We present a method to robustly discriminate clustered from randomly distributed molecules detected with techniques based on single-molecule localization microscopy, such as PALM and STORM. The approach is based on deliberate variation of labeling density, such as titration of fluorescent antibody, combined with quantitative cluster analysis, and it thereby circumvents the problem of cluster artifacts generated by overcounting of blinking fluorophores. The method was used to analyze nanocluster formation in resting and activated immune cells.

It is the main function of T cells to identify harmful antigens as quickly and precisely as possible. Super-resolution microscopy data has indicated that global clustering of the T cell receptor (TCR) occurs prior to T cell activation. Such pre-activation clustering has been interpreted as representing a potential regulatory mechanism that fine-tunes the T cell response. We found here that apparent TCR nanoclustering could be attributed to overcounting artifacts inherent to single-molecule-localization microscopy. Using complementary super-resolution approaches and statistical image analysis, we found no indication of global nanoclustering of the TCR on antigen-experienced CD4 + T cells under non-activating conditions. We also used extensive simulations of super-resolution images to provide quantitative limits for the degree of randomness of the TCR distribution. Together, our results suggest that the distribution of TCRs on the plasma membrane is optimized for fast recognition of antigen in the first phase of T cell activation.

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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...

Superresolution microscopy results have sparked the idea that many membrane proteins are not randomly distributed across the plasma membrane but are instead arranged in nanoclusters. Frequently, these new results seemed to confirm older data based on biochemical and electron microscopy experiments. Recently, however, it was recognized that multiple countings of the very same fluorescently labeled protein molecule can be easily confused with true protein clusters. Various strategies have been developed, which are intended to solve the problem of discriminating true protein clusters from imaging artifacts. We believe that there is currently no perfect algorithm for this problem; instead, different approaches have different strengths and weaknesses. In this review, we discuss single molecule localization microscopy in view of its ability to detect nanoclusters of membrane proteins. To capture the different views on nanoclustering, we chose an unconventional style for this article: we placed its scientific content in the setting of a fictive conference, where five researchers from different fields discuss the problem of detecting and quantifying nanoclusters. Using this style, we feel that the different approaches common for different research areas can be well illustrated. Similarities to a short story by Raymond Carver are not unintentional.