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The mechanisms that govern receptor coalescence into functional clusters – often a critical step in their stimulation by ligand – are poorly understood. We used single-molecule tracking to investigate the dynamics of CD36, a clustering-responsive receptor that mediates oxidized LDL uptake by macrophages. We found that CD36 motion in the membrane was spatially structured by the cortical cytoskeleton. A subpopulation of receptors diffused within linear confinement regions that simultaneously facilitated freedom of movement along one axis while increasing the effective receptor density. Co-confinement within troughs enhanced the probability of collisions between unligated receptors and promoted their clustering. Cytoskeleton perturbations that inhibited diffusion in linear confinement regions reduced receptor clustering in the absence of ligand, and, following ligand addition, suppressed CD36-mediated signaling and internalization. These observations demonstrate a role for the cytoskeleton in controlling signal transduction by structuring receptor diffusion within membrane regions that increase their collision frequency.
Receptor clustering and organization into membrane microdomains is an essential feature of transmembrane signal transduction (Cebecauer et al., 2010; Groves and Kuriyan, 2010; Scott and Pawson, 2009). While receptors were initially thought to cluster upon binding multivalent ligands (Heldin, 1995), there is increasing evidence that receptors can also exist in pre-formed clusters that get reorganized and activated upon ligand binding (Cambi et al., 2006; Chung et al., 2010; Iino et al., 2001; Livnah et al., 1999; Sako et al., 2000; Schamel et al., 2005; Varma and Mayor, 1998). Membrane microdomains enriched in cholesterol and sphingolipids (Foster et al., 2003; Friedrichson and Kurzchalia, 1998; Hess et al., 2007; Pike, 2003; Sharma et al., 2004), interactions between transmembrane proteins and the cytoskeleton (Andrews et al., 2008; Bouzigues et al., 2007; de Keijzer et al., 2011; Goswami et al., 2008; Kaizuka et al., 2009; Plowman et al., 2005; Serge et al., 2003; Suzuki et al., 2007), and interactions between proteins within the membrane (Douglass and Vale, 2005; Espenel et al., 2008) have been implicated in regulating receptor dynamics and clustering. Very little is known about these emerging mechanisms of unligated receptor clustering and their roles in controlling the signaling competence of receptors.
CD36 is a clustering-responsive class B scavenger receptor expressed on the surface of platelets, endothelial cells and macrophages (Febbraio et al., 2001). In macrophages, it binds to multivalent ligands such as oxidized low-density lipoprotein (oxLDL), apoptotic cells and malaria-infected erythrocytes, implicating it in a wide range of processes, from lipid metabolism to innate immunity to tissue remodeling (Endemann et al., 1993; McGilvray et al., 2000; Savill, 1997). Biochemical studies suggest that CD36 clustering at the cell surface upon engagement of multivalent ligands triggers signal transduction and receptor-ligand complex internalization (Daviet et al., 1997; McGilvray et al., 2000). However, it is not known whether unligated CD36 exists as monomers or as clusters that facilitate the cellular response to ligand, and what factors contribute to CD36 clustering. To address these questions, we combined quantitative live-cell single-molecule imaging and immunochemical approaches to study the dynamics, clustering and signaling of CD36 in primary human macrophages.
To image single receptors, we immuno-labeled CD36 with a primary anti-CD36 Fab fragment followed by a secondary Fab fragment conjugated largely (>85%) with a single Cy3 fluorophore, and imaged the dorsal surface of macrophages using wide-field epifluorescence microscopy. The resulting images consisted of diffraction-limited spots (Figure 1A), the sub-pixel positions and peak intensities of which were determined by fitting mixtures of Gaussian kernels (Jaqaman et al., 2008) (Figure S1A).
