Serial block-face scanning electron microscopy to reconstruct three-dimensional tissue nanostructure

Deciphering prefrontal circuits underlying stress and depression: exploring the potential of volume electron microscopy

Adapting to environmental changes and formulating behavioral strategies are central to the nervous system, with the prefrontal cortex being crucial. Chronic stress impacts this region, leading to disorders including major depression. This review discusses the roles for prefrontal cortex and the effects of stress, highlighting similarities and differences between human/primates and rodent brains. Notably, the rodent medial prefrontal cortex is analogous to the human subgenual anterior cingulate cortex in terms of emotional regulation, sharing similarities in cytoarchitecture and circuitry, while also performing cognitive functions similar to the human dorsolateral prefrontal cortex. It has been shown that chronic stress induces atrophic changes in the rodent mPFC, which mirrors the atrophy observed in the subgenual anterior cingulate cortex and dorsolateral prefrontal cortex of depression patients. However, the precise alterations in neural circuitry due to chronic stress are yet to be fully unraveled. The use of advanced imaging techniques, particularly volume electron microscopy, is emphasized as critical for the detailed examination of synaptic changes, providing a deeper understanding of stress and depression at the molecular, cellular and circuit levels. This approach offers invaluable insights into the alterations in neuronal circuits within the medial prefrontal cortex caused by chronic stress, significantly enriching our understanding of stress and depression pathologies.

Application of machine learning in the study of development, behavior, nerve, and genotoxicity of zebrafish

Machine learning (ML) as a novel model-based approach has been used in studying aquatic toxicology in the environmental field. Zebrafish, as an ideal model organism in aquatic toxicology research, has been widely used to study the toxic effects of various pollutants. However, toxicity testing on organisms may cause significant harm, consume considerable time and resources, and raise ethical concerns. Therefore, ML is used in related research to reduce animal experiments and assist researchers in conducting toxicological research. Although ML techniques have matured in various fields, research on ML-based aquatic toxicology is still in its infancy due to the lack of comprehensive large-scale toxicity databases for environmental pollutants and model organisms. Therefore, to better understand the recent research progress of ML in studying the development, behavior, nerve, and genotoxicity of zebrafish, this review mainly focuses on using ML modeling to assess and predict the toxic effects of zebrafish exposure to different toxic chemicals. Meanwhile, the opportunities and challenges faced by ML in the field of toxicology were analyzed. Finally, suggestions and perspectives were proposed for the toxicity studies of ML on zebrafish in future applications.

Neuronal wiring diagram of an adult brain

Connections between neurons can be mapped by acquiring and analysing electron microscopic brain images. In recent years, this approach has been applied to chunks of brains to reconstruct local connectivity maps that are highly informative1-6, but nevertheless inadequate for understanding brain function more globally. Here we present a neuronal wiring diagram of a whole brain containing 5 × 107 chemical synapses7 between 139,255 neurons reconstructed from an adult female Drosophila melanogaster8,9. The resource also incorporates annotations of cell classes and types, nerves, hemilineages and predictions of neurotransmitter identities10-12. Data products are available for download, programmatic access and interactive browsing and have been made interoperable with other fly data resources. We derive a projectome-a map of projections between regions-from the connectome and report on tracing of synaptic pathways and the analysis of information flow from inputs (sensory and ascending neurons) to outputs (motor, endocrine and descending neurons) across both hemispheres and between the central brain and the optic lobes. Tracing from a subset of photoreceptors to descending motor pathways illustrates how structure can uncover putative circuit mechanisms underlying sensorimotor behaviours. The technologies and open ecosystem reported here set the stage for future large-scale connectome projects in other species.

Three-dimensional analysis of mitochondria in a patient-derived xenograft model of triple negative breast cancer reveals mitochondrial network remodeling following chemotherapy treatments

Mitochondria are hubs of metabolism and signaling and play an important role in tumorigenesis, therapeutic resistance, and metastasis in many cancer types. Various laboratory models of cancer demonstrate the extraordinary dynamics of mitochondrial structure, but little is known about the role of mitochondrial structure in resistance to anticancer therapy. We previously demonstrated the importance of mitochondrial structure and oxidative phosphorylation in the survival of chemotherapy-refractory triple negative breast cancer (TNBC) cells. As TNBC is a highly aggressive breast cancer subtype with few targeted therapy options, conventional chemotherapies remain the backbone of early TNBC treatment. Unfortunately, approximately 45% of TNBC patients retain substantial residual tumor burden following chemotherapy, associated with abysmal prognoses. Using an orthotopic patient-derived xenograft mouse model of human TNBC, we compared mitochondrial structures between treatment-naïve tumors and residual tumors after conventional chemotherapeutics were administered singly or in combination. We reconstructed 1,750 mitochondria in three dimensions from serial block-face scanning electron micrographs, providing unprecedented insights into the complexity and intra-tumoral heterogeneity of mitochondria in TNBC. Following exposure to carboplatin or docetaxel given individually, residual tumor mitochondria exhibited significant increases in mitochondrial complexity index, area, volume, perimeter, width, and length relative to treatment-naïve tumor mitochondria. In contrast, residual tumors exposed to those chemotherapies given in combination exhibited diminished mitochondrial structure changes. Further, we document extensive intra-tumoral heterogeneity of mitochondrial structure, especially prior to chemotherapeutic exposure. These results highlight the potential for structure-based monitoring of chemotherapeutic responses and reveal potential molecular mechanisms that underlie chemotherapeutic resistance in TNBC.

The desmosome comes into focus

The desmosome is a cell-cell adhesive junction that provides integrity and mechanical resistance to tissues through its attachment to the intermediate filament cytoskeleton. Defects in desmosomes cause diseases impacting the heart, epidermis, and other epithelia. In this review, we provide a historical perspective on the discovery of the desmosome and how the evolution of cellular imaging technologies revealed insights into desmosome structure and function. We also discuss recent findings using contemporary imaging approaches that have informed the molecular order, three-dimensional architecture, and associations of desmosomes with organelles such as the endoplasmic reticulum. Finally, we provide an updated model of desmosome molecular organization and speculate upon novel functions of this cell junction as a signaling center for sensing mechanical and other forms of cell stress.

