A Review of Super-Resolution Single-Molecule Localization Microscopy Cluster Analysis and Quantification Methods

Self-Blinking Thioflavin T for Super-resolution Imaging

Thioflavin T (ThT) is a typical dye used to visualize the aggregation and formation of fibrillar structures, e.g., amyloid fibrils and peptide nanofibrils. ThT has been considered to produce stable fluorescence when interacting with aggregated proteins. For single-molecule localization microscopy (SMLM)-based optical super-resolution imaging, a photoswitching/blinking fluorescence property is required. Here we demonstrate that, in contrast to previous reports, ThT exhibits intrinsic stochastic blinking properties, without the need for blinking imaging buffer, in stable binding conditions. The blinking properties (photon number, blinking time, and on-off duty cycle) of ThT at the single-molecule level (for ultralow concentrations) were investigated under different conditions. As a proof of concept, we performed SMLM imaging of ThT-labeled α-synuclein fibrils measured in air and PBS buffer.

Machine Learning for Single-Molecule Localization Microscopy: From Data Analysis to Quantification

Single-molecule localization microscopy (SMLM) is a versatile tool for realizing nanoscale imaging with visible light and providing unprecedented opportunities to observe bioprocesses. The integration of machine learning with SMLM enhances data analysis by improving efficiency and accuracy. This tutorial aims to provide a comprehensive overview of the data analysis process and theoretical aspects of SMLM, while also highlighting the typical applications of machine learning in this field. By leveraging advanced analytical techniques, SMLM is becoming a powerful quantitative analysis tool for biological research.

An UltraMNIST classification benchmark to train CNNs for very large images

Current convolutional neural networks (CNNs) are not designed for large scientific images with rich multi-scale features, such as in satellite and microscopy domain. A new phase of development of CNNs especially designed for large images is awaited. However, application-independent high-quality and challenging datasets needed for such development are still missing. We present the 'UltraMNIST dataset' and associated benchmarks for this new research problem of 'training CNNs for large images'. The dataset is simple, representative of wide-ranging challenges in scientific data, and easily customizable for different levels of complexity, smallest and largest features, and sizes of images. Two variants of the problem are discussed: standard version that facilitates the development of novel CNN methods for effective use of the best available GPU resources and the budget-aware version to promote the development of methods that work under constrained GPU memory. Several baselines are presented and the effect of reduced resolution is studied. The presented benchmark dataset and baselines will hopefully trigger the development of new CNN methods for large scientific images.

Heterogeneous density-based clustering with a dual-functional memristive array

In the big data era, the requirement for data clustering methods that can handle massive and heterogeneous datasets with varying distributions increases. This study proposes a clustering algorithm for data sets with heterogeneous density using a dual-mode memristor crossbar array for data clustering. The array consists of a Ta/HfO2/RuO2 memristor operating in analog or digital modes, controlled by the reset voltage. The digital mode shows low dispersion and a high resistance ratio, and the analog mode enables precise conductance tuning. The local outlier factor is introduced to handle a heterogeneous density, and the required Euclidean and K-distances within the given dataset are calculated in the analog mode in parallel. In the digital mode, clustering is performed based on the connectivity among data points after excluding the detected outliers. The proposed algorithm boasts linear time complexity for the entire process. Extensive evaluations of synthetic datasets demonstrate significant improvement over representative density-based algorithms, and the datasets with heterogeneous density are clustered feasibly. Finally, the proposed algorithm is used to cluster the single-molecule localization microscopy data, demonstrating the feasibility of the suggested method for real-world problems.

Fluorescent chemosensors facilitate the visualization of plant health and their living environment in sustainable agriculture

Globally, 91% of plant production encounters diverse environmental stresses that adversely affect their growth, leading to severe yield losses of 50-60%. In this case, monitoring the connection between the environment and plant health can balance population demands with environmental protection and resource distribution. Fluorescent chemosensors have shown great progress in monitoring the health and environment of plants due to their high sensitivity and biocompatibility. However, to date, no comprehensive analysis and systematic summary of fluorescent chemosensors used in monitoring the correlation between plant health and their environment have been reported. Thus, herein, we summarize the current fluorescent chemosensors ranging from their design strategies to applications in monitoring plant-environment interaction processes. First, we highlight the types of fluorescent chemosensors with design strategies to resolve the bottlenecks encountered in monitoring the health and living environment of plants. In addition, the applications of fluorescent small-molecule, nano and supramolecular chemosensors in the visualization of the health and living environment of plants are discussed. Finally, the major challenges and perspectives in this field are presented. This work will provide guidance for the design of efficient fluorescent chemosensors to monitor plant health, and then promote sustainable agricultural development.

Molecular probes for super-resolution imaging of drug dynamics

Super-resolution molecular probes (SRMPs) are essential tools for visualizing drug dynamics within cells, transcending the resolution limits of conventional microscopy. In this review, we provide an overview of the principles and design strategies of SRMPs, emphasizing their role in accurately tracking drug molecules. By illuminating the intricate processes of drug distribution, diffusion, uptake, and metabolism at a subcellular and molecular level, SRMPs offer crucial insights into therapeutic interventions. Additionally, we explore the practical applications of super-resolution imaging in disease treatment, highlighting the significance of SRMPs in advancing our understanding of drug action. Finally, we discuss future perspectives, envisioning potential advancements and innovations in this field. Overall, this review serves to inform and practitioners about the utility of SRMPs in driving innovation and progress in pharmacology, providing valuable insights for drug development and optimization.

Dissecting gene activation and chromatin remodeling dynamics in single human cells undergoing reprogramming

During cell fate transitions, cells remodel their transcriptome, chromatin, and epigenome; however, it has been difficult to determine the temporal dynamics and cause-effect relationship between these changes at the single-cell level. Here, we employ the heterokaryon-mediated reprogramming system as a single-cell model to dissect key temporal events during early stages of pluripotency conversion using super-resolution imaging. We reveal that, following heterokaryon formation, the somatic nucleus undergoes global chromatin decompaction and removal of repressive histone modifications H3K9me3 and H3K27me3 without acquisition of active modifications H3K4me3 and H3K9ac. The pluripotency gene OCT4 (POU5F1) shows nascent and mature RNA transcription within the first 24 h after cell fusion without requiring an initial open chromatin configuration at its locus. NANOG, conversely, has significant nascent RNA transcription only at 48 h after cell fusion but, strikingly, exhibits genomic reopening early on. These findings suggest that the temporal relationship between chromatin compaction and gene activation during cellular reprogramming is gene context dependent.

