A pairwise distance distribution correction (DDC) algorithm to eliminate blinking-caused artifacts in SMLM

ECLiPSE: a versatile classification technique for structural and morphological analysis of 2D and 3D single-molecule localization microscopy data

Single-molecule localization microscopy (SMLM) has gained widespread use for visualizing the morphology of subcellular organelles and structures with nanoscale spatial resolution. However, analysis tools for automatically quantifying and classifying SMLM images have lagged behind. Here we introduce Enhanced Classification of Localized Point clouds by Shape Extraction (ECLiPSE), an automated machine learning analysis pipeline specifically designed to classify cellular structures captured through two-dimensional or three-dimensional SMLM. ECLiPSE leverages a comprehensive set of shape descriptors, the majority of which are directly extracted from the localizations to minimize bias during the characterization of individual structures. ECLiPSE has been validated using both unsupervised and supervised classification on datasets, including various cellular structures, achieving near-perfect accuracy. We apply two-dimensional ECLiPSE to classify morphologically distinct protein aggregates relevant for neurodegenerative diseases. Additionally, we employ three-dimensional ECLiPSE to identify relevant biological differences between healthy and depolarized mitochondria. ECLiPSE will enhance the way we study cellular structures across various biological contexts.

Distinct mobility patterns of BRCA2 molecules at DNA damage sites

BRCA2 is an essential tumor suppressor protein involved in promoting faithful repair of DNA lesions. The activity of BRCA2 needs to be tuned precisely to be active when and where it is needed. Here, we quantified the spatio-temporal dynamics of BRCA2 in living cells using aberration-corrected multifocal microscopy (acMFM). Using multicolor imaging to identify DNA damage sites, we were able to quantify its dynamic motion patterns in the nucleus and at DNA damage sites. While a large fraction of BRCA2 molecules localized near DNA damage sites appear immobile, an additional fraction of molecules exhibits subdiffusive motion, providing a potential mechanism to retain an increased number of molecules at DNA lesions. Super-resolution microscopy revealed inhomogeneous localization of BRCA2 relative to other DNA repair factors at sites of DNA damage. This suggests the presence of multiple nanoscale compartments in the chromatin surrounding the DNA lesion, which could play an important role in the contribution of BRCA2 to the regulation of the repair process.

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.

Membrane topography and the overestimation of protein clustering in single molecule localisation microscopy - identification and correction

According to single-molecule localisation microscopy almost all plasma membrane proteins are clustered. We demonstrate that clusters can arise from variations in membrane topography where the local density of a randomly distributed membrane molecule to a degree matches the variations in the local amount of membrane. Further, we demonstrate that this false clustering can be differentiated from genuine clustering by using a membrane marker to report on local variations in the amount of membrane. In dual colour live cell single molecule localisation microscopy using the membrane probe DiI alongside either the transferrin receptor or the GPI-anchored protein CD59, we found that pair correlation analysis reported both proteins and DiI as being clustered, as did its derivative pair correlation-photoactivation localisation microscopy and nearest neighbour analyses. After converting the localisations into images and using the DiI image to factor out topography variations, no CD59 clusters were visible, suggesting that the clustering reported by the other methods is an artefact. However, the TfR clusters persisted after topography variations were factored out. We demonstrate that membrane topography variations can make membrane molecules appear clustered and present a straightforward remedy suitable as the first step in the cluster analysis pipeline.

Temporal analysis of relative distances (TARDIS) is a robust, parameter-free alternative to single-particle tracking

In single-particle tracking, individual particles are localized and tracked over time to probe their diffusion and molecular interactions. Temporal crossing of trajectories, blinking particles, and false-positive localizations present computational challenges that have remained difficult to overcome. Here we introduce a robust, parameter-free alternative to single-particle tracking: temporal analysis of relative distances (TARDIS). In TARDIS, an all-to-all distance analysis between localizations is performed with increasing temporal shifts. These pairwise distances represent either intraparticle distances originating from the same particle, or interparticle distances originating from unrelated particles, and are fitted analytically to obtain quantitative measures on particle dynamics. We showcase that TARDIS outperforms tracking algorithms, benchmarked on simulated and experimental data of varying complexity. We further show that TARDIS performs accurately in complex conditions characterized by high particle density, strong emitter blinking or false-positive localizations, and is in fact limited by the capabilities of localization algorithms. TARDIS' robustness enables fivefold shorter measurements without loss of information.

