Summaryp21-Activated kinase 4 (PAK4), a serine/threonine kinase, is purported to localize to podosomes: transient adhesive structures that degrade the extracellular matrix to facilitate rapid myeloid cell migration. We find that treatment of transforming growth factor β (TGF-β)-differentiated monocytic (THP-1) cells with a PAK4-targeted inhibitor significantly reduces podosome formation and induces the formation of focal adhesions. This switch in adhesions confers a diminution of matrix degradation and reduced cell migration. Furthermore, reduced PAK4 expression causes a significant reduction in podosome number that cannot be rescued by kinase-dead PAK4, supporting a kinase-dependent role. Concomitant with PAK4 depletion, phosphorylation of Akt is perturbed, whereas a specific phospho-Akt signal is detected within the podosomes. Using superresolution analysis, we find that PAK4 specifically localizes in the podosome ring, nearer to the actin core than other ring proteins. We propose PAK4 kinase activity intersects with the Akt pathway at the podosome ring:core interface to drive regulation of macrophage podosome turnover.

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.

MotivationClustering 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).ResultsHere, 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 implementationThe 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.

In the originally published version of this article, the author name Victoria Sanz Moreno was spelled incorrectly and should be Victoria Sanz-Moreno. This has now been corrected online.

Localisation microscopy is a super‐resolution imaging technique based on detecting randomly activated single molecules in a sequence of images. A super‐resolution image is then reconstructed as a collection of discrete points, using all of the localised single molecule positions. Clustering analysis of these points can provide quantitative information about sample structure, size of features and/or their number. The information obtained from clustering analysis allows characterisation of the functions or properties of biological systems, for example examining signalling pathways in T‐cells antigen receptors. However, quantitative analysis of clusters in localisation microscopy images is challenging because the clusters are usually small and surrounded by relatively high noise. The Rényi divergence quantifies differences between two distributions (in this case the observed data and a reference distribution). Its sensitivity to the degree to which one distribution differed from another can be tuned with a scaling parameter α, which allows us to adapt its robustness to noise. We approximated the data distribution by counting all points which were positioned closer to each other than a set threshold and the reference distribution as all the points concentrated in a single cluster. Ripley's K function, which is widely used for performing localisation microscopy analysis, is a special case of the Rényi divergence. Here we present a comparison of the accuracy and precision of cluster size measurement performed using the Rényi divergence and Ripley's K function. Initial tests involved establishing noise adaptability of the two analysis methods using simulated data sets with characteristics similar to experimental images (with increasing noise levels), and optimising the tunable parameter of the Rényi divergence. We find that the adaptability of the Renyi divergence method is a particular advantage when dealing with localisation microscopy data, in which characteristics can vary a great deal between datasets.