Content-aware frame interpolation (CAFI): deep learning-based temporal super-resolution for fast bioimaging

The development of high-resolution microscopes has made it possible to investigate cellular processes in 3D and over time. However, observing fast cellular dynamics remains challenging because of photobleaching and phototoxicity. Here we report the implementation of two content-aware frame interpolation (CAFI) deep learning networks, Zooming SlowMo and Depth-Aware Video Frame Interpolation, that are highly suited for accurately predicting images in between image pairs, therefore improving the temporal resolution of image series post-acquisition. We show that CAFI is capable of understanding the motion context of biological structures and can perform better than standard interpolation methods. We benchmark CAFI's performance on 12 different datasets, obtained from four different microscopy modalities, and demonstrate its capabilities for single-particle tracking and nuclear segmentation. CAFI potentially allows for reduced light exposure and phototoxicity on the sample for improved long-term live-cell imaging. The models and the training and testing data are available via the ZeroCostDL4Mic platform.

High-fidelity 3D live-cell nanoscopy through data-driven enhanced super-resolution radial fluctuation

Live-cell super-resolution microscopy enables the imaging of biological structure dynamics below the diffraction limit. Here we present enhanced super-resolution radial fluctuations (eSRRF), substantially improving image fidelity and resolution compared to the original SRRF method. eSRRF incorporates automated parameter optimization based on the data itself, giving insight into the trade-off between resolution and fidelity. We demonstrate eSRRF across a range of imaging modalities and biological systems. Notably, we extend eSRRF to three dimensions by combining it with multifocus microscopy. This realizes live-cell volumetric super-resolution imaging with an acquisition speed of ~1 volume per second. eSRRF provides an accessible super-resolution approach, maximizing information extraction across varied experimental conditions while minimizing artifacts. Its optimal parameter prediction strategy is generalizable, moving toward unbiased and optimized analyses in super-resolution microscopy.

Fast4DReg - fast registration of 4D microscopy datasets

Unwanted sample drift is a common issue that plagues microscopy experiments, preventing accurate temporal visualization and quantification of biological processes. Although multiple methods and tools exist to correct images post acquisition, performing drift correction of three-dimensional (3D) videos using open-source solutions remains challenging and time consuming. Here, we present a new tool developed for ImageJ or Fiji called Fast4DReg that can quickly correct axial and lateral drift in 3D video-microscopy datasets. Fast4DReg works by creating intensity projections along multiple axes and estimating the drift between frames using two-dimensional cross-correlations. Using synthetic and acquired datasets, we demonstrate that Fast4DReg can perform better than other state-of-the-art open-source drift-correction tools and significantly outperforms them in speed. We also demonstrate that Fast4DReg can be used to register misaligned channels in 3D using either calibration slides or misaligned images directly. Altogether, Fast4DReg provides a quick and easy-to-use method to correct 3D imaging data before further visualization and analysis.

ALFA-PRF: a novel approach to detect murine perforin release from CTLs into the immune synapse

When killing through the granule exocytosis pathway, cytotoxic lymphocytes release key effector molecules into the immune synapse, perforin and granzymes, to initiate target cell killing. The pore-forming perforin is essential for the function of cytotoxic lymphocytes, as its pores disrupt the target cell membrane and allow diffusion of pro-apoptotic serine proteases, granzyme, into the target cell, where they initiate various cell death cascades. Unlike human perforin, the detection of its murine counterpart in a live cell system has been problematic due its relatively low expression level and the lack of sensitive antibodies. The lack of a suitable methodology to visualise murine perforin secretion into the synapse hinders the study of the cytotoxic lymphocyte secretory machinery in murine models of human disease. Here, we describe a novel recombinant technology, whereby a short ALFA-tag sequence has been fused with the amino-terminus of a mature murine perforin, and this allowed its detection by the highly specific FluoTag^®-X2 anti-ALFA nanobodies using both Total Internal Reflection Fluorescence (TIRF) microscopy of an artificial synapse, and confocal microscopy of the physiological immune synapse with a target cell. This methodology can have broad application in the field of cytotoxic lymphocyte biology and for the many models of human disease.

