Live imaging of primary neural cells is crucial for monitoring neuronal activity, especially multiscale and multifunctional imaging that offers excellent biocompatibility. Multiscale imaging can provide insights into cellular structure and function from the nanoscale to the millimeter scale. Multifunctional imaging can monitor different activities in the brain. However, this remains a challenge because of the lack of dyes with a high signal-to-background ratio, water solubility, and multiscale and multifunctional imaging capabilities. In this study, we present a neural dye with near-infrared (NIR) emissions (>700 nm) that enables ultrafast staining (in less than 1 min) for the imaging of primary neurons. This dye not only enables multiscale neural live-cell imaging from vesicles in neurites, neural membranes, and single neurons to the whole brain but also facilitates multifunctional imaging, such as the monitoring and quantifying of synaptic vesicles and the changes in membrane potential. We also explore the potential of this NIR neural dye for staining brain slices and live brains. The NIR neural dye exhibits superior binding with neural membranes compared to commercial dyes, thereby achieving multiscale and multifunctional brain neuroimaging. In conclusion, our findings introduce a significant breakthrough in neuroimaging dyes by developing a category of small molecular dyes.

Gamete formation and subsequent offspring development often involve extended phases of suspended cellular development or even dormancy. How cells adapt to recover and resume growth remains poorly understood. Here, we visualized budding yeast cells undergoing meiosis by cryo-electron tomography (cryoET) and discovered elaborate filamentous assemblies decorating the nucleus, cytoplasm, and mitochondria. To determine filament composition, we developed a \"filament identification\" (FilamentID) workflow that combines multiscale cryoET/cryo-electron microscopy (cryoEM) analyses of partially lysed cells or organelles. FilamentID identified the mitochondrial filaments as being composed of the conserved aldehyde dehydrogenase Ald4ALDH2 and the nucleoplasmic/cytoplasmic filaments as consisting of acetyl-coenzyme A (CoA) synthetase Acs1ACSS2. Structural characterization further revealed the mechanism underlying polymerization and enabled us to genetically perturb filament formation. Acs1 polymerization facilitates the recovery of chronologically aged spores and, more generally, the cell cycle re-entry of starved cells. FilamentID is broadly applicable to characterize filaments of unknown identity in diverse cellular contexts.

Correlative light and electron microscopy (CLEM) methods are powerful methods that combine molecular organization (from light microscopy) with ultrastructure (from electron microscopy). However, CLEM methods pose high cost/difficulty barriers to entry and have very low experimental throughput. Therefore, we have developed an indirect correlative light and electron microscopy (iCLEM) pipeline to sidestep the rate-limiting steps of CLEM (i.e., preparing and imaging the same samples on multiple microscopes) and correlate multiscale structural data gleaned from separate samples imaged using different modalities by exploiting biological structures identifiable by both light and electron microscopy as intrinsic fiducials. We demonstrate here an application of iCLEM, where we utilized gap junctions and mechanical junctions between muscle cells in the heart as intrinsic fiducials to correlate ultrastructural measurements from transmission electron microscopy (TEM), and focused ion beam scanning electron microscopy (FIB-SEM) with molecular organization from confocal microscopy and single molecule localization microscopy (SMLM). We further demonstrate how iCLEM can be integrated with computational modeling to discover structure-function relationships. Thus, we present iCLEM as a novel approach that complements existing CLEM methods and provides a generalizable framework that can be applied to any set of imaging modalities, provided suitable intrinsic fiducials can be identified.

Slow multiphase flow in porous media is intriguing because its underlying dynamics is almost deterministic, yet depends on a hierarchy of spatiotemporal processes. There has been great progress in the experimental study of such multiphase flows, but three-dimensional (3D) microscopy methods probing the pore-scale fluid dynamics with millisecond resolution have been lacking. Yet, it is precisely at these length and time scales that the crucial pore-filling events known as Haines jumps take place. Here, we report four-dimensional (4D) (3D + time) observations of multiphase flow in a consolidated porous medium as captured in situ by stroboscopic X-ray micro-tomography. With a total duration of 6.5 s and 2 kHz frame rate, our experiments provide unprecedented access to the multiscale liquid dynamics. Our tomography strategy relies on the fact that Haines jumps, although irregularly spaced in time, are almost deterministic, and therefore repeatable during imbibition-drainage cycling. We studied the time-dependent flow pattern in a porous medium consisting of sintered glass shards. Exploiting the repeatability, we could combine the radiographic projections recorded under different angles during successive cycles into a 3D movie, allowing us to reconstruct pore-scale events, such as Haines jumps, with a spatiotemporal resolution that is two orders of magnitude higher than was hitherto possible. This high resolution allows us to explore the detailed interfacial dynamics during drainage, including fluid-front displacements and velocities. Our experimental approach opens the way to the study of fast, yet deterministic mesoscopic processes also other than flow in porous media.

Live-cell and high-resolution fluorescence microscopy are powerful tools to study the organization and dynamics of DNA double-strand break repair foci and specific repair proteins in single cells. This requires specific induction of DNA double-strand breaks and fluorescent markers to follow the DNA lesions in living cells. In this review, where we focused on mammalian cell studies, we discuss different methods to induce DNA double-strand breaks, how to visualize and quantify repair foci in living cells., We describe different (live-cell) imaging modalities that can reveal details of the DNA double-strand break repair process across multiple time and spatial scales. In addition, recent developments are discussed in super-resolution imaging and single-molecule tracking, and how these technologies can be applied to elucidate details on structural compositions or dynamics of DNA double-strand break repair.

