dSTORM images were analyzed and reconstructed with custom-built MATLAB functions as described previously (43, 44). For each image frame, subregions were selected on the basis of local maximum intensity. Each subregion was then fitted to a pixelated Gaussian intensity distribution using a maximum likelihood estimator. Fitted results were rejected on the basis of log-likelihood ratio and the fit precision, which was estimated using the Cramér-Rao lower-bound values for each parameter, as well as intensity and background cutoffs.

Analysis of dSTORM FcγRI cluster data was performed using the density-based DBSCAN algorithm (20) implemented in MATLAB (45) as part of a package of local clustering tools (https://stmc.unm.edu/). Parameters chosen were a maximal distance between neighboring cluster points of ε = 50 nm and a minimal cluster size of six observations. Cluster boundaries were produced with the MATLAB “boundary” function using a default methodology that produced contours halfway between a convex hull and a maximally compact surface enclosing the points. The cluster areas within these boundaries were then converted into the radii of circles of equivalent area for a more intuitive interpretation. ROIs of size 2 μm × 2 μm (4 μm2) were selected from the set of images from which statistics for the equivalent radii were collected per ROI.

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