Method for co-cluster analysis in multichannel single-molecule localisation data

Ligand-induced segregation from large cell-surface phosphatases is a critical step in γδ TCR triggering

Gamma/delta (γδ) T cells are unconventional adaptive lymphocytes that recognize structurally diverse ligands via somatically-recombined antigen receptors (γδ TCRs). The molecular mechanism by which ligand recognition initiates γδ TCR signaling, a process known as TCR triggering, remains elusive. Unlike αβ TCRs, γδ TCRs are not mechanosensitive, and do not require coreceptors or typical binding-induced conformational changes for triggering. Here, we show that γδ TCR triggering by nonclassical MHC class Ib antigens, a major class of ligands recognized by γδ T cells, requires steric segregation of the large cell-surface phosphatases CD45 and CD148 from engaged TCRs at synaptic close contact zones. Increasing access of these inhibitory phosphatases to sites of TCR engagement, by elongating MHC class Ib ligands or truncating CD45/148 ectodomains, abrogates TCR triggering and T cell activation. Our results identify a critical step in γδ TCR triggering and provide insight into the core triggering mechanism of endogenous and synthetic tyrosine-phosphorylated immunoreceptors.

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.

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.

A coordinate-based co-localization index to quantify and visualize spatial associations in single-molecule localization microscopy

Visualizing the subcellular distribution of proteins and determining whether specific proteins co-localize is one of the main strategies in determining the organization and potential interactions of protein complexes in biological samples. The development of super-resolution microscopy techniques such as single-molecule localization microscopy (SMLM) has tremendously increased the ability to resolve protein distribution at nanometer resolution. As super-resolution imaging techniques are becoming instrumental in revealing novel biological insights, new quantitative approaches that exploit the unique nature of SMLM datasets are required. Here, we present a new, local density-based algorithm to quantify co-localization in dual-color SMLM datasets. We show that this method is broadly applicable and only requires molecular coordinates and their localization precision as inputs. Using simulated point patterns, we show that this method robustly measures the co-localization in dual-color SMLM datasets, independent of localization density, but with high sensitivity towards local enrichments. We further validated our method using SMLM imaging of the microtubule network in epithelial cells and used it to study the spatial association between proteins at neuronal synapses. Together, we present a simple and easy-to-use, but powerful method to analyze the spatial association of molecules in dual-color SMLM datasets.

Raw Data to Results: A Hands-On Introduction and Overview of Computational Analysis for Single-Molecule Localization Microscopy

Single-molecule localization microscopy (SMLM) is an advanced microscopy method that uses the blinking of fluorescent molecules to determine the position of these molecules with a resolution below the diffraction limit (∼5-40 nm). While SMLM imaging itself is becoming more popular, the computational analysis surrounding the technique is still a specialized area and often remains a "black box" for experimental researchers. Here, we provide an introduction to the required computational analysis of SMLM imaging, post-processing and typical data analysis. Importantly, user-friendly, ready-to-use and well-documented code in Python and MATLAB with exemplary data is provided as an interactive experience for the reader, as well as a starting point for further analysis. Our code is supplemented by descriptions of the computational problems and their implementation. We discuss the state of the art in computational methods and software suites used in SMLM imaging and data analysis. Finally, we give an outlook into further computational challenges in the field.

Multi-color Molecular Visualization of Signaling Proteins Reveals How C-Terminal Src Kinase Nanoclusters Regulate T Cell Receptor Activation

Elucidating the mechanisms that controlled T cell activation requires visualization of the spatial organization of multiple proteins on the submicron scale. Here, we use stoichiometrically accurate, multiplexed, single-molecule super-resolution microscopy (DNA-PAINT) to image the nanoscale spatial architecture of the primary inhibitor of the T cell signaling pathway, Csk, and two binding partners implicated in its membrane association, PAG and TRAF3. Combined with a newly developed co-clustering analysis framework, we find that Csk forms nanoscale clusters proximal to the plasma membrane that are lost post-stimulation and are re-recruited at later time points. Unexpectedly, these clusters do not co-localize with PAG at the membrane but instead provide a ready pool of monomers to downregulate signaling. By generating CRISPR-Cas9 knockout T cells, our data also identify that a major risk factor for autoimmune diseases, the protein tyrosine phosphatase non-receptor type 22 (PTPN22) locus, is essential for Csk nanocluster re-recruitment and for maintenance of the synaptic PAG population.

Quantitative Data Analysis in Single-Molecule Localization Microscopy

Super-resolution microscopy, and specifically single-molecule localization microscopy (SMLM), is becoming a transformative technology for cell biology, as it allows the study of cellular structures with nanometer resolution. Here, we review a wide range of data analyses approaches for SMLM that extract quantitative information about the distribution, size, shape, spatial organization, and stoichiometry of macromolecular complexes to guide biological interpretation. We present a case study using the nuclear pore complex as an example that highlights the power of combining complementary approaches by identifying its symmetry, ringlike structure, and protein copy number. In face of recent technical and computational advances, this review serves as a guideline for selecting appropriate analysis tools and controls to exploit the potential of SMLM for a wide range of biological questions.

