Systematic Nanoscale Analysis of Endocytosis Links Efficient Vesicle Formation to Patterned Actin Nucleation.

Clathrin-mediated endocytosis is an essential cellular function in all eukaryotes that is driven by a self-assembled macromolecular machine of over 50 different proteins in tens to hundreds of copies. How these proteins are organized to produce endocytic vesicles with high precision and efficiency is not understood. Here, we developed high-throughput superresolution microscopy to reconstruct the nanoscale structural organization of 23 endocytic proteins from over 100,000 endocytic sites in yeast. We found that proteins assemble by radially ordered recruitment according to function. WASP family proteins form a circular nanoscale template on the membrane to spatially control actin nucleation during vesicle formation. Mathematical modeling of actin polymerization showed that this WASP nano-template optimizes force generation for membrane invagination and substantially increases the efficiency of endocytosis. Such nanoscale pre-patterning of actin nucleation may represent a general design principle for directional force generation in membrane remodeling processes such as during cell migration and division.

Annotation and automated segmentation of single-molecule localisation microscopy data.

Single Molecule Localisation Microscopy (SMLM) is becoming a widely used technique in cell biology. After processing the images, the molecular localisations are typically stored in a table as xy (or xyz) coordinates, with additional information, such as number of photons, etc. This set of coordinates can be used to generate an image to visualise the molecular distribution, for example, a 2D or 3D histogram of localisations. Many different methods have been devised to analyse SMLM data, among which cluster analysis of the localisations is popular. However, it can be useful to first segment the data, to extract the localisations in a specific region of a cell or in individual cells, prior to downstream analysis. Here we describe a pipeline for annotating localisations in an SMLM dataset in which we compared membrane segmentation approaches, including Otsu thresholding and machine learning models, and subsequent cell segmentation. We used an SMLM dataset derived from dSTORM images of sectioned cell pellets, stained for the membrane proteins EGFR (epidermal growth factor receptor) and EREG (epiregulin) as a test dataset. We found that a Cellpose model retrained on our data performed the best in the membrane segmentation task, allowing us to perform downstream cluster analysis of membrane versus cell interior localisations. We anticipate this will be generally useful for SMLM analysis.

Supramolecular assembly of the Escherichia coli LdcI upon acid stress.

Pathogenic and commensal bacteria often have to resist the harsh acidity of the host stomach. The inducible lysine decarboxylase LdcI buffers the cytosol and the local extracellular environment to ensure enterobacterial survival at low pH. Here, we investigate the acid stress-response regulation of Escherichia coli LdcI by combining biochemical and biophysical characterization with negative stain and cryoelectron microscopy (cryo-EM) and wide-field and superresolution fluorescence imaging. Due to deleterious effects of fluorescent protein fusions on native LdcI decamers, we opt for three-dimensional localization of nanobody-labeled endogenous wild-type LdcI in acid-stressed E. coli cells and show that it organizes into distinct patches at the cell periphery. Consistent with recent hypotheses that in vivo clustering of metabolic enzymes often reflects their polymerization as a means of stimulus-induced regulation, we show that LdcI assembles into filaments in vitro at physiologically relevant low pH. We solve the structures of these filaments and of the LdcI decamer formed at neutral pH by cryo-EM and reveal the molecular determinants of LdcI polymerization, confirmed by mutational analysis. Finally, we propose a model for LdcI function inside the enterobacterial cell, providing a structural and mechanistic basis for further investigation of the role of its supramolecular organization in the acid stress response.

Near-Infrared Blinking Carbon Dots Designed for Quantitative Nanoscopy.

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

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

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

Correlative super-resolution microscopy with deep UV reactivation.

Correlative super-resolution microscopy has the potential to accurately visualize and validate new biological structures past the diffraction limit. However, combining different super-resolution modalities, such as deterministic stimulated emission depletion (STED) and stochastic single-molecule localization microscopy (SMLM), is a challenging endeavour. For correlative STED and SMLM, the following poses a significant challenge: (1) the photobleaching of the fluorophores in STED; (2) the subsequent reactivation of the fluorophores for SMLM and (3) finding the right fluorochrome and imaging buffer for both imaging modalities. Here, we highlight how the deep ultraviolet (DBUE) wavelengths of the Mercury (Hg) arc lamp can help recover STED bleaching and allow for the reactivation of single molecules for SMLM imaging. We also show that Alexa Fluor 594 and the commercially available Prolong Diamond to be excellent fluorophores and imaging media for correlative STED and SMLM.

