MIiSR: Molecular Interactions in Super-Resolution Imaging Enables the Analysis of Protein Interactions, Dynamics and Formation of Multi-protein Structures

Fish Evo-Devo: Moving Toward Species-Specific and Knowledge-Based Interactome

A knowledge-based interactome maps interactions among proteins and molecules within a cell using experimental data, computational predictions, and literature mining. These interactomes are vital for understanding cellular functions, pathways, and the evolutionary conservation of protein interactions. They reveal how interactions regulate growth, differentiation, and development. Transitioning to functionally validated interactomes is crucial in evolutionary developmental biology (Evo-Devo), especially for non-model species, to uncover unique regulatory networks, evolutionary novelties, and reliable gene interaction models. This enhances our understanding of complex trait evolution across species. The European Evo-Devo 2024 conference in Helsinki hosted the first fish-specific Evo-Devo symposium, highlighting the growing interest in fish models. Advances in genome annotation, genome editing, imaging, and molecular screening are expanding fish Evo-Devo research. High-throughput molecular data have enabled the deduction of gene regulatory networks. The next steps involve creating species-specific interactomes, validating them functionally, and integrating additional molecular data to deepen the understanding of complex regulatory interactions in fish Evo-Devo. This short review aims to address the logical steps for this transition, as well as the necessities and limitations of this journey.

Super-resolution microscopy to study membrane nanodomains and transport mechanisms in the plasma membrane

Biological membranes are complex, heterogeneous, and dynamic systems that play roles in the compartmentalization and protection of cells from the environment. It is still a challenge to elucidate kinetics and real-time transport routes for molecules through biological membranes in live cells. Currently, by developing and employing super-resolution microscopy; increasing evidence indicates channels and transporter nano-organization and dynamics within membranes play an important role in these regulatory mechanisms. Here we review recent advances and discuss the major advantages and disadvantages of using super-resolution microscopy to investigate protein organization and transport within plasma membranes.

In-section Click-iT detection and super-resolution CLEM analysis of nucleolar ultrastructure and replication in plants

Correlative light and electron microscopy (CLEM) is an important tool for the localisation of target molecule(s) and their spatial correlation with the ultrastructural map of subcellular features at the nanometre scale. Adoption of these advanced imaging methods has been limited in plant biology, due to challenges with plant tissue permeability, fluorescence labelling efficiency, indexing of features of interest throughout the complex 3D volume and their re-localization on micrographs of ultrathin cross-sections. Here, we demonstrate an imaging approach based on tissue processing and embedding into methacrylate resin followed by imaging of sections by both, single-molecule localization microscopy and transmission electron microscopy using consecutive CLEM and same-section CLEM correlative workflow. Importantly, we demonstrate that the use of a particular type of embedding resin is not only compatible with single-molecule localization microscopy but shows improvements in the fluorophore blinking behavior relative to the whole-mount approaches. Here, we use a commercially available Click-iT ethynyl-deoxyuridine cell proliferation kit to visualize the DNA replication sites of wild-type Arabidopsis thaliana seedlings, as well as fasciata1 and nucleolin1 plants and apply our in-section CLEM imaging workflow for the analysis of S-phase progression and nucleolar organization in mutant plants with aberrant nucleolar phenotypes.

Super-resolution microscopy reveals focal organization of ER-associated Y-complexes in mitosis

During mitotic entry of vertebrate cells, nuclear pore complexes (NPCs) are rapidly disintegrated. NPC disassembly is initiated by hyperphosphorylation of linker nucleoporins (Nups), which leads to the dissociation of FG repeat Nups and relaxation of the nuclear permeability barrier. However, less is known about disintegration of the huge nuclear and cytoplasmic rings, which are formed by annular assemblies of Y-complexes that are dissociated from NPCs as intact units. Surprisingly, we observe that Y-complex Nups display slower dissociation kinetics compared with other Nups during in vitro NPC disassembly, indicating a mechanistic difference in the disintegration of Y-based rings. Intriguingly, biochemical experiments reveal that a fraction of Y-complexes remains associated with mitotic ER membranes, supporting recent microscopic observations. Visualization of mitotic Y-complexes by super-resolution microscopy demonstrates that they form two classes of higher order assemblies: large clusters at kinetochores and small, focal ER-associated assemblies. These, however, lack features qualifying them as persisting ring-shaped subassemblies previously proposed to serve as structural templates for NPC reassembly during mitotic exit, which helps to refine current models of nuclear reassembly.