TissUExM enables quantitative ultrastructural analysis in whole vertebrate embryos by expansion microscopy

Super-resolution microscopy reveals the molecular organization of biological structures down to the nanoscale. While it allows the study of protein complexes in single cells, small organisms, or thin tissue sections, there is currently no versatile approach for ultrastructural analysis compatible with whole vertebrate embryos. Here, we present tissue ultrastructure expansion microscopy (TissUExM), a method to expand millimeter-scale and mechanically heterogeneous whole embryonic tissues, including Drosophila wing discs, whole zebrafish, and mouse embryos. TissUExM is designed for the observation of endogenous proteins. It permits quantitative characterization of protein complexes in various organelles at super-resolution in a range of ∼3 mm-sized tissues using conventional microscopes. We demonstrate its strength by investigating tissue-specific ciliary architecture heterogeneity and ultrastructural defects observed upon ciliary protein overexpression. Overall, TissUExM is ideal for performing ultrastructural studies and molecular mapping in situ in whole embryos.

DeepBacs for multi-task bacterial image analysis using open-source deep learning approaches

This work demonstrates and guides how to use a range of state-of-the-art artificial neural-networks to analyse bacterial microscopy images using the recently developed ZeroCostDL4Mic platform. We generated a database of image datasets used to train networks for various image analysis tasks and present strategies for data acquisition and curation, as well as model training. We showcase different deep learning (DL) approaches for segmenting bright field and fluorescence images of different bacterial species, use object detection to classify different growth stages in time-lapse imaging data, and carry out DL-assisted phenotypic profiling of antibiotic-treated cells. To also demonstrate the ability of DL to enhance low-phototoxicity live-cell microscopy, we showcase how image denoising can allow researchers to attain high-fidelity data in faster and longer imaging. Finally, artificial labelling of cell membranes and predictions of super-resolution images allow for accurate mapping of cell shape and intracellular targets. Our purposefully-built database of training and testing data aids in novice users' training, enabling them to quickly explore how to analyse their data through DL. We hope this lays a fertile ground for the efficient application of DL in microbiology and fosters the creation of tools for bacterial cell biology and antibiotic research.

A method for the fast and photon-efficient analysis of time-domain fluorescence lifetime image data over large dynamic ranges

Fluorescence lifetime imaging (FLIM) allows the quantification of sub‐cellular processes in situ, in living cells. A number of approaches have been developed to extract the lifetime from time‐domain FLIM data, but they are often limited in terms of speed, photon efficiency, precision or the dynamic range of lifetimes they can measure. Here, we focus on one of the best performing methods in the field, the centre‐of‐mass method (CMM), that conveys advantages in terms of speed and photon efficiency over others. In this paper, however, we identify a loss of photon efficiency of CMM for short lifetimes when background noise is present. We subsequently present a new development and generalization of CMM that provides for the rapid and accurate extraction of fluorescence lifetime over a large lifetime dynamic range. We provide software tools to simulate, validate and analyse FLIM data sets and compare the performance of our approach against the standard CMM and the commonly employed least‐square minimization (LSM) methods. Our method features a better photon efficiency than standard CMM and LSM and is robust in the presence of background noise. The algorithm is applicable to any time‐domain FLIM data set.

TrackMate 7: integrating state-of-the-art segmentation algorithms into tracking pipelines

TrackMate is an automated tracking software used to analyze bioimages and is distributed as a Fiji plugin. Here, we introduce a new version of TrackMate. TrackMate 7 is built to address the broad spectrum of modern challenges researchers face by integrating state-of-the-art segmentation algorithms into tracking pipelines. We illustrate qualitatively and quantitatively that these new capabilities function effectively across a wide range of bio-imaging experiments.