Photoactivatable BODIPYs for Live-Cell PALM.

Photoactivated localization microscopy (PALM) relies on fluorescence photoactivation and single-molecule localization to overcome optical diffraction and reconstruct images of biological samples with spatial resolution at the nanoscale. The implementation of this subdiffraction imaging method, however, requires fluorescent probes with photochemical and photophysical properties specifically engineered to enable the localization of single photoactivated molecules with nanometer precision. The synthetic versatility and outstanding photophysical properties of the borondipyrromethene (BODIPY) chromophore are ideally suited to satisfy these stringent requirements. Specifically, synthetic manipulations of the BODIPY scaffold can be invoked to install photolabile functional groups and photoactivate fluorescence under photochemical control. Additionally, targeting ligands can be incorporated in the resulting photoactivatable fluorophores (PAFs) to label selected subcellular components in live cells. Indeed, photoactivatable BODIPYs have already allowed the sub-diffraction imaging of diverse cellular substructures in live cells using PALM and can evolve into invaluable analytical probes for bioimaging applications.

ReCSAI: recursive compressed sensing artificial intelligence for confocal lifetime localization microscopy.

BACKGROUND: Localization-based super-resolution microscopy resolves macromolecular structures down to a few nanometers by computationally reconstructing fluorescent emitter coordinates from diffraction-limited spots. The most commonly used algorithms are based on fitting parametric models of the point spread function (PSF) to a measured photon distribution. These algorithms make assumptions about the symmetry of the PSF and thus, do not work well with irregular, non-linear PSFs that occur for example in confocal lifetime imaging, where a laser is scanned across the sample. An alternative method for reconstructing sparse emitter sets from noisy, diffraction-limited images is compressed sensing, but due to its high computational cost it has not yet been widely adopted. Deep neural network fitters have recently emerged as a new competitive method for localization microscopy. They can learn to fit arbitrary PSFs, but require extensive simulated training data and do not generalize well. A method to efficiently fit the irregular PSFs from confocal lifetime localization microscopy combining the advantages of deep learning and compressed sensing would greatly improve the acquisition speed and throughput of this method. RESULTS: Here we introduce ReCSAI, a compressed sensing neural network to reconstruct localizations for confocal dSTORM, together with a simulation tool to generate training data. We implemented and compared different artificial network architectures, aiming to combine the advantages of compressed sensing and deep learning. We found that a U-Net with a recursive structure inspired by iterative compressed sensing showed the best results on realistic simulated datasets with noise, as well as on real experimentally measured confocal lifetime scanning data. Adding a trainable wavelet denoising layer as prior step further improved the reconstruction quality. CONCLUSIONS: Our deep learning approach can reach a similar reconstruction accuracy for confocal dSTORM as frame binning with traditional fitting without requiring the acquisition of multiple frames. In addition, our work offers generic insights on the reconstruction of sparse measurements from noisy experimental data by combining compressed sensing and deep learning. We provide the trained networks, the code for network training and inference as well as the simulation tool as python code and Jupyter notebooks for easy reproducibility.

Robust deep learning optical autofocus system applied to automated multiwell plate single molecule localization microscopy.

We presenta robust, long-range optical autofocus system for microscopy utilizing machine learning. This can be useful for experiments with long image data acquisition times that may be impacted by defocusing resulting from drift of components, for example due to changes in temperature or mechanical drift. It is also useful for automated slide scanning or multiwell plate imaging where the sample(s) to be imaged may not be in the same horizontal plane throughout the image data acquisition. To address the impact of (thermal or mechanical) fluctuations over time in the optical autofocus system itself, we utilize a convolutional neural network (CNN) that is trained over multiple days to account for such fluctuations. To address the trade-off between axial precision and range of the autofocus, we implement orthogonal optical readouts with separate CNN training data, thereby achieving an accuracy well within the 600 nm depth of field of our 1.3 numerical aperture objective lens over a defocus range of up to approximately +/-100 µm. We characterize the performance of this autofocus system and demonstrate its application to automated multiwell plate single molecule localization microscopy.