Quantitative 3D electron microscopy characterization of mitochondrial structure, mitophagy, and organelle interactions in murine atrial fibrillation

Atrial fibrillation (AF) is the most common clinical arrhythmia, however there is limited understanding of its pathophysiology including the cellular and ultrastructural changes rendered by the irregular rhythm, which limits pharmacological therapy development. Prior work has demonstrated the importance of reactive oxygen species (ROS) and mitochondrial dysfunction in the development of AF. Mitochondrial structure, interactions with other organelles such as sarcoplasmic reticulum (SR) and T-tubules (TT), and degradation of dysfunctional mitochondria via mitophagy are important processes to understand ultrastructural changes due to AF. However, most analysis of mitochondrial structure and interactome in AF has been limited to two-dimensional (2D) modalities such as transmission electron microscopy (EM), which does not fully visualize the morphological evolution of the mitochondria during mitophagy. Herein, we utilize focused ion beam-scanning electron microscopy (FIB-SEM) and perform reconstruction of three-dimensional (3D) EM from murine left atrial samples and measure the interactions of mitochondria with SR and TT. We developed a novel 3D quantitative analysis of FIB-SEM in a murine model of AF to quantify mitophagy stage, mitophagosome size in cardiomyocytes, and mitochondrial structural remodeling when compared with control mice. We show that in our murine model of spontaneous and continuous AF due to persistent late sodium current, left atrial cardiomyocytes have heterogenous mitochondria, with a significant number which are enlarged with increased elongation and structural complexity. Mitophagosomes in AF cardiomyocytes are located at Z-lines where they neighbor large, elongated mitochondria. Mitochondria in AF cardiomyocytes show increased organelle interaction, with 5X greater contact area with SR and are 4X as likely to interact with TT when compared to control. We show that mitophagy in AF cardiomyocytes involves 2.5X larger mitophagosomes that carry increased organelle contents. In conclusion, when oxidative stress overcomes compensatory mechanisms, mitophagy in AF faces a challenge of degrading bulky complex mitochondria, which may result in increased SR and TT contacts, perhaps allowing for mitochondrial Ca2+ maintenance and antioxidant production.

Neurobiological mechanisms of electroconvulsive therapy for depression: Insights into hippocampal volumetric increases from clinical and preclinical studies

Depression is a highly prevalent and disabling psychiatric disorder. The hippocampus, which plays a central role in mood regulation and memory, has received considerable attention in depression research. Electroconvulsive therapy (ECT) is the most effective treatment for severe pharmacotherapy-resistant depression. Although the working mechanism of ECT remains unclear, recent magnetic resonance imaging (MRI) studies have consistently reported increased hippocampal volumes following ECT. The clinical implications of these volumetric increases and the specific cellular and molecular significance are not yet fully understood. This narrative review brings together evidence from animal models and human studies to provide a detailed examination of hippocampal volumetric increases following ECT. In particular, our preclinical MRI research using a mouse model is consistent with human findings, demonstrating a marked increase in hippocampal volume following ECT. Notable changes were observed in the ventral hippocampal CA1 region, including dendritic growth and increased synaptic density at excitatory synapses. Interestingly, inhibition of neurogenesis did not affect the ECT-related hippocampal volumetric increases detected on MRI. However, it remains unclear whether these histological and volumetric changes would be correlated with the clinical effect of ECT. Hence, future research on the relationships between cellular changes, ECT-related brain volumetric changes, and antidepressant effect could benefit from a bidirectional translational approach that integrates human and animal models. Such translational research may provide important insights into the mechanisms and potential biomarkers associated with ECT-induced hippocampal volumetric changes, thereby advancing our understanding of ECT for the treatment of depression.

Study of curtaining effect reduction methods in Inconel 718 using a plasma focused ion beam

The curtaining effect is a common challenge in focused ion beam (FIB) surface preparation. This study investigates methods to reduce this effect during plasma FIB milling of Inconel 718 (nickel-based superalloy). Platinum deposition, silicon mask and XeF2 gas injection were explored as potential solutions. These methods were evaluated for two ion beam current conditions; a high ion beam intensity condition (30 kV-1 µA) and a medium one (30 kV-100 nA) and their impact on curtaining reduction and resulting cross-section quality was assessed quantitatively thanks to topographic measurements done by atomic force microscopy (AFM). XeF2 assistance notably improved cross-section quality at medium current level. Pt deposition and Si mask individually mitigated the curtaining effect, with greater efficacy at 100 nA. Both methods also contributed to reducing cross-section curvature, with the Si mask outperforming Pt deposition. However, combining Pt deposition and Si mask with XeF2 injection led to deterioration of these protective layers and the reappearance of the curtaining effect after a quite short exposure time.

Fast imaging of millimeter-scale areas with beam deflection transmission electron microscopy

Serial section transmission electron microscopy (TEM) has proven to be one of the leading methods for millimeter-scale 3D imaging of brain tissues at nanoscale resolution. It is important to further improve imaging efficiency to acquire larger and more brain volumes. We report here a threefold increase in the speed of TEM by using a beam deflecting mechanism to enable highly efficient acquisition of multiple image tiles (nine) for each motion of the mechanical stage. For millimeter-scale areas, the duty cycle of imaging doubles to more than 30%, yielding a net average imaging rate of 0.3 gigapixels per second. If fully utilized, an array of four beam deflection TEMs should be capable of imaging a dataset of cubic millimeter scale in five weeks.

Sample preparation and data collection for serial block face scanning electron microscopy of mammalian cell monolayers

Volume electron microscopy encompasses a set of electron microscopy techniques that can be used to examine the ultrastructure of biological tissues and cells in three dimensions. Two block face techniques, focused ion beam scanning electron microscopy (FIB-SEM) and serial block face scanning electron microscopy (SBF-SEM) have often been used to study biological tissue samples. More recently, these techniques have been adapted to in vitro tissue culture samples. Here we describe step-by-step protocols for two sample embedding methods for in vitro tissue culture cells intended to be studied using SBF-SEM. The first focuses on cell pellet embedding and the second on en face embedding. En face embedding can be combined with light microscopy, and this CLEM workflow can be used to identify specific biological events by light microscopy, which can then be imaged using SBF-SEM. We systematically outline the steps necessary to fix, stain, embed and image adherent tissue culture cell monolayers by SBF-SEM. In addition to sample preparation, we discuss optimization of parameters for data collection. We highlight the challenges and key steps of sample preparation, and the consideration of imaging variables.