Cereal endosperm is solely devoted to the storage of proteins and starch that will be used by the embryo upon germination. The high degree of specialization of this tissue is reflected in its endomembrane system, in which ER derived protein bodies and protein storage vacuoles (PSVs) are of particular interest. In maize seeds, the main storage proteins are zeins, that form transport incompetent aggregates within the ER lumen and finally build protein bodies that bud from the ER. In contrast to the zeins, the maize globulins are not very abundant and the vacuolar storage compartment of maize endosperm is not fully described. Whereas in other cereals, including wheat and barley, the PSV serves as the main protein storage compartment, only small, globulin-containing PSVs have been identified in maize so far. We present here a multi-scale set of data, ranging from live-cell imaging to more sophisticated 3D electron microscopy techniques (SBF-SEM), that has allowed us to investigate in detail the vacuoles in maize endosperm cells, including a novel, autophagic vacuole that is present in early developmental stages.

With recent advances in cancer therapeutics, there is a great need for improved imaging methods for characterizing cancer onset and progression in a quantitative and actionable way. Collagen, the most abundant extracellular matrix protein in the tumor microenvironment (and the body in general), plays a multifaceted role, both hindering and promoting cancer invasion and progression. Collagen deposition can defend the tumor with immunosuppressive effects, while aligned collagen fiber structures can enable tumor cell migration, aiding invasion and metastasis. Given the complex role of collagen fiber organization and topology, imaging has been a tool of choice to characterize these changes on multiple spatial scales, from the organ and tumor scale to cellular and subcellular level. Macroscale density already aids in the detection and diagnosis of solid cancers, but progress is being made to integrate finer microscale features into the process. Here we review imaging modalities ranging from optical methods of second harmonic generation (SHG), polarized light microscopy (PLM), and optical coherence tomography (OCT) to the medical imaging approaches of ultrasound and magnetic resonance imaging (MRI). These methods have enabled scientists and clinicians to better understand the impact collagen structure has on the tumor environment, at both the bulk scale (density) and microscale (fibrillar structure) levels. We focus on imaging methods with the potential to both examine the collagen structure in as natural a state as possible and still be clinically amenable, with an emphasis on label-free strategies, exploiting intrinsic optical properties of collagen fibers.

Acoustic-resolution photoacoustic microscopy (AR-PAM) image resolution is determined by the point spread function (PSF) of the imaging system. Previous algorithms, including Richardson-Lucy (R-L) deconvolution and model-based (MB) deconvolution, improve spatial resolution by taking advantage of the PSF as prior knowledge. However, these methods encounter the problems of inaccurate deconvolution, meaning the deconvolved feature size and the original one are not consistent (e.g., the former can be smaller than the latter). We present a novel deep convolution neural network (CNN)-based algorithm featuring high-fidelity recovery of multiscale feature size to improve lateral resolution of AR-PAM. The CNN is trained with simulated image pairs of line patterns, which is to mimic blood vessels. To investigate the suitable CNN model structure and elaborate on the effectiveness of CNN methods compared with non-learning methods, we select five different CNN models, while R-L and directional MB methods are also applied for comparison. Besides simulated data, experimental data including tungsten wires, leaf veins, and in vivo blood vessels are also evaluated. A custom-defined metric of relative size error (RSE) is used to quantify the multiscale feature recovery ability of different methods. Compared to other methods, enhanced deep super resolution (EDSR) network and residual in residual dense block network (RRDBNet) model show better recovery in terms of RSE for tungsten wires with diameters ranging from 30 μ m to 120 μ m . Moreover, AR-PAM images of leaf veins are tested to demonstrate the effectiveness of the optimized CNN methods (by EDSR and RRDBNet) for complex patterns. Finally, in vivo images of mouse ear blood vessels and rat ear blood vessels are acquired and then deconvolved, and the results show that the proposed CNN method (notably RRDBNet) enables accurate deconvolution of multiscale feature size and thus good fidelity.

The SARS-CoV-2 pandemic has had a global impact and has put scientific endeavour in the spotlight, perhaps more than any previous viral outbreak. Fortuitously, the pandemic came at a time when decades of research in multiple scientific fields could be rapidly brought to bear, and a new generation of vaccine platforms was on the cusp of clinical maturity. SARS-CoV-2 also emerged at the inflection point of a technological revolution in macromolecular imaging by cryo-electron microscopy, fuelled by a confluence of major technological advances in sample preparation, optics, detectors and image processing software, that complemented pre-existing techniques. Together, these advances enabled us to visualize SARS-CoV-2 and its components more rapidly, in greater detail, and in a wider variety of biologically relevant contexts than would have been possible even a few years earlier. The resulting ultrastructural information on SARS-CoV-2 and how it interacts with the host cell has played a critical role in the much-needed accelerated development of COVID-19 vaccines and therapeutics. Here, we review key imaging modalities used to visualize SARS-CoV-2 and present select example data, which have provided us with an exceptionally detailed picture of this virus.

We have studied the three-dimensional (3D) cytoarchitecture of the human hippocampus in neuropathologically healthy and Alzheimer\'s disease (AD) individuals, based on phase-contrast X-ray computed tomography of postmortem human tissue punch biopsies. In view of recent findings suggesting a nuclear origin of AD, we target in particular the nuclear structure of the dentate gyrus (DG) granule cells. Tissue samples of 20 individuals were scanned and evaluated using a highly automated approach of measurement and analysis, combining multiscale recordings, optimized phase retrieval, segmentation by machine learning, representation of structural properties in a feature space, and classification based on the theory of optimal transport. Accordingly, we find that the prototypical transformation between a structure representing healthy granule cells and the pathological state involves a decrease in the volume of granule cell nuclei, as well as an increase in the electron density and its spatial heterogeneity. The latter can be explained by a higher ratio of heterochromatin to euchromatin. Similarly, many other structural properties can be derived from the data, reflecting both the natural polydispersity of the hippocampal cytoarchitecture between different individuals in the physiological context and the structural effects associated with AD pathology.