RegiSTORM: channel registration for multi-color stochastic optical reconstruction microscopy

BACKGROUND

Stochastic optical reconstruction microscopy (STORM), a super-resolution microscopy technique based on single-molecule localizations, has become popular to characterize sub-diffraction limit targets. However, due to lengthy image acquisition, STORM recordings are prone to sample drift. Existing cross-correlation or fiducial marker-based algorithms allow correcting the drift within each channel, but misalignment between channels remains due to interchannel drift accumulating during sequential channel acquisition. This is a major drawback in multi-color STORM, a technique of utmost importance for the characterization of various biological interactions.

RESULTS

We developed RegiSTORM, a software for reducing channel misalignment by accurately registering STORM channels utilizing fiducial markers in the sample. RegiSTORM identifies fiducials from the STORM localization data based on their non-blinking nature and uses them as landmarks for channel registration. We first demonstrated accurate registration on recordings of fiducials only, as evidenced by significantly reduced target registration error with all the tested channel combinations. Next, we validated the performance in a more practically relevant setup on cells multi-stained for tubulin. Finally, we showed that RegiSTORM successfully registers two-color STORM recordings of cargo-loaded lipid nanoparticles without fiducials, demonstrating the broader applicability of this software.

CONCLUSIONS

The developed RegiSTORM software was demonstrated to be able to accurately register multiple STORM channels and is freely available as open-source (MIT license) at https://github.com/oystein676/RegiSTORM.git and https://doi.org/10.5281/zenodo.5509861 (archived), and runs as a standalone executable (Windows) or via Python (Mac OS, Linux).

Analysis of Efferocytic Receptor Dynamics and Synapse Formation in a Frustrated Efferocytosis Model

Efferocytes express multiple receptors that mediate the recognition and engulfment of apoptotic cells through a process known as efferocytosis. Ligation of these receptors induces the formation of a structured efferocytic synapse that mediates the engulfment of the apoptotic cell by the efferocyte. The lateral diffusion of these receptors allows for clustering-mediated receptor activation and is central for the formation of the efferocytic synapse. This chapter describes a single particle tracking protocol to analyze the diffusion of efferocytic receptors within a frustrated efferocytosis model. This enables high-resolution tracking of efferocytic receptors throughout synapse formation, allowing the user to simultaneously quantify synapse formation and the dynamics of receptor diffusion as the efferocytic synapse evolves.

A new mechanism of fibronectin fibril assembly revealed by live imaging and super-resolution microscopy

Fibronectin (Fn1) fibrils have long been viewed as continuous fibers composed of extended, periodically aligned Fn1 molecules. However, our live-imaging and single-molecule localization microscopy data are inconsistent with this traditional view and show that Fn1 fibrils are composed of roughly spherical nanodomains containing six to eleven Fn1 dimers. As they move toward the cell center, Fn1 nanodomains become organized into linear arrays, in which nanodomains are spaced with an average periodicity of 105±17 nm. Periodical Fn1 nanodomain arrays can be visualized between cells in culture and within tissues; they are resistant to deoxycholate treatment and retain nanodomain periodicity in the absence of cells. The nanodomain periodicity in fibrils remained constant when probed with antibodies recognizing distinct Fn1 epitopes or combinations of antibodies recognizing epitopes spanning the length of Fn1. Treatment with FUD, a peptide that binds the Fn1 N-terminus and disrupts Fn1 fibrillogenesis, blocked the organization of Fn1 nanodomains into periodical arrays. These studies establish a new paradigm of Fn1 fibrillogenesis. This article has an associated First Person interview with the first author of the paper.

Chromatin dynamics through mouse preimplantation development revealed by single molecule localisation microscopy

Most studies addressing chromatin behaviour during preimplantation development are based on biochemical assays that lack spatial and cell-specific information, crucial during early development. Here, we describe the changes in chromatin taking place at the transition from totipotency to lineage specification, by using direct stochastical optical reconstruction microscopy (dSTORM) in whole-mount embryos during the first stages of mouse development. Through the study of two post-translational modifications of Histone 3 related to active and repressed chromatin, H3K4me3 and H3K9me3 respectively, we obtained a time-course of chromatin states, showing spatial differences between cell types, related to their differentiation state. This analysis adds a new layer of information to previous biochemical studies and provides novel insight to current models of chromatin organisation during the first stages of development.

