STORM Microscopy and Cluster Analysis for PcG Studies.

Nanoscopic imaging techniques provide a powerful set of tools for static and dynamic fluorescence microscopy below the diffraction limit of light. Among these super-resolution techniques, photoactivated localization microscopy (PALM) and stochastic optical reconstruction microscopy (STORM) are based on the detection of single fluorophores, whose signals are localized with great precision. The strength of single-molecule approaches lies in their high localization accuracy and their molecular specificity and in the development of multicolor approaches, three-dimensional imaging, and the quantification of absolute molecule numbers. These capabilities make PALM and STORM imaging ideally suited to visualize the ultrastructures of synapses, to track the movements of synaptic components, and to quantify the molecular plasticity at synapses.Key wordsActive zone (AZ)Dendritic spineGluA1 (AMPA receptor subunit GluR1)GluN2B (NMDA receptor subunit NR2B)Photoactivated localization microscopy (PALM)Postsynaptic density (PSD)Stochastic optical reconstruction microscopy (STORM)

Light microscopy is a widely used imaging method in biomedical research. However, the resolution of conventional optical microcopy is limited by the diffraction of light, making structures smaller than 200 nm difficult to resolve. To overcome this limit, we have developed a new form of fluorescence microscopy - Stochastic Optical Reconstruction Microscopy (STORM). STORM makes use of single-molecule imaging methods and photo-switchable fluorescent probes to temporally separate the otherwise spatially overlapping images of individual molecules. An STORM image is acquired over a number of imaging cycles, and in each cycle only a subset of the fluorescent labels is switched on such that each of the active fluorophores is optically resolvable from the rest. This allows the position of these fluorophores to be determined with nanometer accuracy. Over the course of many such cycles, the positions of numerous fluorophores are determined and used to construct a super-resolution image. Using this method, we have demonstrated multi-color, three-dimensional (3D) imaging of biomolecules and cells with ∼ 20 nm lateral and ∼ 50 nm axial resolutions. In principle, the resolution of this technique can reach the molecular scale.

Stochastic optical reconstruction microscopy (STORM) can achieve resolutions of better than 20nm imaging single fluorescently labeled cells. However, when optical aberrations induced by larger biological samples degrade the point spread function (PSF), the localization accuracy and number of localizations are both reduced, destroying the resolution of STORM. Adaptive optics (AO) can be used to correct the wavefront, restoring the high resolution of STORM. A challenge for AO-STORM microscopy is the development of robust optimization algorithms which can efficiently correct the wavefront from stochastic raw STORM images. Here we present the implementation of a particle swarm optimization (PSO) approach with a Fourier metric for real-time correction of wavefront aberrations during STORM acquisition. We apply our approach to imaging boutons 100 μm deep inside the central nervous system (CNS) of Drosophila melanogaster larvae achieving a resolution of 146 nm.

New technologies are ready to revolutionize glomerular imaging and significantly improve or replace immunofluorescence and electron microscopy, which have driven research and diagnosis of glomerular diseases for over 50 years. Advanced forms of transmission and scanning electron microscopy have revealed the detailed spatial relationships of the glomerular basement membrane, podocytes, and endothelial cells. These may be overshadowed by super resolution microscopy (SRM), which combines the advantages of immunofluorescence and electron microscopy, offers high resolution identification of specific molecules, and images large, physiologically relevant volumes of the glomerulus. Rapidity, ease of use and low cost with some types of SRM make them potentially suitable for routine diagnosis. SRM visualizes structures below the classical diffraction limit of conventional light microscopy by adding a time variable to either the illumination of the specimen, or to the fluorescence signal emitted by it. Ensemble techniques vary illumination and include Structured Illumination Microscopy (SIM) and Stimulation Emission Depletion Microscopy (STED). Single molecule localization techniques vary the light emission by fluorescence labels in the specimen, and include Photoactivated Localization Microscopy (PALM) and Stochastic Optical Reconstruction Microscopy (STORM). Technologies such as expansion microscopy and genetic labeling can also create effective super resolution imaging by non-optical, specialized preparation techniques. All technologies require dark field fluorescence and some require computer image analysis and reconstruction. Replicating successful application in other areas of biology, SIM, STED, and STORM have visualized normal and nephrotic disease podocytes, and have confirmed their appearances to be similar to those seen by electron microscopy, but with added new information on cell configuration and protein localization. STORM has also localized podocyte cytoskeleton and adhesion proteins, and glomerular basement membrane proteins at a resolution never before possible. These pioneering efforts show the promise of super resolution microscopy, and lay the groundwork for future study and new diagnostic tools for glomerular diseases.