Volume electron microscopy analysis of synapses in primary regions of the human cerebral cortex

Functional and structural studies investigating macroscopic connectivity in the human cerebral cortex suggest that high-order associative regions exhibit greater connectivity compared to primary ones. However, the synaptic organization of these brain regions remains unexplored. In the present work, we conducted volume electron microscopy to investigate the synaptic organization of the human brain obtained at autopsy. Specifically, we examined layer III of Brodmann areas 17, 3b, and 4, as representative areas of primary visual, somatosensorial, and motor cortex. Additionally, we conducted comparative analyses with our previous datasets of layer III from temporopolar and anterior cingulate associative cortical regions (Brodmann areas 24, 38, and 21). 9,690 synaptic junctions were 3D reconstructed, showing that certain synaptic characteristics are specific to particular regions. The number of synapses per volume, the proportion of the postsynaptic targets, and the synaptic size may distinguish one region from another, regardless of whether they are associative or primary cortex. By contrast, other synaptic characteristics were common to all analyzed regions, such as the proportion of excitatory and inhibitory synapses, their shapes, their spatial distribution, and a higher proportion of synapses located on dendritic spines. The present results provide further insights into the synaptic organization of the human cerebral cortex.

Work smart, not hard: How array tomography can help increase the ultrastructure data output

Transmission electron microscopy has been essential for understanding cell biology for over six decades. Volume electron microscopy tools, such as serial block face and focused ion beam scanning electron microscopy acquisition, brought a new era to ultrastructure analysis. 'Array Tomography' (AT) refers to sequential image acquisition of resin-embedded sample sections on a large support (coverslip, glass slide, silicon wafers) for immunolabelling with multiple fluorescent labels, occasionally combined with ultrastructure observation. Subsequently, the term was applied to generating and imaging a series of sections to acquire a 3D representation of a structure using scanning electron microscopy (SEM). Although this is a valuable application, the potential of AT is to facilitate many tasks that are difficult or even impossible to obtain by Transmission Electron Microscopy (TEM). Due to the straightforward nature and versatility of AT sample preparation and image acquisition, the technique can be applied practically to any biological sample for selected sections or volume electron microscopy analysis. Furthermore, in addition to the benefits described here, AT is compatible with morphological analysis, multiplex immunolabelling, immune-gold labelling, and correlative light and electron microscopy workflow applicable for single cells, tissue and small organisms. This versatility makes AT attractive not only for basic research but as a diagnostic tool with a simplified routine.

Tracing nerve fibers with volume electron microscopy to quantitatively analyze brain connectivity

The highly complex structure of the brain requires an approach that can unravel its connectivity. Using volume electron microscopy and a dedicated software we can trace and measure all nerve fibers present within different samples of brain tissue. With this software tool, individual dendrites and axons are traced, obtaining a simplified "skeleton" of each fiber, which is linked to its corresponding synaptic contacts. The result is an intricate meshwork of axons and dendrites interconnected by a cloud of synaptic junctions. To test this methodology, we apply it to the stratum radiatum of the hippocampus and layers 1 and 3 of the somatosensory cortex of the mouse. We find that nerve fibers are densely packed in the neuropil, reaching up to 9 kilometers per cubic mm. We obtain the number of synapses, the number and lengths of dendrites and axons, the linear densities of synapses established by dendrites and axons, and their location on dendritic spines and shafts. The quantitative data obtained through this method enable us to identify subtle traits and differences in the synaptic organization of the samples, which might have been overlooked in a qualitative analysis.

SmartEM: machine-learning guided electron microscopy

Connectomics provides essential nanometer-resolution, synapse-level maps of neural circuits to understand brain activity and behavior. However, few researchers have access to the high-throughput electron microscopes necessary to generate enough data for whole circuit or brain reconstruction. To date, machine-learning methods have been used after the collection of images by electron microscopy (EM) to accelerate and improve neuronal segmentation, synapse reconstruction and other data analysis. With the computational improvements in processing EM images, acquiring EM images has now become the rate-limiting step. Here, in order to speed up EM imaging, we integrate machine-learning into real-time image acquisition in a singlebeam scanning electron microscope. This SmartEM approach allows an electron microscope to perform intelligent, data-aware imaging of specimens. SmartEM allocates the proper imaging time for each region of interest - scanning all pixels equally rapidly, then re-scanning small subareas more slowly where a higher quality signal is required to achieve accurate segmentability, in significantly less time. We demonstrate that this pipeline achieves a 7-fold acceleration of image acquisition time for connectomics using a commercial single-beam SEM. We apply SmartEM to reconstruct a portion of mouse cortex with the same accuracy as traditional microscopy but in less time.

Deciphering the genetic code of neuronal type connectivity through bilinear modeling

Understanding how different neuronal types connect and communicate is critical to interpreting brain function and behavior. However, it has remained a formidable challenge to decipher the genetic underpinnings that dictate the specific connections formed between neuronal types. To address this, we propose a novel bilinear modeling approach that leverages the architecture similar to that of recommendation systems. Our model transforms the gene expressions of presynaptic and postsynaptic neuronal types, obtained from single-cell transcriptomics, into a covariance matrix. The objective is to construct this covariance matrix that closely mirrors a connectivity matrix, derived from connectomic data, reflecting the known anatomical connections between these neuronal types. When tested on a dataset of Caenorhabditis elegans, our model achieved a performance comparable to, if slightly better than, the previously proposed spatial connectome model (SCM) in reconstructing electrical synaptic connectivity based on gene expressions. Through a comparative analysis, our model not only captured all genetic interactions identified by the SCM but also inferred additional ones. Applied to a mouse retinal neuronal dataset, the bilinear model successfully recapitulated recognized connectivity motifs between bipolar cells and retinal ganglion cells, and provided interpretable insights into genetic interactions shaping the connectivity. Specifically, it identified unique genetic signatures associated with different connectivity motifs, including genes important to cell-cell adhesion and synapse formation, highlighting their role in orchestrating specific synaptic connections between these neurons. Our work establishes an innovative computational strategy for decoding the genetic programming of neuronal type connectivity. It not only sets a new benchmark for single-cell transcriptomic analysis of synaptic connections but also paves the way for mechanistic studies of neural circuit assembly and genetic manipulation of circuit wiring.