Avoiding a replication crisis in deep-learning-based bioimage analysis

Deep learning algorithms are powerful tools to analyse, restore and transform bioimaging data, increasingly used in life sciences research. These approaches now outperform most other algorithms for a broad range of image analysis tasks. In particular, one of the promises of deep learning is the possibility to provide parameter-free, one-click data analysis achieving expert-level performances in a fraction of the time previously required. However, as with most new and upcoming technologies, the potential for inappropriate use is raising concerns among the biomedical research community. This perspective aims to provide a short overview of key concepts that we believe are important for researchers to consider when using deep learning for their microscopy studies. These comments are based on our own experience gained while optimising various deep learning tools for bioimage analysis and discussions with colleagues from both the developer and user community. In particular, we focus on describing how results obtained using deep learning can be validated and discuss what should, in our views, be considered when choosing a suitable tool. We also suggest what aspects of a deep learning analysis would need to be reported in publications to describe the use of such tools to guarantee that the work can be reproduced. We hope this perspective will foster further discussion between developers, image analysis specialists, users and journal editors to define adequate guidelines and ensure that this transformative technology is used appropriately.

Imaging in focus: An introduction to denoising bioimages in the era of deep learning

Fluorescence microscopy enables the direct observation of previously hidden dynamic processes of life, allowing profound insights into mechanisms of health and disease. However, imaging of live samples is fundamentally limited by the toxicity of the illuminating light and images are often acquired using low light conditions. As a consequence, images can become very noisy which severely complicates their interpretation. In recent years, deep learning (DL) has emerged as a very successful approach to remove this noise while retaining the useful signal. Unlike classical algorithms which use well-defined mathematical functions to remove noise, DL methods learn to denoise from example data, providing a powerful content-aware approach. In this review, we first describe the different types of noise that typically corrupt fluorescence microscopy images and introduce the denoising task. We then present the main DL-based denoising methods and their relative advantages and disadvantages. We aim to provide insights into how DL-based denoising methods operate and help users choose the most appropriate tools for their applications.

Democratising deep learning for microscopy with ZeroCostDL4Mic

Deep Learning (DL) methods are powerful analytical tools for microscopy and can outperform conventional image processing pipelines. Despite the enthusiasm and innovations fuelled by DL technology, the need to access powerful and compatible resources to train DL networks leads to an accessibility barrier that novice users often find difficult to overcome. Here, we present ZeroCostDL4Mic, an entry-level platform simplifying DL access by leveraging the free, cloud-based computational resources of Google Colab. ZeroCostDL4Mic allows researchers with no coding expertise to train and apply key DL networks to perform tasks including segmentation (using U-Net and StarDist), object detection (using YOLOv2), denoising (using CARE and Noise2Void), super-resolution microscopy (using Deep-STORM), and image-to-image translation (using Label-free prediction - fnet, pix2pix and CycleGAN). Importantly, we provide suitable quantitative tools for each network to evaluate model performance, allowing model optimisation. We demonstrate the application of the platform to study multiple biological processes.

Comparative Studies in the A30P and A53T α-Synuclein C. elegans Strains to Investigate the Molecular Origins of Parkinson's Disease