To assess whether the spots corresponded to single fluorophores, we imaged fixed cells using a range of primary Fab fragment dilutions at a fixed concentration of secondary Fab fragment. Modal analysis of the particle intensity histograms (Yang et al., 2007) revealed multiple intensity modes with conserved mean intensities across all dilutions (Figure 1B). Moreover, individual particles photobleached in a stepwise fashion, with a step size similar to the mean of the first mode in the intensity histograms (Figure 1A). Thus, the first mode of the intensity histograms most likely corresponded to a single Cy3 fluorophore, demonstrating our ability to detect single molecules. Of note, not all secondary Fab fragments were conjugated to exactly one Cy3 and, at the labeling densities used, not every CD36 molecule on the surface was labeled. For these reasons, the following analysis does not assume that one fluorophore represents one CD36 molecule.
To measure the dynamics of CD36 in live cells, we chose an intermediate labeling density (Figure 1B, right panel) that balanced the conflicting requirements of tracking single receptors while at the same time capturing interactions between them. Movies were collected with a frame rate of 10 Hz for 10 s (Movie S1, left), over which period photobleaching was negligible (Figure S1B, C). Receptor trajectories were reconstructed using a multiple-particle tracking algorithm (Jaqaman et al., 2008) designed to follow individual particles in densely populated fields and to explicitly capture their merging and splitting with other particles (Movie S1, right; Movie S2).
In unstimulated macrophages CD36 exhibited several trajectory types (Figure 2A), which we classified using two measures: The first characterized trajectories as linear or isotropic based on the scatter of receptor positions regardless of the underlying mobility (Huet et al., 2006; Jaqaman et al., 2008). The second identified the mobility by a moment scaling spectrum (MSS) analysis of receptor displacements (Ewers et al., 2005; Ferrari et al., 2001) (Figure S2A). The combination of these two measures revealed that 27 ± 1% of receptors had linear trajectories, 18 ± 1% had isotropic trajectories generated by unconfined diffusion (referred to as isotropic-unconfined), and 55 ± 1% had isotropic trajectories generated by confined diffusion (referred to as isotropic-confined) (Figure 2B).
The linear trajectories of CD36 radiated from the perinuclear region (Figure 2A, C; Figure S2B). Since unligated CD36 is thought to reside exclusively at the cell surface (Collins et al., 2009), this unexpected linear movement raised the possibility that binding to Fab fragments triggered CD36 internalization and displacement along microtubules (MTs). However, following an acute acid wash that preserved cell integrity, only 15% of the fluorescent particles remained (Figure S2C–H). This fraction was smaller than the fraction of linearly-moving receptors, and none of the remaining label exhibited linear motion (Figure S2F). These results indicated that the majority of the imaged receptors were surface-bound, including those moving linearly.
The imaged receptors, even though unligated, underwent merging and splitting events (Figure 2D–F; Movie S2). These events could be apparent fusions reflecting incidental colocalization of receptors within distances closer than the resolution limit (~300 nm), or they could be genuine reversible clustering events formed, for example, through direct interactions between receptors, indirect interactions via other molecules, and/or receptor co-confinement within membrane nanodomains. We used several measures to distinguish genuine clustering from incidental colocalization. First, we compared the measured distribution of fusion times (Figure 2G) to the distribution expected were merging and splitting events solely due to incidental colocalization (Kasai et al., 2011). Specifically, we simulated non-interacting receptors that moved on the cell surface in a manner similar to CD36 and obtained the distribution of apparent fusion times caused by resolution limitations. With this distribution, if the probability of observing a simulated apparent fusion time ≥ X s was p(X), then we defined the confidence that an experimental fusion lasting for X s represented a true clustering event as 1 – p(X) (Figure 2G). We found that CD36 fusion events could not be accounted for solely by incidental encounters: 60% of the experimentally observed fusions lasted longer than 1 s, the 90% confidence threshold. Second, since a protein’s diffusion speed in the membrane is linked to its dimension (Gambin et al., 2006), we investigated whether fused receptors moved slower than before merging or after splitting. We found that 65% of receptors indeed exhibited slower speeds while fused (Figure S2I, J). In addition, we found a significant negative cross-correlation between particle intensity and mobility (Figure S2K). These results implied that at least 60–65% of the observed merging and splitting events reflected genuine reversible clustering events, while the rest were most likely apparent mergers due to resolution limitations.
