual trajectory (D_eff). Plots are shown for control and PMA- and cytoD-treated wild type cells (A and B) and for untreated N-5, N5,15, and 14–15 cells and 14–15 cells treated with PMA (C and D). Trend lines were added for clarity. wt, wild type.

FIGURE 6.

Proposed CD45-ankyrin binding interaction. We propose an ankyrin-binding site on CD45 based on an alignment of AE1 (B3AT) and CD44 to the CD45 cytoplasmic domain (32, 33) using the ClustalW algorithm (42) (supplemental Fig. S4). A, the known fodrin/spectrin-binding site is shown in magenta (33), and the putative ankyrin-binding domain is shown in green. Both sites are mapped on the structure of CD45 Domain 1 and Domain 2 (26). The residues that form the putative site map to a contiguous groove on the back of the catalytic domain. B, a hypothetical model was constructed by docking four ankyrin repeat domains to the proposed binding domain. Ankyrin is shown in cyan (27). The model suggests that the groove has a complementary topography to the ankyrin repeats. (Protein Data Bank codes 1YGR for CD45 and 2BKG for ankyrin). C and D, an electrostatic map of the proposed binding groove is shown (C), as well as a complete electrostatic map of the CD45 cytoplasmic domain (D).

FIGURE 7.

Model of CD45 lateral diffusion. We propose a model of CD45 cytoskeletal interactions, based on the work of Pradhan and Morrow (13) and the data presented here. A, in resting cells CD45 has only transient attachment to the cytoskeleton, likely through spectrin, and modest membrane interactions through the PH domain of spectrin isoforms. B, upon cellular activation with PMA, CD45 becomes immobilized by multiple cytoskeletal and membrane contacts, which include interactions with ankyrin, spectrin, PIP2, or PKCβ. C, inhibition of the ankyrin-spectrin interaction by the N-5 peptide leads to an increase in D₁ by macrodiffusion, although microdiffusion and HMM analysis suggest that transient cytoskeletal contacts may occur at the now vacant CD45 sites. D, disruption of the spectrin-ankyrin interaction by the 14–15 peptide shows an effect similar to that of the N-5 peptide, implicating ankyrin binding as a regulatory step for CD45 contacts. E, cells that express the N-5,15 peptide are found to be similar to wild type cells by all analyses employed and thus reconstitute native CD45 regulation of lateral mobility. A potential molecular mechanism for these effects is the overlap in the spectrin and ankyrin-binding sites on CD45 (see Fig. 6). Activation of cells expressing spectrin peptides prevents the almost complete immobilization of CD45 seen in wild type cells, although an increase in the immobile fraction is seen. This interaction likely represents a direct contact between spectrin and the CD45 cytoplasmic domain (11).