Direct Imaging of Protein Clusters in Metal-Organic Frameworks

Protein@metal-organic frameworks (P@MOFs) prepared by coprecipitation of protein, metal ions, and organic ligands represent an effective method for protein stabilization with a wide spectrum of applications. However, the formation mechanism of P@MOFs via the coprecipitation process and the reason why proteins can retain their biological activity in the frameworks with highly concentrated metal ions remain unsettled. Here, by a combined methodology of single molecule localization microscopy and clustering analysis, we discovered that in this process enzyme molecules form clusters with metal ions and organic ligands, contributing to both the nucleation and subsequent crystal growth. We proposed that the clusters played an important role in the retention of overall enzymatic activity by sacrificing protein molecules on the cluster surface. This work offers fresh perspectives on protein behaviors in the formation of P@MOFs, inspiring future endeavors in the design and development of artificial bionanocomposites with high biological activities.

Estimation with Ultimate Quantum Precision of the Transverse Displacement between Two Photons via Two-Photon Interference Sampling Measurements

We present a quantum sensing scheme achieving the ultimate quantum sensitivity in the estimation of the transverse displacement between two photons interfering at a balanced beam splitter, based on transverse-momentum sampling measurements at the output. This scheme can possibly lead to enhanced high-precision nanoscopic techniques, such as superresolved single-molecule localization microscopy with quantum dots, by circumventing the requirements in standard direct imaging of camera resolution at the diffraction limit, and of highly magnifying objectives. Interestingly, we show that our interferometric technique achieves the ultimate spatial precision in nature irrespectively of the overlap of the two displaced photonic wave packets, while its precision is only reduced of a constant factor for photons differing in any nonspatial degrees of freedom. This opens a new research paradigm based on the interface between spatially resolved quantum interference and quantum-enhanced spatial sensitivity.

Scaffolds and the scaffolding domain: an alternative paradigm for caveolin-1 signaling

Caveolin-1 (Cav1) is a 22 kDa intracellular protein that is the main protein constituent of bulb-shaped membrane invaginations known as caveolae. Cav1 can be also found in functional non-caveolar structures at the plasma membrane called scaffolds. Scaffolds were originally described as SDS-resistant oligomers composed of 10-15 Cav1 monomers observable as 8S complexes by sucrose velocity gradient centrifugation. Recently, cryoelectron microscopy (cryoEM) and super-resolution microscopy have shown that 8S complexes are interlocking structures composed of 11 Cav1 monomers each, which further assemble modularly to form higher-order scaffolds and caveolae. In addition, Cav1 can act as a critical signaling regulator capable of direct interactions with multiple client proteins, in particular, the endothelial nitric oxide (NO) synthase (eNOS), a role believed by many to be attributable to the highly conserved and versatile scaffolding domain (CSD). However, as the CSD is a hydrophobic domain located by cryoEM to the periphery of the 8S complex, it is predicted to be enmeshed in membrane lipids. This has led some to challenge its ability to interact directly with client proteins and argue that it impacts signaling only indirectly via local alteration of membrane lipids. Here, based on recent advances in our understanding of higher-order Cav1 structure formation, we discuss how the Cav1 CSD may function through both lipid and protein interaction and propose an alternate view in which structural modifications to Cav1 oligomers may impact exposure of the CSD to cytoplasmic client proteins, such as eNOS.

Squaraine Dyes Exhibit Spontaneous Fluorescence Blinking That Enables Live-Cell Nanoscopy

Hampered by their susceptibility to nucleophilic attack and chemical bleaching, electron-deficient squaraine dyes have long been considered unsuitable for biological imaging. This study unveils a surprising twist: in aqueous environments, bleaching is not irreversible but rather a reversible spontaneous quenching process. Leveraging this new discovery, we introduce a novel deep-red squaraine probe tailored for live-cell super-resolution imaging. This probe enables single-molecule localization microscopy (SMLM) under physiological conditions without harmful additives or intense lasers and exhibits spontaneous blinking orchestrated by biological nucleophiles, such as glutathione or hydroxide anion. With a low duty cycle (∼0.1%) and high-emission rate (∼6 × 104 photons/s under 400 W/cm2), the squaraine probe surpasses the benchmark Cy5 dye by 4-fold and Si-rhodamine by a factor of 1.7 times. Live-cell SMLM with the probe reveals intricate structural details of cell membranes, which demonstrates the high potential of squaraine dyes for next-generation super-resolution imaging.

Increased vesicular dynamics and nanoscale clustering of IL-2 after T cell activation

T cells coordinate intercellular communication through the meticulous regulation of cytokine secretion. Direct visualization of vesicular transport and intracellular distribution of cytokines provides valuable insights into the temporal and spatial mechanisms involved in regulation. Employing Jurkat E6-1 T cells and interleukin-2 (IL-2) as a model system, we investigated vesicular dynamics using single-particle tracking and the nanoscale distribution of intracellular IL-2 in fixed T cells using superresolution microscopy. Live-cell imaging revealed that in vitro activation resulted in increased vesicular dynamics. Direct stochastic optical reconstruction microscopy and 3D structured illumination microscopy revealed nanoscale clustering of IL-2. In vitro activation correlated with spatial accumulation of IL-2 nanoclusters into more pronounced and elongated clusters. These observations provide visual evidence that accelerated vesicular transport and spatial concatenation of IL-2 clusters at the nanoscale may constitute a potential mechanism for modulating cytokine release by Jurkat T cells.

Quantifying nanoscopic alterations associated with mitochondrial dysfunction using three-dimensional single-molecule localization microscopy

Mitochondrial morphology provides unique insights into their integrity and function. Among fluorescence microscopy techniques, 3D super-resolution microscopy uniquely enables the analysis of mitochondrial morphological features individually. However, there is a lack of tools to extract morphological parameters from super-resolution images of mitochondria. We report a quantitative method to extract mitochondrial morphological metrics, including volume, aspect ratio, and local protein density, from 3D single-molecule localization microscopy images, with single-mitochondrion sensitivity. We validated our approach using simulated ground-truth SMLM images of mitochondria. We further tested our morphological analysis on mitochondria that have been altered functionally and morphologically in controlled manners. This work sets the stage to quantitatively analyze mitochondrial morphological alterations associated with disease progression on an individual basis.