The aggregation of α-synuclein is a hallmark of Parkinson's disease (PD) and a variety of related neurological disorders. A number of mutations in this protein, including A30P and A53T, are associated with familial forms of the disease. Patients carrying the A30P mutation typically exhibit a similar age of onset and symptoms as sporadic PD, while those carrying the A53T mutation generally have an earlier age of onset and an accelerated progression. We report two C. elegans models of PD (PD_A30P and PD_A53T), which express these mutational variants in the muscle cells, and probed their behavior relative to animals expressing the wild-type protein (PD_WT). PD_A30P worms showed a reduced speed of movement and an increased paralysis rate, control worms, but no change in the frequency of body bends. By contrast, in PD_A53T worms both speed and frequency of body bends were significantly decreased, and paralysis rate was increased. α-Synuclein was also observed to be less well localized into aggregates in PD_A30P worms compared to PD_A53T and PD_WT worms, and amyloid-like features were evident later in the life of the animals, despite comparable levels of expression of α-synuclein. Furthermore, squalamine, a natural product currently in clinical trials for treating symptomatic aspects of PD, was found to reduce significantly the aggregation of α-synuclein and its associated toxicity in PD_A53T and PD_WT worms, but had less marked effects in PD_A30P. In addition, using an antibody that targets the N-terminal region of α-synuclein, we observed a suppression of toxicity in PD_A30P, PD_A53T and PD_WT worms. These results illustrate the use of these two C. elegans models in fundamental and applied PD research.

Application of Super-Resolution and Advanced Quantitative Microscopy to the Spatio-Temporal Analysis of Influenza Virus Replication

With an estimated three to five million human cases annually and the potential to infect domestic and wild animal populations, influenza viruses are one of the greatest health and economic burdens to our society, and pose an ongoing threat of large-scale pandemics. Despite our knowledge of many important aspects of influenza virus biology, there is still much to learn about how influenza viruses replicate in infected cells, for instance, how they use entry receptors or exploit host cell trafficking pathways. These gaps in our knowledge are due, in part, to the difficulty of directly observing viruses in living cells. In recent years, advances in light microscopy, including super-resolution microscopy and single-molecule imaging, have enabled many viral replication steps to be visualised dynamically in living cells. In particular, the ability to track single virions and their components, in real time, now allows specific pathways to be interrogated, providing new insights to various aspects of the virus-host cell interaction. In this review, we discuss how state-of-the-art imaging technologies, notably quantitative live-cell and super-resolution microscopy, are providing new nanoscale and molecular insights into influenza virus replication and revealing new opportunities for developing antiviral strategies.

Single-Molecule Super-Resolution Imaging of T-Cell Plasma Membrane CD4 Redistribution upon HIV-1 Binding

The first step of cellular entry for the human immunodeficiency virus type-1 (HIV-1) occurs through the binding of its envelope protein (Env) with the plasma membrane receptor CD4 and co-receptor CCR5 or CXCR4 on susceptible cells, primarily CD4+ T cells and macrophages. Although there is considerable knowledge of the molecular interactions between Env and host cell receptors that lead to successful fusion, the precise way in which HIV-1 receptors redistribute to sites of virus binding at the nanoscale remains unknown. Here, we quantitatively examine changes in the nanoscale organisation of CD4 on the surface of CD4+ T cells following HIV-1 binding. Using single-molecule super-resolution imaging, we show that CD4 molecules are distributed mostly as either individual molecules or small clusters of up to 4 molecules. Following virus binding, we observe a local 3-to-10-fold increase in cluster diameter and molecule number for virus-associated CD4 clusters. Moreover, a similar but smaller magnitude reorganisation of CD4 was also observed with recombinant gp120. For one of the first times, our results quantify the nanoscale CD4 reorganisation triggered by HIV-1 on host CD4+ T cells. Our quantitative approach provides a robust methodology for characterising the nanoscale organisation of plasma membrane receptors in general with the potential to link spatial organisation to function.

