Catalysis by hen egg-white lysozyme proceeds via a covalent intermediate

Hen egg-white lysozyme (HEWL) was the first enzyme to have its three-dimensional structure determined by X-ray diffraction techniques. A catalytic mechanism, featuring a long-lived oxocarbenium-ion intermediate, was proposed on the basis of model-building studies. The 'Phillips' mechanism is widely held as the paradigm for the catalytic mechanism of beta-glycosidases that cleave glycosidic linkages with net retention of configuration of the anomeric centre. Studies with other retaining beta-glycosidases, however, provide strong evidence pointing to a common mechanism for these enzymes that involves a covalent glycosyl-enzyme intermediate, as previously postulated. Here we show, in three different cases using electrospray ionization mass spectrometry, a catalytically competent covalent glycosyl-enzyme intermediate during the catalytic cycle of HEWL. We also show the three-dimensional structure of this intermediate as determined by X-ray diffraction. We formulate a general catalytic mechanism for all retaining beta-glycosidases that includes substrate distortion, formation of a covalent intermediate, and the electrophilic migration of C1 along the reaction coordinate.

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.

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.

C-terminal calcium binding of α-synuclein modulates synaptic vesicle interaction

Alpha-synuclein is known to bind to small unilamellar vesicles (SUVs) via its N terminus, which forms an amphipathic alpha-helix upon membrane interaction. Here we show that calcium binds to the C terminus of alpha-synuclein, therewith increasing its lipid-binding capacity. Using CEST-NMR, we reveal that alpha-synuclein interacts with isolated synaptic vesicles with two regions, the N terminus, already known from studies on SUVs, and additionally via its C terminus, which is regulated by the binding of calcium. Indeed, dSTORM on synaptosomes shows that calcium mediates the localization of alpha-synuclein at the pre-synaptic terminal, and an imbalance in calcium or alpha-synuclein can cause synaptic vesicle clustering, as seen ex vivo and in vitro. This study provides a new view on the binding of alpha-synuclein to synaptic vesicles, which might also affect our understanding of synucleinopathies.

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

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.

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.

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.

De novo design of a biologically active amyloid

Most human proteins possess amyloidogenic segments, but only about 30 are associated with amyloid-associated pathologies, and it remains unclear what determines amyloid toxicity. We designed vascin, a synthetic amyloid peptide, based on an amyloidogenic fragment of vascular endothelial growth factor receptor 2 (VEGFR2), a protein that is not associated to amyloidosis. Vascin recapitulates key biophysical and biochemical characteristics of natural amyloids, penetrates cells, and seeds the aggregation of VEGFR2 through direct interaction. We found that amyloid toxicity is observed only in cells that both express VEGFR2 and are dependent on VEGFR2 activity for survival. Thus, amyloid toxicity here appears to be both protein-specific and conditional-determined by VEGFR2 loss of function in a biological context in which target protein function is essential.

The stereochemical configuration of lysobisphosphatidic acid from rat liver, rabbit lung and pig lung

Lysobisphosphatidic acid known also as bis(monoacyl-glycerol)phosphate, was isolated from liver of rats treated with Triton WR1339, and from rabbit and pig lung. Alkaline hydrolysates of all these samples of lysobisphosphatidic acid were essentially similar and contained phosphorus, total glycerol, free glycerol, total glycerophosphates, beta-glycerophosphate, total alpha-glycerophosphates, sn-glycero-1-phosphate and sn-glycero-3-phosphate in a molar ratio of 1.0 : 2.0 : 1.0 : 1.0 :0.6 : 0.4 : 0.38 : 0.04. This proves that the backbone of the principal lysobisphosphatidic acid from all three sources has the structure of 1-sn-glycerophospho-1-sn-glycerol.

FLIM FRET technology for drug discovery: automated multiwell-plate high-content analysis, multiplexed readouts and application in situ

A fluorescence lifetime imaging (FLIM) technology platform intended to read out changes in Förster resonance energy transfer (FRET) efficiency is presented for the study of protein interactions across the drug-discovery pipeline. FLIM provides a robust, inherently ratiometric imaging modality for drug discovery that could allow the same sensor constructs to be translated from automated cell-based assays through small transparent organisms such as zebrafish to mammals. To this end, an automated FLIM multiwell-plate reader is described for high content analysis of fixed and live cells, tomographic FLIM in zebrafish and FLIM FRET of live cells via confocal endomicroscopy. For cell-based assays, an exemplar application reading out protein aggregation using FLIM FRET is presented, and the potential for multiple simultaneous FLIM (FRET) readouts in microscopy is illustrated.

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.

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.

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.