Even though clustering events were rare overall (Figure 2F), they depended on the type of receptor motion. First, there was a gradient in particle intensities: linearly-moving particles had the highest intensity and isotropic-confined particles the lowest (Figure 2H), implying that the chance of a detected particle consisting of multiple CD36 molecules was highest for linearly-moving particles, and lowest for isotropic-confined particles. Second, we observed a gradient in the probability of merging and splitting: again linearly-moving receptors had the highest probability and isotropic-confined receptors the lowest (Figure 2I). These observations collectively indicated that the linear movement of CD36 favored metastable clustering in the absence of ligand.
MSS analysis of the diffusion of linearly-moving receptors rarely classified them as super-diffusive (only ~7%). To further dissect the linear motion characteristics, we determined the orientation axis of each linear trajectory, defined as the axis of largest positional variation within a trajectory (Figure S2B), and extracted two parameters (Figure 3A): (1) the component of the frame-to-frame displacement parallel to the orientation axis, and (2) the run-time, i.e. the number of steps taken in one direction before switching to the opposite direction. We found no difference between motion away from and toward the perinuclear region (Figure 3B). Also, the distribution of run-times resembled that of a 1D random walk, where the probability of taking n consecutive steps in one direction is 2−n (Berg, 1993). These results suggested that the linear motion of unligated CD36 was not motor-driven but rather diffusive.
To test this hypothesis further, we collected movies with higher sampling frequencies (33, 62.5 and 125 Hz) and compared receptor motion across time-scales. For these experiments we labeled CD36 with quantum dots (Qdots) instead of Cy3 because of their brighter and more photostable signal. Qdot blinking was compensated for by the gap-closing feature of the multiple-particle tracking algorithm (Jaqaman et al., 2008). This multi-scale analysis yielded several results: First, the mean parallel component of the frame-to-frame displacements scaled with the square-root of time, as expected for diffusion (Figure 3C; see also Figure S3A). In contrast, motors would scale linearly with time. Second, as expected for a time-scale invariant process such as diffusion (Sethna, 2006), the distributions of run-times expressed in frames did not vary with frame rate (Figure 3D). In contrast, motor-driven motion should have a characteristic processivity, in which case frame rate changes would alter the run-time distribution expressed in frames. Third, in agreement with the isotropic nature of diffusion, the average receptor displacement component perpendicular to the orientation axis (Figure 3A) was very similar to the average parallel displacement component at 125 Hz sampling (Figure 3C; at lower sampling rates, confinement in the perpendicular direction prevented the full square-root of time scaling of the perpendicular component). Fourth, receptors visited all positions within their linear confinement regions, albeit with a bias toward the edges, providing further evidence for motion isotropy (Figure 3E; Figure S3B–D). Combined, these observations provided strong evidence that the linear motion of CD36 on the surface of macrophages resulted from diffusion within linear confinement regions.
Differences or similarities between the motion characteristics of the linear, isotropic-unconfined and isotropic-confined receptors could give further insight into the regulation of CD36 motion in the membrane. Thus, we first compared the confinement width of receptors in linear trajectories to the confinement dimension of isotropic-confined receptors. We approximated linear confinement regions by rectangles and isotropic confinement regions by squares. Interestingly, the confinement width of linearly-moving receptors and the confinement dimension of isotropic-confined receptors were similar, both with a median of 190 nm (Figure 3E, F).