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

Single-molecule localization microscopy (SMLM) is a relatively new imaging modality, winning the 2014 Nobel Prize in Chemistry, and considered as one of the key super-resolution techniques. SMLM resolution goes beyond the diffraction limit of light microscopy and achieves resolution on the order of 10-20 nm. SMLM thus enables imaging single molecules and study of the low-level molecular interactions at the subcellular level. In contrast to standard microscopy imaging that produces 2D pixel or 3D voxel grid data, SMLM generates big data of 2D or 3D point clouds with millions of localizations and associated uncertainties. This unprecedented breakthrough in imaging helps researchers employ SMLM in many fields within biology and medicine, such as studying cancerous cells and cell-mediated immunity and accelerating drug discovery. However, SMLM data quantification and interpretation methods have yet to keep pace with the rapid advancement of SMLM imaging. Researchers have been actively exploring new computational methods for SMLM data analysis to extract biosignatures of various biological structures and functions. In this survey, we describe the state-of-the-art clustering methods adopted to analyze and quantify SMLM data and examine the capabilities and shortcomings of the surveyed methods. We classify the methods according to (1) the biological application (i.e., the imaged molecules/structures), (2) the data acquisition (such as imaging modality, dimension, resolution, and number of localizations), and (3) the analysis details (2D versus 3D, field of view versus region of interest, use of machine-learning and multi-scale analysis, biosignature extraction, etc.). We observe that the majority of methods that are based on second-order statistics are sensitive to noise and imaging artifacts, have not been applied to 3D data, do not leverage machine-learning formulations, and are not scalable for big-data analysis. Finally, we summarize state-of-the-art methodology, discuss some key open challenges, and identify future opportunities for better modeling and design of an integrated computational pipeline to address the key challenges.

Single-Molecule Imaging of Protein Interactions and Dynamics

Live-cell single-molecule fluorescence imaging has become a powerful analytical tool to investigate cellular processes that are not accessible to conventional biochemical approaches. This has greatly enriched our understanding of the behaviors of single biomolecules in their native environments and their roles in cellular events. Here, we review recent advances in fluorescence-based single-molecule bioimaging of proteins in living cells. We begin with practical considerations of the design of single-molecule fluorescence imaging experiments such as the choice of imaging modalities, fluorescent probes, and labeling methods. We then describe analytical observables from single-molecule data and the associated molecular parameters along with examples of live-cell single-molecule studies. Lastly, we discuss computational algorithms developed for single-molecule data analysis.

Analysis of sparse molecular distributions in fibrous arrangements based on the distance to the first neighbor in single molecule localization microscopy

Single Molecule Localization Microscopy (SMLM) currently attains a lateral resolution of around 10 nm approaching molecular size. Together with increasingly specific fluorescent labeling, it opens the possibility to quantitatively analyze molecular organization. When the labeling density is high enough, SMLM provides clear images of the molecular organization. However, either due to limited labeling efficiency or due to intrinsically low molecular abundance, SMLM delivers a small set of sparse and highly precise localizations. In this work, we introduce a correlation analysis of molecular locations based on the functional dependence of the complementary cumulative distribution function (CCDF) of the distance to the first neighbor (r₁). We demonstrate that the log(-log(CCDF(r₁))) vs. log(r₁) is characterized by a scaling exponent n that takes extreme values of 2 for a random 2D distribution and 1 for a strictly linear arrangement, and find that n is a robust and sensitive metric to distinguish characteristics of the underlying structure responsible for the molecular distribution, even at a very low labeling density. The method enables the detection of fibrillary organization and the estimation of the diameter of host fibers under conditions where a visual inspection provides no clue.

Imaging the fibroblast growth factor receptor network on the plasma membrane with DNA-assisted single-molecule super-resolution microscopy

Fibroblast growth factor receptors (FGFRs) are a subfamily of receptor tyrosine kinases and central players in health and disease. Following ligand binding and the formation of homo- and heteromeric complexes, FGFRs initiate a cellular response. Challenges in studying FGFR activation are inner-subfamily interactions and a complex heterogeneity of these in the cell membrane, which demand for observation techniques that can resolve individual protein complexes and that are compatible with endogenous protein levels. Here, we established an imaging and analysis pipeline for multiplexed single-molecule localization microscopy (SMLM) of the FGFR network at the plasma membrane. Using DNA-labeled primary antibodies, we visualize all four FGFRs in the same cell with near-molecular spatial resolution. From the super-resolution imaging data, we extract information on FGFR density, spatial distribution, and inner-subfamily colocalization. Our approach is straightforward and easily adaptable to other multiplexed SMLM data of membrane proteins.

Verifying molecular clusters by 2-color localization microscopy and significance testing

While single-molecule localization microscopy (SMLM) offers the invaluable prospect to visualize cellular structures below the diffraction limit of light microscopy, its potential has not yet been fully capitalized due to its inherent susceptibility to blinking artifacts. Particularly, overcounting of single molecule localizations has impeded a reliable and sensitive detection of biomolecular nanoclusters. Here we introduce a 2-Color Localization microscopy And Significance Testing Approach (2-CLASTA), providing a parameter-free statistical framework for the qualitative analysis of two-dimensional SMLM data via significance testing methods. 2-CLASTA yields p-values for the null hypothesis of random biomolecular distributions, independent of the blinking behavior of the chosen fluorescent labels. The method is parameter-free and does not require any additional measurements nor grouping of localizations. We validated the method both by computer simulations as well as experimentally, using protein concatemers as a mimicry of biomolecular clustering. As the new approach is not affected by overcounting artifacts, it is able to detect biomolecular clustering of various shapes at high sensitivity down to a level of dimers.