Comparison of the SMLM technique and the MSSR algorithm in confocal microscopy for super-resolved imaging of cellulose fibres

Nowadays, the use of super-resolution microscopy (SRM) is increasing globally due to its potential application in several fields of life sciences. However, a detailed and comprehensive guide is necessary for understanding a single-frame image's resolution limit. This study was performed to provide information about the structural organisation of isolated cellulose fibres from garlic and agave wastes through fluorophore-based techniques and image analysis algorithms. Confocal microscopy provided overall information on the cellulose fibres' microstructure, while techniques such as total internal reflection fluorescence microscopy facilitated the study of the plant fibres' surface structures at a sub-micrometric scale. Furthermore, SIM and single-molecule localisation microscopy (SMLM) using the PALM reconstruction wizard can resolve the network of cellulose fibres at the nanometric level. In contrast, the mean shift super-resolution (MSSR) algorithm successfully determined nanometric structures from confocal microscopy images. Atomic force microscopy was used as a microscopy technique for measuring the size of the fibres. Similar fibre sizes to those evaluated with SIM and SMLM were found using the MSSR algorithm and AFM. However, the MSSR algorithm must be cautiously applied because the selection of thresholding parameters still depends on human visual perception. Therefore, this contribution provides a comparative study of SRM techniques and MSSR algorithm using cellulose fibres as reference material to evaluate the performance of a mathematical algorithm for image processing of bioimages at a nanometric scale. In addition, this work could act as a simple guide for improving the lateral resolution of single-frame fluorescence bioimages when SRM facilities are unavailable.

Cell-Material Interplay in Focal Adhesion Points

The complex interplay between cells and materials is a key focus of this research, aiming to develop optimal scaffolds for regenerative medicine. The need for tissue regeneration underscores understanding cellular behavior on scaffolds, especially cell adhesion to polymer fibers forming focal adhesions. Key proteins, paxillin and vinculin, regulate cell signaling, migration, and mechanotransduction in response to the extracellular environment. This study utilizes advanced microscopy, specifically the AiryScan technique, along with advanced image analysis employing the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) cluster algorithm, to investigate protein distribution during osteoblast cell adhesion to polymer fibers and glass substrates. During cell attachment to both glass and polymer fibers, a noticeable shift in the local maxima of paxillin and vinculin signals is observed at the adhesion sites. The focal adhesion sites on polymer fibers are smaller and elliptical but exhibit higher protein density than on the typical glass surface. The characteristics of focal adhesions, influenced by paxillin and vinculin, such as size and density, can potentially reflect the strength and stability of cell adhesion. Efficient adhesion correlates with well-organized, larger focal adhesions characterized by increased accumulation of paxillin and vinculin. These findings offer promising implications for enhancing scaffold design, evaluating adhesion to various substrates, and refining cellular interactions in biomedical applications.

SEMORE: SEgmentation and MORphological fingErprinting by machine learning automates super-resolution data analysis

The morphology of protein assemblies impacts their behaviour and contributes to beneficial and aberrant cellular responses. While single-molecule localization microscopy provides the required spatial resolution to investigate these assemblies, the lack of universal robust analytical tools to extract and quantify underlying structures limits this powerful technique. Here we present SEMORE, a semi-automatic machine learning framework for universal, system- and input-dependent, analysis of super-resolution data. SEMORE implements a multi-layered density-based clustering module to dissect biological assemblies and a morphology fingerprinting module for quantification by multiple geometric and kinetics-based descriptors. We demonstrate SEMORE on simulations and diverse raw super-resolution data: time-resolved insulin aggregates, and published data of dSTORM imaging of nuclear pore complexes, fibroblast growth receptor 1, sptPALM of Syntaxin 1a and dynamic live-cell PALM of ryanodine receptors. SEMORE extracts and quantifies all protein assemblies, their temporal morphology evolution and provides quantitative insights, e.g. classification of heterogeneous insulin aggregation pathways and NPC geometry in minutes. SEMORE is a general analysis platform for super-resolution data, and being a time-aware framework can also support the rise of 4D super-resolution data.

The European Polysaccharide Network of Excellence (EPNOE) research roadmap 2040: Advanced strategies for exploiting the vast potential of polysaccharides as renewable bioresources

Polysaccharides are among the most abundant bioresources on earth and consequently need to play a pivotal role when addressing existential scientific challenges like climate change and the shift from fossil-based to sustainable biobased materials. The Research Roadmap 2040 of the European Polysaccharide Network of Excellence (EPNOE) provides an expert's view on how future research and development strategies need to evolve to fully exploit the vast potential of polysaccharides as renewable bioresources. It is addressed to academic researchers, companies, as well as policymakers and covers five strategic areas that are of great importance in the context of polysaccharide related research: (I) Materials & Engineering, (II) Food & Nutrition, (III) Biomedical Applications, (IV) Chemistry, Biology & Physics, and (V) Skills & Education. Each section summarizes the state of research, identifies challenges that are currently faced, project achievements and developments that are expected in the upcoming 20 years, and finally provides outlines on how future research activities need to evolve.

Minimal information for studies of extracellular vesicles (MISEV2023): From basic to advanced approaches

Extracellular vesicles (EVs), through their complex cargo, can reflect the state of their cell of origin and change the functions and phenotypes of other cells. These features indicate strong biomarker and therapeutic potential and have generated broad interest, as evidenced by the steady year-on-year increase in the numbers of scientific publications about EVs. Important advances have been made in EV metrology and in understanding and applying EV biology. However, hurdles remain to realising the potential of EVs in domains ranging from basic biology to clinical applications due to challenges in EV nomenclature, separation from non-vesicular extracellular particles, characterisation and functional studies. To address the challenges and opportunities in this rapidly evolving field, the International Society for Extracellular Vesicles (ISEV) updates its 'Minimal Information for Studies of Extracellular Vesicles', which was first published in 2014 and then in 2018 as MISEV2014 and MISEV2018, respectively. The goal of the current document, MISEV2023, is to provide researchers with an updated snapshot of available approaches and their advantages and limitations for production, separation and characterisation of EVs from multiple sources, including cell culture, body fluids and solid tissues. In addition to presenting the latest state of the art in basic principles of EV research, this document also covers advanced techniques and approaches that are currently expanding the boundaries of the field. MISEV2023 also includes new sections on EV release and uptake and a brief discussion of in vivo approaches to study EVs. Compiling feedback from ISEV expert task forces and more than 1000 researchers, this document conveys the current state of EV research to facilitate robust scientific discoveries and move the field forward even more rapidly.

Location, location, location: Protein kinase nanoclustering for optimised signalling output

Protein kinases (PKs) are proteins at the core of cellular signalling and are thereby responsible for most cellular physiological processes and their regulations. As for all intracellular proteins, PKs are subjected to Brownian thermal energy that tends to homogenise their distribution throughout the volume of the cell. To access their substrates and perform their critical functions, PK localisation is therefore tightly regulated in space and time, relying upon a range of clustering mechanisms. These include post-translational modifications, protein-protein and protein-lipid interactions, as well as liquid-liquid phase separation, allowing spatial restriction and ultimately regulating access to their substrates. In this review, we will focus on key mechanisms mediating PK nanoclustering in physiological and pathophysiological processes. We propose that PK nanoclusters act as a cellular quantal unit of signalling output capable of integration and regulation in space and time. We will specifically outline the various super-resolution microscopy approaches currently used to elucidate the composition and mechanisms driving PK nanoscale clustering and explore the pathological consequences of altered kinase clustering in the context of neurodegenerative disorders, inflammation, and cancer.