Automated cell tracking using StarDist and TrackMate

The ability of cells to migrate is a fundamental physiological process involved in embryonic development, tissue homeostasis, immune surveillance, and wound healing. Therefore, the mechanisms governing cellular locomotion have been under intense scrutiny over the last 50 years. One of the main tools of this scrutiny is live-cell quantitative imaging, where researchers image cells over time to study their migration and quantitatively analyze their dynamics by tracking them using the recorded images. Despite the availability of computational tools, manual tracking remains widely used among researchers due to the difficulty setting up robust automated cell tracking and large-scale analysis. Here we provide a detailed analysis pipeline illustrating how the deep learning network StarDist can be combined with the popular tracking software TrackMate to perform 2D automated cell tracking and provide fully quantitative readouts. Our proposed protocol is compatible with both fluorescent and widefield images. It only requires freely available and open-source software (ZeroCostDL4Mic and Fiji), and does not require any coding knowledge from the users, making it a versatile and powerful tool for the field. We demonstrate this pipeline's usability by automatically tracking cancer cells and T cells using fluorescent and brightfield images. Importantly, we provide, as supplementary information, a detailed step-by-step protocol to allow researchers to implement it with their images.

Automated cell tracking using StarDist and TrackMate

The ability of cells to migrate is a fundamental physiological process involved in embryonic development, tissue homeostasis, immune surveillance, and wound healing. Therefore, the mechanisms governing cellular locomotion have been under intense scrutiny over the last 50 years. One of the main tools of this scrutiny is live-cell quantitative imaging, where researchers image cells over time to study their migration and quantitatively analyze their dynamics by tracking them using the recorded images. Despite the availability of computational tools, manual tracking remains widely used among researchers due to the difficulty setting up robust automated cell tracking and large-scale analysis. Here we provide a detailed analysis pipeline illustrating how the deep learning network StarDist can be combined with the popular tracking software TrackMate to perform 2D automated cell tracking and provide fully quantitative readouts. Our proposed protocol is compatible with both fluorescent and widefield images. It only requires freely available and open-source software (ZeroCostDL4Mic and Fiji), and does not require any coding knowledge from the users, making it a versatile and powerful tool for the field. We demonstrate this pipeline's usability by automatically tracking cancer cells and T cells using fluorescent and brightfield images. Importantly, we provide, as supplementary information, a detailed step-by-step protocol to allow researchers to implement it with their images.

Fluctuation-Based Super-Resolution Traction Force Microscopy

Cellular mechanics play a crucial role in tissue homeostasis and are often misregulated in disease. Traction force microscopy is one of the key methods that has enabled researchers to study fundamental aspects of mechanobiology; however, traction force microscopy is limited by poor resolution. Here, we propose a simplified protocol and imaging strategy that enhances the output of traction force microscopy by increasing i) achievable bead density and ii) the accuracy of bead tracking. Our approach relies on super-resolution microscopy, enabled by fluorescence fluctuation analysis. Our pipeline can be used on spinning-disk confocal or widefield microscopes and is compatible with available analysis software. In addition, we demonstrate that our workflow can be used to gain biologically relevant information and is suitable for fast long-term live measurement of traction forces even in light-sensitive cells. Finally, using fluctuation-based traction force microscopy, we observe that filopodia align to the force field generated by focal adhesions.

OptiJ: Open-source optical projection tomography of large organ samples

The three-dimensional imaging of mesoscopic samples with Optical Projection Tomography (OPT) has become a powerful tool for biomedical phenotyping studies. OPT uses visible light to visualize the 3D morphology of large transparent samples. To enable a wider application of OPT, we present OptiJ, a low-cost, fully open-source OPT system capable of imaging large transparent specimens up to 13 mm tall and 8 mm deep with 50 µm resolution. OptiJ is based on off-the-shelf, easy-to-assemble optical components and an ImageJ plugin library for OPT data reconstruction. The software includes novel correction routines for uneven illumination and sample jitter in addition to CPU/GPU accelerated reconstruction for large datasets. We demonstrate the use of OptiJ to image and reconstruct cleared lung lobes from adult mice. We provide a detailed set of instructions to set up and use the OptiJ framework. Our hardware and software design are modular and easy to implement, allowing for further open microscopy developments for imaging large organ samples.