Nanoscale Distribution of Nuclear Sites by Super-Resolved Image Cross-Correlation Spectroscopy

Deciphering the spatiotemporal coordination between nuclear functions is important to understand its role in the maintenance of human genome. In this context, super-resolution microscopy has gained considerable interest because it can be used to probe the spatial organization of functional sites in intact single-cell nuclei in the 20-250 nm range. Among the methods that quantify colocalization from multicolor images, image cross-correlation spectroscopy (ICCS) offers several advantages, namely it does not require a presegmentation of the image into objects and can be used to detect dynamic interactions. However, the combination of ICCS with super-resolution microscopy has not been explored yet. Here, we combine dual-color stimulated emission depletion (STED) nanoscopy with ICCS (STED-ICCS) to quantify the nanoscale distribution of functional nuclear sites. We show that super-resolved ICCS provides not only a value of the colocalized fraction but also the characteristic distances associated to correlated nuclear sites. As a validation, we quantify the nanoscale spatial distribution of three different pairs of functional nuclear sites in MCF10A cells. As expected, transcription foci and a transcriptionally repressive histone marker (H3K9me3) are not correlated. Conversely, nascent DNA replication foci and the proliferating cell nuclear antigen(PCNA) protein have a high level of proximity and are correlated at a nanometer distance scale that is close to the limit of our experimental approach. Finally, transcription foci are found at a distance of 130 nm from replication foci, indicating a spatial segregation at the nanoscale. Overall, our data demonstrate that STED-ICCS can be a powerful tool for the analysis of the nanoscale distribution of functional sites in the nucleus.

Differential nanoscale organisation of LFA-1 modulates T-cell migration

Effector T-cells rely on integrins to drive adhesion and migration to facilitate their immune function. The heterodimeric transmembrane integrin LFA-1 (αLβ2 integrin) regulates adhesion and migration of effector T-cells through linkage of the extracellular matrix with the intracellular actin treadmill machinery. Here, we quantified the velocity and direction of F-actin flow in migrating T-cells alongside single-molecule localisation of transmembrane and intracellular LFA-1. Results showed that actin retrograde flow positively correlated and immobile actin negatively correlated with T-cell velocity. Plasma membrane-localised LFA-1 forms unique nano-clustering patterns in the leading edge, compared to the mid-focal zone, of migrating T-cells. Deleting the cytosolic phosphatase PTPN22, loss-of-function mutations of which have been linked to autoimmune disease, increased T-cell velocity, and leading-edge co-clustering of pY397 FAK, pY416 Src family kinases and LFA-1. These data suggest that differential nanoclustering patterns of LFA-1 in migrating T-cells may instruct intracellular signalling. Our data presents a paradigm where T-cells modulate the nanoscale organisation of adhesion and signalling molecules to fine tune their migration speed, with implications for the regulation of immune and inflammatory responses.This article has an associated First Person interview with the first author of the paper.

3DClusterViSu: 3D clustering analysis of super-resolution microscopy data by 3D Voronoi tessellations

Motivation

Single-molecule localization microscopy (SMLM) can play an important role in integrated structural biology approaches to identify, localize and determine the 3D structure of cellular structures. While many tools exist for the 3D analysis and visualization of crystal or cryo-EM structures little exists for 3D SMLM data, which can provide unique insights but are particularly challenging to analyze in three dimensions especially in a dense cellular context.

Results

We developed 3DClusterViSu, a method based on 3D Voronoi tessellations that allows local density estimation, segmentation and quantification of 3D SMLM data and visualization of protein clusters within a 3D tool. We show its robust performance on microtubules and histone proteins H2B and CENP-A with distinct spatial distributions. 3DClusterViSu will favor multi-scale and multi-resolution synergies to allow integrating molecular and cellular levels in the analysis of macromolecular complexes.

Availability and impementation

3DClusterViSu is available under http://cbi-dev.igbmc.fr/cbi/voronoi3D.

Supplementary information

Supplementary figures are available at Bioinformatics online.

A tessellation-based colocalization analysis approach for single-molecule localization microscopy

Multicolor single-molecule localization microscopy (λSMLM) is a powerful technique to reveal the relative nanoscale organization and potential colocalization between different molecular species. While several standard analysis methods exist for pixel-based images, λSMLM still lacks such a standard. Moreover, existing methods only work on 2D data and are usually sensitive to the relative molecular organization, a very important parameter to consider in quantitative SMLM. Here, we present an efficient, parameter-free colocalization analysis method for 2D and 3D λSMLM using tessellation analysis. We demonstrate that our method allows for the efficient computation of several popular colocalization estimators directly from molecular coordinates and illustrate its capability to analyze multicolor SMLM data in a robust and efficient manner.

Multicolour single-molecule localization microscopy lacks a standard analysis method. Here Levet et al. introduce Coloc-Tesseler, a parameter-free colocalisation analysis method based on tessellation analysis for the efficient analysis of multicolour SMLM data.

Updates on Aptamer Research

For many years, different probing techniques have mainly relied on antibodies for molecular recognition. However, with the discovery of aptamers, this has changed. The science community is currently considering using aptamers in molecular targeting studies because of the many potential advantages they have over traditional antibodies. Some of these possible advantages are their specificity, higher binding affinity, better target discrimination, minimized batch-to-batch variation, and reduced side effects. Overall, these characteristics of aptamers have attracted scholars to use them as molecular probes in place of antibodies, with some aptamer-based targeting products being now available in the market. The present review is aimed at discussing the potential of aptamers as probes in molecular biology and in super-resolution microscopy.