Next we compared the diffusion coefficient between the three motion categories (Figure 3G). To accommodate the anisotropic geometry of linear trajectories, which caused apparent differences between movements parallel and perpendicular to the orientation axis (Figure 3C), we also calculated for linearly-moving receptors their 1D diffusion coefficients parallel and perpendicular to the orientation axis (Figure 3H) (Long and Vu, 2010). The 1D parallel diffusion coefficient was ~0.1 μm2/s, similar to what was previously measured for receptors diffusing freely in the plane of the membrane (Serge et al., 2003). Importantly, the 1D perpendicular diffusion coefficient was similar to the diffusion coefficient of isotropic-confined trajectories, indicating that both motion types were generated by one diffusive movement that was confined within either linear regions or small isotropic regions. The fact that the isotropic-unconfined trajectories had an apparent diffusion coefficient < 0.1 μm2/s also suggested that these trajectories did not undergo truly free diffusion but were subject to many short-term constraints unobservable at 10 Hz (Saxton and Jacobson, 1997).
CD36 has been reported to localize in the cholesterol-enriched microdomains known as rafts (Dorahy et al., 1996; Zeng et al., 2003). Thus we investigated whether rafts played a role in organizing CD36 motion in the membrane. First we tracked raft dynamics using the raft marker cholera toxin subunit B (CTB) conjugated to Alexa555 (Brown and London, 1998) (Figure S4A). We found that most rafts exhibited isotropic, primarily confined diffusion, although a small fraction exhibited radially-arranged linear motion (Figure 4A–C). Of note, radially arranged linear motion was not a general feature of macrophage membrane components; Fcγ receptors, for example, did not exhibit any (Figure S4B–D). Next, we tracked rafts or CD36 after treating the macrophages with methyl-β-cyclodextrin (MβCD; 10 mM for 30 min) which depleted ~50% of cholesterol from the cells (Figure S4E, F). While MβCD treatment disrupted raft motion as previously reported (Kilsdonk et al., 1995; Ohtani et al., 1989), it had no effect on CD36 motion (Figure 4D, E). These results indicated that while some raft-associated molecules could undergo linear motion, the association of CD36 with cholesterol-enriched microdomains was not essential for it to move linearly.
The actin cytoskeleton has been previously implicated in regulating membrane protein dynamics (Andrews et al., 2008; Chung et al., 2010; Goswami et al., 2008; Kaizuka et al., 2009; Plowman et al., 2005; Suzuki et al., 2007). Therefore, we investigated whether CD36 motion depended on the actin cytoskeleton. Indeed, macrophage treatment with latrunculin B (10 μM for 20 min) to depolymerize F-actin markedly reduced the fraction of linearly moving CD36 (Figure 5A, B). These relatively short incubation periods sufficiently preserved the actin cortex to maintain stable cell-substrate adhesion for single-molecule imaging, yet receptor motion was disrupted. Macrophage treatment with blebbistatin (10 μM for 10 min), a specific inhibitor of myosin II, also decreased the fraction of linearly-moving receptors (Figure 5A, C). Thus, the motion of CD36 in linear confinement regions depended on the integrity and flow of the cortical actomyosin meshwork.
The dependence of CD36 linear motion on the cortical actomyosin meshwork raised the question of what could underlie the formation of the linear structures in the path of CD36. The radial arrangement of the linear trajectories around the nucleus suggested that MTs, closely apposed to the cell cortex in macrophages (Figure S6A), could play a role. Previous studies have implicated MTs in regulating receptor dynamics (Bouzigues et al., 2007; de Keijzer et al., 2011; Serge et al., 2003), although generally resulting in directed movement and not “1D diffusion” as observed for CD36.
Using two-color imaging of Qdot-labeled CD36 and Cy3-immunolabeled MTs in fixed cells, we found a significant fraction of receptors colocalizing with MTs, 27 ± 1% (Figure 6A, B; Figures S6B–D), in remarkable agreement with the fraction of CD36 diffusing within linear confinement regions (Figure 2B). Live-cell imaging of Qdot-labeled CD36 in macrophages transduced with baculovirus to express tubulin-GFP revealed that linear CD36 trajectories primarily colocalized with MTs (Figure 6C), and were more abundant in areas where MTs were more organized (right vs. left side of cell in Figure 6D). Consistent with these observations, macrophage treatment with nocodazole (50 μM for 30 min) to depolymerize MTs significantly decreased the fraction of receptors undergoing linear motion (Figure 6E, F). Therefore, in addition to the cortical actomyosin meshwork, MTs also played a role in mediating the radially-arranged linear motion of CD36.