Fatty chains of different lipid classes of Semliki forest virus and host cell membranes

Semliki Forest virus was grown in BHK-21 cells. The major classes of phospho-and glycolipids of the virus were analyzed for the compositions of fatty acids, aldehydes, and sphingosine bases, and the major glycerophospholipids were analyzed for the relative proportions of alkenyl-acyl, alkyl-acyl, and diacyl forms. All viral lipid classes proved to be mixtures of several molecular species. Each class contained a characteristic mixture of fatty chains, which was different in all other classes. All viral lipid classes resembled their counterparts of the host plasma membrane and also those of the endoplasmic reticulum. The gangliosides of the virus and the plasma membrane proved to be similar even at the level of individual molecular species. The number of certain lipid molecules in an average virion was less than the number of the protein molecules.

Human myeloma cells acquire resistance to difluoromethylornithine by amplification of ornithine decarboxylase gene

Stepwise increments of the concentration of 2-difluoromethylornithine (DFMO), a mechanism-based irreversible inhibitor of mammalian ornithine decarboxylase (ODC), resulted in a selection of cultured human IgG-myeloma cells (Sultan cell line) capable of growing in the presence of up to 3 mM-DFMO. This capacity was associated with 10-fold increase in ODC activity in the dialysed extracts of drug-resistant myeloma cells, markedly enhanced synthesis rate for ODC enzyme molecules, as revealed by a 20 min [35S]methionine labelling of cellular proteins, followed by specific immunoprecipitation and SDS/polyacrylamide-gel electrophoresis, dose-dependently increased expression of ODC mRNA in resistant cells (effective dose causing 50% inhibition), dose-dependent amplification of ODC gene sequences in a 9-kilobase-pairs EcoRI genomic DNA fragment, and (v) a 10-fold increase in the ED50 (effective dose causing 50% inhibition) for the anti-proliferative action of DFMO in these myeloma cells. These results represent one of the few gene amplifications described in cultured human cells.

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.

Retarded PDI diffusion and a reductive shift in poise of the calcium depleted endoplasmic reticulum

Background

Endoplasmic reticulum (ER) lumenal protein thiol redox balance resists dramatic variation in unfolded protein load imposed by diverse physiological challenges including compromise in the key upstream oxidases. Lumenal calcium depletion, incurred during normal cell signaling, stands out as a notable exception to this resilience, promoting a rapid and reversible shift towards a more reducing poise. Calcium depletion induced ER redox alterations are relevant to physiological conditions associated with calcium signaling, such as the response of pancreatic cells to secretagogues and neuronal activity. The core components of the ER redox machinery are well characterized; however, the molecular basis for the calcium-depletion induced shift in redox balance is presently obscure.

Results

In vitro, the core machinery for generating disulfides, consisting of ERO1 and the oxidizing protein disulfide isomerase, PDI1A, was indifferent to variation in calcium concentration within the physiological range. However, ER calcium depletion in vivo led to a selective 2.5-fold decline in PDI1A mobility, whereas the mobility of the reducing PDI family member, ERdj5 was unaffected. In vivo, fluorescence resonance energy transfer measurements revealed that declining PDI1A mobility correlated with formation of a complex with the abundant ER chaperone calreticulin, whose mobility was also inhibited by calcium depletion and the calcium depletion-mediated reductive shift was attenuated in cells lacking calreticulin. Measurements with purified proteins confirmed that the PDI1A-calreticulin complex dissociated as Ca²⁺ concentrations approached those normally found in the ER lumen ([Ca²⁺]K_0.5max = 190 μM).

Conclusions

Our findings suggest that selective sequestration of PDI1A in a calcium depletion-mediated complex with the abundant chaperone calreticulin attenuates the effective concentration of this major lumenal thiol oxidant, providing a plausible and simple mechanism for the observed shift in ER lumenal redox poise upon physiological calcium depletion.

Electronic supplementary material

The online version of this article (doi:10.1186/s12915-014-0112-2) contains supplementary material, which is available to authorized users.

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.

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.

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.

Fluorescence lifetime optical projection tomography

We describe a quantitative fluorescence projection tomography technique which measures the 3-D fluorescence lifetime distribution in optically cleared specimens up 1 cm in diameter. This is achieved by acquiring a series of wide-field time-gated images at different relative time delays with respect to a train of excitation pulses, at a number of projection angles. For each time delay, the 3-D time-gated intensity distribution is reconstructed using a filtered back projection algorithm and the fluorescence lifetime subsequently determined for each reconstructed horizontal plane by iterative fitting to a mono-exponential decay. Due to its inherently ratiometric nature, fluorescence lifetime is robust against intensity based artefacts as well as producing a quantitative measure of the fluorescence signal. We present a 3-D fluorescence lifetime reconstruction of a mouse embryo labelled with an alexa-488 conjugated antibody targeted to the neurofilament, which clearly differentiates between the extrinsic label and the autofluorescence, particularly from the heart and dorsal aorta.

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.