A robust transformer-based pipeline of 3D cell alignment, denoise and instance segmentation on electron microscopy sequence images

Germline cells are critical for transmitting genetic information to subsequent generations in biological organisms. While their differentiation from somatic cells during embryonic development is well-documented in most animals, the regulatory mechanisms initiating plant germline cells are not well understood. To thoroughly investigate the complex morphological transformations of their ultrastructure over developmental time, nanoscale 3D reconstruction of entire plant tissues is necessary, achievable exclusively through electron microscopy imaging. This paper presents a full-process framework designed for reconstructing large-volume plant tissue from serial electron microscopy images. The framework ensures end-to-end direct output of reconstruction results, including topological networks and morphological analysis. The proposed 3D cell alignment, denoise, and instance segmentation pipeline (3DCADS) leverages deep learning to provide a cell instance segmentation workflow for electron microscopy image series, ensuring accurate and robust 3D cell reconstructions with high computational efficiency. The pipeline involves five stages: the registration of electron microscopy serial images; image enhancement and denoising; semantic segmentation using a Transformer-based neural network; instance segmentation through a supervoxel-based clustering algorithm; and an automated analysis and statistical assessment of the reconstruction results, with the mapping of topological connections. The 3DCADS model's precision was validated on a plant tissue ground-truth dataset, outperforming traditional baseline models and deep learning baselines in overall accuracy. The framework was applied to the reconstruction of early meiosis stages in the anthers of Arabidopsis thaliana, resulting in a topological connectivity network and analysis of morphological parameters and characteristics of cell distribution. The experiment underscores the 3DCADS model's potential for biological tissue identification and its significance in quantitative analysis of plant cell development, crucial for examining samples across different genetic phenotypes and mutations in plant development. Additionally, the paper discusses the regulatory mechanisms of Arabidopsis thaliana's germline cells and the development of stamen cells before meiosis, offering new insights into the transition from somatic to germline cell fate in plants.

Diffusion-based deep learning method for augmenting ultrastructural imaging and volume electron microscopy

Electron microscopy (EM) revolutionized the way to visualize cellular ultrastructure. Volume EM (vEM) has further broadened its three-dimensional nanoscale imaging capacity. However, intrinsic trade-offs between imaging speed and quality of EM restrict the attainable imaging area and volume. Isotropic imaging with vEM for large biological volumes remains unachievable. Here, we developed EMDiffuse, a suite of algorithms designed to enhance EM and vEM capabilities, leveraging the cutting-edge image generation diffusion model. EMDiffuse generates realistic predictions with high resolution ultrastructural details and exhibits robust transferability by taking only one pair of images of 3 megapixels to fine-tune in denoising and super-resolution tasks. EMDiffuse also demonstrated proficiency in the isotropic vEM reconstruction task, generating isotropic volume even in the absence of isotropic training data. We demonstrated the robustness of EMDiffuse by generating isotropic volumes from seven public datasets obtained from different vEM techniques and instruments. The generated isotropic volume enables accurate three-dimensional nanoscale ultrastructure analysis. EMDiffuse also features self-assessment functionalities on predictions' reliability. We envision EMDiffuse to pave the way for investigations of the intricate subcellular nanoscale ultrastructure within large volumes of biological systems.

Dynamic microvilli sculpt bristles at nanometric scale

Organisms generate shapes across size scales. Whereas patterning and morphogenesis of macroscopic tissues has been extensively studied, the principles underlying the formation of micrometric and submicrometric structures remain largely enigmatic. Individual cells of polychaete annelids, so-called chaetoblasts, are associated with the generation of chitinous bristles of highly stereotypic geometry. Here we show that bristle formation requires a chitin-producing enzyme specifically expressed in the chaetoblasts. Chaetoblasts exhibit dynamic cell surfaces with stereotypical patterns of actin-rich microvilli. These microvilli can be matched with internal and external structures of bristles reconstructed from serial block-face electron micrographs. Individual chitin teeth are deposited by microvilli in an extension-disassembly cycle resembling a biological 3D printer. Consistently, pharmacological interference with actin dynamics leads to defects in tooth formation. Our study reveals that both material and shape of bristles are encoded by the same cell, and that microvilli play a role in micro- to submicrometric sculpting of biomaterials.

Bioethanol Production from Characterized Pre-treated Sugarcane Trash and Jatropha Agrowastes

Low production costs and a potential feedstock supply make lignocellulosic ethanol (bioethanol) an important source of advanced biofuels. The physical and chemical preparation of this kind of lignocellulosic feedstock led to a high ethanol yield. In order to increase the yield of fermentable sugars, pretreatment is an essential process step that alters the lignocellulosic structure and improves its accessibility for the expensive hydrolytic enzymes. In this context, the chemical composition of sugarcane trash (dry leaves, green leaves, and tops) and jatropha (shell and seed cake) was determined to be mainly cellulose, hemicellulose, and lignin. Hydrogen peroxide and sodium hydroxide were applied in an attempt to facilitate the solubilization of lignin and hemicelluloses in five agrowastes. The extraction of hydrogen peroxide was much better than that of sodium hydroxide. A comparative study was done using SEM, EDXA, and FTIR to evaluate the difference between the two methods. The pretreated wastes were subjected to saccharification by commercial cellulases (30 IU/g substrate). The obtained glucose was fortified with nutrients and fermented statically by Saccharomyces cerevisiae F-307 for bioethanol production. The results revealed the bioethanol yields were 325.4, 310.8, 282.9, 302.4 and 264.0 mg ethanol/g treated agrowastes from green leaves of sugarcane, jatropha deolied seed cake, tops sugarcane, dry leaves of sugarcane, and jatropha shell, respectively. This study emphasizes the value of lignocellulosic agricultural waste as a resource for the production of biofuels as well as the significance of the extraction process.

RoboEM: automated 3D flight tracing for synaptic-resolution connectomics

Mapping neuronal networks from three-dimensional electron microscopy (3D-EM) data still poses substantial reconstruction challenges, in particular for thin axons. Currently available automated image segmentation methods require manual proofreading for many types of connectomic analysis. Here we introduce RoboEM, an artificial intelligence-based self-steering 3D 'flight' system trained to navigate along neurites using only 3D-EM data as input. Applied to 3D-EM data from mouse and human cortex, RoboEM substantially improves automated state-of-the-art segmentations and can replace manual proofreading for more complex connectomic analysis problems, yielding computational annotation cost for cortical connectomes about 400-fold lower than the cost of manual error correction.