SUMMARY: We have applied super-resolution microscopy to analyse changes in the state of chromatin during the first stages of mouse development, from the two-cell stage to the blastocyst.

Analysis of conditional colocalization relationships and hierarchies in three-color microscopy images

Colocalization analysis of multicolor microscopy images is a cornerstone approach in cell biology. It provides information on the localization of molecules within subcellular compartments and allows the interrogation of known molecular interactions in their cellular context. However, almost all colocalization analyses are designed for two-color images, limiting the type of information that they reveal. Here, we describe an approach, termed "conditional colocalization analysis," for analyzing the colocalization relationships between three molecular entities in three-color microscopy images. Going beyond the question of whether colocalization is present or not, it addresses the question of whether the colocalization between two entities is influenced, positively or negatively, by their colocalization with a third entity. We benchmark the approach and showcase its application to investigate receptor-downstream adaptor colocalization relationships in the context of functionally relevant plasma membrane locations. The software for conditional colocalization analysis is available at https://github.com/kjaqaman/conditionalColoc.

Nanoscale Dynamics of Actin Filaments in the Red Blood Cell Membrane Skeleton

Red blood cell (RBC) shape and deformability are supported by a planar network of short actin filament (F-actin) nodes (∼37 nm length, 15–18 subunits) interconnected by long spectrin strands at the inner surface of the plasma membrane. Spectrin-F-actin network structure underlies quantitative modeling of forces controlling RBC shape, membrane curvature, and deformation, yet the nanoscale organization and dynamics of the F-actin nodes in situ are not well understood. We examined F-actin distribution and dynamics in RBCs using fluorescent-phalloidin labeling of F-actin imaged by multiple microscopy modalities. Total internal reflection fluorescence and Zeiss Airyscan confocal microscopy demonstrate that F-actin is concentrated in multiple brightly stained F-actin foci ∼200–300 nm apart interspersed with dimmer F-actin staining regions. Single molecule stochastic optical reconstruction microscopy imaging of Alexa 647-phalloidin-labeled F-actin and computational analysis also indicates an irregular, nonrandom distribution of F-actin nodes. Treatment of RBCs with latrunculin A and cytochalasin D indicates that F-actin foci distribution depends on actin polymerization, while live cell imaging reveals dynamic local motions of F-actin foci, with lateral movements, appearance and disappearance. Regulation of F-actin node distribution and dynamics via actin assembly/disassembly pathways and/or via local extension and retraction of spectrin strands may provide a new mechanism to control spectrin-F-actin network connectivity, RBC shape, and membrane deformability.

Measuring Nanoscale Distances by Structured Illumination Microscopy and Image Cross-Correlation Spectroscopy (SIM-ICCS)

Since the introduction of super-resolution microscopy, there has been growing interest in quantifying the nanoscale spatial distributions of fluorescent probes to better understand cellular processes and their interactions. One way to check if distributions are correlated or not is to perform colocalization analysis of multi-color acquisitions. Among all the possible methods available to study and quantify the colocalization between multicolor images, there is image cross-correlation spectroscopy (ICCS). The main advantage of ICCS, in comparison with other co-localization techniques, is that it does not require pre-segmentation of the sample into single objects. Here we show that the combination of structured illumination microscopy (SIM) with ICCS (SIM-ICCS) is a simple approach to quantify colocalization and measure nanoscale distances from multi-color SIM images. We validate the SIM-ICCS analysis on SIM images of optical nanorulers, DNA-origami-based model samples containing fluorophores of different colors at a distance of 80 nm. The SIM-ICCS analysis is compared with an object-based analysis performed on the same samples. Finally, we show that SIM-ICCS can be used to quantify the nanoscale spatial distribution of functional nuclear sites in fixed cells.

Practical Aspects of Super-Resolution Imaging and Segmentation of Macromolecular Complexes by dSTORM

Super-resolution fluorescence microscopy allows imaging macromolecular complexes down to the nanoscopic scale and thus is a great tool to combine and integrate cellular imaging in the native cellular environment with structural analysis by X-ray crystallography or high-resolution cryo electron microscopy or tomography. Here we describe practical aspects of SMLM imaging by dSTORM, from the initial sample preparation using mounting media, antibodies and fluorescent markers, the experimental setup for data acquisition including multi-color colocalization and 3D data acquisition, and finally tips and clues on advanced data processing that includes image reconstruction and data segmentation using 2D or 3D clustering methods. This approach opens the path toward multi-resolution integration in cellular structural biology.