The propensity of unligated CD36 to form metastable clusters depended on its mobility (Figure 2H, I). Thus, we suspected that cytoskeleton perturbations that altered CD36 motion would also perturb its clustering. To investigate this, we compared clustering in the different conditions using two measures: (1) modal analysis of the particle intensity histograms (similar to Figure 1B) and (2) the probability of receptor merging and splitting (similar to Figure 2F). At face value, all drug treatments reduced receptor clustering, with nocodazole showing the weakest reduction (Figure 7A, B). However, in addition to altering receptor motion, these drug treatments reduced receptor density on the cell surface (Figure 7C). To separate the effects of motion perturbation and density reduction, we repeated the clustering comparisons between conditions using a subset of cells (called “density-normalized subset”) that had comparable receptor densities (Figure 7D, E). In this subset, latrunculin and blebbistatin treatments reduced clustering to a similar extent as they did in all cells, implying that motion changes upon these treatments were the dominant factor in reducing receptor clustering. The effects of nocodazole on the density-normalized subset were not significant, implying that with this drug the reduction in receptor density was more likely the cause of decreased receptor clustering. Overall, this analysis provided evidence that clustering of unligated CD36 was regulated by geometric constraints mediated by cortical cytoskeleton organization.
Biochemical evidence suggests that CD36 clustering is essential for its signaling and internalization upon engagement to multivalent ligands (Daviet et al., 1997; McGilvray et al., 2000). The metastable clustering of unligated CD36 might prime the cell and facilitate its response when exposed to ligand that, in turn, could stabilize the clusters and/or increase their size, leading to receptor activation. Our observation that CD36 diffusion within linear confinement regions promoted unengaged receptor clustering thus led us to hypothesize that the cytoskeleton-mediated organization of receptor diffusion in the membrane might enhance CD36 responsiveness to ligand.
To test this, we monitored the response of macrophages to oxLDL, a physiologically important ligand that binds to macrophages largely via CD36 (Figure S7A). We monitored the internalization of oxLDL and the activation of c-Jun N-terminal kinase (JNK), a well-established effector of CD36 (Kennedy et al., 2011; Rahaman et al., 2006).
In unperturbed macrophages, DiI-labeled oxLDL bound rapidly to the surface. Within 5 min of DiI-oxLDL addition, a fraction of the receptor-ligand complexes, 25 ± 2%, moved along linear trajectories as described for CD36 (Figure 7F, G). This behavior was observed before any significant internalization occurred, as verified by acid-stripping the cells, which removed most of the bound oxLDL and eliminated most of the linearly-moving complexes (Figure 7G). After 20 min of oxLDL addition, ~60% of the oxLDL was internalized and could no longer be displaced from the cells by an acid wash (not shown). Binding and internalization of oxLDL were associated with JNK activation, as assessed using antibodies that specifically recognized the phosphorylated form of its substrate cJun (phospho-cJun; Figure 7H, Figure S7B).
To assess the effect of perturbing CD36 motion and clustering on its ability to signal and internalize oxLDL, we pretreated the cells with latrunculin, blebbistatin or nocodazole before adding oxLDL. Pretreatment with all three agents reduced ligand internalization (Figure 7I), in all cases to a larger extent than what would be expected from the reduction in receptor density alone (Table S1). This reduction did not result from wholesale inhibition of endocytosis, as the same drug treatments did not significantly alter transferrin internalization (Figure 7J). Pretreatment with blebbistatin or nocodazole also suppressed c-Jun phosphorylation (Figure 7H, Figure S7B). The effect of latrunculin on CD36-mediated JNK activation could not be evaluated; as described in other systems (Subbaramaiah et al., 2000; Yujiri et al., 1999), the actin-perturbing agent itself markedly activated JNK, precluding subsequent stimulation via CD36 (Figure 7H, Figure S7B). The results of these experiments combined thus supported the hypothesis that the cytoskeleton-mediated organization of CD36 diffusion was essential for its proper signaling and ability to internalize ligands.