Believing is seeing - the deceptive influence of bias in quantitative microscopy

The visual allure of microscopy makes it an intuitively powerful research tool. Intuition, however, can easily obscure or distort the reality of the information contained in an image. Common cognitive biases, combined with institutional pressures that reward positive research results, can quickly skew a microscopy project towards upholding, rather than rigorously challenging, a hypothesis. The impact of these biases on a variety of research topics is well known. What might be less appreciated are the many forms in which bias can permeate a microscopy experiment. Even well-intentioned researchers are susceptible to bias, which must therefore be actively recognized to be mitigated. Importantly, although image quantification has increasingly become an expectation, ostensibly to confront subtle biases, it is not a guarantee against bias and cannot alone shield an experiment from cognitive distortions. Here, we provide illustrative examples of the insidiously pervasive nature of bias in microscopy experiments - from initial experimental design to image acquisition, analysis and data interpretation. We then provide suggestions that can serve as guard rails against bias.

Visualizing HIV-1 Assembly at the T-Cell Plasma Membrane Using Single-Molecule Localization Microscopy

The 20-year revolution in optical fluorescence microscopy, supported by the optimization of both spatial resolution and timely acquisition, allows the visualization of nanoscaled objects in cell biology. Currently, the use of a recent generation of super-resolution fluorescence microscope coupled with improved fluorescent probes gives the possibility to study the replicative cycle of viruses in living cells, at the single-virus particle or protein level. Here, we highlight the protocol for visualizing HIV-1 Gag assembly at the host T-cell plasma membrane using super-resolution light microscopy. Total internal reflection fluorescence microscopy (TIRF-M) coupled with single-molecule localization microscopy (SMLM) enables the detection and characterization of the assembly of viral proteins at the plasma membrane of infected host cells at the single protein level. Here, we describe the TIRF equipment, the T-cell culture for HIV-1, the sample preparation for single-molecule localization microscopies such as PALM and STORM, acquisition protocols, and Gag assembling cluster analysis.

Single fluorogen imaging reveals distinct environmental and structural features of biomolecular condensates

Recent computations suggest that biomolecular condensates that form via macromolecular phase separation are network fluids featuring spatially inhomogeneous organization of the underlying molecules. Computations also point to unique conformations of molecules at condensate interfaces. Here, we test these predictions using high-resolution structural characterizations of condensates formed by intrinsically disordered prion-like low complexity domains (PLCDs). We leveraged the localization and orientational preferences of freely diffusing fluorogens and the solvatochromic effect whereby specific fluorogens are turned on in response to the physic-chemical properties of condensate microenvironments to facilitate single-molecule tracking and super-resolution imaging. We deployed three different fluorogens to probe internal microenvironments and molecular organization of PLCD condensates. The spatiotemporal resolution and environmental sensitivity afforded by single-fluorogen imaging shows that the internal environments of condensates are more hydrophobic than coexisting dilute phases. Molecules within condensates are organized in a spatially inhomogeneous manner featuring slow-moving nanoscale molecular clusters or hubs that coexist with fast-moving molecules. Finally, molecules at interfaces of condensates are found to have distinct orientational preferences when compared to the interiors. Our findings, which affirm computational predictions, help provide a structural basis for condensate viscoelasticity and dispel the notion of protein condensates being isotropic liquids defined by uniform internal densities.

Insulin Receptor Isoforms and Insulin Growth Factor-like Receptors: Implications in Cell Signaling, Carcinogenesis, and Chemoresistance

This comprehensive review thoroughly explores the intricate involvement of insulin receptor (IR) isoforms and insulin-like growth factor receptors (IGFRs) in the context of the insulin and insulin-like growth factor (IGF) signaling (IIS) pathway. This elaborate system encompasses ligands, receptors, and binding proteins, giving rise to a wide array of functions, including aspects such as carcinogenesis and chemoresistance. Detailed genetic analysis of IR and IGFR structures highlights their distinct isoforms, which arise from alternative splicing and exhibit diverse affinities for ligands. Notably, the overexpression of the IR-A isoform is linked to cancer stemness, tumor development, and resistance to targeted therapies. Similarly, elevated IGFR expression accelerates tumor progression and fosters chemoresistance. The review underscores the intricate interplay between IRs and IGFRs, contributing to resistance against anti-IGFR drugs. Consequently, the dual targeting of both receptors could present a more effective strategy for surmounting chemoresistance. To conclude, this review brings to light the pivotal roles played by IRs and IGFRs in cellular signaling, carcinogenesis, and therapy resistance. By precisely modulating these receptors and their complex signaling pathways, the potential emerges for developing enhanced anti-cancer interventions, ultimately leading to improved patient outcomes.

Trends in Single-Molecule Total Internal Reflection Fluorescence Imaging and Their Biological Applications with Lab-on-a-Chip Technology

Single-molecule imaging technologies, especially those based on fluorescence, have been developed to probe both the equilibrium and dynamic properties of biomolecules at the single-molecular and quantitative levels. In this review, we provide an overview of the state-of-the-art advancements in single-molecule fluorescence imaging techniques. We systematically explore the advanced implementations of in vitro single-molecule imaging techniques using total internal reflection fluorescence (TIRF) microscopy, which is widely accessible. This includes discussions on sample preparation, passivation techniques, data collection and analysis, and biological applications. Furthermore, we delve into the compatibility of microfluidic technology for single-molecule fluorescence imaging, highlighting its potential benefits and challenges. Finally, we summarize the current challenges and prospects of fluorescence-based single-molecule imaging techniques, paving the way for further advancements in this rapidly evolving field.

An Image-Based High-Throughput and High-Content Drug Screening Method Based on Microarray and Expansion Microscopy

A high-efficiency drug screening method is urgently needed due to the expanding number of potential targets and the extremely long time required to assess them. To date, high throughput and high content have not been successfully combined in image-based drug screening, which is the main obstacle to improve the efficiency. Here, we establish a high-throughput and high-content drug screening method by preparing a superhydrophobic microwell array plate (SMAP) and combining it with protein-retention expansion microscopy (proExM). Primarily, we described a flexible method to prepare the SMAP based on photolithography. Cells were cultured in the SMAP and treated with different drugs using a microcolumn-microwell sandwiching technology. After drug treatment, proExM was applied to realize super-resolution imaging. As a demonstration, a 7 × 7 image array of microtubules was successfully collected within 3 h with 68 nm resolution using this method. Qualitative and quantitative analyses of microtubule and mitochondria morphological changes after drug treatment suggested that more details were revealed after applying proExM, demonstrating the successful combination of high throughput and high content.

