Localization Microscopy Methods Research Articles

ThunderSTORM: a comprehensive ImageJ plug-in for PALM and STORM data analysis and super-resolution imaging.

Summary: ThunderSTORM is an open-source, interactive and modular plug-in for ImageJ designed for automated processing, analysis and visualization of data acquired by single-molecule localization microscopy methods such as photo-activated localization microscopy and stochastic optical reconstruction microscopy. ThunderSTORM offers an extensive collection of processing and post-processing methods so that users can easily adapt the process of analysis to their data. ThunderSTORM also offers a set of tools for creation of simulated data and quantitative performance evaluation of localization algorithms using Monte Carlo simulations.Availability and implementation: ThunderSTORM and the online documentation are both freely accessible at https://code.google.com/p/thunder-storm/Contact: guy.hagen@lf1.cuni.czSupplementary information: Supplementary data are available at Bioinformatics online.

Fluorophore localization algorithms for super-resolution microscopy

Super-resolution localization microscopy methods provide powerful new capabilities for probing biology at the nanometer scale via fluorescence. These methods rely on two key innovations: switchable fluorophores (which blink on and off and can be sequentially imaged) and powerful localization algorithms (which estimate the positions of the fluorophores in the images). These techniques have spurred a flurry of innovation in algorithm development over the last several years. In this Review, we survey the fundamental issues for single-fluorophore fitting routines, localization algorithms based on principles other than fitting, three-dimensional imaging, dipole imaging and techniques for estimating fluorophore positions from images of multiple activated fluorophores. We offer practical advice for users and adopters of algorithms, and we identify areas for further development.

Eight years of single-molecule localization microscopy

Super-resolution imaging by single-molecule localization (localization microscopy) provides the ability to unravel the structural organization of cells and the composition of biomolecular assemblies at a spatial resolution that is well below the diffraction limit approaching virtually molecular resolution. Constant improvements in fluorescent probes, efficient and specific labeling techniques as well as refined data analysis and interpretation strategies further improved localization microscopy. Today, it allows us to interrogate how the distribution and stoichiometry of interacting proteins in subcellular compartments and molecular machines accomplishes complex interconnected cellular processes. Thus, it exhibits potential to address fundamental questions of cell and developmental biology. Here, we briefly introduce the history, basic principles, and different localization microscopy methods with special focus on direct stochastic optical reconstruction microscopy (dSTORM) and summarize key developments and examples of two- and three-dimensional localization microscopy of the last 8 years.

Spatial cluster analysis of nanoscopically mapped serotonin receptors for classification of fixed brain tissue

We present a cluster spatial analysis method using nanoscopic dSTORM images to determine changes in protein cluster distributions within brain tissue. Such methods are suitable to investigate human brain tissue and will help to achieve a deeper understanding of brain disease along with aiding drug development. Human brain tissue samples are usually treated postmortem via standard fixation protocols, which are established in clinical laboratories. Therefore, our localization microscopy-based method was adapted to characterize protein density and protein cluster localization in samples fixed using different protocols followed by common fluorescent immunohistochemistry techniques. The localization microscopy allows nanoscopic mapping of serotonin 5-HT1A receptor groups within a two-dimensional image of a brain tissue slice. These nanoscopically mapped proteins can be confined to clusters by applying the proposed statistical spatial analysis. Selected features of such clusters were subsequently used to characterize and classify the tissue. Samples were obtained from different types of patients, fixed with different preparation methods, and finally stored in a human tissue bank. To verify the proposed method, samples of a cryopreserved healthy brain have been compared with epitope-retrieved and paraffin-fixed tissues. Furthermore, samples of healthy brain tissues were compared with data obtained from patients suffering from mental illnesses (e.g., major depressive disorder). Our work demonstrates the applicability of localization microscopy and image analysis methods for comparison and classification of human brain tissues at a nanoscopic level. Furthermore, the presented workflow marks a unique technological advance in the characterization of protein distributions in brain tissue sections.