Many microscopy experiments involve extended imaging of samples over timescales from minutes to days, during which the microscope can 'drift' out of focus. When imaging at high magnification, the depth of field is of the order of one micron and so the imaging system should keep the sample in the focal plane of the microscope objective lens to this precision. Unfortunately, temperature changes in the laboratory can cause thermal expansion of microscope components that can move the focal plane by more than a micron and such changes can occur on a timescale of minutes. This is a particular issue for super-resolved microscopy experiments using single molecule localization microscopy (SMLM) techniques, for which 1000s of images are acquired, and for automated imaging of multiple samples in multiwell plates. It is possible to maintain the sample in the focal plane focus position by either automatically moving the sample or adjusting the imaging system, for example by moving the objective lens. This is called 'autofocus' and is frequently achieved by reflecting a light beam from the microscope coverslip and measuring its position of beam profile as a function of defocus of the microscope. The correcting adjustment is then usually calculated analytically but there is recent interest in using machine learning techniques to determine the required focussing adjustment. Here, we present a system that uses a neural network to determine the required defocus correcting adjustment from camera images of a laser beam that is reflected from the coverslip. Unfortunately, this approach will only work when the microscope is in the same condition as it was when the neural network was trained - and this can be compromised by the same drift of the optical system that causes the defocus needing to be corrected. We show, however, that by training a neural network over an extended period, for example 10 days, this approach can 'learn' about the optical system drifts and provide the required autofocus function. We also show that an optical system utilizing a rectangular slit can make two measurements of the defocus simultaneously, with one measurement being optimized for high accuracy over a limited range (±10 µm) near focus and the other providing lower accuracy but over a much longer range (±100 µm). This robust autofocus system is suitable for automated super-resolved microscopy of arrays of samples in a multiwell plate using SMLM, for which an experiment routinely lasts more than 5 h.

At the molecular resolution with MINFLUX?

MINFLUX is purported as the next revolutionary fluorescence microscopy technique claiming a spatial resolution in the range of 1-3 nm in fixed and living cells. Though the claim of molecular resolution is attractive, I am concerned whether true 1 nm resolution has been attained. Here, I compare the performance with other super-resolution methods focusing particularly on spatial resolution claims, subjective filtering of localizations, detection versus labelling efficiency and the possible limitations when imaging biological samples containing densely labelled structures. I hope the analysis and evaluation parameters presented here are not only useful for future research directions for single-molecule techniques but also microscope users, developers and core facility managers when deciding on an investment for the next 'state-of-the-art' instrument. This article is part of the Theo Murphy meeting issue 'Super-resolution structured illumination microscopy (part 2)'.

Super-Resolution Microscopy to Study Interorganelle Contact Sites.

Interorganelle membrane contact sites (MCS) are areas of close vicinity between the membranes of two organelles that are maintained by protein tethers. Recently, a significant research effort has been made to study MCS, as they are implicated in a wide range of biological functions, such as organelle biogenesis and division, apoptosis, autophagy, and ion and phospholipid homeostasis. Their composition, characteristics, and dynamics can be studied by different techniques, but in recent years super-resolution fluorescence microscopy (SRFM) has emerged as a powerful tool for studying MCS. In this review, we first explore the main characteristics and biological functions of MCS and summarize the different approaches for studying them. Then, we center on SRFM techniques that have been used to study MCS. For each of the approaches, we summarize their working principle, discuss their advantages and limitations, and explore the main discoveries they have uncovered in the field of MCS.

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 (αLß2 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.

About samples, giving examples: Optimized Single Molecule Localization Microscopy.

Super-resolution microscopy has profoundly transformed how we study the architecture of cells, revealing unknown structures and refining our view of cellular assemblies. Among the various techniques, the resolution of Single Molecule Localization Microscopy (SMLM) can reach the size of macromolecular complexes and offer key insights on their nanoscale arrangement in situ. SMLM is thus a demanding technique and taking advantage of its full potential requires specifically optimized procedures. Here we describe how we perform the successive steps of an SMLM workflow, focusing on single-color Stochastic Optical Reconstruction Microscopy (STORM) as well as multicolor DNA Points Accumulation for imaging in Nanoscale Topography (DNA-PAINT) of fixed samples. We provide detailed procedures for careful sample fixation and immunostaining of typical cellular structures: cytoskeleton, clathrin-coated pits, and organelles. We then offer guidelines for optimal imaging and processing of SMLM data in order to optimize reconstruction quality and avoid the generation of artifacts. We hope that the tips and tricks we discovered over the years and detail here will be useful for researchers looking to make the best possible SMLM images, a pre-requisite for meaningful biological discovery.