Ultrafast data mining of molecular assemblies in multiplexed high-density super-resolution images

Multicolor single-molecule localization super-resolution microscopy has enabled visualization of ultrafine spatial organizations of molecular assemblies within cells. Despite many efforts, current approaches for distinguishing and quantifying such organizations remain limited, especially when these are contained within densely distributed super-resolution data. In theory, higher-order correlation such as the Triple-Correlation function is capable of obtaining the spatial configuration of individual molecular assemblies masked within seemingly discorded dense distributions. However, due to their enormous computational cost such analyses are impractical, even for high-end computers. Here, we developed a fast algorithm for Triple-Correlation analyses of high-content multiplexed super-resolution data. This algorithm computes the probability density of all geometric configurations formed by every triple-wise single-molecule localization from three different channels, circumventing impractical 4D Fourier Transforms of the entire megapixel image. This algorithm achieves 10²-folds enhancement in computational speed, allowing for high-throughput Triple-Correlation analyses and robust quantification of molecular complexes in multiplexed super-resolution microscopy.

Analyzing the organization of molecular complexes in multi-color single-molecule localization microscopy data requires heavy computation resources that are impractical for laboratory computers. Here the authors develop a coordinate-based Triple-Correlation algorithm with improved speed and reduced computational cost.

Analysis of Spatial Assembly of GPCRs Using Photoactivatable Dyes and Localization Microscopy

Super-resolution imaging has provided unprecedented insight in the molecular complexities of fundamental cell biological questions. For G protein-coupled receptors (GPCRs), its application to the study of receptor homomers and heteromers have unveiled the diversity of complexes these GPCRs can form at the plasma membrane at a structural and functional level. Here, we describe our methodological approach of photoactivated localization microscopy with photoactivatable dyes (PD-PALM) to visualize and quantify the spatial assembly of GPCR heteromers at the plasma membrane.

Deciphering and engineering chromodomain-methyllysine peptide recognition

Posttranslational modifications (PTMs) play critical roles in regulating protein functions and mediating protein-protein interactions. An important PTM is lysine methylation that orchestrates chromatin modifications and regulates functions of non-histone proteins. Methyllysine peptides are bound by modular domains, of which chromodomains are representative. Here, we conducted the first large-scale study of chromodomains in the human proteome interacting with both histone and non-histone methyllysine peptides. We observed significant degenerate binding between chromodomains and histone peptides, i.e., different histone sites can be recognized by the same set of chromodomains, and different chromodomains can share similar binding profiles to individual histone sites. Such degenerate binding is not dictated by amino acid sequence or PTM motif but rather rooted in the physiochemical properties defined by the PTMs on the histone peptides. This molecular mechanism is confirmed by the accurate prediction of the binding specificity using a computational model that captures the structural and energetic patterns of the domain-peptide interaction. To further illustrate the power and accuracy of our model, we used it to effectively engineer an exceptionally strong H3K9me3-binding chromodomain and to label H3K9me3 in live cells. This study presents a systematic approach to deciphering domain-peptide recognition and reveals a general principle by which histone modifications are interpreted by reader proteins, leading to dynamic regulation of gene expression and other biological processes.

Mapping Transient Protein Interactions at the Nanoscale in Living Mammalian Cells

Protein-protein interactions (PPIs) form the basis of cellular processes, regulating cell behavior and fate. PPIs can be extremely transient in nature, which hinders their detection. In addition, traditional biochemical methods provided limited information on the spatial distribution and temporal dynamics of PPIs that is crucial for their regulation in the crowded cellular environment. Given the pivotal role of membrane micro- and nanodomains in the regulation of PPIs at the plasma membrane, the development of methods to visualize PPIs with a high spatial resolution is imperative. Here, we present a super-resolution fluorescence microscopy technique that can detect and map short-lived transient protein-protein interactions on a nanometer scale in the cellular environment. This imaging method is based on single-molecule fluorescence microscopy and exploits the effect of the difference in the mobility between cytosolic and membrane-bound proteins in the recorded fluorescence signals. After the development of the proof of concept using a model system based on membrane-bound modular protein domains and fluorescently labeled peptides, we applied this imaging approach to investigate the interactions of cytosolic proteins involved in the epidermal growth factor signaling pathway (namely, Grb2, c-Raf, and PLCγ1). The detected clusters of Grb2 and c-Raf were correlated with the distribution of the receptor at the plasma membrane. Additionally, the interactions of wild type PLCγ1 were compared with those detected with truncated mutants, which provided important information regarding the role played by specific domains in the interaction with the membrane. The results presented here demonstrate the potential of this technique to unravel the role of membrane heterogeneity in the spatiotemporal regulation of cell signaling.

Quantitative fibre analysis of single-molecule localization microscopy data

Single molecule localization microscopy (SMLM) methods produce data in the form of a spatial point pattern (SPP) of all localized emitters. Whilst numerous tools exist to quantify molecular clustering in SPP data, the analysis of fibrous structures has remained understudied. Taking the SMLM localization coordinates as input, we present an algorithm capable of tracing fibrous structures in data generated by SMLM. Based upon a density parameter tracing routine, the algorithm outputs several fibre descriptors, such as number of fibres, length of fibres, area of enclosed regions and locations and angles of fibre branch points. The method is validated in a variety of simulated conditions and experimental data acquired using the image reconstruction by integrating exchangeable single-molecule localization (IRIS) technique. For this, the nanoscale architecture of F-actin at the T cell immunological synapse in both untreated and pharmacologically treated cells, designed to perturb actin structure, was analysed.