Disordered C-terminal domain drives spatiotemporal confinement of RNAPII to enhance search for chromatin targets

Efficient gene expression requires RNA polymerase II (RNAPII) to find chromatin targets precisely in space and time. How RNAPII manages this complex diffusive search in three-dimensional nuclear space remains largely unknown. The disordered carboxy-terminal domain (CTD) of RNAPII, which is essential for recruiting transcription-associated proteins, forms phase-separated droplets in vitro, hinting at a potential role in modulating RNAPII dynamics. In the present study, we use single-molecule tracking and spatiotemporal mapping in living yeast to show that the CTD is required for confining RNAPII diffusion within a subnuclear region enriched for active genes, but without apparent phase separation into condensates. Both Mediator and global chromatin organization are required for sustaining RNAPII confinement. Remarkably, truncating the CTD disrupts RNAPII spatial confinement, prolongs target search, diminishes chromatin binding, impairs pre-initiation complex formation and reduces transcription bursting. The present study illuminates the pivotal role of the CTD in driving spatiotemporal confinement of RNAPII for efficient gene expression.

Disordered C-terminal domain drives spatiotemporal confinement of RNAPII to enhance search for chromatin targets

Efficient gene expression requires RNA Polymerase II (RNAPII) to find chromatin targets precisely in space and time. How RNAPII manages this complex diffusive search in 3D nuclear space remains largely unknown. The disordered carboxy-terminal domain (CTD) of RNAPII, which is essential for recruiting transcription-associated proteins, forms phase-separated droplets in vitro, hinting at a potential role in modulating RNAPII dynamics. Here, we use single-molecule tracking and spatiotemporal mapping in living yeast to show that the CTD is required for confining RNAPII diffusion within a subnuclear region enriched for active genes, but without apparent phase separation into condensates. Both Mediator and global chromatin organization are required for sustaining RNAPII confinement. Remarkably, truncating the CTD disrupts RNAPII spatial confinement, prolongs target search, diminishes chromatin binding, impairs pre-initiation complex formation, and reduces transcription bursting. This study illuminates the pivotal role of the CTD in driving spatiotemporal confinement of RNAPII for efficient gene expression.

Quantitatively Lighting up the Spatial Organization of CD47/SIRPα Immune Checkpoints on the Cellular Membrane with Single-Molecule Localization Microscopy

Immunotherapy including immune checkpoint inhibition has reinvigorated the current cancer treatment field. The development of efficient cancer immunotherapies depends on a thorough understanding of the status of immune checkpoints and how they interact. However, the distribution and spatial organization changes of immune checkpoints during their interactions at the single-molecule level remain difficult to directly visualize due to the lack of in situ imaging techniques with appropriate spatial and stoichiometric resolution. Herein, we report the direct visualization and quantification of the spatial distribution and organization of CD47 on the bladder tumor cell membrane and SIRPα on the macrophage membrane by using a single-molecule localization imaging technique called quantitative direct stochastic optical reconstruction microscopy (QdSTORM). Results showed that a portion of CD47 and SIRPα was present on cell membranes as heterogeneous clusters of varying sizes and densities prior to activation. Quantitative analyses of the reconstructed super-resolution images and theoretical simulation revealed that CD47 and SIRPα were reorganized into larger clusters upon binding to each other. Furthermore, we found that blocking the immune checkpoint interaction with small-molecule inhibitors or antibodies significantly impacted the spatial clustering behavior of CD47 on bladder tumor cells, demonstrating the promise of our QdSTORM strategy in elucidating the molecular mechanisms underlying immunotherapy. This work offers a promising strategy to advance our understanding of immune checkpoint state and interactions while also contributing to the fields including signal regulation and cancer therapy.

Monomeric agonist peptide/MHCII complexes activate T-cells in an autonomous fashion

Molecular crowding of agonist peptide/MHC class II complexes (pMHCIIs) with structurally similar, yet per se non-stimulatory endogenous pMHCIIs is postulated to sensitize T-cells for the recognition of single antigens on the surface of dendritic cells and B-cells. When testing this premise with the use of advanced live cell microscopy, we observe pMHCIIs as monomeric, randomly distributed entities diffusing rapidly after entering the APC surface. Synaptic TCR engagement of highly abundant endogenous pMHCIIs is low or non-existent and affects neither TCR engagement of rare agonist pMHCII in early and advanced synapses nor agonist-induced TCR-proximal signaling. Our findings highlight the capacity of single freely diffusing agonist pMHCIIs to elicit the full T-cell response in an autonomous and peptide-specific fashion with consequences for adaptive immunity and immunotherapeutic approaches.