Imaging tripartite synapses using super-resolution microscopy.

Astroglia are vital facilitators of brain development, homeostasis, and metabolic support. In addition, they are also essential to the formation and regulation of synaptic circuits. Due to the extraordinary complex, nanoscopic morphology of astrocytes, the underlying cellular mechanisms have been poorly understood. In particular, fine astrocytic processes that can be found in the vicinity of synapses have been difficult to study using traditional imaging techniques. Here, we describe a 3D three-colour super-resolution microscopy approach to unravel the nanostructure of tripartite synapses. The method is based on the SMLM technique direct stochastic optical reconstruction microscopy (dSTORM) which uses conventional fluorophore-labelled antibodies. This approach enables reconstructing the nanoscale localisation of individual astrocytic glutamate transporter (GLT-1) molecules surrounding presynaptic (bassoon) and postsynaptic (Homer1) protein localisations in fixed mouse brain sections. However, the technique is readily adaptable to other types of targets and tissues.

Characterizing Binding Interactions That Are Essential for Selective Transport through the Nuclear Pore Complex.

Specific macromolecules are rapidly transported across the nuclear envelope via the nuclear pore complex (NPC). The selective transport process is facilitated when nuclear transport receptors (NTRs) weakly and transiently bind to intrinsically disordered constituents of the NPC, FG Nups. These two types of proteins help maintain the selective NPC barrier. To interrogate their binding interactions in vitro, we deployed an NPC barrier mimic. We created the stationary phase by covalently attaching fragments of a yeast FG Nup called Nsp1 to glass coverslips. We used a tunable mobile phase containing NTR, nuclear transport factor 2 (NTF2). In the stationary phase, three main factors affected binding: the number of FG repeats, the charge of fragments, and the fragment density. We also identified three main factors affecting binding in the mobile phase: the avidity of the NTF2 variant for Nsp1, the presence of nonspecific proteins, and the presence of additional NTRs. We used both experimentally determined binding parameters and molecular dynamics simulations of Nsp1FG fragments to create an agent-based model. The results suggest that NTF2 binding is negatively cooperative and dependent on the density of Nsp1FG molecules. Our results demonstrate the strengths of combining experimental and physical modeling approaches to study NPC-mediated transport.

Elucidation of the Clustered Nano-Architecture of Radiation-Induced DNA Damage Sites and Surrounding Chromatin in Cancer Cells: A Single Molecule Localization Microscopy Approach.

In cancer therapy, the application of (fractionated) harsh radiation treatment is state of the art for many types of tumors. However, ionizing radiation is a "double-edged sword"-it can kill the tumor but can also promote the selection of radioresistant tumor cell clones or even initiate carcinogenesis in the normal irradiated tissue. Individualized radiotherapy would reduce these risks and boost the treatment, but its development requires a deep understanding of DNA damage and repair processes and the corresponding control mechanisms. DNA double strand breaks (DSBs) and their repair play a critical role in the cellular response to radiation. In previous years, it has become apparent that, beyond genetic and epigenetic determinants, the structural aspects of damaged chromatin (i.e., not only of DSBs themselves but also of the whole damage-surrounding chromatin domains) form another layer of complex DSB regulation. In the present article, we summarize the application of super-resolution single molecule localization microscopy (SMLM) for investigations of these structural aspects with emphasis on the relationship between the nano-architecture of radiation-induced repair foci (IRIFs), represented here by γH2AX foci, and their chromatin environment. Using irradiated HeLa cell cultures as an example, we show repair-dependent rearrangements of damaged chromatin and analyze the architecture of γH2AX repair clusters according to topological similarities. Although HeLa cells are known to have highly aberrant genomes, the topological similarity of γH2AX was high, indicating a functional, presumptively genome type-independent relevance of structural aspects in DSB repair. Remarkably, nano-scaled chromatin rearrangements during repair depended both on the chromatin domain type and the treatment. Based on these results, we demonstrate how the nano-architecture and topology of IRIFs and chromatin can be determined, point to the methodological relevance of SMLM, and discuss the consequences of the observed phenomena for the DSB repair network regulation or, for instance, radiation treatment outcomes.