Impact of Obesity and Age on Mouse Corneal Innervation at the Epithelial-Stromal Interface

Purpose

The corneal epithelium is the most highly innervated structure in the body. Previously, we reported a novel event whereby stromal axons fuse with basal epithelial cells, limiting nerve penetration into the epithelium. Although corneal-epithelial nerves undergo changes in sensitivity and distribution throughout life and in response to an obesogenic diet, it is unknown if neuronal-epithelial cell fusion is altered. Here, we sought to determine if neuronal-epithelial cell fusion frequency correlates with obesogenic diet consumption and age.

Methods

Corneas were collected from C57BL/6 mice and evaluated for neuronal-epithelial cell fusion frequency using serial block-face scanning electron microscopy. To assess the correlation between diet-induced obesity and fusion frequency, 6-week-old mice were fed either a normal diet or an obesogenic diet for 10 weeks. To assess changes in fusion frequency between young and adult mice under normal dietary conditions, 9- and 24-week-old mice were used.

Results

Mice fed a 10-week obesogenic diet showed 87% of central-cornea stromal nerves engaged in fusion compared with only 54% in age-matched controls (16 weeks old). In 9-week-old normal-diet animals, 48% of central-cornea stromal nerves contained fusing axons and increased to 81% at 24 weeks of age. Corneal sensitivity loss correlated with increased body weight and adiposity regardless of age and diet.

Conclusions

Neuronal-epithelial cell fusion positively correlates with age and obesogenic diet consumption, and corneal nerve sensitivity loss correlates with increased body weight and adiposity, regardless of age and diet. As such, neuronal-epithelial cell fusion may play a role in corneal nerve density and sensitivity regulation.

Zooming into lipid droplet biology through the lens of electron microscopy

Electron microscopy (EM), in its various flavors, has significantly contributed to our understanding of lipid droplets (LD) as central organelles in cellular metabolism. For example, EM has illuminated that LDs, in contrast to all other cellular organelles, are uniquely enclosed by a single phospholipid monolayer, revealed the architecture of LD contact sites with different organelles, and provided near-atomic resolution maps of key enzymes that regulate neutral lipid biosynthesis and LD biogenesis. In this review, we first provide a brief history of pivotal findings in LD biology unveiled through the lens of an electron microscope. We describe the main EM techniques used in the context of LD research and discuss their current capabilities and limitations, thereby providing a foundation for utilizing suitable EM methodology to address LD-related questions with sufficient level of structural preservation, detail, and resolution. Finally, we highlight examples where EM has recently been and is expected to be instrumental in expanding the frontiers of LD biology.

Spinning Disk Confocal Microscopy for Optimized and Quantified Live Imaging of 3D Mitochondrial Network

Mitochondria are the energy factories of a cell, and depending on the metabolic requirements, the mitochondrial morphology, quantity, and membrane potential in a cell change. These changes are frequently assessed using commercially available probes. In this study, we tested the suitability of three commercially available probes-namely 5',6,6'-tetrachloro-1,1',3,3'-tetraethylbenzimidazolo-carbocyanine iodide (JC-1), MitoTracker Red CMX Rox (CMXRos), and tetramethylrhodamine methyl ester (TMRM)-for assessing the mitochondrial quantity, morphology, and membrane potential in living human mesoangioblasts in 3D with confocal laser scanning microscope (CLSM) and scanning disk confocal microscope (SDCM). Using CLSM, JC-1, and CMXRos-but not TMRM-uncovered considerable background and variation. Using SDCM, the background signal only remained apparent for the JC-1 monomer. Repetitive imaging of CMXRos and JC-1-but not TMRM-demonstrated a 1.5-2-fold variation in signal intensity between cells using CLSM. The use of SDCM drastically reduced this variation. The slope of the relative signal intensity upon repetitive imaging using CLSM was lowest for TMRM (-0.03) and highest for CMXRos (0.16). Upon repetitive imaging using SDCM, the slope varied from 0 (CMXRos) to a maximum of -0.27 (JC-1 C1). Conclusively, our data show that TMRM staining outperformed JC-1 and CMXRos dyes in a (repetitive) 3D analysis of the entire mitochondrial quantity, morphology, and membrane potential in living cells.

Synaptic architecture of a memory engram in the mouse hippocampus

Memory engrams are formed through experience-dependent remodeling of neural circuits, but their detailed architectures have remained unresolved. Using 3D electron microscopy, we performed nanoscale reconstructions of the hippocampal CA3-CA1 pathway following chemogenetic labeling of cellular ensembles with a remote history of correlated excitation during associative learning. Projection neurons involved in memory acquisition expanded their connectomes via multi-synaptic boutons without altering the numbers and spatial arrangements of individual axonal terminals and dendritic spines. This expansion was driven by presynaptic activity elicited by specific negative valence stimuli, regardless of the co-activation state of postsynaptic partners. The rewiring of initial ensembles representing an engram coincided with local, input-specific changes in the shapes and organelle composition of glutamatergic synapses, reflecting their weights and potential for further modifications. Our findings challenge the view that the connectivity among neuronal substrates of memory traces is governed by Hebbian mechanisms, and offer a structural basis for representational drifts.

Morphomics via next-generation electron microscopy

The living body is composed of innumerable fine and complex structures. Although these structures have been studied in the past, a vast amount of information pertaining to them still remains unknown. When attempting to observe these ultra-structures, the use of electron microscopy (EM) has become indispensable. However, conventional EM settings are limited to a narrow tissue area, which can bias observations. Recently, new trends in EM research have emerged, enabling coverage of far broader, nano-scale fields of view for two-dimensional wide areas and three-dimensional large volumes. Moreover, cutting-edge bioimage informatics conducted via deep learning has accelerated the quantification of complex morphological bioimages. Taken together, these technological and analytical advances have led to the comprehensive acquisition and quantification of cellular morphology, which now arises as a new omics science termed 'morphomics'.