Nano illumination microscopy: a technique based on scanning with an array of individually addressable nanoLEDs

In lensless microscopy, spatial resolution is usually provided by the pixel density of current digital cameras, which are reaching a hard-to-surpass pixel size / resolution limit over 1 µm. As an alternative, the dependence of the resolving power can be moved from the detector to the light sources, offering a new kind of lensless microscopy setups. The use of continuously scaled-down Light-Emitting Diode (LED) arrays to scan the sample allows resolutions on order of the LED size, giving rise to compact and low-cost microscopes without mechanical scanners or optical accessories. In this paper, we present the operation principle of this new approach to lensless microscopy, with simulations that demonstrate the possibility to use it for super-resolution, as well as a first prototype. This proof-of-concept setup integrates an 8 × 8 array of LEDs, each 5 × 5 μm² pixel size and 10 μm pitch, and an optical detector. We characterize the system using Electron-Beam Lithography (EBL) pattern. Our prototype validates the imaging principle and opens the way to improve resolution by further miniaturizing the light sources.

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.

Super-resolution imaging reveals the evolution of higher-order chromatin folding in early carcinogenesis

Genomic DNA is folded into a higher-order structure that regulates transcription and maintains genomic stability. Although progress has been made on understanding biochemical characteristics of epigenetic modifications in cancer, the in-situ higher-order folding of chromatin structure during malignant transformation remains largely unknown. Here, using optimized stochastic optical reconstruction microscopy (STORM) for pathological tissue (PathSTORM), we uncover a gradual decompaction and fragmentation of higher-order chromatin folding throughout all stages of carcinogenesis in multiple tumor types, and prior to tumor formation. Our integrated imaging, genomic, and transcriptomic analyses reveal functional consequences in enhanced transcription activities and impaired genomic stability. We also demonstrate the potential of imaging higher-order chromatin disruption to detect high-risk precursors that cannot be distinguished by conventional pathology. Taken together, our findings reveal gradual decompaction and fragmentation of higher-order chromatin structure as an enabling characteristic in early carcinogenesis to facilitate malignant transformation, which may improve cancer diagnosis, risk stratification, and prevention.

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.

Membrane Diffusion Occurs by Continuous-Time Random Walk Sustained by Vesicular Trafficking

Diffusion in cellular membranes is regulated by processes that occur over a range of spatial and temporal scales. These processes include membrane fluidity, interprotein and interlipid interactions, interactions with membrane microdomains, interactions with the underlying cytoskeleton, and cellular processes that result in net membrane movement. The complex, non-Brownian diffusion that results from these processes has been difficult to characterize, and moreover, the impact of factors such as membrane recycling on membrane diffusion remains largely unexplored. We have used a careful statistical analysis of single-particle tracking data of the single-pass plasma membrane protein CD93 to show that the diffusion of this protein is well described by a continuous-time random walk in parallel with an aging process mediated by membrane corrals. The overall result is an evolution in the diffusion of CD93: proteins initially diffuse freely on the cell surface but over time become increasingly trapped within diffusion-limiting membrane corrals. Stable populations of freely diffusing and corralled CD93 are maintained by an endocytic/exocytic process in which corralled CD93 is selectively endocytosed, whereas freely diffusing CD93 is replenished by exocytosis of newly synthesized and recycled CD93. This trafficking not only maintained CD93 diffusivity but also maintained the heterogeneous distribution of CD93 in the plasma membrane. These results provide insight into the nature of the biological and biophysical processes that can lead to significantly non-Brownian diffusion of membrane proteins and demonstrate that ongoing membrane recycling is critical to maintaining steady-state diffusion and distribution of proteins in the plasma membrane.

