Super-resolution fluorescence imaging has offered unprecedented insights and revolutionized our understanding of biology. In particular, localized plasmonic structured illumination microscopy (LPSIM) achieves video-rate super-resolution imaging with ∼50 nm spatial resolution by leveraging subdiffraction-limited nearfield patterns generated by plasmonic nanoantenna arrays. However, the conventional trial-and-error design process for LPSIM arrays is time-consuming and computationally intensive, limiting the exploration of optimal designs. Here, we propose a hybrid inverse design framework combining deep learning and genetic algorithms to refine LPSIM arrays. A population of designs is evaluated using a trained convolutional neural network, and a multiobjective optimization method optimizes them through iteration and evolution. Simulations demonstrate that the optimized LPSIM substrate surpasses traditional substrates, exhibiting higher reconstruction accuracy, robustness against noise, and increased tolerance for fewer measurements. This framework not only proves the efficacy of inverse design for tailoring LPSIM substrates but also opens avenues for exploring new plasmonic nanostructures in imaging applications.

Recent advancements in optical metamaterials have opened new possibilities in the exciting field of super-resolution microscopies. The far-field metamaterial-assisted illumination nanoscopies (MAINs) have, very recently, enhanced the lateral resolution to one-fifteenth of the optical wavelength. However, the axial localization accuracy of fluorophores in the MAINs remains rarely explored. Here, a MAIN with a nanometer-scale axial localization accuracy is demonstrated by monitoring the distance-dependent photobleaching dynamics of the fluorophores on top of an organic hyperbolic metamaterial (OHM) substrate under a wide-field single-objective microscope. With such a regular experimental configuration, 3D imaging of various biological samples with the resolution of ≈40 nm in the lateral dimensions and ≈5 nm in the axial dimension is realized. The demonstrated imaging modality enables the resolution of the 3D morphology of nanoscopic cellular structures with a significantly simplified experimental setup.

UNASSIGNED: Lattice light-sheet structured illumination microscopy (latticeSIM) has proven highly effective in producing three-dimensional images with super resolution rapidly and with minimal photobleaching. However, due to the use of two separate objectives, sample-induced aberrations can result in an offset between the planes of excitation and detection, causing artifacts in the reconstructed images.
UNASSIGNED: We introduce a posterior approach to detect and correct the axial offset between the excitation and detection focal planes in latticeSIM and provide a method to minimize artifacts in the reconstructed images.
UNASSIGNED: We utilized the residual phase information within the overlap regions of the laterally shifted structured illumination microscopy information components in frequency space to retrieve the axial offset between the excitation and the detection focal planes in latticeSIM.
UNASSIGNED: We validated our technique through simulations and experiments, encompassing a range of samples from fluorescent beads to subcellular structures of adherent cells. We also show that using transfer functions with the same axial offset as the one present during data acquisition results in reconstructed images with minimal artifacts and salvages otherwise unusable data.
UNASSIGNED: We envision that our method will be a valuable addition to restore image quality in latticeSIM datasets even for those acquired under non-ideal experimental conditions.

Array tomography (AT) allows one to localize sub-cellular components within the structural context of cells in 3D through the imaging of serial sections. Using this technique, the z-resolution can be improved physically by cutting ultra-thin sections. Nevertheless, conventional immunofluorescence staining of those sections is time consuming and requires relatively large amounts of costly antibody solutions. Moreover, epitopes are only readily accessible at the section\'s surface, leaving the volume of the serial sections unlabeled. Localization of receptors at neuronal synapses in 3D in their native cellular ultrastructural context is important for understanding signaling processes. Here, we present in vivo labeling of receptors via fluorophore-coupled tags in combination with super-resolution AT. We present two workflows where we label receptors at the plasma membrane: first, in vivo labeling via microinjection with a setup consisting of readily available components and self-manufactured microscope table equipment and second, live receptor labeling by using a cell-permeable tag. To take advantage of a near-to-native preservation of tissues for subsequent scanning electron microscopy (SEM), we also apply high-pressure freezing and freeze substitution. The advantages and disadvantages of our workflows are discussed.