Flat clathrin lattices are linked to metastatic potential in colorectal cancer

Clathrin assembles at the cells' plasma membrane in a multitude of clathrin-coated structures (CCSs). Among these are flat clathrin lattices (FCLs), alternative clathrin structures that have been found in specific cell types, including cancer cells. Here we show that these structures are also present in different colorectal cancer (CRC) cell lines, and that they are extremely stable with lifetimes longer than 8 h. By combining cell models representative of CRC metastasis with advanced fluorescence imaging and analysis, we discovered that the metastatic potential of CRC is associated with an aberrant membranous clathrin distribution, resulting in a higher prevalence of FCLs in cells with a higher metastatic potential. These findings suggest that clathrin organization might play an important yet unexplored role in cancer metastasis.

Optimizing Voronoi-based quantifications for reaching interactive analysis of 3D localizations in the million range

Over the last decade, single-molecule localization microscopy (SMLM) has revolutionized cell biology, making it possible to monitor molecular organization and dynamics with spatial resolution of a few nanometers. Despite being a relatively recent field, SMLM has witnessed the development of dozens of analysis methods for problems as diverse as segmentation, clustering, tracking or colocalization. Among those, Voronoi-based methods have achieved a prominent position for 2D analysis as robust and efficient implementations were available for generating 2D Voronoi diagrams. Unfortunately, this was not the case for 3D Voronoi diagrams, and existing methods were therefore extremely time-consuming. In this work, we present a new hybrid CPU-GPU algorithm for the rapid generation of 3D Voronoi diagrams. Voro3D allows creating Voronoi diagrams of datasets composed of millions of localizations in minutes, making any Voronoi-based analysis method such as SR-Tesseler accessible to life scientists wanting to quantify 3D datasets. In addition, we also improve ClusterVisu, a Voronoi-based clustering method using Monte-Carlo simulations, by demonstrating that those costly simulations can be correctly approximated by a customized gamma probability distribution function.

Heterogeneity of Signaling Complex Nanostructure in T Cells Activated Via the T Cell Antigen Receptor

Activation of the T cell antigen receptor (TCR) is a key step in initiating the adaptive immune response. Single-molecule localization techniques have been used to investigate the arrangement of proteins within the signaling complexes formed around activated TCRs, but a clear picture of nanoscale organization in stimulated T cells has not emerged. Here, we have improved the examination of T cell nanostructure by visualizing individual molecules of six different proteins in a single sample of activated Jurkat T cells using the multiplexed antibody-size limited direct stochastic optical reconstruction microscopy (madSTORM) technique. We formally define irregularly shaped regions of interest, compare areas where signaling complexes are concentrated with other areas, and improve the statistical analyses of the locations of molecules. We show that nanoscale organization of proteins is mainly confined to the areas with dense concentrations of TCR-based signaling complexes. However, randomly distributed molecules are also found in some areas containing concentrated signaling complexes. These results are consistent with the view that the proteins within signaling complexes are connected by numerous weak interactions, leading to flexible, dynamic, and mutable structures which produce large variations in the nanostructure found in activated T cells.

Introducing Diinamic, a flexible and robust method for clustering analysis in single-molecule localization microscopy

Super-resolution microscopy allowed major improvements in our capacity to describe and explain biological organization at the nanoscale. Single-molecule localization microscopy (SMLM) uses the positions of molecules to create super-resolved images, but it can also provide new insights into the organization of molecules through appropriate pointillistic analyses that fully exploit the sparse nature of SMLM data. However, the main drawback of SMLM is the lack of analytical tools easily applicable to the diverse types of data that can arise from biological samples. Typically, a cloud of detections may be a cluster of molecules or not depending on the local density of detections, but also on the size of molecules themselves, the labeling technique, the photo-physics of the fluorophore, and the imaging conditions. We aimed to set an easy-to-use clustering analysis protocol adaptable to different types of data. Here, we introduce Diinamic, which combines different density-based analyses and optional thresholding to facilitate the detection of clusters. On simulated or real SMLM data, Diinamic correctly identified clusters of different sizes and densities, being performant even in noisy datasets with multiple detections per fluorophore. It also detected subdomains ("nanodomains") in clusters with non-homogeneous distribution of detections.

Single Extracellular VEsicle Nanoscopy

Extracellular vesicles (EVs) and their cargo constitute novel biomarkers. EV subpopulations have been defined not only by abundant tetraspanins (e.g., CD9, CD63 and CD81) but also by specific markers derived from their source cells. However, it remains a challenge to robustly isolate and characterize EV subpopulations. Here, we combined affinity isolation with super-resolution imaging to comprehensively assess EV subpopulations from human plasma. Our Single Extracellular VEsicle Nanoscopy (SEVEN) assay successfully quantified the number of affinity-isolated EVs, their size, shape, molecular tetraspanin content, and heterogeneity. The number of detected tetraspanin-enriched EVs positively correlated with sample dilution in a 64-fold range (for SEC-enriched plasma) and a 50-fold range (for crude plasma). Importantly, SEVEN robustly detected EVs from as little as ∼0.1 μL of crude plasma. We further characterized the size, shape and molecular tetraspanin content (with corresponding heterogeneities) for CD9-, CD63- and CD81-enriched EV subpopulations. Finally, we assessed EVs from the plasma of four pancreatic ductal adenocarcinoma patients with resectable disease. Compared to healthy plasma, CD9-enriched EVs from patients were smaller while IGF1R-enriched EVs from patients were larger, rounder and contained more tetraspanin molecules, suggestive of a unique pancreatic cancer-enriched EV subpopulation. This study provides the method validation and demonstrates that SEVEN could be advanced into a platform for characterizing both disease-associated and organ-associated EV subpopulations.

Recent Advances in Single-Molecule Tracking and Imaging Techniques

Since the early 1990s, single-molecule detection in solution at room temperature has enabled direct observation of single biomolecules at work in real time and under physiological conditions, providing insights into complex biological systems that the traditional ensemble methods cannot offer. In particular, recent advances in single-molecule tracking techniques allow researchers to follow individual biomolecules in their native environments for a timescale of seconds to minutes, revealing not only the distinct pathways these biomolecules take for downstream signaling but also their roles in supporting life. In this review, we discuss various single-molecule tracking and imaging techniques developed to date, with an emphasis on advanced three-dimensional (3D) tracking systems that not only achieve ultrahigh spatiotemporal resolution but also provide sufficient working depths suitable for tracking single molecules in 3D tissue models. We then summarize the observables that can be extracted from the trajectory data. Methods to perform single-molecule clustering analysis and future directions are also discussed.