Our study reveals that the diffusion of CD36 in the membrane of human macrophages is regulated by interactions between CD36 and the cytoskeleton. Indeed, Triton extraction experiments provide evidence that in macrophages CD36 interacts with F-actin (Figure S5), although most likely transiently and indirectly, perhaps via integrins (Thorne et al., 2000). It is tempting to speculate that some lipid microdomains exhibit radially-arranged linear motion like CD36 because of similar interactions with F-actin (Harder et al., 1997; Viola and Gupta, 2007).
While details of the molecular mechanism by which the cytoskeleton controls CD36 diffusion in the membrane remain to be determined, our current data suggest two models: in regions without MTs, the submembranous actin meshwork is isotropic; thus receptors diffuse isotropically and, due to CD36-actin interactions, would get slowed down (Saxton and Jacobson, 1997) and often confined. On the other hand, where MTs are apposed to the membrane (Manneville et al., 2003), they might disrupt the integrity of the submembranous actomyosin meshwork by chemical and/or mechanical interactions (Rodriguez et al., 2003), generating actin-delimited channels along which CD36 would move relatively unobstructed (Model 1 in Figure S6E). Alternatively, the submembranous actin meshwork is isotropic everywhere, including regions with MTs, however MTs might locally detach the actin meshwork from the plasmalemma, generating linear patches of bare membrane where receptor diffusion is unimpeded by actin (Model 2 in Figure S6E). The remarkable conservation of confinement width between isotropic compartments and linear channels favors the first model. In either case, the reversible interactions between CD36 and F-actin would lead to the observed bias of CD36 localization toward the channel edges (Figure 3E).
The compartmentalization of CD36 diffusion in the membrane is reminiscent of the membrane matrix corrals proposed by Kusumi at al. (Kusumi et al., 2005a; Kusumi et al., 2005b). However, there are two main differences between CD36 compartments and those described previously. First, for CD36 we observe not only isotropic compartments but also linear channels. Second, the confinement of CD36 seems to be more long-lived than the previously observed corrals (at least 10 s vs. 1 ms timescale (Kusumi et al., 2005a)). Of note, cortical actin turnover is also on the order of tens of seconds (McGrath et al., 1998; Ponti et al., 2005). Therefore, we propose that the linear and isotropic compartments described here are salient features of the cortical architecture in macrophages, controlling receptor diffusion over long periods and thus having major implications for the steady-state of CD36-mediated signal transduction.
A critical implication of the compartmentalization of CD36 diffusion is its impact on receptor interactions. In particular, our data show that CD36 diffusion in linear channels promotes receptor encounters and clustering., which can be attributed to the unique geometry of linear channels: When compared to the small regions of isotropic confinement, the comparatively long linear channels accommodate more receptors and offer them greater freedom of movement parallel to the orientation axis. Conversely, when compared to free diffusion, linear channels restrict movement perpendicular to the orientation axis, thereby increasing the effective local density by ~5-fold.
While the exact molecular nature of the CD36 clusters remains to be determined, our study provides evidence that the metastable clusters of unligated CD36 prime the cell to respond when exposed to multivalent ligands. At present the link between cytoskeleton organization and signaling could be probed only by global disruption of actin and MT dynamics. Thus we cannot formally exclude that effects besides reduced receptor clustering contribute to the documented shifts in signaling downstream of CD36. However, the fact that cytoskeleton perturbants working via different molecular mechanisms had similar effects on CD36 function provides compelling evidence that reducing unligated CD36 clustering – common among all the perturbants – is a major contributor to the inhibition of CD36 function upon disruption of cytoskeleton organization.
