STED and confocal images were analyzed for clusters using a custom script in Java for ImageJ. The pipeline is based on the Laplacian of Gaussian (59) filter and morphological segmentation techniques to identify and segment bright spots in the image at sites of signaling against the noisy and uneven background. First, the Laplacian of Gaussian filter standard deviation σLoG was convolved with the raw image. A binary map was then generated by applying a threshold tLoG to the filtered image; pixels that fell below the threshold were considered as background and set to zero, whereas bright spots were set to 1. A region-labeling algorithm [provided in the MorphoLibJ library for ImageJ (60)] was then applied to the binary map to identify isolated disjoint regions of nonzero valued connected pixels. The area of each region was computed, Ai, and any region that contained Ai ≤ 9 pixels was considered as noise due to nonspecific binding (when compared to the isotype-matched control) and therefore discarded. For each remaining region (labeled as “masks” in figures), the local background intensity was estimated from the raw image by computing the median intensity of the pixels directly outside the perimeter of the region. Last, the pixel intensity descriptors for each region, such as the mean or integrated intensity, were computed by subtracting the local background estimate from the pixels inside the region and computing the respective statistic on each set of pixels. The parameters used in the aforementioned analysis pipeline were the mean radius of the bright spots, r = σLoG = 5 pixels, and the threshold applied to the filtered image tLoG = 15 arbitrary units. These values were chosen empirically and kept constant between conditions. Mander’s colocalization of clusters was applied to the STED clusters defined earlier with a custom ImageJ script. For a negative control, the clusters in one channel were randomly placed within the cell region of interest. Spread area was measured by drawing around the outline of cells in interference reflection microscopy (IRM) images in ImageJ.

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