BNP-Track: A framework for superresolved tracking

Assessing dynamic processes at single molecule scales is key toward capturing life at the level of its molecular actors. Widefield superresolution methods, such as STORM, PALM, and PAINT, provide nanoscale localization accuracy, even when distances between fluorescently labeled single molecules ("emitters") fall below light's diffraction limit. However, as these superresolution methods rely on rare photophysical events to distinguish emitters from both each other and background, they are largely limited to static samples. In contrast, here we leverage spatiotemporal correlations of dynamic widefield imaging data to extend superresolution to simultaneous multiple emitter tracking without relying on photodynamics even as emitter distances from one another fall below the diffraction limit. We simultaneously determine emitter numbers and their tracks (localization and linking) with the same localization accuracy per frame as widefield superresolution does for immobilized emitters under similar imaging conditions (≈50nm). We demonstrate our results for both in cellulo data and, for benchmarking purposes, on synthetic data. To this end, we avoid the existing tracking paradigm relying on completely or partially separating the tasks of emitter number determination, localization of each emitter, and linking emitter positions across frames. Instead, we develop a fully joint posterior distribution over the quantities of interest, including emitter tracks and their total, otherwise unknown, number within the Bayesian nonparametric paradigm. Our posterior quantifies the full uncertainty over emitter numbers and their associated tracks propagated from origins including shot noise and camera artefacts, pixelation, stochastic background, and out-of-focus motion. Finally, it remains accurate in more crowded regimes where alternative tracking tools cannot be applied.

Advanced Microscopy Techniques for Molecular Biophysics

Though microscopy is most often intended as a technique for providing qualitative assessment of cellular and subcellular properties, when coupled with other instruments such as wavelength selectors, lasers, photoelectric devices and computers, it can perform a wide variety of quantitative measurements, which are demanding in establishing relationships between the properties and structures of biological material in all their spatial and temporal complexities. These combinations of instruments are a powerful approach to improve non-destructive investigations of cellular and subcellular properties (both physical and chemical) at a macromolecular scale resolution. Since many subcellular compartments in living cells are characterized by structurally organized molecules, this review deals with three advanced microscopy techniques well-suited for these kind of investigations, i.e., microspectrophotometry (MSP), super-resolution localization microscopy (SRLM) and holotomographic microscopy (HTM). These techniques can achieve an insight view into the role intracellular molecular organizations such as photoreceptive and photosynthetic structures and lipid bodies play in many cellular processes as well as their biophysical properties. Microspectrophotometry uses a set-up based on the combination of a wide-field microscope and a polychromator, which allows the measurement of spectroscopic features such as absorption spectra. Super resolution localization microscopy combines dedicated optics and sophisticated software algorithms to overcome the diffraction limit of light and allow the visualization of subcellular structures and dynamics in greater detail with respect to conventional optical microscopy. Holotomographic microscopy combines holography and tomography techniques into a single microscopy set-up, and allows 3D reconstruction by means of the phase separation of biomolecule condensates. This review is organized in sections, which for each technique describe some general aspects, a peculiar theoretical aspect, a specific experimental configuration and examples of applications (fish and algae photoreceptors, single labeled proteins and endocellular aggregates of lipids).

Super-resolved trajectory-derived nanoclustering analysis using spatiotemporal indexing

Single-molecule localization microscopy techniques are emerging as vital tools to unravel the nanoscale world of living cells by understanding the spatiotemporal organization of protein clusters at the nanometer scale. Current analyses define spatial nanoclusters based on detections but neglect important temporal information such as cluster lifetime and recurrence in "hotspots" on the plasma membrane. Spatial indexing is widely used in video games to detect interactions between moving geometric objects. Here, we use the R-tree spatial indexing algorithm to determine the overlap of the bounding boxes of individual molecular trajectories to establish membership in nanoclusters. Extending the spatial indexing into the time dimension allows the resolution of spatial nanoclusters into multiple spatiotemporal clusters. Using spatiotemporal indexing, we found that syntaxin1a and Munc18-1 molecules transiently cluster in hotspots, offering insights into the dynamics of neuroexocytosis. Nanoscale spatiotemporal indexing clustering (NASTIC) has been implemented as a free and open-source Python graphic user interface.

Tracking the Message: Applying Single Molecule Localization Microscopy to Cellular RNA Imaging

RNA function is increasingly appreciated to be more complex than merely communicating between DNA sequence and protein structure. RNA localization has emerged as a key contributor to the intricate roles RNA plays in the cell, and the link between dysregulated spatiotemporal localization and disease warrants an exploration beyond sequence and structure. However, the tools needed to visualize RNA with precise resolution are lacking in comparison to methods available for studying proteins. In the past decade, many techniques have been developed for imaging RNA, and in parallel super resolution and single-molecule techniques have enabled imaging of single molecules in cells. Of these methods, single molecule localization microscopy (SMLM) has shown significant promise for probing RNA localization. In this review, we highlight current approaches that allow super resolution imaging of specific RNA transcripts and summarize challenges and future opportunities for developing innovative RNA labeling methods that leverage the power of SMLM.

Cycle Registration in Persistent Homology With Applications in Topological Bootstrap

We propose a novel approach for comparing the persistent homology representations of two spaces (or filtrations). Commonly used methods are based on numerical summaries such as persistence diagrams and persistence landscapes, along with suitable metrics (e.g., Wasserstein). These summaries are useful for computational purposes, but they are merely a marginal of the actual topological information that persistent homology can provide. Instead, our approach compares between two topological representations directly in the data space. We do so by defining a correspondence relation between individual persistent cycles of two different spaces, and devising a method for computing this correspondence. Our matching of cycles is based on both the persistence intervals and the spatial placement of each feature. We demonstrate our new framework in the context of topological inference, where we use statistical bootstrap methods in order to differentiate between real features and noise in point cloud data.

Single-molecule localization microscopy goes quantitative

In the last few years, single-molecule localization (SMLM) techniques have been used to address biological questions in different research fields. More recently, super-resolution has also been proposed as a quantitative tool for quantifying protein copy numbers at the nanoscale level. In this scenario, quantitative approaches, mainly based on stepwise photobleaching and quantitative SMLM assisted by calibration standards, offer an exquisite tool for investigating protein complexes. This primer focuses on the basic concepts behind quantitative super-resolution microscopy, also providing strategies to overcome the technical hurdles that could limit their application.

