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The goal when imaging bioprocesses with optical microscopy is to acquire the most spatiotemporal information with the least invasiveness. Deep neural networks have substantially improved optical microscopy, including image super-resolution and restoration, but still have substantial potential for artifacts. In this study, we developed rationalized deep learning (rDL) for structured illumination microscopy and lattice light sheet microscopy (LLSM) by incorporating prior knowledge of illumination patterns and, thereby, rationally guiding the network to denoise raw images. Here we demonstrate that rDL structured illumination microscopy eliminates spectral bias-induced resolution degradation and reduces model uncertainty by five-fold, improving the super-resolution information by more than ten-fold over other computational approaches. Moreover, rDL applied to LLSM enables self-supervised training by using the spatial or temporal continuity of noisy data itself, yielding results similar to those of supervised methods. We demonstrate the utility of rDL by imaging the rapid kinetics of motile cilia, nucleolar protein condensation during light-sensitive mitosis and long-term interactions between membranous and membrane-less organelles. Rationalized deep learning improves image reconstruction by incorporating prior knowledge of illumination patterns.

Deep neural networks have enabled astonishing transformations from low-resolution (LR) to super-resolved images. However, whether, and under what imaging conditions, such deep-learning models outperform super-resolution (SR) microscopy is poorly explored. Here, using multimodality structured illumination microscopy (SIM), we first provide an extensive dataset of LR–SR image pairs and evaluate the deep-learning SR models in terms of structural complexity, signal-to-noise ratio and upscaling factor. Second, we devise the deep Fourier channel attention network (DFCAN), which leverages the frequency content difference across distinct features to learn precise hierarchical representations of high-frequency information about diverse biological structures. Third, we show that DFCAN’s Fourier domain focalization enables robust reconstruction of SIM images under low signal-to-noise ratio conditions. We demonstrate that DFCAN achieves comparable image quality to SIM over a tenfold longer duration in multicolor live-cell imaging experiments, which reveal the detailed structures of mitochondrial cristae and nucleoids and the interaction dynamics of organelles and cytoskeleton.This study explores the performance of deep-learning models for super-resolution imaging and introduces models that utilize frequency content information in the Fourier domain to improve SIM reconstruction under low-SNR conditions.

U-ExM enables near-native expansion microscopy of samples in vitro and in cells. The combination of U-ExM with confocal microscopy and HyVolution revealed details of centriole chirality that were previously accessible only by electron microscopy.

Exosomes and extracellular vesicles (EV) are increasingly being explored as circulating biomarkers, but their heterogenous composition will likely mandate the development of multiplexed EV technologies. Iteratively multiplexed analyses of near single EVs have been challenging to implement beyond a few colors during spectral sensing. Here we developed a multiplexed analysis of EV technique (MASEV) to interrogate thousands of individual EVs during 5 cycles of multi-channel fluorescence staining for 15 EV biomarkers. Contrary to the common belief, we show that: several markers proposed to be ubiquitous are less prevalent than believed; multiple biomarkers concur in single vesicles but only in small fractions; affinity purification can lead to loss of rare EV subtypes; and deep profiling allows detailed analysis of EV, potentially improving the diagnostic content. These findings establish the potential of MASEV for uncovering fundamental EV biology and heterogeneity and increasing diagnostic specificity. Multiplexed analyses of near single EVs is currently challenging. Here the authors report the method MASEV, multiplexed analysis of EVs, to interrogate thousands of individual EVs during 5 cycles of multi-channel fluorescence staining for 15 EV biomarkers.

The ability of cells to respond to mechanical forces is critical for numerous biological processes. Emerging evidence indicates that external mechanical forces trigger changes in nuclear envelope structure and composition, chromatin organization and gene expression. However, it remains unclear if these processes originate in the nucleus or are downstream of cytoplasmic signals. Here we discuss recent findings that support a direct role of the nucleus in cellular mechanosensing and highlight novel tools to study nuclear mechanotransduction. Mechanical forces influence both cytoplasmic and nuclear events. Kirby and Lammerding discuss recent evidence suggesting that the nucleus itself is a mechanosensor and methods to study nuclear mechanotransduction.