Exposure to biological fluid envelops a nanoparticle in layers of proteins and biomolecules, which has a profound impact on the nanoparticle's biological fate. Although the identities and amounts of the proteins in this "corona" have been thoroughly examined, the spatial arrangement of the proteins is unclear, a problem that is compounded on porous nanoparticles due to penetration of proteins within the porous network. To address this problem, we have developed a procedure based on information derived from stochastic optical reconstruction microscopy. We employed a mathematical model to reveal the penetration depth of several proteins within porous nanoparticles. Understanding protein penetration depth provides an explanation for the composition of the protein corona, aiding in the development of safe and effective particle-based therapies.

ABSTRACTMycoplasmas are the smallest free-living organisms. These bacteria are important models for both fundamental and synthetic biology, owing to their highly reduced genomes. They are also relevant in the medical and veterinary fields, as they are pathogenic to both humans and most livestock species. Mycoplasma cells have minute sizes, often in the 300- to 800-nm range. As these dimensions are close to the diffraction limit of visible light, fluorescence imaging in mycoplasmas is often poorly informative. Recently developed superresolution imaging techniques can break this diffraction limit, improving the imaging resolution by an order of magnitude and offering a new nanoscale vision of the organization of these bacteria. These techniques have, however, not been applied to mycoplasmas before. Here, we describe an efficient and reliable protocol to perform single-molecule localization microscopy (SMLM) imaging in mycoplasmas. We provide a polyvalent transposon-based system to express the photoconvertible fluorescent protein mEos3.2, enabling photo-activated localization microscopy (PALM) in most Mycoplasma species. We also describe the application of direct stochastic optical reconstruction microscopy (dSTORM). We showcase the potential of these techniques by studying the subcellular localization of two proteins of interest. Our work highlights the benefits of state-of-the-art microscopy techniques for mycoplasmology and provides an incentive to further the development of SMLM strategies to study these organisms in the future.IMPORTANCE Mycoplasmas are important models in biology, as well as highly problematic pathogens in the medical and veterinary fields. The very small sizes of these bacteria, well below a micron, limits the usefulness of traditional fluorescence imaging methods, as their resolution limit is similar to the dimensions of the cells. Here, to bypass this issue, we established a set of state-of-the-art superresolution microscopy techniques in a wide range of Mycoplasma species. We describe two strategies: PALM, based on the expression of a specific photoconvertible fluorescent protein, and dSTORM, based on fluorophore-coupled antibody labeling. With these methods, we successfully performed single-molecule imaging of proteins of interest at the surface of the cells and in the cytoplasm, at lateral resolutions well below 50 nm. Our work paves the way toward a better understanding of mycoplasma biology through imaging of subcellular structures at the nanometer scale.