In conclusion, by dictating the spatial organization of receptor motion, cortical cytoskeletal structures appear to play a critical role in CD36 signal transduction, where the outside-in activation of pathways that modulate cellular processes is in turn controlled by inside-out feedback regulating receptor clustering. We speculate that this reciprocal interaction may be a general mechanism for enhancing or silencing signals at the level of the plasma membrane.
Human blood samples from healthy volunteers were collected with heparin. Peripheral blood mononuclear cells were isolated by density-gradient centrifugation using Ficoll-Paque Plus (Amersham). Cells were resuspended (107 cells/mL) in RPMI-1640 with L-glutamine containing 10% heat-inactivated fetal calf serum (FCS; from Wisent) and seeded onto 18 mm glass coverslips (Fisher Scientific) at 5×105 cells/coverslip. After 1 h at 37°C, non-adherent cells were removed by multiple washes with Hanks buffered saline solution (HBSS). Adherent cells were incubated in RPMI-1640 with 10% FCS and 100 U/mL penicillin, 100 μg/mL streptomycin and 10 μg/mL polymyxin B (Invitrogen) for 7–14 days.
Monoclonal antibodies to human CD36 (clone 131.1; mouse IgG1) were the gift of Dr. N. Tandon (Otsuka America Pharmaceutical, Inc., Rockville, MD). Monovalent Fab fragments were prepared using the ImmunoPure Fab Preparation Kit (Pierce). To minimize non-specific binding, cells were blocked with 4% donkey serum for 10 min, then incubated with anti-CD36 Fab fragments at 1:2000–1:3000 dilution for 10 min. After washing with HBSS, cells were incubated with either (i) Cy3-conjugated donkey anti-mouse Fab fragments (Jackson ImmunoResearch Laboratories) at a 1:3000 dilution for 10 min; (ii) Qdot® 655-goat F(ab′)2 anti-mouse IgG conjugates at 1:2000–1:3000 dilution for 10–15 min (to prevent cross-linking, unoccupied antibody-binding sites on the Qdots were blocked with non-immune mouse IgG antibody (10 μg/ml)); or (iii) biotinylated secondary Fab (1:1000 dilution) followed by streptavadin-655 Qdots (1:10,000 dilution); medium with excess free biotin was then added to block sites on avidin, thereby preventing cross-linking. All labeling protocols led to comparable results.
All the preceding steps were performed at 4°C, to minimize lateral mobility and clustering. Cells were then warmed to 37ºC before filming.
Live-cell imaging was performed using a Zeiss Axiovert 200 epifluorescence microscope equipped with a 100× oil-immersion objective (NA 1.45), a custom-made 2.5× lens and either a Cy3 filter set or a 32012 cube from Chroma Technology for Qdots. Illumination was provided by an Exfo X-Cite 120 light source, and a Hamamatsu 9100-13 deep-cooled EM-CCD camera was used for recording. Image acquisition was controlled by Volocity (Improvision). Images were acquired continuously at 10, 33, 62.5, and 125 frames per second for 10–20 s.
The imaged molecules were detected and tracked as described in (Jaqaman et al., 2008). Please see Extended Experimental Procedures for a brief description. Tracks lasting at least five frames were retained for further analysis.
Please see Extended Experimental Procedures for reagents, immuno-labeling controls, other labeling protocols, acid-stripping protocol, oxLDL and transferrin uptake assays, phospho-cJun assay, MT visualization, and quantification of cholesterol content and of cytoskeleton-associated CD36; also for particle tracking, motion analysis, simulations and receptor density calculation.
Work in the Grinstein lab was supported by the Heart and Stroke Foundation of Ontario and by Canadian Institutes of Health Research Grant MOP-102474. Work in the Danuser lab was supported by NIH grant U01 GM67230. KJ is an investigator in the Center for Cell Decision Processes (NIH P50 GM068762). HK was supported in part by the Uehara Memorial Foundation, the Mochida Memorial Foundation for Medical and Pharmaceutical Research and the Kanae Foundation for the Promotion of Medical Science. SG is the current holder of the Pitblado Chair in Cell Biology.
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