The low photostability of fluorescent proteins is a limiting factor in many applications of fluorescence microscopy. Here we present StayGold, a green fluorescent protein (GFP) derived from the jellyfish Cytaeis uchidae . StayGold is over one order of magnitude more photostable than any currently available fluorescent protein and has a cellular brightness similar to mNeonGreen. We used StayGold to image the dynamics of the endoplasmic reticulum (ER) with high spatiotemporal resolution over several minutes using structured illumination microscopy (SIM) and observed substantially less photobleaching than with a GFP variant optimized for stability in the ER. Using StayGold fusions and SIM, we also imaged the dynamics of mitochondrial fusion and fission and mapped the viral spike proteins in fixed cells infected with severe acute respiratory syndrome coronavirus 2. As StayGold is a dimer, we created a tandem dimer version that allowed us to observe the dynamics of microtubules and the excitatory post-synaptic density in neurons. StayGold will substantially reduce the limitations imposed by photobleaching, especially in live cell or volumetric imaging. StayGold is over one order of magnitude more photostable than current fluorescent proteins

Modern microscopes used for biological imaging often present themselves as black boxes whose precise operating principle remains unknown, and whose optical resolution and price seem to be in inverse proportion to each other. With UC2 (You. See. Too.) we present a low-cost, 3D-printed, open-source, modular microscopy toolbox and demonstrate its versatility by realizing a complete microscope development cycle from concept to experimental phase. The self-contained incubator-enclosed brightfield microscope monitors monocyte to macrophage cell differentiation for seven days at cellular resolution level (e.g. 2 μm). Furthermore, by including very few additional components, the geometry is transferred into a 400 Euro light sheet fluorescence microscope for volumetric observations of a transgenic Zebrafish expressing green fluorescent protein (GFP). With this, we aim to establish an open standard in optics to facilitate interfacing with various complementary platforms. By making the content and comprehensive documentation publicly available, the systems presented here lend themselves to easy and straightforward replications, modifications, and extensions. Open standard microscopy is urgently needed to give low-cost solutions to researchers and to overcome the reproducibility crisis in science. Here the authors present a 3D-printed, open-source modular microscopy toolbox UC2 (You. See. Too.) for a few hundred Euros.

Specialized epitope tags are widely used for detecting, manipulating or purifying proteins, but often their versatility is limited. Here, we introduce the ALFA-tag, a rationally designed epitope tag that serves a remarkably broad spectrum of applications in life sciences while outperforming established tags like the HA-, FLAG®- or myc-tag. The ALFA-tag forms a small and stable α-helix that is functional irrespective of its position on the target protein in prokaryotic and eukaryotic hosts. We characterize a nanobody (NbALFA) binding ALFA-tagged proteins from native or fixed specimen with low picomolar affinity. It is ideally suited for super-resolution microscopy, immunoprecipitations and Western blotting, and also allows in vivo detection of proteins. We show the crystal structure of the complex that enabled us to design a nanobody mutant (NbALFA PE ) that permits efficient one-step purifications of native ALFA-tagged proteins, complexes and even entire living cells using peptide elution under physiological conditions. Epitope tags are widely used in various applications, but often lack versatility. Here, the authors introduce a small, alpha helical tag, which is recognized by a high affinity nanobody and can be used in a range of different applications, from protein purification to super-resolution imaging and in vivo detection of proteins.

This protocol describes the preparation of calibration bead slides, their use and additional strategies to reduce artifacts of structured illumination microscopy that will allow researchers to exploit the technique's full potential for biological applications. Linear 2D- or 3D-structured illumination microscopy (SIM or3D-SIM, respectively) enables multicolor volumetric imaging of fixed and live specimens with subdiffraction resolution in all spatial dimensions. However, the reliance of SIM on algorithmic post-processing renders it particularly sensitive to artifacts that may reduce resolution, compromise data and its interpretations, and drain resources in terms of money and time spent. Here we present a protocol that allows users to generate high-quality SIM data while accounting and correcting for common artifacts. The protocol details preparation of calibration bead slides designed for SIM-based experiments, the acquisition of calibration data, the documentation of typically encountered SIM artifacts and corrective measures that should be taken to reduce them. It also includes a conceptual overview and checklist for experimental design and calibration decisions, and is applicable to any commercially available or custom platform. This protocol, plus accompanying guidelines, allows researchers from students to imaging professionals to create an optimal SIM imaging environment regardless of specimen type or structure of interest. The calibration sample preparation and system calibration protocol can be executed within 1–2 d.

Super-resolution microscopy depends on steps that can contribute to the formation of image artifacts, leading to misinterpretation of biological information. We present NanoJ-SQUIRREL, an ImageJ-based analytical approach that provides quantitative assessment of super-resolution image quality. By comparing diffraction-limited images and super-resolution equivalents of the same acquisition volume, this approach generates a quantitative map of super-resolution defects and can guide researchers in optimizing imaging parameters.