The structure of the cell nucleus of higher organisms has become a major topic of advanced light microscopy. So far, a variety of methods have been applied, including confocal laser scanning fluorescence microscopy, 4Pi, STED and localisation microscopy approaches, as well as different types of patterned illumination microscopy, modulated either laterally (in the object plane) or axially (along the optical axis). Based on our experience, we discuss here some application perspectives of Modulated Illumination Microscopy (MIM) and its combination with single-molecule localisation microscopy (SMLM). For example, spatially modulated illumination microscopy/SMI (illumination modulation along the optical axis) has been used to determine the axial extension (size) of small, optically isolated fluorescent objects between ≤ 200 nm and ≥ 40 nm diameter with a precision down to the few nm range; it also allows the axial positioning of such structures down to the 1 nm scale; combined with laterally structured illumination/SIM, a 3D localisation precision of ≤1 nm is expected using fluorescence yields typical for SMLM applications. Together with the nanosizing capability of SMI, this can be used to analyse macromolecular nuclear complexes with a resolution approaching that of cryoelectron microscopy.

Non-junctional connexin43 (Cx43) plasma membrane hemichannels have been implicated in several inflammatory diseases, particularly playing a role in ATP release that triggers activation of the inflammasome. Therapies targeting the blocking of the hemichannels to prevent the pathological release or uptake of ions and signalling molecules through its pores are of therapeutic interest. To date, there is no close-to-native, high-definition documentation of the impact of Cx43 hemichannel-mediated inflammation on cellular ultrastructure, neither is there a robust account of the ultrastructural changes that occur following treatment with selective Cx43 hemichannel blockers such as Xentry-Gap19 (XG19). A combination of same-sample correlative high-resolution three-dimensional fluorescence microscopy and soft X-ray tomography at cryogenic temperatures, enabled in the identification of novel 3D molecular interactions within the cellular milieu when comparing behaviour in healthy states and during the early onset or late stages under inflammatory conditions. Notably, our findings suggest that XG19 blockage of connexin hemichannels under pro-inflammatory conditions may be crucial in preventing the direct degradation of connexosomes by lysosomes, without affecting connexin protein translation and trafficking. We also delineated fine and gross cellular phenotypes, characteristic of inflammatory insult or road-to-recovery from inflammation, where XG19 could indirectly prevent and reverse inflammatory cytokine-induced mitochondrial swelling and cellular hypertrophy through its action on Cx43 hemichannels. Our findings suggest that XG19 might have prophylactic and therapeutic effects on the inflammatory response, in line with functional studies.

The introduction of exogenous lipids in the production of infant formula induces significant alterations in milk lipid composition, content, and membrane structure, thus affecting the lipid digestion, absorption, and utilization. This study meticulously tracks these changes throughout the manufacturing process. Pasteurization has a significant effect on phosphatidylcholine and sphingomyelin in the outer membrane, decreasing their relative contents to total polar lipids from 12.52% and 17.34% to 7.72% and 12.59%, respectively. Subsequent processes, including bactericidal-concentration and spray-drying, demonstrate the thermal stability of sphingomyelin and ceramides, while glycerolipids with arachidonic acid/docosahexaenoic acid and glycerophospholipids, particularly phosphatidylethanolamine, diminish significantly. Polar lipids addition and freeze-drying technology significantly enhance the polar lipid content and improve microscopic morphology of infant formula. These findings reveal the diverse effects of technological processes on glycerolipid and polar lipid compositions, concentration, and ultrastructure in infant formulas, thus offering crucial insights for optimizing lipid content and structure within infant formula.