Biological Insight from Super-Resolution Microscopy: What We Can Learn from Localization-Based Images

Super-resolution optical imaging based on the switching and localization of individual fluorescent molecules [photoactivated localization microscopy (PALM), stochastic optical reconstruction microscopy (STORM), etc.] has evolved remarkably over the last decade. Originally driven by pushing technological limits, it has become a tool of biological discovery. The initial demand for impressive pictures showing well-studied biological structures has been replaced by a need for quantitative, reliable data providing dependable evidence for specific unresolved biological hypotheses. In this review, we highlight applications that showcase this development, identify the features that led to their success, and discuss remaining challenges and difficulties. In this context, we consider the complex topic of defining resolution for this imaging modality and address some of the more common analytical methods used with this data.

Super Resolution Network Analysis Defines the Molecular Architecture of Caveolae and Caveolin-1 Scaffolds

Quantitative approaches to analyze the large data sets generated by single molecule localization super-resolution microscopy (SMLM) are limited. We developed a computational pipeline and applied it to analyzing 3D point clouds of SMLM localizations (event lists) of the caveolar coat protein, caveolin-1 (Cav1), in prostate cancer cells differentially expressing CAVIN1 (also known as PTRF), that is also required for caveolae formation. High degree (strongly-interacting) points were removed by an iterative blink merging algorithm and Cav1 network properties were compared with randomly generated networks to retain a sub-network of geometric structures (or blobs). Machine-learning based classification extracted 28 quantitative features describing the size, shape, topology and network characteristics of ∼80,000 blobs. Unsupervised clustering identified small S1A scaffolds corresponding to SDS-resistant Cav1 oligomers, as yet undescribed larger hemi-spherical S2 scaffolds and, only in CAVIN1-expressing cells, spherical, hollow caveolae. Multi-threshold modularity analysis suggests that S1A scaffolds interact to form larger scaffolds and that S1A dimers group together, in the presence of CAVIN1, to form the caveolae coat.

Dynamic pattern generation in cell membranes: Current insights into membrane organization

It has been two decades since the lipid raft hypothesis was first presented. Even today, whether these nanoscale cholesterol-rich domains are present in cell membranes is not completely resolved. However, especially in the last few years, a rich body of literature has demonstrated both the presence and the importance of non-random distribution of biomolecules on the membrane, which is the focus of this review. These new developments have pushed the experimental limits of detection and have brought us closer to observing lipid domains in the plasma membrane of live cells. Characterization of biomolecules associated with lipid rafts has revealed a deep connection between biological regulation and function and membrane compositional heterogeneities. Finally, tantalizing new developments in the field have demonstrated that lipid domains might not just be associated with the plasma membrane of eukaryotes but could potentially be a ubiquitous membrane-organizing principle in several other biological systems. This article is part of a Special Issue entitled: Emergence of Complex Behavior in Biomembranes edited by Marjorie Longo.

Mapping molecular assemblies with fluorescence microscopy and object-based spatial statistics

Elucidating protein functions and molecular organisation requires to localise precisely single or aggregated molecules and analyse their spatial distributions. We develop a statistical method SODA (Statistical Object Distance Analysis) that uses either micro- or nanoscopy to significantly improve on standard co-localisation techniques. Our method considers cellular geometry and densities of molecules to provide statistical maps of isolated and associated (coupled) molecules. We use SODA with three-colour structured-illumination microscopy (SIM) images of hippocampal neurons, and statistically characterise spatial organisation of thousands of synapses. We show that presynaptic synapsin is arranged in asymmetric triangle with the 2 postsynaptic markers homer and PSD95, indicating a deeper localisation of homer. We then determine stoichiometry and distance between localisations of two synaptic vesicle proteins with 3D-STORM. These findings give insights into the protein organisation at the synapse, and prove the efficiency of SODA to quantitatively assess the geometry of molecular assemblies.

Image co-localization – co-occurrence versus correlation

Fluorescence image co-localization analysis is widely utilized to suggest biomolecular interaction. However, there exists some confusion as to its correct implementation and interpretation. In reality, co-localization analysis consists of at least two distinct sets of methods, termed co-occurrence and correlation. Each approach has inherent and often contrasting strengths and weaknesses. Yet, neither one can be considered to always be preferable for any given application. Rather, each method is most appropriate for answering different types of biological question. This Review discusses the main factors affecting multicolor image co-occurrence and correlation analysis, while giving insight into the types of biological behavior that are better suited to one approach or the other. Further, the limits of pixel-based co-localization analysis are discussed in the context of increasingly popular super-resolution imaging techniques.

A fluorescence nanoscopy marker for corticotropin-releasing hormone type 1 receptor: computer design, synthesis, signaling effects, super-resolved fluorescence imaging, and in situ affinity constant in cells

A new fluorescent marker for CRHR1 shows an antagonist effect and suitability for super resolution fluorescence microscopy.

A Method for Quantifying Molecular Interactions Using Stochastic Modelling and Super-Resolution Microscopy

We introduce the Interaction Factor (IF), a measure for quantifying the interaction of molecular clusters in super-resolution microscopy images. The IF is robust in the sense that it is independent of cluster density, and it only depends on the extent of the pair-wise interaction between different types of molecular clusters in the image. The IF for a single or a collection of images is estimated by first using stochastic modelling where the locations of clusters in the images are repeatedly randomized to estimate the distribution of the overlaps between the clusters in the absence of interaction (IF = 0). Second, an analytical form of the relationship between IF and the overlap (which has the random overlap as its only parameter) is used to estimate the IF for the experimentally observed overlap. The advantage of IF compared to conventional methods to quantify interaction in microscopy images is that it is insensitive to changing cluster density and is an absolute measure of interaction, making the interpretation of experiments easier. We validate the IF method by using both simulated and experimental data and provide an ImageJ plugin for determining the IF of an image.

