Introducing Diinamic, a flexible and robust method for clustering analysis in single-molecule localization microscopy

Abstract

Super-resolution microscopy allowed major improvements in our capacity to describe and explain biological organization at the nanoscale. Single-molecule localization microscopy (SMLM) uses the positions of molecules to create super-resolved images, but it can also provide new insights into the organization of molecules through appropriate pointillistic analyses that fully exploit the sparse nature of SMLM data. However, the main drawback of SMLM is the lack of analytical tools easily applicable to the diverse types of data that can arise from biological samples. Typically, a cloud of detections may be a cluster of molecules or not depending on the local density of detections, but also on the size of molecules themselves, the labeling technique, the photo-physics of the fluorophore, and the imaging conditions. We aimed to set an easy-to-use clustering analysis protocol adaptable to different types of data. Here, we introduce Diinamic, which combines different density-based analyses and optional thresholding to facilitate the detection of clusters. On simulated or real SMLM data, Diinamic correctly identified clusters of different sizes and densities, being performant even in noisy datasets with multiple detections per fluorophore. It also detected subdomains ("nanodomains") in clusters with non-homogeneous distribution of detections. Impact StatementSingle molecule localization microscopy not only provide images with higher resolution than classical fluorescence microscopy, but the pointillistic character of its data opened a new field of biological image analysis. The possibility to "see" molecules one by one offers the perfect way to analyse the distribution of molecules, and several analytical tools were proposed to describe the formation of aggregates or clusters. However, clusters in biological samples can be very variable in size and density and the available analytical tools are, in general, effective only for a certain type of distribution. Moreover, the characteristics of clusters depend not only on the molecules themselves, but also on the labelling technique, the photo-physics of the fluorophore and the imaging conditions. We developed Diinamic, which combines different density-based analyses and optional thresholding to facilitate the detection of clusters in biological samples. By combining progressive analysis steps, Diinamic can be easily adapted to a large variety of molecular distributions. In addition, it provides the possibility to introduce biology-based criteria to describe the clustering behaviour of molecules. To help with its application, we provide cues about the strategy to follow depending on the characteristics of the dataset.