Caveolae and scaffold detection from single molecule localization microscopy data using deep learning

Caveolae are plasma membrane invaginations whose formation requires caveolin-1 (Cav1), the adaptor protein polymerase I, and the transcript release factor (PTRF or CAVIN1). Caveolae have an important role in cell functioning, signaling, and disease. In the absence of CAVIN1/PTRF, Cav1 forms non-caveolar membrane domains called scaffolds. In this work, we train machine learning models to automatically distinguish between caveolae and scaffolds from single molecule localization microscopy (SMLM) data. We apply machine learning algorithms to discriminate biological structures from SMLM data. Our work is the first that is leveraging machine learning approaches (including deep learning models) to automatically identifying biological structures from SMLM data. In particular, we develop and compare three binary classification methods to identify whether or not a given 3D cluster of Cav1 proteins is a caveolae. The first uses a random forest classifier applied to 28 hand-crafted/designed features, the second uses a convolutional neural net (CNN) applied to a projection of the point clouds onto three planes, and the third uses a PointNet model, a recent development that can directly take point clouds as its input. We validate our methods on a dataset of super-resolution microscopy images of PC3 prostate cancer cells labeled for Cav1. Specifically, we have images from two cell populations: 10 PC3 and 10 CAVIN1/PTRF-transfected PC3 cells (PC3-PTRF cells) that form caveolae. We obtained a balanced set of 1714 different cellular structures. Our results show that both the random forest on hand-designed features and the deep learning approach achieve high accuracy in distinguishing the intrinsic features of the caveolae and non-caveolae biological structures. More specifically, both random forest and deep CNN classifiers achieve classification accuracy reaching 94% on our test set, while the PointNet model only reached 83% accuracy. We also discuss the pros and cons of the different approaches.

Super-Resolution Imaging of G Protein-Coupled Receptors Using Ground State Depletion Microscopy

G protein-coupled receptors (GPCRs) comprise the largest family of integral membrane proteins, which are coupled to heterotrimeric G proteins to influence cell signaling. Subsequent to G protein activation, agonist-stimulated G protein-coupled receptor kinase (GRK) phosphorylation results in the recruitment of β-arrestin proteins, which form both stable and unstable complexes with GPCRs. β-Arrestins when bound to GPCRs not only contribute to the uncoupling of G protein signaling but also to the redistribution of GPCRs to clathrin-coated pits via their association with both clathrin and β2-adaptin facilitating GPCR endocytosis. This allows β-arrestins to couple GPCRs to additional cell signaling proteins allowing a second wave of receptor signaling. Importantly, the β-arrestin-regulated subcellular localization of these complexes also plays a critical role in regulating how these signals are transduced and which proteins are recruited. Here, we describe a methodology for assessing the GPCR subcellular localization by super-resolution microscopy and suggest that this methodology can be extended to the study of GPCR/protein complexes.

PACS-1 and adaptor protein-1 mediate ACTH trafficking to the regulated secretory pathway

The regulated secretory pathway is a specialized form of protein secretion found in endocrine and neuroendocrine cell types. Pro-opiomelanocortin (POMC) is a pro-hormone that utilizes this pathway to be trafficked to dense core secretory granules (DCSGs). Within this organelle, POMC is processed to multiple bioactive hormones that play key roles in cellular physiology. However, the complete set of cellular membrane trafficking proteins that mediate the correct sorting of POMC to DCSGs remain unknown. Here, we report the roles of the phosphofurin acidic cluster sorting protein - 1 (PACS-1) and the clathrin adaptor protein 1 (AP-1) in the targeting of POMC to DCSGs. Upon knockdown of PACS-1 and AP-1, POMC is readily secreted into the extracellular milieu and fails to be targeted to DCSGs.

Super-Resolution Imaging of Higher-Order Chromatin Structures at Different Epigenomic States in Single Mammalian Cells

Histone modifications influence higher-order chromatin structures at individual epigenomic states and chromatin environments to regulate gene expression. However, genome-wide higher-order chromatin structures shaped by different histone modifications remain poorly characterized. With stochastic optical reconstruction microscopy (STORM), we characterized the higher-order chromatin structures at their epigenomic states, categorized into three major types in interphase: histone acetylation marks form spatially segregated nanoclusters, active histone methylation marks form spatially dispersed larger nanodomains, and repressive histone methylation marks form condensed large aggregates. These distinct structural characteristics are also observed in mitotic chromosomes. Furthermore, active histone marks coincide with less compact chromatin and exhibit a higher degree of co-localization with other active marks and RNA polymerase II (RNAP II), while repressive marks coincide with densely packed chromatin and spatially distant from repressive marks and active RNAP II. Taken together, super-resolution imaging reveals three distinct chromatin structures at various epigenomic states, which may be spatially coordinated to impact transcription.

