The Rényi divergence enables accurate and precise cluster analysis for localization microscopy.

Motivation: Clustering analysis is a key technique for quantitatively characterizing structures in localization microscopy images. To build up accurate information about biological structures, it is critical that the quantification is both accurate (close to the ground truth) and precise (has small scatter and is reproducible). Results: Here, we describe how the Rényi divergence can be used for cluster radius measurements in localization microscopy data. We demonstrate that the Rényi divergence can operate with high levels of background and provides results which are more accurate than Ripley's functions, Voronoi tesselation or DBSCAN. Availability and implementation: The data supporting this research and the software described are accessible at the following site: https://dx.doi.org/10.18742/RDM01-316. Correspondence and requests for materials should be addressed to the corresponding author. Supplementary information: Supplementary data are available at Bioinformatics online.

Local dimensionality determines imaging speed in localization microscopy.

Localization microscopy allows biological samples to be imaged at a length scale of tens of nanometres. Live-cell super-resolution imaging is rare, as it is generally assumed to be too slow for dynamic samples. The speed of data acquisition can be optimized by tuning the density of activated fluorophores in each time frame. Here, we show that the maximum achievable imaging speed for a particular structure varies by orders of magnitude, depending on the sample dimensionality (that is, whether the sample is more like a point, a strand or an extended structure such as a focal adhesion). If too high an excitation density is used, we demonstrate that the analysis undergoes silent failure, resulting in reconstruction artefacts. We are releasing a tool to allow users to identify areas of the image in which the activation density was too high and correct for them, in both live- and fixed-cell experiments.

Investigation of podosome ring protein arrangement using localization microscopy images.

Podosomes are adhesive structures formed on the plasma membrane abutting the extracellular matrix of macrophages, osteoclasts, and dendritic cells. They consist of an f-actin core and a ring structure composed of integrins and integrin-associated proteins. The podosome ring plays a major role in adhesion to the underlying extracellular matrix, but its detailed structure is poorly understood. Recently, it has become possible to study the nano-scale structure of podosome rings using localization microscopy. Unlike traditional microscopy images, localization microscopy images are reconstructed using discrete points, meaning that standard image analysis methods cannot be applied. Here, we present a pipeline for podosome identification, protein position calculation, and creating a podosome ring model for use with localization microscopy data.

Fixed pattern noise in localization microscopy.

Localization microscopy vastly improves the resolution achieved by fluorescence microscopy by fitting the positions of individual fluorophores. We examine the reconstructions produced by different fitting algorithms for instances of fixed pattern noise--systematic tendencies to alter estimated emitter positions according to their subpixel location in a way that does not reflect the ground truth structure. We show that while not readily visible at standard empirical signal strengths, fixed pattern noise can occur when performing sub-pixel fitting, and that its degree varies according to the algorithm used and the relative size of the pixels compared to the point spread function. For pixel sizes in the range 80-170 nm, this results in variations in accuracy of the order of 2-4 nm-comparatively small for many applications, but non-negligible in scenarios where very high accuracy is sought.