Prior to pancreatic ductal adenocarcinoma (PDAC), pancreatic acini cells change their morphology through pancreatic intraepithelial neoplasia (PanIN) stages, becoming increasingly dysplastic as stroma and tissue hypoxia increase. This increasing hypoxia forms a cancer promoting microenvironment, wherein we propose metabolic changes trigger aberrant mitochondrial networks form healthy tissue. We developed tissue superresolution imaging to directly quantify structural mitochondrial response through patient histology, whereby DNA-PAINT can provide super-resolution detail decoupled from photo bleaching to visualize mitochondria through tissue layers. Expanding from preliminary data, target mitochondrial dynamics proteins’ organization will also be correlated to PanIN stages. Introduction: Early oncogene involvement in PDAC progression links to metabolic and mitochondrial regulatory changes. Typically nutrient deprivation and hypoxia trigger cell death from increased reactive oxygen species production and organelle damage which trigger apoptosis, yet these prolonged effects can be cancer promoting when less severe. Mitochondrial dynamics and proteins related to mitochondrial fission and fusion can reduce apoptotic signaling, enhance aerobic glycolysis, and increase ROS to allow cancer progression. Materials and Methods: Adapting from previous proof of concept, mitochondria (TOM20) within formalin-fixed paraffin embedded (FFPE) tissue sections were imaged with stochastic optical reconstruction microscopy (STORM). In brief: cadaver healthy pancreas FFPE tissue samples underwent deparafinization, antigen retrieval, indirect immuno-labeling with AlexaFluor647, TIRF illumination with a 60x objective was used for data collection, whereby data processing was conducted using the open-source FIJI and custom MATLAB software. Results and Discussion: Preliminary data shows proof of principle that both STORM and DNA-PAINT can be correlated to histological staining of human pancreas. Conclusions: Superresolution imaging reveals ultrastructural details of mitochondria in FFPE patient samples not resolved via conventional fluorescence imaging. Through quantitative feature analysis, we would be able to correlate aberrant mitochondria structure and abundance to PDAC progression. Acknowledgements: Funding provided by the Cancer Early Detection Advanced Research (CEDAR) Center of OHSU Knight Cancer Institute. Citation Format: Matthew J. Rames, Fehmi Civitci, Ting Zheng, Josiah Wagner, Jason Link, Xiaolin Nan. Aberrant mitochondrial protein involvement through early PDAC initiation and progression using multiplexed DNA-PAINT and correlative histology [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 799.

The structure of the centrosome was resolved by EM many years ago to reveal a pair of centrioles embedded in a dense network of proteins. More recently, the molecular composition of the centrosome was catalogued by mass spectroscopy and many novel components were identified. Determining precisely where a novel component localizes to within the centrosome remains a challenge, and until now it has required the use of immuno-EM. This technique is both time-consuming and unreliable, as it often fails due to problems with antigen accessibility. We have investigated the use of two nanoscopic techniques, photoactivated localization microscopy (PALM) and stochastic optical reconstruction microscopy (STORM), as alternative techniques for localizing centrosomal proteins. The localization of a known centrosomal component, the distal appendage protein Cep164 was investigated by direct STORM (dSTORM) and resolved with a high spatial resolution. We further validated the use of nanoscopic PALM imaging by showing that the previously uncharacterized centrosomal protein CCDC123 (Cep123) localizes to the distal appendages, forming ring-like structures with a diameter of 500 nm. Our results demonstrate that both PALM and STORM imaging have great potential as alternatives to immuno-EM.

Single-molecule-localization-based super-resolution microscopic technologies, such as stochastic optical reconstruction microscopy (STORM), require lengthy runtimes. Compressed sensing (CS) can partially overcome this inherent disadvantage, but its effect on super-resolution reconstruction has not been thoroughly examined. In CS, measurement matrices play more important roles than reconstruction algorithms. Larger measurement matrices have better restricted isometry properties (RIPs). This paper proposes, analyzes, and compares uses of higher resolution cameras and interpolation to achieve better outcomes. Statistical results demonstrate that super-resolution reconstructions is significantly improved by interpolating low-resolution STORM raw images and using point-spread-function-based measurement matrices with better RIPs. The analysis of publically accessible experimental data confirms this conclusion.