Single-molecule tracking of PprI in D. radiodurans without interference of autoblinking

Autoblinking is a widespread phenomenon and exhibits high level of intensity in some bacteria. In Deinococcus radiodurans (D. radiodurans), strong autoblinking was found to be indistinguishable from PAmCherry and greatly prevented single-molecule tracking of proteins of interest. Here we employed the bright photoswitchable fluorescent protein mMaple3 to label PprI, one essential DNA repair factor, and characterized systematically the fluorescence intensity and bleaching kinetics of both autoblinking and PprI-mMaple3 molecules within cells grown under three different conditions. Under minimal media, we can largely separate autoblinking from mMaple3 molecules and perform reliably single-molecule tracking of PprI in D. radiodurans, by means of applying signal-to-noise ratio and constraining the minimal length for linking the trajectories. We observed three states of PprI molecules, which bear different subcellular localizations and distinct functionalities. Our strategy provides a useful means to study the dynamics and distributions of proteins of interest in bacterial cells with high level of autoblinking.

Absolute quantification of protein number and dynamics in single cells

Quantitative characterization of protein abundance and interactions in live cells is necessary to understand and predict cellular behavior. The accurate determination of copy number for individual proteins and heterologous complexes in individual cells is critical because small changes in protein dosage, often less than two-fold, can have strong phenotypic consequences. Here, we review the merits and pitfalls of different quantitative fluorescence imaging methods for single-cell determination of protein abundance, localization, interactions, and dynamics. In particular, we discuss how scanning number and brightness (sN&B) and its variation, Raster scanning image correlation spectroscopy (RICS), exploit stochastic noise in small measurement volumes to quantify protein abundance, stoichiometry, and dynamics with high accuracy.

ECLiPSE: A Versatile Classification Technique for Structural and Morphological Analysis of Super-Resolution Microscopy Data

We introduce a new automated machine learning analysis pipeline to precisely classify cellular structures captured through single molecule localization microscopy, which we call ECLiPSE (Enhanced Classification of Localized Pointclouds by Shape Extraction). ECLiPSE leverages 67 comprehensive shape descriptors encompassing geometric, boundary, skeleton and other properties, the majority of which are directly extracted from the localizations to accurately characterize individual structures. We validate ECLiPSE through unsupervised and supervised classification on a dataset featuring five distinct cellular structures, achieving exceptionally high classification accuracies nearing 100%. Moreover, we demonstrate the versatility of our approach by applying it to two novel biological applications: quantifying the clearance of tau protein aggregates, a critical marker for neurodegenerative diseases, and differentiating between two distinct morphological features (morphotypes) of TAR DNA-binding protein 43 proteinopathy, potentially associated to different TDP-43 strains, each exhibiting unique seeding and spreading properties. We anticipate that this versatile approach will significantly enhance the way we study cellular structures across various biological contexts, elucidating their roles in disease development and progression.

Quantitative Single-Molecule Localization Microscopy

Super-resolution fluorescence microscopy allows the investigation of cellular structures at nanoscale resolution using light. Current developments in super-resolution microscopy have focused on reliable quantification of the underlying biological data. In this review, we first describe the basic principles of super-resolution microscopy techniques such as stimulated emission depletion (STED) microscopy and single-molecule localization microscopy (SMLM), and then give a broad overview of methodological developments to quantify super-resolution data, particularly those geared toward SMLM data. We cover commonly used techniques such as spatial point pattern analysis, colocalization, and protein copy number quantification but also describe more advanced techniques such as structural modeling, single-particle tracking, and biosensing. Finally, we provide an outlook on exciting new research directions to which quantitative super-resolution microscopy might be applied.

Synthetic analysis of chromatin tracing and live-cell imaging indicates pervasive spatial coupling between genes

The role of the spatial organization of chromosomes in directing transcription remains an outstanding question in gene regulation. Here, we analyze two recent single-cell imaging methodologies applied across hundreds of genes to systematically analyze the contribution of chromosome conformation to transcriptional regulation. Those methodologies are (1) single-cell chromatin tracing with super-resolution imaging in fixed cells; and (2) high-throughput labeling and imaging of nascent RNA in living cells. Specifically, we determine the contribution of physical distance to the coordination of transcriptional bursts. We find that individual genes adopt a constrained conformation and reposition toward the centroid of the surrounding chromatin upon activation. Leveraging the variability in distance inherent in single-cell imaging, we show that physical distance - but not genomic distance - between genes on individual chromosomes is the major factor driving co-bursting. By combining this analysis with live-cell imaging, we arrive at a corrected transcriptional correlation of [Formula: see text] for genes separated by < 400 nm. We propose that this surprisingly large correlation represents a physical property of human chromosomes and establishes a benchmark for future experimental studies.