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.

Antagonistic Coevolution of MER Tyrosine Kinase Expression and Function

TYRO3, AXL, and MERTK (TAM) receptors are a family of receptor tyrosine kinases that maintain homeostasis through the clearance of apoptotic cells, and when defective, contribute to chronic inflammatory and autoimmune diseases such as atherosclerosis, multiple sclerosis, systemic lupus erythematosus, rheumatoid arthritis, and Crohn's disease. In addition, certain enveloped viruses utilize TAM receptors for immune evasion and entry into host cells, with several viruses preferentially hijacking MERTK for these purposes. Despite the biological importance of TAM receptors, little is understood of their recent evolution and its impact on their function. Using evolutionary analysis of primate TAM receptor sequences, we identified strong, recent positive selection in MERTK's signal peptide and transmembrane domain that was absent from TYRO3 and AXL. Reconstruction of hominid and primate ancestral MERTK sequences revealed three nonsynonymous single nucleotide polymorphisms in the human MERTK signal peptide, with a G14C mutation resulting in a predicted non-B DNA cruciform motif, producing a significant decrease in MERTK expression with no significant effect on MERTK trafficking or half-life. Reconstruction of MERTK's transmembrane domain identified three amino acid substitutions and four amino acid insertions in humans, which led to significantly higher levels of self-clustering through the creation of a new interaction motif. This clustering counteracted the effect of the signal peptide mutations through enhancing MERTK avidity, whereas the lower MERTK expression led to reduced binding of Ebola virus-like particles. The decreased MERTK expression counterbalanced by increased avidity is consistent with antagonistic coevolution to evade viral hijacking of MERTK.

Robust nonparametric quantification of clustering density of molecules in single-molecule localization microscopy

We report a robust nonparametric descriptor, J'(r), for quantifying the density of clustering molecules in single-molecule localization microscopy. J'(r), based on nearest neighbor distribution functions, does not require any parameter as an input for analyzing point patterns. We show that J'(r) displays a valley shape in the presence of clusters of molecules, and the characteristics of the valley reliably report the clustering features in the data. Most importantly, the position of the J'(r) valley ([Formula: see text]) depends exclusively on the density of clustering molecules (ρc). Therefore, it is ideal for direct estimation of the clustering density of molecules in single-molecule localization microscopy. As an example, this descriptor was applied to estimate the clustering density of ptsG mRNA in E. coli bacteria.

Quantitative Localization Microscopy Reveals a Novel Organization of a High-Copy Number Plasmid

The maintenance of high-copy number plasmids within bacteria had been commonly thought to result from free diffusion and random segregation. Recent microscopy experiments, however, observed high-copy number plasmids clustering into discrete foci, which seemed to contradict this model, and hinted at an undiscovered active mechanism, as often found in low-copy number plasmids. We recently investigated the cellular organization of a ColE1-derivative plasmid in Escherichia coli bacteria using quantitative superresolved microscopy based on single-molecule localization in combination with single-molecule fluorescence in situ hybridization (smFISH). We observed that many of the plasmids aggregated into large clusters, although most of the plasmids were randomly distributed throughout the bacteria, minus an excluded volume about the chromosomal DNA. Our results indicate that neither of the previous models completely encompasses the behavior of high-copy number plasmids. We also found many plasmids within the chromosomal volume, providing further evidence that the nucleoid does not fully exclude DNA and RNA.

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.

HIV-1 Nef sequesters MHC-I intracellularly by targeting early stages of endocytosis and recycling

A defining characteristic of HIV-1 infection is the ability of the virus to persist within the host. Specifically, MHC-I downregulation by the HIV-1 accessory protein Nef is of critical importance in preventing infected cells from cytotoxic T-cell mediated killing. Nef downregulates MHC-I by modulating the host membrane trafficking machinery, resulting in the endocytosis and eventual sequestration of MHC-I within the cell. In the current report, we utilized the intracellular protein-protein interaction reporter system, bimolecular fluorescence complementation (BiFC), in combination with super-resolution microscopy, to track the Nef/MHC-I interaction and determine its subcellular localization in cells. We demonstrate that this interaction occurs upon Nef binding the MHC-I cytoplasmic tail early during endocytosis in a Rab5-positive endosome. Disruption of early endosome regulation inhibited Nef-dependent MHC-I downregulation, demonstrating that Nef hijacks the early endosome to sequester MHC-I within the cell. Furthermore, super-resolution imaging identified that the Nef:MHC-I BiFC complex transits through both early and late endosomes before ultimately residing at the trans-Golgi network. Together we demonstrate the importance of the early stages of the endocytic network in the removal of MHC-I from the cell surface and its re-localization within the cell, which allows HIV-1 to optimally evade host immune responses.

