Optical single-channel resolution imaging of the ryanodine receptor distribution in rat cardiac myocytes

Abstract

We have applied an optical super-resolution technique based on single-molecule localization to examine the peripheral distribution of a cardiac signaling protein, the ryanodine receptor (RyR), in rat ventricular myocytes. RyRs form clusters with a mean size of approximately 14 RyRs per cluster, which is almost an order of magnitude smaller than previously estimated. Clusters were typically not circular (as previously assumed) but elongated with an average aspect ratio of 1.9. Edge-to-edge distances between adjacent RyR clusters were often <50 nm, suggesting that peripheral RyR clusters may exhibit strong intercluster signaling. The wide variation of cluster size, which follows a near-exponential distribution, is compatible with a stochastic cluster assembly process. We suggest that calcium sparks may be the result of the concerted activation of several RyR clusters forming a functional "supercluster" whose gating is controlled by both cytosolic and sarcoplasmic reticulum luminal calcium levels.

Fig. 1.

Imaging of peripheral ryanodine receptor (RyR) clusters with single protein resolution. (A) Image of a small region on the surface of a rat cardiac myocyte showing four successive z-lines with double rows of RyR clusters at each z-line. The conventional (diffraction-limited) fluorescence image in which all clusters appear as shapeless blurs is shown in red. Overlaid on this image is the localization image (green) revealing high-resolution structure within these blurs. Note the wide variation in cluster size and morphology. Arrows identify several smaller clusters which could be identified in the localization image but not in the conventional image. (Scale bar, 1 μm.) (B) Magnified view of two clusters alongside a reconstruction with candidate positions for the underlying approximately 30 nm wide RyR channels (each shown as a quatrefoil structure adapted from ref. 18). Note the sharp, straight, cluster edges and the edge dislocations corresponding to the width of single RyRs. Based on the reconstructions the clusters contain 61 and 39 RyRs, respectively. (Scale bar, 200 nm.)

Fig. 2.

Quantitative analysis of RyR cluster properties. (A) Distribution of cluster diameters and nearest neighbor distances. Clusters are considerably smaller than previously thought and neighbouring clusters are often close enough to be inseparable at conventional imaging resolution. (B) A region of the surface sarcolemma of a cardiac myocyte in which segmented RyR clusters are shown color-coded according to the number of RyRs they contain. Note the presence of many small clusters (red corresponds to N_RyRs ≤ 7). (Scale bar, 1 μm.) (C) The number of RyRs per cluster follows an approximately exponential distribution with a mean RyR number of 13.6. Shaded area represents ± 1 SD (n = 22 cells). Inset: the same data on a log scale with a maximum-likelihood fit to an exponential distribution (red). (D) Fraction of the total number of RyRs in clusters of a given size. This is interesting from a functional perspective as it tells us which sizes of clusters are likely to make the largest contribution to calcium signaling. The shaded area represents ± 1 SD.

Fig. 3.

Morphology of RyR clusters. (A) A collage of randomly selected larger RyR clusters that illustrate the variable morphology. (Scale bar, 200 nm.) (B) The distribution of RyR cluster aspect ratios shows that clusters are typically elongated (mean ratio = 1.9). (C) The circularity, which quantifies how compact RyR clusters are, is much smaller than expected for a circle (circularity = 1) with a mean of 0.2 and reflects the complex outlines of these clusters.

Fig. 4.

Monte-Carlo simulation of stochastic self-assembly of RyR clusters using a simple growth model. The model yields cluster sizes and geometries which are very similar to those measured. (A) The simulation grid at the end of one simulation run (100 iterations) in which RyRs are shown schematically as circles. (Scale bar, 100 nm.) (B) The size distribution obtained from 1,000 runs. Inset: the same data on a log scale with a maximum-likelihood fit to an exponential distribution (red). The simulation parameters were (probabilities per pixel and iteration step): P_nucleation = 2.5 × 10⁻⁴, P_growth = 2.5 × 10⁻² x N_neighbors, P_retention = 0.937.