Ensemble learning and ground-truth validation of synaptic connectivity inferred from spike trains

Probing the architecture of neuronal circuits and the principles that underlie their functional organization remains an important challenge of modern neurosciences. This holds true, in particular, for the inference of neuronal connectivity from large-scale extracellular recordings. Despite the popularity of this approach and a number of elaborate methods to reconstruct networks, the degree to which synaptic connections can be reconstructed from spike-train recordings alone remains controversial. Here, we provide a framework to probe and compare connectivity inference algorithms, using a combination of synthetic ground-truth and in vitro data sets, where the connectivity labels were obtained from simultaneous high-density microelectrode array (HD-MEA) and patch-clamp recordings. We find that reconstruction performance critically depends on the regularity of the recorded spontaneous activity, i.e., their dynamical regime, the type of connectivity, and the amount of available spike-train data. We therefore introduce an ensemble artificial neural network (eANN) to improve connectivity inference. We train the eANN on the validated outputs of six established inference algorithms and show how it improves network reconstruction accuracy and robustness. Overall, the eANN demonstrated strong performance across different dynamical regimes, worked well on smaller datasets, and improved the detection of synaptic connectivity, especially inhibitory connections. Results indicated that the eANN also improved the topological characterization of neuronal networks. The presented methodology contributes to advancing the performance of inference algorithms and facilitates our understanding of how neuronal activity relates to synaptic connectivity.

Array tomography: trails to discovery

Tissue slicing is at the core of many approaches to studying biological structures. Among the modern volume electron microscopy (vEM) methods, array tomography (AT) is based on serial ultramicrotomy, section collection onto solid support, imaging via light and/or scanning electron microscopy, and re-assembly of the serial images into a volume for analysis. While AT largely uses standard EM equipment, it provides several advantages, including long-term preservation of the sample and compatibility with multi-scale and multi-modal imaging. Furthermore, the collection of serial ultrathin sections improves axial resolution and provides access for molecular labeling, which is beneficial for light microscopy and immunolabeling, and facilitates correlation with EM. Despite these benefits, AT techniques are underrepresented in imaging facilities and labs, due to their perceived difficulty and lack of training opportunities. Here we point towards novel developments in serial sectioning and image analysis that facilitate the AT pipeline, and solutions to overcome constraints. Because no single vEM technique can serve all needs regarding field of view and resolution, we sketch a decision tree to aid researchers in navigating the plethora of options available. Lastly, we elaborate on the unexplored potential of AT approaches to add valuable insight in diverse biological fields.

The application and development of electron microscopy for three-dimensional reconstruction in life science: a review

Imaging technologies have played a pivotal role in advancing biological research by enabling visualization of biological structures and processes. While traditional electron microscopy (EM) produces two-dimensional images, emerging techniques now allow high-resolution three-dimensional (3D) characterization of specimens in situ, meeting growing needs in molecular and cellular biology. Combining transmission electron microscopy (TEM) with serial sectioning inaugurated 3D imaging, attracting biologists seeking to explore cell ultrastructure and driving advancement of 3D EM reconstruction. By comprehensively and precisely rendering internal structure and distribution, 3D TEM reconstruction provides unparalleled ultrastructural insights into cells and molecules, holding tremendous value for elucidating structure-function relationships and broadly propelling structural biology. Here, we first introduce the principle of 3D reconstruction of cells and tissues by classical approaches in TEM and then discuss modern technologies utilizing TEM and on new SEM-based as well as cryo-electron microscope (cryo-EM) techniques. 3D reconstruction techniques from serial sections, electron tomography (ET), and the recent single-particle analysis (SPA) are examined; the focused ion beam scanning electron microscopy (FIB-SEM), the serial block-face scanning electron microscopy (SBF-SEM), and automatic tape-collecting lathe ultramicrotome (ATUM-SEM) for 3D reconstruction of large volumes are discussed. Finally, we review the challenges and development prospects of these technologies in life science. It aims to provide an informative reference for biological researchers.

Three-Dimensional Ultrastructure of Arabidopsis Cotyledons Infected with Colletotrichum higginsianum

We used serial block-face scanning electron microscopy (SBF-SEM) to study the host-pathogen interface between Arabidopsis cotyledons and the hemibiotrophic fungus Colletotrichum higginsianum. By combining high-pressure freezing and freeze-substitution with SBF-SEM, followed by segmentation and reconstruction of the imaging volume using the freely accessible software IMOD, we created 3D models of the series of cytological events that occur during the Colletotrichum-Arabidopsis susceptible interaction. We found that the host cell membranes underwent massive expansion to accommodate the rapidly growing intracellular hypha. As the fungal infection proceeded from the biotrophic to the necrotrophic stage, the host cell membranes went through increasing levels of disintegration culminating in host cell death. Intriguingly, we documented autophagosomes in proximity to biotrophic hyphae using transmission electron microscopy (TEM) and a concurrent increase in autophagic flux between early to mid/late biotrophic phase of the infection process. Occasionally, we observed osmiophilic bodies in the vicinity of biotrophic hyphae using TEM only and near necrotrophic hyphae under both TEM and SBF-SEM. Overall, we established a method for obtaining serial SBF-SEM images, each with a lateral (x-y) pixel resolution of 10 nm and an axial (z) resolution of 40 nm, that can be reconstructed into interactive 3D models using the IMOD. Application of this method to the Colletotrichum-Arabidopsis pathosystem allowed us to more fully understand the spatial arrangement and morphological architecture of the fungal hyphae after they penetrate epidermal cells of Arabidopsis cotyledons and the cytological changes the host cell undergoes as the infection progresses toward necrotrophy. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

GAUSS-EM, guided accumulation of ultrathin serial sections with a static magnetic field for volume electron microscopy

Serial sectioning electron microscopy (EM) of millimeter-scale three-dimensional (3D) anatomical volumes requires the collection of thousands of ultrathin sections. Here, we report a high-throughput automated approach, GAUSS-EM (guided accumulation of ultrathin serial sections-EM), utilizing a static magnetic field to collect and densely pack thousands of sections onto individual silicon wafers. The method is capable of sectioning hundreds of microns of tissue per day at section thicknesses down to 35 nm. Relative to other automated volume EM approaches, GAUSS-EM democratizes the ability to collect large 3D EM volumes because it is simple and inexpensive to implement. We present two exemplar EM volumes of a zebrafish eye and mouse olfactory bulb collected with the method.

Neuronal ensembles: Building blocks of neural circuits

Neuronal ensembles, defined as groups of neurons displaying recurring patterns of coordinated activity, represent an intermediate functional level between individual neurons and brain areas. Novel methods to measure and optically manipulate the activity of neuronal populations have provided evidence of ensembles in the neocortex and hippocampus. Ensembles can be activated intrinsically or in response to sensory stimuli and play a causal role in perception and behavior. Here we review ensemble phenomenology, developmental origin, biophysical and synaptic mechanisms, and potential functional roles across different brain areas and species, including humans. As modular units of neural circuits, ensembles could provide a mechanistic underpinning of fundamental brain processes, including neural coding, motor planning, decision-making, learning, and adaptability.