Immunoglobulin G-dependent inhibition of inflammatory bone remodeling requires pattern recognition receptor Dectin-1

Immunoglobulin G (IgG) antibodies are major drivers of inflammation during infectious and autoimmune diseases. In pooled serum IgG (IVIg), however, antibodies have a potent immunomodulatory and anti-inflammatory activity, but how this is mediated is unclear. We studied IgG-dependent initiation of resolution of inflammation in cytokine- and autoantibody-driven models of rheumatoid arthritis and found IVIg sialylation inhibited joint inflammation, whereas inhibition of osteoclastogenesis was sialic acid independent. Instead, IVIg-dependent inhibition of osteoclastogenesis was abrogated in mice lacking receptors Dectin-1 or FcγRIIb. Atomistic molecular dynamics simulations and super-resolution microscopy revealed that Dectin-1 promoted FcγRIIb membrane conformations that allowed productive IgG binding and enhanced interactions with mouse and human IgG subclasses. IVIg reprogrammed monocytes via FcγRIIb-dependent signaling that required Dectin-1. Our data identify a pathogen-independent function of Dectin-1 as a co-inhibitory checkpoint for IgG-dependent inhibition of mouse and human osteoclastogenesis. These findings may have implications for therapeutic targeting of autoantibody and cytokine-driven inflammation.

Advanced image-free analysis of the nano-organization of chromatin and other biomolecules by Single Molecule Localization Microscopy (SMLM)

The cell as a system of many components, governed by the laws of physics and chemistry drives molecular functions having an impact on the spatial organization of these systems and vice versa. Since the relationship between structure and function is an almost universal rule not only in biology, appropriate methods are required to parameterize the relationship between the structure and function of biomolecules and their networks, the mechanisms of the processes in which they are involved, and the mechanisms of regulation of these processes. Single molecule localization microscopy (SMLM), which we focus on here, offers a significant advantage for the quantitative parametrization of molecular organization: it provides matrices of coordinates of fluorescently labeled biomolecules that can be directly subjected to advanced mathematical analytical procedures without the need for laborious and sometimes misleading image processing. Here, we propose mathematical tools for comprehensive quantitative computer data analysis of SMLM point patterns that include Ripley distance frequency analysis, persistent homology analysis, persistent 'imaging', principal component analysis and co-localization analysis. The application of these methods is explained using artificial datasets simulating different, potentially possible and interpretatively important situations. Illustrative analyses of real complex biological SMLM data are presented to emphasize the applicability of the proposed algorithms. This manuscript demonstrated the extraction of features and parameters quantifying the influence of chromatin (re)organization on genome function, offering a novel approach to study chromatin architecture at the nanoscale. However, the ability to adapt the proposed algorithms to analyze essentially any molecular organizations, e.g., membrane receptors or protein trafficking in the cytosol, offers broad flexibility of use.

A framework for evaluating the performance of SMLM cluster analysis algorithms

Single-molecule localization microscopy (SMLM) generates data in the form of coordinates of localized fluorophores. Cluster analysis is an attractive route for extracting biologically meaningful information from such data and has been widely applied. Despite a range of cluster analysis algorithms, there exists no consensus framework for the evaluation of their performance. Here, we use a systematic approach based on two metrics to score the success of clustering algorithms in simulated conditions mimicking experimental data. We demonstrate the framework using seven diverse analysis algorithms: DBSCAN, ToMATo, KDE, FOCAL, CAML, ClusterViSu and SR-Tesseler. Given that the best performer depended on the underlying distribution of localizations, we demonstrate an analysis pipeline based on statistical similarity measures that enables the selection of the most appropriate algorithm, and the optimized analysis parameters for real SMLM data. We propose that these standard simulated conditions, metrics and analysis pipeline become the basis for future analysis algorithm development and evaluation.

Precision and Accuracy of Receptor Quantification on Synthetic and Biological Surfaces Using DNA-PAINT

Characterization of the number and distribution of biological molecules on 2D surfaces is of foremost importance in biology and biomedicine. Synthetic surfaces bearing recognition motifs are a cornerstone of biosensors, while receptors on the cell surface are critical/vital targets for the treatment of diseases. However, the techniques used to quantify their abundance are qualitative or semi-quantitative and usually lack sensitivity, accuracy, or precision. Detailed herein a simple and versatile workflow based on super-resolution microscopy (DNA-PAINT) was standardized to improve the quantification of the density and distribution of molecules on synthetic substrates and cell membranes. A detailed analysis of accuracy and precision of receptor quantification is presented, based on simulated and experimental data. We demonstrate enhanced accuracy and sensitivity by filtering out non-specific interactions and artifacts. While optimizing the workflow to provide faithful counting over a broad range of receptor densities. We validated the workflow by specifically quantifying the density of docking strands on a synthetic sensor surface and the densities of PD1 and EGF receptors (EGFR) on two cellular models.

Stimulated Raman versus Inverse Raman: Investigating Depletion Mechanisms for Super-Resolution Raman Microscopy

Super-resolution fluorescence microscopy has been critical in elucidating the nanoscale structure of biological systems. However, fluorescent labels bring difficulties such as perturbative labeling steps and photobleaching. Thus, label-free super-resolution techniques are of great interest, like our group's 2016 stimulated Raman scattering (SRS) technique, stimulated Raman depletion microscopy (SRDM). Inspired by stimulated emission depletion microscopy, SRDM uses a toroidally shaped beam to deplete the signal formed on the edges of the focal spot, resulting in SRS signal being detected from only a subdiffraction limited region. In initial works, the cause of the depletion was not thoroughly characterized. Here, we conclusively demonstrate suppression mechanisms in SRDM, while also contrasting approaches to super-resolution Raman microscopy on the Stokes and anti-Stokes sides of the spectrum. By monitoring the depletion of both the SRS and inverse Raman scattering (IRS) signal at a range of depletion powers, we observed other four-wave coherent Raman pathways that correspond to the introduction of the femtosecond depletion beam. In addition, we showed the depletion of the IRS signal, paving the way for a super-resolution imaging technique based on IRS, inverse raman depletion microscopy (IRDM). Combined, SRDM and IRDM offer label-free super-resolution imaging over a large spectral range to accommodate a variety of different sample constraints.

Recent development of computational cluster analysis methods for single-molecule localization microscopy images

With the development of super-resolution imaging techniques, it is crucial to understand protein structure at the nanoscale in terms of clustering and organization in a cell. However, cluster analysis from single-molecule localization microscopy (SMLM) images remains challenging because the classical computational cluster analysis methods developed for conventional microscopy images do not apply to pointillism SMLM data, necessitating the development of distinct methods for cluster analysis from SMLM images. In this review, we discuss the development of computational cluster analysis methods for SMLM images by categorizing them into classical and machine-learning-based methods. Finally, we address possible future directions for machine learning-based cluster analysis methods for SMLM data.