Convolutional neural network (CNN)-based methods have shown excellent performance in denoising and reconstruction of super-resolved structured illumination microscopy (SR-SIM) data. Therefore, CNN-based architectures have been the focus of existing studies. However, Swin Transformer, an alternative and recently proposed deep learning-based image restoration architecture, has not been fully investigated for denoising SR-SIM images. Furthermore, it has not been fully explored how well transfer learning strategies work for denoising SR-SIM images with different noise characteristics and recorded cell structures for these different types of deep learning-based methods. Currently, the scarcity of publicly available SR-SIM datasets limits the exploration of the performance and generalization capabilities of deep learning methods.
In this work, we present SwinT-fairSIM, a novel method based on the Swin Transformer for restoring SR-SIM images with a low signal-to-noise ratio. The experimental results show that SwinT-fairSIM outperforms previous CNN-based denoising methods. Furthermore, as a second contribution, two types of transfer learning-namely, direct transfer and fine-tuning-were benchmarked in combination with SwinT-fairSIM and CNN-based methods for denoising SR-SIM data. Direct transfer did not prove to be a viable strategy, but fine-tuning produced results comparable to conventional training from scratch while saving computational time and potentially reducing the amount of training data required. As a third contribution, we publish four datasets of raw SIM images and already reconstructed SR-SIM images. These datasets cover two different types of cell structures, tubulin filaments and vesicle structures. Different noise levels are available for the tubulin filaments.
The SwinT-fairSIM method is well suited for denoising SR-SIM images. By fine-tuning, already trained models can be easily adapted to different noise characteristics and cell structures. Furthermore, the provided datasets are structured in a way that the research community can readily use them for research on denoising, super-resolution, and transfer learning strategies.

Viruses are obligate intracellular pathogens that depend on their host cell machinery and metabolism for their replicative life cycle. Virus entry, replication, and assembly are dynamic processes that lead to the reorganisation of host cell components. Therefore, a complete understanding of the viral processes requires their study in the cellular context where advanced imaging has been proven valuable in providing the necessary information. Among the available imaging techniques, soft X-ray tomography (SXT) at cryogenic temperatures can provide three-dimensional mapping to 25 nm resolution and is ideally suited to visualise the internal organisation of virus-infected cells. In this chapter, the principles and practices of synchrotron-based cryo-soft X-ray tomography (cryo-SXT) in virus research are presented. The potential of the cryo-SXT in correlative microscopy platforms is also demonstrated through working examples of reovirus and hepatitis research at Beamline B24 (Diamond Light Source Synchrotron, UK) and BL09-Mistral beamline (ALBA Synchrotron, Spain), respectively.

Given the growing concerns about nanotoxicity, numerous studies have focused on providing mechanistic insights into nanotoxicity by imaging the intracellular fate of nanoparticles. A suitable imaging strategy is necessary to uncover the intracellular behavior of nanoparticles. Although each conventional technique has its own limitations, scanning transmission electron microscopy (STEM) and three-dimensional structured illumination microscopy (3D-SIM) combine the advantages of chemical element mapping, ultrastructural analysis, and cell dynamic tracking. Gold nanoclusters (AuNCs), synthesized using 6-aza-2 thiothymine (ATT) and L-arginine (Arg) as reducing and protecting ligands, referred to as Arg@ATT-AuNCs, have been widely used in biological sensing and imaging, medicine, and catalyst yield. Based on their intrinsic fluorescence and high electron density, Arg@ATT-AuNCs were selected as a model. STEM imaging showed that both the single-particle and aggregated states of Arg@ATT-AuNCs were compartmentally distributed within a single cell. Real-time 3D-SIM imaging showed that the fluorescent Arg@ATT-AuNCs gradually aggregated after being located in the lysosomes of living cells, causing lysosomal damage. The aggregate formation of Arg@ATT-AuNCs was triggered by the low-pH medium, particularly in the lysosomal acidic environment. The proposed dual imaging strategy was verified using other types of AuNCs, which is valuable for studying nano-cell interactions and any associated cytotoxicity, and has the potential to be a useful approach for exploring the interaction of cells with various nanoparticles.