High-resolution 3D ultrastructural analysis of developing mouse neocortex reveals long slender processes of endothelial cells that enter neural cells

The development of the neocortex involves an interplay between neural cells and the vasculature. However, little is known about this interplay at the ultrastructural level. To gain a 3D insight into the ultrastructure of the developing neocortex, we have analyzed the embryonic mouse neocortex by serial block-face scanning electron microscopy (SBF-SEM). In this study, we report a first set of findings that focus on the interaction of blood vessels, notably endothelial tip cells (ETCs), and the neural cells in this tissue. A key observation was that the processes of ETCs, located either in the ventricular zone (VZ) or subventricular zone (SVZ)/intermediate zone (IZ), can enter, traverse the cytoplasm, and even exit via deep plasma membrane invaginations of the host cells, including apical progenitors (APs), basal progenitors (BPs), and newborn neurons. More than half of the ETC processes were found to enter the neural cells. Striking examples of this ETC process "invasion" were (i) protrusions of apical progenitors or newborn basal progenitors into the ventricular lumen that contained an ETC process inside and (ii) ETC process-containing protrusions of neurons that penetrated other neurons. Our observations reveal a - so far unknown - complexity of the ETC-neural cell interaction.

Structural Plasticity of GABAergic Pallidothalamic Terminals in MPTP-Treated Parkinsonian Monkeys: A 3D Electron Microscopic Analysis

The internal globus pallidus (GPi) is a major source of tonic GABAergic inhibition to the motor thalamus. In parkinsonism, the firing rate of GPi neurons is increased, and their pattern switches from a tonic to a burst mode, two pathophysiological changes associated with increased GABAergic pallidothalamic activity. In this study, we used high-resolution 3D electron microscopy to demonstrate that GPi terminals in the parvocellular ventral anterior nucleus (VApc) and the centromedian nucleus (CM), the two main GPi-recipient motor thalamic nuclei in monkeys, undergo significant morphometric changes in parkinsonian monkeys including (1) increased terminal volume in both nuclei; (2) increased surface area of synapses in both nuclei; (3) increased number of synapses/GPi terminals in the CM, but not VApc; and (4) increased total volume, but not number, of mitochondria/terminals in both nuclei. In contrast to GPi terminals, the ultrastructure of putative GABAergic nonpallidal terminals was not affected. Our results also revealed striking morphological differences in terminal volume, number/area of synapses, and volume/number of mitochondria between GPi terminals in VApc and CM of control monkeys. In conclusion, GABAergic pallidothalamic terminals are endowed with a high level of structural plasticity that may contribute to the development and maintenance of the abnormal increase in pallidal GABAergic outflow to the thalamus in the parkinsonian state. Furthermore, the evidence for ultrastructural differences between GPi terminals in VApc and CM suggests that morphologically distinct pallidothalamic terminals from single pallidal neurons may underlie specific physiological properties of pallidal inputs to VApc and CM in normal and diseased states.

Synaptic wiring motifs in posterior parietal cortex support decision-making

The posterior parietal cortex exhibits choice-selective activity during perceptual decision-making tasks1-10. However, it is not known how this selective activity arises from the underlying synaptic connectivity. Here we combined virtual-reality behaviour, two-photon calcium imaging, high-throughput electron microscopy and circuit modelling to analyse how synaptic connectivity between neurons in the posterior parietal cortex relates to their selective activity. We found that excitatory pyramidal neurons preferentially target inhibitory interneurons with the same selectivity. In turn, inhibitory interneurons preferentially target pyramidal neurons with opposite selectivity, forming an opponent inhibition motif. This motif was present even between neurons with activity peaks in different task epochs. We developed neural-circuit models of the computations performed by these motifs, and found that opponent inhibition between neural populations with opposite selectivity amplifies selective inputs, thereby improving the encoding of trial-type information. The models also predict that opponent inhibition between neurons with activity peaks in different task epochs contributes to creating choice-specific sequential activity. These results provide evidence for how synaptic connectivity in cortical circuits supports a learned decision-making task.

Connectivity Matrix Seriation via Relaxation

Volume electron microscopy together with computer-based image analysis are yielding neural circuit diagrams of ever larger regions of the brain. These datasets are usually represented in a cell-to-cell connectivity matrix and contain important information about prevalent circuit motifs allowing to directly test various theories on the computation in that brain structure. Of particular interest are the detection of cell assemblies and the quantification of feedback, which can profoundly change circuit properties. While the ordering of cells along the rows and columns doesn't change the connectivity, it can make special connectivity patterns recognizable. For example, ordering the cells along the flow of information, feedback and feedforward connections are segregated above and below the main matrix diagonal, respectively. Different algorithms are used to renumber matrices such as to minimize a given cost function, but either their performance becomes unsatisfying at a given size of the circuit or the CPU time needed to compute them scales in an unfavorable way with increasing number of neurons. Based on previous ideas, I describe an algorithm which is effective in matrix reordering with respect to both its performance as well as to its scaling in computing time. Rather than trying to reorder the matrix in discrete steps, the algorithm transiently relaxes the integer program by assigning a real-valued parameter to each cell describing its location on a continuous axis ('smooth-index') and finds the parameter set that minimizes the cost. I find that the smooth-index algorithm outperforms all algorithms I compared it to, including those based on topological sorting.

Modeling the 3-dimensional structure of the silkworm's spinning apparatus in silk production

The silk-spinning process of the silkworms transforms the liquid silk solution to a solid state under mild conditions, making it an attractive model for bioinspiration However, the precise mechanism behind silk expulsion remains largely unknown. Here we selected the silkworms as representative models to investigate the silk-spinning mechanism. We used serial block-face scanning electron microscopy (SBF-SEM) to reconstruct the three-dimensional structures of the spinnerets in silkworms at various stages and with different gene backgrounds. By comparing the musculature and duct deformation of these spinneret models during the spinning process, we were able to simulate the morphological changes of the spinneret. Based on the results, we proposed three essential factors for silkworm spinning: the pressure generated by the silk gland, the opening duct, and the pulling force generated by head movement. Understanding the silkworm spinning process provides insights into clarify the fluid-ejecting mechanism of a group of animals. Moreover, these findings are helpful to the development of biomimetic spinning device that mimics the push-and-pull dual-force system in silkworms. STATEMENT OF SIGNIFICANCE: The silkworms' spinning system produces fibers under mild conditions, making it an ideal candidate for bioinspiration. However, the mechanism of silk expulsion is unknown, and the three-dimensional structure of the spinneret is still uncertain. In this study, we reconstructed a detailed 3-dimensional model of the spinneret at near-nanometer resolution, and for the first time, we observed the changes that occur before and during the silk-spinning process. Our reconstructed models suggested that silkworms have the ability to control the spinning process by opening or closing the spinning duct. During the continuously spinning period, both the pressure generated by the silk gland and the pulling force resulting from head movement work in tandem to expel the silk solution. We believe that gaining a full understanding of the spinning process steps can advance our ability to spin synthetic fibers with properties comparable to those of native fibers by mimicking the natural spinning process.

