While single-molecule localization microscopy (SMLM) offers the invaluable prospect to visualize cellular structures below the diffraction limit of light microscopy, its potential has not yet been fully capitalized due to its inherent susceptibility to blinking artifacts. Particularly, overcounting of single molecule localizations has impeded a reliable and sensitive detection of biomolecular nanoclusters. Here we introduce a 2-Color Localization microscopy And Significance Testing Approach (2-CLASTA), providing a parameter-free statistical framework for the qualitative analysis of two-dimensional SMLM data via significance testing methods. 2-CLASTA yields p-values for the null hypothesis of random biomolecular distributions, independent of the blinking behavior of the chosen fluorescent labels. The method is parameter-free and does not require any additional measurements nor grouping of localizations. We validated the method both by computer simulations as well as experimentally, using protein concatemers as a mimicry of biomolecular clustering. As the new approach is not affected by overcounting artifacts, it is able to detect biomolecular clustering of various shapes at high sensitivity down to a level of dimers. Single Molecule Localization Microscopy (SMLM) has boosted our insights into cellular structures below the diffraction limit of light microscopy 1. Common to all SMLM variants is the stochastic switching of single dye molecules between a bright and a dark state. Conditions are chosen such that only a marginal portion of the molecules is in the bright state, so that single molecule signals are well separated on each frame. The final superresolution image is reconstructed from the localizations of all single molecule signals. Researchers have been particularly intrigued by the possibility to determine the spatial distribution of biomolecules in their natural environment, in most cases the intact cell. For example, models for cellular signaling are crucially affected by the spatial organization of receptor and downstream signaling molecules at the plasma membrane 2,3. Application of SMLM to various plasma membrane proteins revealed the presence of nanoclusters to different degrees 4. More recently concerns were raised that the stochastic activation process of the fluorophores, along with the presence of more than one dye molecule per labeled biomolecule, may lead to multiple observations of the same biomolecule in the superresolution image 5,6. Different attempts were undertaken to approach this problem 5,7-11 , e.g. by merging localization bursts into one localization 12 , by analyzing the number of blinking events per localization cluster 10,11 , or by evaluating the spatial spread of the localization clusters 7. A disadvantage of existing methods is the requirement of user-defined parameters 7,12 or additional experiments to characterize the blinking statistics of the chosen fluorophores 10,11. We recently developed a parameter-free method to identify global protein clustering based on a label titrati...

techniques available to study chromatin organization at the nanoscale, optical super-resolution microscopy is emerging for its potential to reveal the spatiotemporal organization of molecules, within intact single cell nuclei. Here we explore the use of structured illumination microscopy (SIM) [2], to investigate oncogene-induced alterations in the organization of cells nuclei. In particular, we use the U937-PR9 in vitro model, a cell line engineered to activate an oncogene that causes the formation of the PML-RARalpha fusion protein. Thanks to this model, we monitor alterations in chromatin organization before and after oncogene activation. Specifically, we exploit the resolution improvement provided by multi-color SIM to detect alterations in the spatial distribution of replication and transcription foci, in relation to the expression of the oncoprotein. The relative spatial distributions of nuclear sites, before and after activation of the oncogene, is quantified through object-based co-localization and a recently developed image cross-correlation spectroscopy (ICCS)-based algorithm [3].