Fast Fluorescence Lifetime Imaging Reveals the Aggregation Processes of α-Synuclein and Polyglutamine in Aging Caenorhabditis elegans

The nematode worm Caenorhabditis elegans has emerged as an important model organism in the study of the molecular mechanisms of protein misfolding diseases associated with amyloid formation because of its small size, ease of genetic manipulation, and optical transparency. Obtaining a reliable and quantitative read-out of protein aggregation in this system, however, remains a challenge. To address this problem, we here present a fast time-gated fluorescence lifetime imaging (TG-FLIM) method and show that it provides functional insights into the process of protein aggregation in living animals by enabling the rapid characterization of different types of aggregates. Specifically, in longitudinal studies of C. elegans models of Parkinson's and Huntington's diseases, we observed marked differences in the aggregation kinetics and the nature of the protein inclusions formed by α-synuclein and polyglutamine. In particular, we found that α-synuclein inclusions do not display amyloid-like features until late in the life of the worms, whereas polyglutamine forms amyloid characteristics rapidly in early adulthood. Furthermore, we show that the TG-FLIM method is capable of imaging live and non-anaesthetized worms moving in specially designed agarose microchambers. Taken together, our results show that the TG-FLIM method enables high-throughput functional imaging of living C. elegans that can be used to study in vivo mechanisms of protein aggregation and that has the potential to aid the search for therapeutic modifiers of protein aggregation and toxicity.

Intrinsically aggregation-prone proteins form amyloid-like aggregates and contribute to tissue aging in Caenorhabditis elegans

Reduced protein homeostasis leading to increased protein instability is a common molecular feature of aging, but it remains unclear whether this is a cause or consequence of the aging process. In neurodegenerative diseases and other amyloidoses, specific proteins self-assemble into amyloid fibrils and accumulate as pathological aggregates in different tissues. More recently, widespread protein aggregation has been described during normal aging. Until now, an extensive characterization of the nature of age-dependent protein aggregation has been lacking. Here, we show that age-dependent aggregates are rapidly formed by newly synthesized proteins and have an amyloid-like structure resembling that of protein aggregates observed in disease. We then demonstrate that age-dependent protein aggregation accelerates the functional decline of different tissues in C. elegans. Together, these findings imply that amyloid-like aggregates contribute to the aging process and therefore could be important targets for strategies designed to maintain physiological functions in the late stages of life.

NanoJ: a high-performance open-source super-resolution microscopy toolbox

Super-resolution microscopy (SRM) has become essential for the study of nanoscale biological processes. This type of imaging often requires the use of specialised image analysis tools to process a large volume of recorded data and extract quantitative information. In recent years, our team has built an open-source image analysis framework for SRM designed to combine high performance and ease of use. We named it NanoJ-a reference to the popular ImageJ software it was developed for. In this paper, we highlight the current capabilities of NanoJ for several essential processing steps: spatio-temporal alignment of raw data (NanoJ-Core), super-resolution image reconstruction (NanoJ-SRRF), image quality assessment (NanoJ-SQUIRREL), structural modelling (NanoJ-VirusMapper) and control of the sample environment (NanoJ-Fluidics). We expect to expand NanoJ in the future through the development of new tools designed to improve quantitative data analysis and measure the reliability of fluorescent microscopy studies.

Automating multimodal microscopy with NanoJ-Fluidics

Combining and multiplexing microscopy approaches is crucial to understand cellular events, but requires elaborate workflows. Here, we present a robust, open-source approach for treating, labelling and imaging live or fixed cells in automated sequences. NanoJ-Fluidics is based on low-cost Lego hardware controlled by ImageJ-based software, making high-content, multimodal imaging easy to implement on any microscope with high reproducibility. We demonstrate its capacity on event-driven, super-resolved live-to-fixed and multiplexed STORM/DNA-PAINT experiments.