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.

Near-Infrared Blinking Carbon Dots Designed for Quantitative Nanoscopy

Blinking carbon dots (CDs) have attracted attention as a probe for single molecule localization microscopy (SMLM), yet quantitative analysis is limited because of inept blinking and low signal-to-noise ratio (SNR). Here we report the design and synthesis of near-infrared (NIR) blinking CDs with a maximum emission of around 750 nm by weaving a nitrogen-doped aromatic backbone with surplus carboxyl groups on the surface. The NIR-CDs allow conjugation to monovalent antibody fragments for labeling and imaging of cellular receptors as well as afford increases of 52% in SNR and 33% in localization precision over visible CDs. Analysis of fluorescent bursts allows for accurate counting of cellular receptors at the nanoscale resolution. Using NIR-CDs-based SMLM, we demonstrate oligomerization and internalization of programmed cell death-ligand 1 by a small molecule inhibitor for checkpoint blockade. Our NIR-CDs can become a generally applicable probe for quantitative nanoscopy in chemistry and biology.

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.

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.

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.

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.

Convex hull as diagnostic tool in single-molecule localization microscopy

MOTIVATION

Single-molecule localization microscopy resolves individual fluorophores or fluorescence-labeled biomolecules. Data are provided as a set of localizations that distribute normally around the true fluorophore position with a variance determined by the localization precision. Characterizing the spatial fluorophore distribution to differentiate between resolution-limited localization clusters, which resemble individual biomolecules, and extended structures, which represent aggregated molecular complexes, is a common challenge.

RESULTS

We demonstrate the use of the convex hull and related hull properties of localization clusters for diagnostic purposes, as a parameter for cluster selection or as a tool to determine localization precision.

AVAILABILITY AND IMPLEMENTATION

https://github.com/super-resolution/Ebert-et-al-2022-supplement.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

High-precision estimation of emitter positions using Bayesian grouping of localizations

Single-molecule localization microscopy super-resolution methods rely on stochastic blinking/binding events, which often occur multiple times from each emitter over the course of data acquisition. Typically, the blinking/binding events from each emitter are treated as independent events, without an attempt to assign them to a particular emitter. Here, we describe a Bayesian method of inferring the positions of the tagged molecules by exploring the possible grouping and combination of localizations from multiple blinking/binding events. The results are position estimates of the tagged molecules that have improved localization precision and facilitate nanoscale structural insights. The Bayesian framework uses the localization precisions to learn the statistical distribution of the number of blinking/binding events per emitter and infer the number and position of emitters. We demonstrate the method on a range of synthetic data with various emitter densities, DNA origami constructs and biological structures using DNA-PAINT and dSTORM data. We show that under some experimental conditions it is possible to achieve sub-nanometer precision.

Single-molecule counting applied to the study of GPCR oligomerization

Single-molecule counting techniques enable a precise determination of the intracellular abundance and stoichiometry of proteins and macromolecular complexes. These details are often challenging to quantitatively assess yet are essential for our understanding of cellular function. Consider G-protein-coupled receptors-an expansive class of transmembrane signaling proteins that participate in many vital physiological functions making them a popular target for drug development. While early evidence for the role of oligomerization in receptor signaling came from ensemble biochemical and biophysical assays, innovations in single-molecule measurements are now driving a paradigm shift in our understanding of its relevance. Here, we review recent developments in single-molecule counting with a focus on photobleaching step counting and the emerging technique of quantitative single-molecule localization microscopy-with a particular emphasis on the potential for these techniques to advance our understanding of the role of oligomerization in G-protein-coupled receptor signaling.

Photoswitching fingerprint analysis bypasses the 10-nm resolution barrier

Advances in super-resolution microscopy have demonstrated single-molecule localization precisions of a few nanometers. However, translation of such high localization precisions into sub-10-nm spatial resolution in biological samples remains challenging. Here we show that resonance energy transfer between fluorophores separated by less than 10 nm results in accelerated fluorescence blinking and consequently lower localization probabilities impeding sub-10-nm fluorescence imaging. We demonstrate that time-resolved fluorescence detection in combination with photoswitching fingerprint analysis can be used to determine the number and distance even of spatially unresolvable fluorophores in the sub-10-nm range. In combination with genetic code expansion with unnatural amino acids and bioorthogonal click labeling with small fluorophores, photoswitching fingerprint analysis can be used advantageously to reveal information about the number of fluorophores present and their distances in the sub-10-nm range in cells.