The Drosophila melanogaster brain comprises different neuronal cell types that interconnect with precise patterns of synaptic connections. These patterns are essential for the normal function of the brain. To understand the connectivity patterns requires characterizing them at single-cell resolution, for which a fluorescence microscope becomes an indispensable tool. Additionally, because the neurons connect at the nanoscale, the investigation often demands super-resolution microscopy. Here, we adopt one super-resolution microscopy technique, called stochastic optical reconstruction microscopy (STORM), improving the lateral and axial resolution to ∼20 nm. This article extensively describes our methods along with considerations for sample preparation of neurons in vitro and in vivo, conjugation of dyes to antibodies, immunofluorescence labeling, and acquisition and processing of STORM data. With these tools and techniques, we open up the potential to investigate cell-cell interactions using STORM in the Drosophila nervous system. © 2021 Wiley Periodicals LLC. Basic Protocol 1: Preparation of Drosophila primary neuronal culture and embryonic fillets Basic Protocol 2: Immunofluorescence labeling of samples Basic Protocol 3: Single-molecule fluorescence imaging Basic Protocol 4: Localization and visualization of single-molecule data Supporting Protocol: Conjugation of antibodies with STORM-compatible dyes.

Gene expression is dynamic and heterogeneous across all cell types. It serves several fundamental functions such as adaptation for changing environment and developing tissue specific phenotype and functionality during development and in adult organism. Control of gene expression mostly occurs at the transcription stage. Two main factors that regulate transcription in high eukaryotes are chromatin organization and transcription factor (TF) protein dynamics. Until recently they were mostly studied by methods that are performed in vitro or/and require ensemble averaging. We have been contributing to a development of new single molecule imaging methods that allow quantifying chromatin organization and TF dynamics in vivo and at single cell/single molecule level. Here we present our methodological developments and apply them to study how chromatin organization and TF dynamics regulate transcription. On the technical side of the study we, first, developed a method of imaging DNA with enhanced resolution in living cells. Super-resolution (SR) point localization based microscopy allows visualizing sub-diffraction organization of cellular structures and their dynamics in vivo. However, until recently almost exclusively proteins were imaged in SR microscopy. We found photoswitching conditions to image with stochastic optical reconstruction microscopy (STORM) DNA directly using the site-specific DNA –binding dye Picogreen (Chapter 2). We achieved a resolution of 50-70 nm which is 5 fold better than conventional microscopy. Due to the excellent dye preservation we were able to do time lapse SR imaging. This study was a first demonstration of using a site-specific dye for STORM SR imaging in living cells. Photoswitching principle used in SR microscopy can be applied to single molecule (SM) tracking. It provides up to 1000x increase in density of trajectories compared to classic SM tracking. Initially, photoactivatable (PA) fusion proteins were used for high density SR based tracking. However, PA proteins are relatively dim and there is a limited choice of them. We demonstrated a new approach to SR based high density tracking by using organic dyes’ photoswitching to track proteins (Chapter 4). We showed that different dyes can be photoswitched in similar live-cell compatible media on membrane and in different organelles. Together with orthogonal protein labelling schemes this allows us to perform multicolour high density tracking across different compartments. This approach gives flexibility of labelling and increased track length compared to PA-FP SR based tracking. In a separate study, using tracking we developed algorithm which can account for long-living molecules in live cell SR imaging and allows us to correct clustering artefacts (Chapter 5). [...]

Stochastic optical reconstruction microscopy (STORM) is extensively utilized in the fields of cell and molecular biology as a super-resolution imaging technique for visualizing cells and molecules. Nonetheless, the imaging process of STORM is frequently susceptible to noise, which can significantly impact the subsequent image analysis. Moreover, there is currently a lack of a comprehensive automated processing approach for analyzing protein aggregation states from a large number of STORM images. This paper initially applies our previously proposed denoising algorithm, UNet-Att, in STORM image denoising. This algorithm was constructed based on attention mechanism and multi-scale features, showcasing a remarkably efficient performance in denoising. Subsequently, we propose a collection of automated image processing algorithms for the ultimate feature extractions and data analyses of the STORM images. The information extraction workflow effectively integrates automated methods of image denoising, objective image segmentation and binarization, and object information extraction, and a novel image information clustering algorithm specifically developed for the morphological analysis of the objects in the STORM images. This automated workflow significantly improves the efficiency of the effective data analysis for large-scale original STORM images.