Automating multimodal microscopy with NanoJ-Fluidics

Combining and multiplexing microscopy approaches is crucial to understand cellular events, but requires elaborate workflows. Here, we present a robust, open-source approach for treating, labelling and imaging live or fixed cells in automated sequences. NanoJ-Fluidics is based on low-cost Lego hardware controlled by ImageJ-based software, making high-content, multimodal imaging easy to implement on any microscope with high reproducibility. We demonstrate its capacity on event-driven, super-resolved live-to-fixed and multiplexed STORM/DNA-PAINT experiments.

Probing the Growth Kinetics for the Formation of Uniform 1D Block Copolymer Nanoparticles by Living Crystallization-Driven Self-Assembly

Living crystallization-driven self-assembly (CDSA) is a seeded growth method for crystallizable block copolymers (BCPs) and related amphiphiles in solution and has recently emerged as a highly promising and versatile route to uniform core-shell nanoparticles (micelles) with control of dimensions and architecture. However, the factors that influence the rate of nanoparticle growth have not been systematically studied. Using transmission electron microscopy, small- and wide-angle X-ray scattering, and super-resolution fluorescence microscopy techniques, we have investigated the kinetics of the seeded growth of poly(ferrocenyldimethylsilane)- b-(polydimethylsiloxane) (PFS- b-PDMS), as a model living CDSA system for those employing, for example, crystallizable emissive and biocompatible polymers. By altering various self-assembly parameters including concentration, temperature, solvent, and BCP composition our results have established that the time taken to prepare fiber-like micelles via the living CDSA method can be reduced by decreasing temperature, by employing solvents that are poorer for the crystallizable PFS core-forming block, and by increasing the length of the PFS core-forming block. These results are of general importance for the future optimization of a wide variety of living CDSA systems. Our studies also demonstrate that the growth kinetics for living CDSA do not exhibit the first-order dependence of growth rate on unimer concentration anticipated by analogy with living covalent polymerizations of molecular monomers. This difference may be caused by the combined influence of chain conformational effects of the BCP on addition to the seed termini and chain length dispersity.

Structured illumination microscopy combined with machine learning enables the high throughput analysis and classification of virus structure

Optical super-resolution microscopy techniques enable high molecular specificity with high spatial resolution and constitute a set of powerful tools in the investigation of the structure of supramolecular assemblies such as viruses. Here, we report on a new methodology which combines Structured Illumination Microscopy (SIM) with machine learning algorithms to image and classify the structure of large populations of biopharmaceutical viruses with high resolution. The method offers information on virus morphology that can ultimately be linked with functional performance. We demonstrate the approach on viruses produced for oncolytic viriotherapy (Newcastle Disease Virus) and vaccine development (Influenza). This unique tool enables the rapid assessment of the quality of viral production with high throughput obviating the need for traditional batch testing methods which are complex and time consuming. We show that our method also works on non-purified samples from pooled harvest fluids directly from the production line.

RNA Docking and Local Translation Regulate Site-Specific Axon Remodeling In Vivo

Nascent proteins can be positioned rapidly at precise subcellular locations by local protein synthesis (LPS) to facilitate localized growth responses. Axon arbor architecture, a major determinant of synaptic connectivity, is shaped by localized growth responses, but it is unknown whether LPS influences these responses in vivo. Using high-resolution live imaging, we examined the spatiotemporal dynamics of RNA and LPS in retinal axons during arborization in vivo. Endogenous RNA tracking reveals that RNA granules dock at sites of branch emergence and invade stabilized branches. Live translation reporter analysis reveals that de novo β-actin hotspots colocalize with docked RNA granules at the bases and tips of new branches. Inhibition of axonal β-actin mRNA translation disrupts arbor dynamics primarily by reducing new branch emergence and leads to impoverished terminal arbors. The results demonstrate a requirement for LPS in building arbor complexity and suggest a key role for pre-synaptic LPS in assembling neural circuits.