Superresolution Localization Methods

Superresolution localization microscopy methods produce nanoscale images via a combination of intermittently active fluorescent probes and algorithms that can precisely determine the positions of these probes from single-molecule or few-molecule images. These algorithms vary widely in their underlying principles, complexity, and accuracy. In this review, we begin by surveying the principles of localization microscopy and describing the fundamental limits to localization precision. We then examine several different families of fluorophore localization algorithms, comparing their complexity, performance, and range of applicability (e.g., whether they require particular types of experimental information, are optimized for specific situations, or are more general). Whereas our focus is on the localization of single isotropic emitters in two dimensions, we also consider oriented dipoles, three-dimensional localization, and algorithms that can handle overlapping images of several nearby fluorophores. Throughout the review, we try to highlight practical advice for users of fluorophore localization algorithms, as well as open questions.

Measuring localization performance of super-resolution algorithms on very active samples

Super-resolution fluorescence imaging based on single-molecule localization relies critically on the availability of efficient processing algorithms to distinguish, identify, and localize emissions of single fluorophores. In multiple current applications, such as three-dimensional, time-resolved or cluster imaging, high densities of fluorophore emissions are common. Here, we provide an analytic tool to test the performance and quality of localization microscopy algorithms and demonstrate that common algorithms encounter difficulties for samples with high fluorophore density. We demonstrate that, for typical single-molecule localization microscopy methods such as dSTORM and the commonly used rapidSTORM scheme, computational precision limits the acceptable density of concurrently active fluorophores to 0.6 per square micrometer and that the number of successfully localized fluorophores per frame is limited to 0.2 per square micrometer.

PALM and STORM: Unlocking live‐cell super‐resolution

Live-cell fluorescence light microscopy has emerged as an important tool in the study of cellular biology. The development of fluorescent markers in parallel with super-resolution imaging systems has pushed light microscopy into the realm of molecular visualization at the nanometer scale. Resolutions previously only attained with electron microscopes are now within the grasp of light microscopes. However, until recently, live-cell imaging approaches have eluded super-resolution microscopy, hampering it from reaching its full potential for revealing the dynamic interactions in biology occurring at the single molecule level. Here we examine recent advances in the super-resolution imaging of living cells by reviewing recent breakthroughs in single molecule localization microscopy methods such as PALM and STORM to achieve this important goal.

Coherent nonlinear single-molecule microscopy

We investigate a nonlinear localization microscopy method based on Rabi oscillations of single emitters. We demonstrate the fundamental working principle of this new technique using a cryogenic far-field experiment in which subwavelength features smaller than $\lambda$/10 are obtained. Using Monte Carlo simulations, we show the superior localization accuracy of this method under realistic conditions and a potential for higher acquisition speed or a lower number of required photons as compared to conventional linear schemes. The method can be adapted to other emitters than molecules and allows for the localization of several emitters at different distances to a single measurement pixel.

Real‐time computation of subdiffraction‐resolution fluorescence images

In the recent past, single-molecule based localization or photoswitching microscopy methods such as stochastic optical reconstruction microscopy (STORM) or photoactivated localization microscopy (PALM) have been successfully implemented for subdiffraction-resolution fluorescence imaging. However, the computational effort needed to localize numerous fluorophores is tremendous, causing long data processing times and thereby limiting the applicability of the technique. Here we present a new computational scheme for data processing consisting of noise reduction, detection of likely fluorophore positions, high-precision fluorophore localization and subsequent visualization of found fluorophore positions in a super-resolution image. We present and benchmark different algorithms for noise reduction and demonstrate the use of non-maximum suppression to quickly find likely fluorophore positions in high depth and very noisy images. The algorithm is evaluated and compared in terms of speed, accuracy and robustness by means of simulated data. On real biological samples, we find that real-time data processing is possible and that super-resolution imaging with organic fluorophores of cellular structures with approximately 20 nm optical resolution can be completed in less than 10 s.

Light-Induced Dark States of Organic Fluochromes Enable 30 nm Resolution Imaging in Standard Media

We show that high quantum efficiency fluorophores can exhibit reversible photobleaching. This observation provides the basis for an imaging technique we call reversible photobleaching microscopy. We demonstrate applicability of this technique using antibody labeled biological samples in standard aqueous (or glycerol based) media to produce far-field images at ∼30 nm resolution. Our novel method relies on intense illumination to reversibly induce a very long-lived (>10 s) dark state from which single fluorochromes slowly return stochastically. As in other localization microscopy methods, reversible photobleaching microscopy localizes single fluorochromes, but has the advantage that specialized photoactivatible and photoswitchable molecules or special immersion/embedding media are not required.