Architectural plasticity of human BRCA2-RAD51 complexes in DNA break repair

The tumor suppressor BRCA2 is a large multifunctional protein mutated in 50–60% of familial breast cancers. BRCA2 interacts with many partners and includes multiple regions with potentially disordered structure. In homology directed DNA repair BRCA2 delivers RAD51 to DNA resulting in removal of RPA and assembly of a RAD51 nucleoprotein filament. Dynamic rearrangements of BRCA2 likely drive this molecular hand-off initiating DNA strand exchange. We show human BRCA2 forms oligomers which can have an extended shape. Scanning force microscopy and quantitative single molecule fluorescence define the variety of BRCA2 complexes, reveal dramatic rearrangements upon RAD51 binding and the loading of RAD51 patches on single strand DNA. At sites of repair in cell nuclei, super-resolution microscopy shows BRCA2 and RAD51 arranged in largely separate locations. We identified dynamic structural transitions in BRCA2 complexes suggested to facilitate loading of RAD51 onto RPA coated single strand DNA and subsequent release of BRCA2.

Localizing order to boost signaling

B-cell receptors form ordered clusters to recruit kinases and exclude phosphatases.

A Bayesian cluster analysis method for single-molecule localization microscopy data

Cell function is regulated by the spatiotemporal organization of the signaling machinery, and a key facet of this is molecular clustering. Here, we present a protocol for the analysis of clustering in data generated by 2D single-molecule localization microscopy (SMLM)-for example, photoactivated localization microscopy (PALM) or stochastic optical reconstruction microscopy (STORM). Three features of such data can cause standard cluster analysis approaches to be ineffective: (i) the data take the form of a list of points rather than a pixel array; (ii) there is a non-negligible unclustered background density of points that must be accounted for; and (iii) each localization has an associated uncertainty in regard to its position. These issues are overcome using a Bayesian, model-based approach. Many possible cluster configurations are proposed and scored against a generative model, which assumes Gaussian clusters overlaid on a completely spatially random (CSR) background, before every point is scrambled by its localization precision. We present the process of generating simulated and experimental data that are suitable to our algorithm, the analysis itself, and the extraction and interpretation of key cluster descriptors such as the number of clusters, cluster radii and the number of localizations per cluster. Variations in these descriptors can be interpreted as arising from changes in the organization of the cellular nanoarchitecture. The protocol requires no specific programming ability, and the processing time for one data set, typically containing 30 regions of interest, is ∼18 h; user input takes ∼1 h.

Clus-DoC: a combined cluster detection and colocalization analysis for single-molecule localization microscopy data

Advances in fluorescence microscopy are providing increasing evidence that the spatial organization of proteins in cell membranes may facilitate signal initiation and integration for appropriate cellular responses. Our understanding of how changes in spatial organization are linked to function has been hampered by the inability to directly measure signaling activity or protein association at the level of individual proteins in intact cells. Here we solve this measurement challenge by developing Clus-DoC, an analysis strategy that quantifies both the spatial distribution of a protein and its colocalization status. We apply this approach to the triggering of the T-cell receptor during T-cell activation, as well as to the functionality of focal adhesions in fibroblasts, thereby demonstrating an experimental and analytical workflow that can be used to quantify signaling activity and protein colocalization at the level of individual proteins.

Inhomogeneity Based Characterization of Distribution Patterns on the Plasma Membrane

Cell surface protein and lipid molecules are organized in various patterns: randomly, along gradients, or clustered when segregated into discrete micro- and nano-domains. Their distribution is tightly coupled to events such as polarization, endocytosis, and intracellular signaling, but challenging to quantify using traditional techniques. Here we present a novel approach to quantify the distribution of plasma membrane proteins and lipids. This approach describes spatial patterns in degrees of inhomogeneity and incorporates an intensity-based correction to analyze images with a wide range of resolutions; we have termed it Quantitative Analysis of the Spatial distributions in Images using Mosaic segmentation and Dual parameter Optimization in Histograms (QuASIMoDOH). We tested its applicability using simulated microscopy images and images acquired by widefield microscopy, total internal reflection microscopy, structured illumination microscopy, and photoactivated localization microscopy. We validated QuASIMoDOH, successfully quantifying the distribution of protein and lipid molecules detected with several labeling techniques, in different cell model systems. We also used this method to characterize the reorganization of cell surface lipids in response to disrupted endosomal trafficking and to detect dynamic changes in the global and local organization of epidermal growth factor receptors across the cell surface. Our findings demonstrate that QuASIMoDOH can be used to assess protein and lipid patterns, quantifying distribution changes and spatial reorganization at the cell surface. An ImageJ/Fiji plugin of this analysis tool is provided.

Plasma membrane organization is fundamental to cellular signaling, transport of molecules, and cell adhesion. To achieve this, plasma membrane proteins and lipids are spatially organized: they form clusters, aggregate in signaling platforms, distribute into gradients on polarized cells, or randomly distribute across the membrane. It is also clear that these organizations can be affected in various contexts. For example, in aging or neurodegenerative diseases, the composition of the plasma membrane is altered and, consequently, the protein and lipid distributions in the membrane fluctuate. In addition, cancer progression is characterized by changes in cellular polarity, lipid content, and the redistribution of cell surface receptors and adhesion molecules. Here we have developed a method to quantify such alterations that, unlike current tools, is compatible with diverse types of cellular organization, including polarity. Our tool can be employed to screen for changes in a straightforward manner and to elucidate distributions of cell surface components in different disciplines, ranging from neurobiology to cancer research.