A density-based enrichment measure for assessing colocalization in single-molecule localization microscopy data

Dual-color single-molecule localization microscopy (SMLM) provides unprecedented possibilities for detailed studies of colocalization of different molecular species in a cell. However, the informational richness of the data is not fully exploited by current analysis tools that often reduce colocalization to a single value. Here, we describe a tool specifically designed for determination of co-localization in both 2D and 3D from SMLM data. The approach uses a function that describes the relative enrichment of one molecular species on the density distribution of a reference species. The function reframes the question of colocalization by providing a density-context relevant to multiple biological questions. Moreover, the function visualize enrichment (i.e. colocalization) directly in the images for easy interpretation. We demonstrate the approach's functionality on both simulated data and cultured neurons, and compare it to current alternative measures. The method is available in a Python function for easy and parameter-free implementation.

Correction of multiple-blinking artifacts in photoactivated localization microscopy

Photoactivated localization microscopy (PALM) produces an array of localization coordinates by means of photoactivatable fluorescent proteins. However, observations are subject to fluorophore multiple blinking and each protein is included in the dataset an unknown number of times at different positions, due to localization error. This causes artificial clustering to be observed in the data. We present a 'model-based correction' (MBC) workflow using calibration-free estimation of blinking dynamics and model-based clustering to produce a corrected set of localization coordinates representing the true underlying fluorophore locations with enhanced localization precision, outperforming the state of the art. The corrected data can be reliably tested for spatial randomness or analyzed by other clustering approaches, and descriptors such as the absolute number of fluorophores per cluster are now quantifiable, which we validate with simulated data and experimental data with known ground truth. Using MBC, we confirm that the adapter protein, the linker for activation of T cells, is clustered at the T cell immunological synapse.

Don’t Be Fooled by Randomness: Valid p-Values for Single Molecule Microscopy

The human mind shows extraordinary capability at recognizing patterns, while at the same time tending to underestimate the natural scope of random processes. Taken together, this easily misleads researchers in judging whether the observed characteristics of their data are of significance or just the outcome of random effects. One of the best tools to assess whether observed features fall into the scope of pure randomness is statistical significance testing, which quantifies the probability to falsely reject a chosen null hypothesis. The central parameter in this context is the p-value, which can be calculated from the recorded data sets. In case of p-values smaller than the level of significance, the null hypothesis is rejected, otherwise not. While significance testing has found widespread application in many sciences including the life sciences, it is hardly used in (bio-)physics. We propose here that significance testing provides an important and valid addendum to the toolbox of quantitative (single molecule) biology. It allows to support a quantitative judgement (the hypothesis) about the data set with a probabilistic assessment. In this manuscript we describe ways for obtaining valid p-values in two selected applications of single molecule microscopy: (i) Nanoclustering in single molecule localization microscopy. Previously, we developed a method termed 2-CLASTA, which allows to calculate a valid p-value for the null hypothesis of an underlying random distribution of molecules of interest while circumventing overcounting issues. Here, we present an extension to this approach, yielding a single overall p-value for data pooled from multiple cells or experiments. (ii) Single molecule trajectories. Data from a single molecule trajectory are inherently correlated, thus prohibiting a direct analysis via conventional statistical tools. Here, we introduce a block permutation test, which yields a valid p-value for the analysis and comparison of single molecule trajectory data. We exemplify the approach based on FRET trajectories.

Technological advances in super-resolution microscopy to study cellular processes

Since its initial demonstration in 2000, far-field super-resolution light microscopy has undergone tremendous technological developments. In parallel, these developments have opened a new window into visualizing the inner life of cells at unprecedented levels of detail. Here, we review the technical details behind the most common implementations of super-resolution microscopy and highlight some of the recent, promising advances in this field.

Towards a 'Spot On' Understanding of Transcription in the Nucleus

Regulation of transcription by RNA Polymerase II (RNAPII) is a rapidly evolving area of research. Technological developments in microscopy have revealed insight into the dynamics, structure, and localization of transcription components within single cells. A frequent observation in many studies is the appearance of 'spots' in cell nuclei associated with the transcription process. In this review we highlight studies that characterize the temporal and spatial characteristics of these spots, examine possible pitfalls in interpreting these kind of imaging data, and outline directions where single-cell imaging may advance in ways to further our understanding of transcription regulation.