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Tracking endogenous RNA shows that RNA docking predicts axon branch emergence in vivo

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Axon arbor complexity in vivo depends on local protein synthesis

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Axonal β-actin synthesis regulates branching by increased branch initiation

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Live imaging reveals de novo synthesis of β-actin hotspots during branch formation

Heparin acts as a structural component of β-endorphin amyloid fibrils rather than a simple aggregation promoter

The aggregation promoter heparin is commonly used to study the aggregation kinetics and biophysical properties of protein amyloids. However, the underlying mechanism for amyloid promotion by heparin remains poorly understood. In the case of the neuropeptide β-endorphin that can reversibly adopt a functional amyloid form in nature, aggregation in the presence of heparin leads to a loss of function. Applying correlative optical super-resolution microscopy methods, we show that heparin incorporates into emerging β-endorphin fibrils forming an integral component and is essential for amyloid templating. This will have direct implications on β-endorphin's normal physiological function and raises concerns on the biological relevance of heparin-promoted amyloid models.

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.

A method to quantify FRET stoichiometry with phasor plot analysis and acceptor lifetime ingrowth

FRET is widely used for the study of protein-protein interactions in biological samples. However, it is difficult to quantify both the FRET efficiency (E) and the affinity (Kd) of the molecular interaction from intermolecular FRET signals in samples of unknown stoichiometry. Here, we present a method for the simultaneous quantification of the complete set of interaction parameters, including fractions of bound donors and acceptors, local protein concentrations, and dissociation constants, in each image pixel. The method makes use of fluorescence lifetime information from both donor and acceptor molecules and takes advantage of the linear properties of the phasor plot approach. We demonstrate the capability of our method in vitro in a microfluidic device and also in cells, via the determination of the binding affinity between tagged versions of glutathione and glutathione S-transferase, and via the determination of competitor concentration. The potential of the method is explored with simulations.

In Situ Visualization of Block Copolymer Self-Assembly in Organic Media by Super-Resolution Fluorescence Microscopy

Analytical methods that enable visualization of nanomaterials derived from solution self‐assembly processes in organic solvents are highly desirable. Herein, we demonstrate the use of stimulated emission depletion microscopy (STED) and single molecule localization microscopy (SMLM) to map living crystallization‐driven block copolymer (BCP) self‐assembly in organic media at the sub‐diffraction scale. Four different dyes were successfully used for single‐colour super‐resolution imaging of the BCP nanostructures allowing micelle length distributions to be determined in situ. Dual‐colour SMLM imaging was used to measure and compare the rate of addition of red fluorescent BCP to the termini of green fluorescent seed micelles to generate block comicelles. Although well‐established for aqueous systems, the results highlight the potential of super‐resolution microscopy techniques for the interrogation of self‐assembly processes in organic media.

HSV-1 Glycoproteins Are Delivered to Virus Assembly Sites Through Dynamin-Dependent Endocytosis

Herpes simplex virus‐1 (HSV‐1) is a large enveloped DNA virus that belongs to the family of Herpesviridae. It has been recently shown that the cytoplasmic membranes that wrap the newly assembled capsids are endocytic compartments derived from the plasma membrane. Here, we show that dynamin‐dependent endocytosis plays a major role in this process. Dominant‐negative dynamin and clathrin adaptor AP180 significantly decrease virus production. Moreover, inhibitors targeting dynamin and clathrin lead to a decreased transport of glycoproteins to cytoplasmic capsids, confirming that glycoproteins are delivered to assembly sites via endocytosis. We also show that certain combinations of glycoproteins colocalize with each other and with the components of clathrin‐dependent and ‐independent endocytosis pathways. Importantly, we demonstrate that the uptake of neutralizing antibodies that bind to glycoproteins when they become exposed on the cell surface during virus particle assembly leads to the production of non‐infectious HSV‐1. Our results demonstrate that transport of viral glycoproteins to the plasma membrane prior to endocytosis is the major route by which these proteins are localized to the cytoplasmic virus assembly compartments. This highlights the importance of endocytosis as a major protein‐sorting event during HSV‐1 envelopment.