From single-molecule spectroscopy to super-resolution imaging of the neuron: a review

For more than 20 years, single-molecule spectroscopy has been providing invaluable insights into nature at the molecular level. The field has received a powerful boost with the development of the technique into super-resolution imaging methods, ca. 10 years ago, which overcome the limitations imposed by optical diffraction. Today, single molecule super-resolution imaging is routinely used in the study of macromolecular function and structure in the cell. Concomitantly, computational methods have been developed that provide information on numbers and positions of molecules at the nanometer-scale. In this overview, we outline the technical developments that have led to the emergence of localization microscopy techniques from single-molecule spectroscopy. We then provide a comprehensive review on the application of the technique in the field of neuroscience research.

ClusterViSu, a method for clustering of protein complexes by Voronoi tessellation in super-resolution microscopy

Super-resolution microscopy (PALM, STORM etc.) provides a plethora of fluorescent signals in dense cellular environments which can be difficult to interpret. Here we describe ClusterViSu, a method for image reconstruction, visualization and quantification of labelled protein clusters, based on Voronoi tessellation of the individual fluorescence events. The general applicability of this clustering approach for the segmentation of super-resolution microscopy data, including for co-localization, is illustrated on a series of important biological objects such as chromatin complexes, RNA polymerase, nuclear pore complexes and microtubules.

Protein clustering and spatial organization in T-cells

T-cell protein microclusters have until recently been investigable only as microscale entities with their composition and structure being discerned by biochemistry or diffraction-limited light microscopy. With the advent of super resolution microscopy comes the ability to interrogate the structure and function of these clusters at the single molecule level by producing highly accurate pointillist maps of single molecule locations at ~20nm resolution. Analysis tools have also been developed to provide rich descriptors of the pointillist data, allowing us to pose questions about the nanoscale organization which governs the local and cell wide responses required of a migratory T-cell.

Super-resolution imaging of STAT3 cellular clustering during nuclear transport

STAT3 cellular clustering revealed by super-resolution fluorescence microscopy.

Tetraspanin CD37 Regulates β2 Integrin-Mediated Adhesion and Migration in Neutrophils

Deciphering the molecular basis of leukocyte recruitment is critical to the understanding of inflammation. In this study, we investigated the contribution of the tetraspanin CD37 to this key process. CD37-deficient mice showed impaired neutrophil recruitment in a peritonitis model. Intravital microscopic analysis indicated that the absence of CD37 impaired the capacity of leukocytes to follow a CXCL1 chemotactic gradient accurately in the interstitium. Moreover, analysis of CXCL1-induced leukocyte-endothelial cell interactions in postcapillary venules revealed that CXCL1-induced neutrophil adhesion and transmigration were reduced in the absence of CD37, consistent with a reduced capacity to undergo β2 integrin-dependent adhesion. This result was supported by in vitro flow chamber experiments that demonstrated an impairment in adhesion of CD37-deficient neutrophils to the β2 integrin ligand, ICAM-1, despite the normal display of high-affinity β2 integrins. Superresolution microscopic assessment of localization of CD37 and CD18 in ICAM-1-adherent neutrophils demonstrated that these molecules do not significantly cocluster in the cell membrane, arguing against the possibility that CD37 regulates β2 integrin function via a direct molecular interaction. Moreover, CD37 ablation did not affect β2 integrin clustering. In contrast, the absence of CD37 in neutrophils impaired actin polymerization, cell spreading and polarization, dysregulated Rac-1 activation, and accelerated β2 integrin internalization. Together, these data indicate that CD37 promotes neutrophil adhesion and recruitment via the promotion of cytoskeletal function downstream of integrin-mediated adhesion.

The nanoscale organization of signaling domains at the plasma membrane

In this chapter, we present an overview of the role of the nanoscale organization of signaling domains in regulating key cellular processes. In particular, we illustrate the importance of protein and lipid nanodomains as triggers and mediators of cell signaling. As particular examples, we summarize the state of the art of understanding the role of nanodomains in the mounting of an immune response, cellular adhesion, intercellular communication, and cell proliferation. Thus, this chapter underlines the essential role the nanoscale organization of key signaling proteins and lipid domains. We will also see how nanodomains play an important role in the lifecycle of many pathogens relevant to human disease and therefore illustrate how these structures may become future therapeutic targets.

The Histochemistry and Cell Biology pandect: the year 2014 in review

This review encompasses a brief synopsis of the articles published in 2014 in Histochemistry and Cell Biology. Out of the total of 12 issues published in 2014, two special issues were devoted to "Single-Molecule Super-Resolution Microscopy." The present review is divided into 11 categories, providing an easy format for readers to quickly peruse topics of particular interest to them.

Statistical analysis of molecule colocalization in bioimaging

The quantitative analysis of molecule interactions in bioimaging is key for understanding the molecular orchestration of cellular processes and is generally achieved through the study of the spatial colocalization between the different populations of molecules. Colocalization methods are traditionally divided into pixel-based methods that measure global correlation coefficients from the overlap between pixel intensities in different color channels, and object-based methods that first segment molecule spots and then analyze their spatial distributions with second-order statistics. Here, we present a review of such colocalization methods and give a quantitative comparison of their relative merits in different types of biological applications and contexts. We show on synthetic and biological images that object-based methods are more robust statistically than pixel-based methods, and allow moreover to quantify accurately the number of colocalized molecules.