[Illuminating the nanoscopic world of viruses by fluorescence super-resolution microscopy]. / Apport de la microscopie de fluorescence super-résolue : dans l'intimité des virus !

The recent revolution in optical fluorescence microscopy, supported by the optimization of both spatial resolution and acquisition speed, led to the ability to visualize nano-scaled objects. Currently, the use of a new generation of super-resolution fluorescence microscopes coupled to improved fluorescent probes gives the possibility to study the replicative cycle of viruses in living cells, at the single-virus and molecule level. In this review, after a brief chronological description of these new approaches, we highlight several examples of super-resolution microscopies that have allowed to revisit our understanding of several human viruses and of host-pathogen interactions.

Illuminating the nanoscopic world of viruses by fluorescence super-resolution microscopy.

The recent revolution in optical fluorescence microscopy, supported by the optimization of both spatial resolution and acquisition speed, led to the ability to visualize nano-scaled objects. Currently, the use of a new generation of super-resolution fluorescence microscopes coupled to improved fluorescent probes gives the possibility to study the replicative cycle of viruses in living cells, at the single-virus and molecule level. In this review, after a brief chronological description of these new approaches, we highlight several examples of super-resolution microscopies that have allowed to revisit our understanding of several human viruses and of host-pathogen interactions.

dSTORM microscopy evidences in HeLa cells clustered and scattered γH2AX nanofoci sensitive to ATM, DNA-PK, and ATR kinase inhibitors.

In response to DNA double-strand breaks (DSB), histone H2AX is phosphorylated around the lesion by a feed forward signal amplification loop, originating γH2AX foci detectable by immunofluorescence and confocal microscopy as elliptical areas of uniform intensity. We exploited the significant increase in resolution (~ × 10) provided by single-molecule localization microscopy (SMLM) to investigate at nanometer scale the distribution of γH2AX signals either endogenous (controls) or induced by the radiomimetic bleomycin (BLEO) in HeLa cells. In both conditions, clustered substructures (nanofoci) confined to γH2AX foci and scattered nanofoci throughout the remnant nuclear area were detected. SR-Tesseler software (Voronoï tessellation-based segmentation) was combined with a custom Python script to first separate clustered nanofoci inside γH2AX foci from scattered nanofoci, and then to perform a cluster analysis upon each nanofoci type. Compared to controls, γH2AX foci in BLEO-treated nuclei presented on average larger areas (0.41 versus 0.19 µm2), more nanofoci per focus (22.7 versus 13.2) and comparable nanofoci densities (~ 60 nanofoci/µm2). Scattered γH2AX nanofoci were equally present (~ 3 nanofoci/µm2), suggesting an endogenous origin. BLEO-treated cells were challenged with specific inhibitors of canonical H2AX kinases, namely: KU-55933, VE-821 and NU-7026 for ATM, ATR and DNA-PK, respectively. Under treatment with pooled inhibitors, clustered nanofoci vanished from super-resolution images while scattered nanofoci decreased (~ 50%) in density. Residual scattered nanofoci could reflect, among other alternatives, H2AX phosphorylation mediated by VRK1, a recently described non-canonical H2AX kinase. In addition to H2AX findings, an analytical approach to quantify clusters of highly differing density from SMLM data is put forward.

Toward Absolute Molecular Numbers in DNA-PAINT.

Single-molecule localization microscopy (SMLM) has revolutionized optical microscopy, extending resolution down to the level of individual molecules. However, the actual counting of molecules relies on preliminary knowledge of the blinking behavior of individual targets or on a calibration to a reference. In particular for biological applications, great care has to be taken because a plethora of factors influence the quality and applicability of calibration-dependent approaches to count targets in localization clusters particularly in SMLM data obtained from heterogeneous samples. Here, we present localization-based fluorescence correlation spectroscopy (lbFCS) as the first absolute molecular counting approach for DNA-points accumulation for imaging in nanoscale topography (PAINT) microscopy and, to our knowledge, for SMLM in general. We demonstrate that lbFCS overcomes the limitation of previous DNA-PAINT counting and allows the quantification of target molecules independent of the localization cluster density. In accordance with the promising results of our systematic proof-of-principle study on DNA origami structures as idealized targets, lbFCS could potentially also provide quantitative access to more challenging biological targets featuring heterogeneous cluster sizes in the future.