Advanced Imaging Approaches to Reveal Molecular Mechanisms Governing Neuroendocrine Secretion

Identification of the molecular mechanisms governing neuroendocrine secretion and resulting intercellular communication is one of the great challenges of cell biology to better understand organism physiology and neurosecretion disruption-related pathologies such as hypertension, neurodegenerative, or metabolic diseases. To visualize molecule distribution and dynamics at the nanoscale, many imaging approaches have been developed and are still emerging. In this review, we provide an overview of the pioneering studies using transmission electron microscopy, atomic force microscopy, total internal reflection microscopy, and super-resolution microscopy in neuroendocrine cells to visualize molecular mechanisms driving neurosecretion processes, including exocytosis and associated fusion pores, endocytosis and associated recycling vesicles, and protein-protein or protein-lipid interactions. Furthermore, the potential and the challenges of these different advanced imaging approaches for application in the study of neuroendocrine cell biology are discussed, aiming to guide researchers to select the best approach for their specific purpose around the crucial but not yet fully understood neurosecretion process.

STORM Super-Resolution Imaging of CB1 Receptors in Tissue Preparations

Single-molecule localization microscopy (SMLM) opened new possibilities to study the spatial arrangement of molecular distribution and disease-associated redistribution at a previously unprecedented resolution that was not achievable with optical microscopy approaches. Recent discoveries based on SMLM techniques uncovered specific nanoscale organizational principles of signaling proteins in several biological systems including the chemical synapses in the brain. Emerging data suggest that the spatial arrangement of the molecular players of the endocannabinoid system is also precisely regulated at the nanoscale level in synapses and in other neuronal and glial subcellular compartments. The precise nanoscale distribution pattern is likely to be important to subserve several specific signaling functions of this important messenger system in a cell-type- and subcellular domain-specific manner.STochastic Optical Reconstruction Microscopy (STORM) is an especially suitable SMLM modality for cell-type-specific nanoscale molecular imaging due to its compatibility with traditional diffraction-limited microscopy approaches and classical staining methods. Here, we describe a detailed protocol for STORM imaging in mouse brain tissue samples with a focus on the CB₁ cannabinoid receptor, one of the most abundant synaptic receptors in the brain. We also summarize important conceptual and methodical details that are essential for the valid interpretation of single-molecule localization microscopy data.

High- and Super-Resolution Imaging of Cell-Cell Interfaces

Physical interfaces mediate interactions between multiple types of cells. Despite the importance of such interfaces to the cells' function, their high-resolution optical imaging has been typically limited due to poor alignment of the interfaces relative to the optical plane of imaging. Here, we present a simple and robust method to align cell-cell interfaces in parallel to the coverslip by adhering the interacting cells to two opposing coverslips and bringing them into contact in a controlled and stable fashion. We demonstrate aberration-free high-resolution imaging of interfaces between live T cells and antigen-presenting cells, known as immune synapses, as an outstanding example. Imaging methods may include multiple diffraction-limited and super-resolution microscopy techniques (e.g., bright-field, confocal, STED, and dSTORM). Thus, our simple and widely compatible approach allows imaging with high- and super-resolution the intricate structure and molecular organization within a variety of cell-cell interfaces.

Characterizing Properties of Biomolecular Condensates Below the Diffraction Limit In Vivo

Fluorescence microscopy assays enable the investigation of endogenous biomolecular condensates directly in their cellular context. With appropriate experimental designs, these assays yield quantitative information on condensate material properties and inform on biophysical mechanisms of condensate formation. Single-molecule super-resolution and tracking experiments grant access to the smallest condensates and early condensation stages not resolved by conventional imaging approaches. Here, we discuss considerations for using single-molecule assays to extract quantitative information about biomolecular condensates directly in their cellular context.

Quantitative Analysis of PcG-Associated Condensates by Stochastic Optical Reconstruction Microscopy (STORM)

The polycomb group (PcG) proteins play a central role in the maintenance of a repressive state of gene expression. Recent findings demonstrate that PcG components are organized into nuclear condensates, contributing to the reshaping of chromatin architecture in physiological and pathological conditions, thus affecting the nuclear mechanics. In this context, direct stochastic optical reconstruction microscopy (dSTORM) provides an effective tool to achieve a detailed characterization of PcG condensates by visualizing them at a nanometric level. Furthermore, by analyzing dSTORM datasets with cluster analysis algorithms, quantitative information can be yielded regarding protein numbers, grouping, and spatial organization. Here, we describe how to set up a dSTORM experiment and perform the data analysis to study PcG complexes' components in adhesion cells quantitatively.

Super-resolution microscopy reveals the number and distribution of topoisomerase IIα and CENH3 molecules within barley metaphase chromosomes

Topoisomerase IIα (Topo IIα) and the centromere-specific histone H3 variant CENH3 are key proteins involved in chromatin condensation and centromere determination, respectively. Consequently, they are required for proper chromosome segregation during cell divisions. We combined two super-resolution techniques, structured illumination microscopy (SIM) to co-localize Topo IIα and CENH3, and photoactivated localization microscopy (PALM) to determine their molecule numbers in barley metaphase chromosomes. We detected a dispersed Topo IIα distribution along chromosome arms but an accumulation at centromeres, telomeres, and nucleolus-organizing regions. With a precision of 10-50 nm, we counted ~ 20,000-40,000 Topo IIα molecules per chromosome, 28% of them within the (peri)centromere. With similar precision, we identified ~13,500 CENH3 molecules per centromere where Topo IIα proteins and CENH3-containing chromatin intermingle. In short, we demonstrate PALM as a useful method to count and localize single molecules with high precision within chromosomes. The ultrastructural distribution and the detected amount of Topo IIα and CENH3 are instrumental for a better understanding of their functions during chromatin condensation and centromere determination.

Unbiased choice of global clustering parameters for single-molecule localization microscopy

Single-molecule localization microscopy resolves objects below the diffraction limit of light via sparse, stochastic detection of target molecules. Single molecules appear as clustered detection events after image reconstruction. However, identification of clusters of localizations is often complicated by the spatial proximity of target molecules and by background noise. Clustering results of existing algorithms often depend on user-generated training data or user-selected parameters, which can lead to unintentional clustering errors. Here we suggest an unbiased algorithm (FINDER) based on adaptive global parameter selection and demonstrate that the algorithm is robust to noise inclusion and target molecule density. We benchmarked FINDER against the most common density based clustering algorithms in test scenarios based on experimental datasets. We show that FINDER can keep the number of false positive inclusions low while also maintaining a low number of false negative detections in densely populated regions.