Super-resolution microscopy has been widely used to study protein interactions and subcellular structures in many organisms. In photosynthetic organisms, however, the lateral resolution of super-resolution imaging is only ~100 nm. The low resolution is mainly due to the high autofluorescence background of photosynthetic cells caused by high-intensity lasers that are required for super-resolution imaging, such as stochastic optical reconstruction microscopy (STORM). Here, we describe a photobleaching-assisted STORM method which was developed recently for imaging the marine picocyanobacterium Prochlorococcus. After photobleaching, the autofluorescence of Prochlorococcus is effectively reduced so that STORM can be performed with a lateral resolution of ~10 nm. Using this method, we acquire the in vivo three-dimensional (3-D) organization of the FtsZ protein and characterize four different FtsZ ring morphologies during the cell cycle of Prochlorococcus. The method we describe here might be adopted for the super-resolution imaging of other photosynthetic organisms.

Far-field optical microscopy using focused light is an important tool in a number of scientific disciplines including chemical, (bio)physical and biomedical research, particularly with respect to the study of living cells and organisms. Unfortunately, the applicability of the optical microscope is limited, since the diffraction of light imposes limitations on the spatial resolution of the image. Consequently the details of, for example, cellular protein distributions, can be visualized only to a certain extent. Fortunately, recent years have witnessed the development of ‘super-resolution’ far-field optical microscopy (nanoscopy) techniques such as stimulated emission depletion (STED), ground state depletion (GSD), reversible saturated optical (fluorescence) transitions (RESOLFT), photoactivation localization microscopy (PALM), stochastic optical reconstruction microscopy (STORM), structured illumination microscopy (SIM) or saturated structured illumination microscopy (SSIM), all in one way or another addressing the problem of the limited spatial resolution of far-field optical microscopy. While SIM achieves a two-fold improvement in spatial resolution compared to conventional optical microscopy, STED, RESOLFT, PALM/STORM, or SSIM have all gone beyond, pushing the limits of optical image resolution to the nanometer scale. Consequently, all super-resolution techniques open new avenues of biomedical research. Because the field is so young, the potential capabilities of different super-resolution microscopy approaches have yet to be fully explored, and uncertainties remain when considering the best choice of methodology. Thus, even for experts, the road to the future is sometimes shrouded in mist. The super-resolution optical microscopy roadmap of Journal of Physics D: Applied Physics addresses this need for clarity. It provides guidance to the outstanding questions through a collection of short review articles from experts in the field, giving a thorough discussion on the concepts underlying super-resolution optical microscopy, the potential of different approaches, the importance of label optimization (such as reversible photoswitchable proteins) and applications in which these methods will have a significant impact.Mark Bates, Christian Eggeling

The field of optical nanoscopy , a paradigm referring to the recent cutting-edge developments aimed at surpassing the widely acknowledged 200nm-diffraction limit in traditional optical microscopy, has gained recent prominence & traction in the $21^{\mathrm {st}}$ century. Numerous optical implementations allowing for a new frontier in traditional confocal laser scanning fluorescence microscopy to be explored (termed super-resolution fluorescence microscopy ) have been realized through the development of techniques such as stimulated emission and depletion (STED) microscopy, photoactivated localization microscopy (PALM) and stochastic optical reconstruction microscopy (STORM), amongst others. Nonetheless, it would be apt to mention at this juncture that optical nanoscopy has been explored since the mid-late $20^{\mathrm {th}}$ century, through several computational techniques such as deblurring and deconvolution algorithms. In this review, we take a step back in the field, evaluating the various in silico methods used to achieve optical nanoscopy today, ranging from traditional deconvolution algorithms (such as the Nearest Neighbors algorithm) to the latest developments in the field of computational nanoscopy, founded on artificial intelligence (AI). An insight is provided into some of the commercial applications of AI-based super-resolution imaging, prior to delving into the potentially promising future implications of computational nanoscopy. This is facilitated by recent advancements in the field of AI, deep learning (DL) and convolutional neural network (CNN) architectures, coupled with the growing size of data sources and rapid improvements in computing hardware, such as multi-core CPUs & GPUs, low-latency RAM and hard-drive capacities.