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.

Where in the Cell Are You? Probing HIV-1 Host Interactions through Advanced Imaging Techniques

Viruses must continuously evolve to hijack the host cell machinery in order to successfully replicate and orchestrate key interactions that support their persistence. The type-1 human immunodeficiency virus (HIV-1) is a prime example of viral persistence within the host, having plagued the human population for decades. In recent years, advances in cellular imaging and molecular biology have aided the elucidation of key steps mediating the HIV-1 lifecycle and viral pathogenesis. Super-resolution imaging techniques such as stimulated emission depletion (STED) and photoactivation and localization microscopy (PALM) have been instrumental in studying viral assembly and release through both cell-cell transmission and cell-free viral transmission. Moreover, powerful methods such as Forster resonance energy transfer (FRET) and bimolecular fluorescence complementation (BiFC) have shed light on the protein-protein interactions HIV-1 engages within the host to hijack the cellular machinery. Specific advancements in live cell imaging in combination with the use of multicolor viral particles have become indispensable to unravelling the dynamic nature of these virus-host interactions. In the current review, we outline novel imaging methods that have been used to study the HIV-1 lifecycle and highlight advancements in the cell culture models developed to enhance our understanding of the HIV-1 lifecycle.

Short-Lived Cages Restrict Protein Diffusion in the Plasma Membrane

The plasma membrane is a heterogeneous environment characterized by anomalous diffusion and the presence of microdomains that are molecularly distinct from the bulk membrane. Using single particle tracking of the C-type lectin CD93, we have identified for the first time the transient trapping of transmembrane proteins in cage-like microdomains which restrict protein diffusion. These cages are stabilized by actin-dependent confinement regions, but are separate structures with sizes and lifespans uncorrelated to those of the underlying actin corral. These membrane cages require cholesterol for their strength and stability, with cholesterol depletion decreasing both. Despite this, cages are much larger in size and are longer lived than lipid rafts, suggesting instead that cholesterol-dependent effects on membrane fluidity or molecular packing play a role in cage formation. This diffusional compartment in the plasma membrane has characteristics of both a diffusional barrier and a membrane microdomain, with a size and lifespan intermediate between short-lived microdomains such as lipid rafts and long-lasting diffusional barriers created by the actin cytoskeleton.

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.

82-kDa choline acetyltransferase and SATB1 localize to β-amyloid induced matrix attachment regions

The M-transcript of human choline acetyltransferase (ChAT) produces an 82-kDa protein (82-kDa ChAT) that concentrates in nuclei of cholinergic neurons. We assessed the effects of acute exposure to oligomeric amyloid-β_1–42 (Aβ_1–42) on 82-kDa ChAT disposition in SH-SY5Y neural cells, finding that acute exposure to Aβ_1–42 results in increased association of 82-kDa ChAT with chromatin and formation of 82-kDa ChAT aggregates in nuclei. When measured by chromatin immunoprecipitation with next-generation sequencing (ChIP-seq), we identified that Aβ_1–42 -exposure increases 82-kDa ChAT association with gene promoters and introns. The Aβ_1–42 -induced 82-kDa ChAT aggregates co-localize with special AT-rich binding protein 1 (SATB1), which anchors DNA to scaffolding/matrix attachment regions (S/MARs). SATB1 had a similar genomic association as 82-kDa ChAT, with both proteins associating with synapse and cell stress genes. After Aβ_1–42 -exposure, both SATB1 and 82-kDa ChAT are enriched at the same S/MAR on the APP gene, with 82-kDa ChAT expression attenuating an increase in an isoform-specific APP mRNA transcript. Finally, 82-kDa ChAT and SATB1 have patterned genomic association at regions enriched with S/MAR binding motifs. These results demonstrate that 82-kDa ChAT and SATB1 play critical roles in the response of neural cells to acute Aβ -exposure.