Single molecule analysis of functionally asymmetric G protein-coupled receptor (GPCR) oligomers reveals diverse spatial and structural assemblies

Formation of G protein-coupled receptors (GPCRs) into dimers and higher order oligomers represents a key mechanism in pleiotropic signaling, yet how individual protomers function within oligomers remains poorly understood. We present a super-resolution imaging approach, resolving single GPCR molecules to ∼8 nm resolution in functional asymmetric dimers and oligomers using dual-color photoactivatable dyes and localization microscopy (PD-PALM). PD-PALM of two functionally defined mutant luteinizing hormone receptors (LHRs), a ligand-binding deficient receptor (LHR^B−) and a signaling-deficient (LHR^S−) receptor, which only function via intermolecular cooperation, favored oligomeric over dimeric formation. PD-PALM imaging of trimers and tetramers revealed specific spatial organizations of individual protomers in complexes where the ratiometric composition of LHR^B− to LHR^S− modulated ligand-induced signal sensitivity. Structural modeling of asymmetric LHR oligomers strongly aligned with PD-PALM-imaged spatial arrangements, identifying multiple possible helix interfaces mediating inter-protomer associations. Our findings reveal that diverse spatial and structural assemblies mediating GPCR oligomerization may acutely fine-tune the cellular signaling profile.

Quantitative analysis of single-molecule superresolution images

This review highlights the quantitative capabilities of single-molecule localization-based superresolution imaging methods. In addition to revealing fine structural details, the molecule coordinate lists generated by these methods provide the critical ability to quantify the number, clustering, and colocalization of molecules with 10-50 nm resolution. Here we describe typical workflows and precautions for quantitative analysis of single-molecule superresolution images. These guidelines include potential pitfalls and essential control experiments, allowing critical assessment and interpretation of superresolution images.

Super-resolution imaging of C-type lectin spatial rearrangement within the dendritic cell plasma membrane at fungal microbe contact sites

Dendritic cells express DC-SIGN and CD206, C-type lectins (CTLs) that bind a variety of pathogens and may facilitate pathogen uptake for subsequent antigen presentation. Both proteins form punctate membrane nanodomains (∼80 nm) on naïve cells. We analyzed the spatiotemporal distribution of CTLs following host-fungal particle contact using confocal microscopy and three distinct methods of cluster identification and measurement of receptor clusters in super-resolution datasets: DBSCAN, Pair Correlation and a custom implementation of the Getis spatial statistic. Quantitative analysis of confocal and super-resolution images demonstrated that CTL nanodomains become concentrated in the contact site relative to non-contact membrane after the first hour of exposure and established that this recruitment is sustained out to 4 h. DC-SIGN nanodomains in fungal contact sites exhibit a 70% area increase and a 38% decrease in interdomain separation. Contact site CD206 nanodomains possess 90% greater area and 42% lower interdomain separation relative to non-contact regions. Contact site CTL clusters appear as disk-shaped domains of approximately 150-175 nm in diameter. The increase in length scale of CTL nanostructure in contact sites suggests that the smaller nanodomains on resting membranes may merge during fungal recognition, or that they become packed closely enough to achieve sub-resolution inter-domain edge separations of <30 nm. This study provides evidence of local receptor spatial rearrangements on the nanoscale that occur in the plasma membrane upon pathogen binding and may direct important signaling interactions required to recognize and respond to the presence of a relatively large pathogen.

Transient cholesterol effects on nicotinic acetylcholine receptor cell-surface mobility

To what extent do cholesterol-rich lipid platforms modulate the supramolecular organization of the nicotinic acetylcholine receptor (AChR)? To address this question, the dynamics of AChR particles at high density and its cholesterol dependence at the surface of mammalian cells were studied by combining total internal reflection fluorescence microscopy and single-particle tracking. AChR particles tagged with a monovalent ligand, fluorescent α-bungarotoxin (αBTX), exhibited two mobile pools: i) a highly mobile one undergoing simple Brownian motion (16%) and ii) one with restricted motion (∼50%), the rest being relatively immobile (∼44%). Depletion of membrane cholesterol by methyl-α-cyclodextrin increased the fraction of the first pool to 22% and 33% after 15 and 40 min, respectively; the pool undergoing restricted motion diminished from 50% to 44% and 37%, respectively. Monoclonal antibody binding results in AChR crosslinking-internalization after 2 h; here, antibody binding immobilized within minutes ∼20% of the totally mobile AChR. This proportion dramatically increased upon cholesterol depletion, especially during the initial 10 min (83.3%). Thus, antibody crosslinking and cholesterol depletion exhibited a mutually synergistic effect, increasing the average lifetime of cell-surface AChRs∼10 s to ∼20 s. The instantaneous (microscopic) diffusion coefficient D2-4 of the AChR obtained from the MSD analysis diminished from ∼0.001 µm2 s(-1) to ∼0.0001-0.00033 µm2 s(-1) upon cholesterol depletion, ∼30% of all particles falling into the stationary mode. Thus, muscle-type AChR exhibits heterogeneous motional regimes at the cell surface, modulated by the combination of intrinsic (its supramolecular organization) and extrinsic (membrane cholesterol content) factors.