We present Localizer, a freely available and open source software package that implements the computational data processing inherent to several types of superresolution fluorescence imaging, such as localization (PALM/STORM/GSDIM) and fluctuation imaging (SOFI/pcSOFI). Localizer delivers high accuracy and performance and comes with a fully featured and easy-to-use graphical user interface but is also designed to be integrated in higher-level analysis environments. Due to its modular design, Localizer can be readily extended with new algorithms as they become available, while maintaining the same interface and performance. We provide front-ends for running Localizer from Igor Pro, Matlab, or as a stand-alone program. We show that Localizer performs favorably when compared with two existing superresolution packages, and to our knowledge is the only freely available implementation of SOFI/pcSOFI microscopy. By dramatically improving the analysis performance and ensuring the easy addition of current and future enhancements, Localizer strongly improves the usability of superresolution imaging in a variety of biomedical studies.

In wide-field super-resolution microscopy, investigating the nanoscale structure of cellular processes, and resolving fast dynamics and morphological changes in cells requires algorithms capable of working with a high-density of emissive fluorophores. Current deconvolution algorithms estimate fluorophore density by using representations of the signal that promote sparsity of the super-resolution images via an L₁-norm penalty. This penalty imposes a restriction on the sum of absolute values of the estimates of emitter brightness. By implementing an L₀-norm penalty – on the number of fluorophores rather than on their overall brightness – we present a penalized regression approach that can work at high-density and allows fast super-resolution imaging. We validated our approach on simulated images with densities up to 15 emitters per μm^-2and investigated total internal reflection fluorescence (TIRF) data of mitochondria in a HEK293-T cell labeled with DAKAP-Dronpa. We demonstrated super-resolution imaging of the dynamics with a resolution down to 55 nm and a 0.5 s time sampling.

It has become increasingly clear that protein-protein interactions (PPIs) are compartmentalized in nanoscale domains that define the biochemical architecture of the cell. Despite tremendous advances in super-resolution imaging, strategies to observe PPIs at sufficient resolution to discern their organization are just emerging. Here we describe a strategy in which PPIs induce reconstitution of fluorescent proteins (FPs) that are capable of exhibiting single-molecule fluctuations suitable for stochastic optical fluctuation imaging (SOFI). Subsequently, spatial maps of these interactions can be resolved in super-resolution in living cells. Using this strategy, termed reconstituted fluorescence-based SOFI (refSOFI), we investigated the interaction between the endoplasmic reticulum (ER) Ca(2+) sensor STIM1 and the pore-forming channel subunit ORAI1, a crucial process in store-operated Ca(2+) entry (SOCE). Stimulating SOCE does not appear to change the size of existing STIM1/ORAI1 interaction puncta at the ER-plasma membrane junctions, but results in an apparent increase in the number of interaction puncta.