Localization-based super-resolution techniques open the door to unprecedented analysis of molecular organization. This task often involves complex image processing adapted to the specific topology and quality of the image to be analyzed. Here we present a segmentation framework based on Voronoï tessellation constructed from the coordinates of localized molecules, implemented in freely available and open-source SR-Tesseler software. This method allows precise, robust and automatic quantification of protein organization at different scales, from the cellular level down to clusters of a few fluorescent markers. We validated our method on simulated data and on various biological experimental data of proteins labeled with genetically encoded fluorescent proteins or organic fluorophores. In addition to providing insight into complex protein organization, this polygon-based method should serve as a reference for the development of new types of quantifications, as well as for the optimization of existing ones.

The advent of super-resolution imaging (SRI) has created a need for optimized labelling strategies. We present a new method relying on fluorophore-conjugated monomeric streptavidin (mSA) to label membrane proteins carrying a short, enzymatically biotinylated tag, compatible with SRI techniques including uPAINT, STED and dSTORM. We demonstrate efficient and specific labelling of target proteins in confined intercellular and organotypic tissues, with reduced steric hindrance and no crosslinking compared with multivalent probes. We use mSA to decipher the dynamics and nanoscale organization of the synaptic adhesion molecules neurexin-1β, neuroligin-1 (Nlg1) and leucine-rich-repeat transmembrane protein 2 (LRRTM2) in a dual-colour configuration with GFP nanobody, and show that these proteins are diffusionally trapped at synapses where they form apposed trans-synaptic adhesive structures. Furthermore, Nlg1 is dynamic, disperse and sensitive to synaptic stimulation, whereas LRRTM2 is organized in compact and stable nanodomains. Thus, mSA is a versatile tool to image membrane proteins at high resolution in complex live environments, providing novel information about the nano-organization of biological structures.