Transport of Nanoparticles into Plants and Their Detection Methods

Nanoparticle transport into plants is an evolving field of research with diverse applications in agriculture and biotechnology. This article provides an overview of the challenges and prospects associated with the transport of nanoparticles in plants, focusing on delivery methods and the detection of nanoparticles within plant tissues. Passive and assisted delivery methods, including the use of roots and leaves as introduction sites, are discussed, along with their respective advantages and limitations. The barriers encountered in nanoparticle delivery to plants are highlighted, emphasizing the need for innovative approaches (e.g., the stem as a new recognition site) to optimize transport efficiency. In recent years, research efforts have intensified, leading to an evendeeper understanding of the intricate mechanisms governing the interaction of nanomaterials with plant tissues and cells. Investigations into the uptake pathways and translocation mechanisms within plants have revealed nuanced responses to different types of nanoparticles. Additionally, this article delves into the importance of detection methods for studying nanoparticle localization and quantification within plant tissues. Various techniques are presented as valuable tools for comprehensively understanding nanoparticle-plant interactions. The reliance on multiple detection methods for data validation is emphasized to enhance the reliability of the research findings. The future outlooks of this field are explored, including the potential use of alternative introduction sites, such as stems, and the continued development of nanoparticle formulations that improve adhesion and penetration. By addressing these challenges and fostering multidisciplinary research, the field of nanoparticle transport in plants is poised to make significant contributions to sustainable agriculture and environmental management.

A Novel Sandwich Method for Serial Block Face SEM Imaging of Airway Multiciliated Epithelium

Volume electron microscopy technologies such as serial block face scanning electron microscopy (SBF-SEM) allow the characterization of tissue organization and cellular content in three dimensions at nanoscale resolution. Here, we describe the procedure to process and image an air-liquid interface culture of human or mouse airway epithelial cells for visualization of the multiciliated epithelium by SBF-SEM in vertical or horizontal cross section.

Microscopy based methods for characterization, drug delivery, and understanding the dynamics of nanoparticles

Nanomedicine is an emerging field that exploits nanotechnology for the development of novel therapeutic and diagnostic modalities. Researches are been focussed in nanoimaging to develop noninvasive, highly sensitive, and reliable tools for diagnosis and visualization in nanomedical field. The application of nanomedicine in healthcare requires in-depth understanding of their structural, physical and morphological properties, internalization inside living system, biodistribution and localization, stability, mode of action and possible toxic health effects. Microscopic techniques including fluorescence-based confocal laser scanning microscopy, super-resolution fluorescence microscopy and multiphoton microscopy; optical-based Raman microscopy, photoacoustic microscopy and optical coherence tomography; photothermal microscopy; electron microscopy (transmission electron microscope and scanning electron microscope); atomic force microscopy; X-ray microscopy and, correlative multimodal imaging are recognized as an indispensable tool in material research and aided in numerous discoveries. Microscopy holds great promise in detecting the fundamental structures of nanoparticles (NPs) that determines their performance and applications. Moreover, the intricate details that allows assessment of chemical composition, surface topology and interfacial properties, molecular, microstructure, and micromechanical properties are also elucidated. With plethora of applications, microscopy-based techniques have been used to characterize novel NPs alongwith their proficient designing and adoption of safe strategies to be exploited in nanomedicine. Consequently, microscopic techniques have been extensively used in the characterization of fabricated NPs, and their biomedical application in diagnostics and therapeutics. The present review provides an overview of the microscopy-based techniques for in vitro and in vivo application in nanomedical investigation alongwith their challenges and advancement to meet the limitations of conventional methods.

Probing the in situ volumes of Arabidopsis leaf plastids using three-dimensional confocal and scanning electron microscopy

Leaf plastids harbor a plethora of biochemical reactions including photosynthesis, one of the most important metabolic pathways on Earth. Scientists are eager to unveil the physiological processes within the organelle but also their interconnection with the rest of the plant cell. An increasingly important feature of this venture is to use experimental data in the design of metabolic models. A remaining obstacle has been the limited in situ volume information of plastids and other cell organelles. To fill this gap for chloroplasts, we established three microscopy protocols delivering in situ volumes based on: (i) chlorophyll fluorescence emerging from the thylakoid membrane, (ii) a CFP marker embedded in the envelope, and (iii) calculations from serial block-face scanning electron microscopy (SBFSEM). The obtained data were corroborated by comparing wild-type data with two mutant lines affected in the plastid division machinery known to produce small and large mesophyll chloroplasts, respectively. Furthermore, we also determined the volume of the much smaller guard cell plastids. Interestingly, their volume is not governed by the same components of the division machinery which defines mesophyll plastid size. Based on our three approaches, the average volume of a mature Col-0 wild-type mesophyll chloroplasts is 93 μm3 . Wild-type guard cell plastids are approximately 18 μm3 . Lastly, our comparative analysis shows that the chlorophyll fluorescence analysis can accurately determine chloroplast volumes, providing an important tool to research groups without access to transgenic marker lines expressing genetically encoded fluorescence proteins or costly SBFSEM equipment.

Correlative Imaging to Detect Rare HIV Reservoirs and Associated Damage in Tissues

Correlative light-electron microscopy (CLEM) has evolved in the last decades, especially after significant developments in sample preparation, imaging acquisition, software, spatial resolution, and equipment, including confocal, live-cell, super-resolution, and electron microscopy (scanning, transmission, focused ion beam, and cryo-electron microscopy). However, the recent evolution of different laser-related techniques, such as mass spectrometry imaging (MSI) and laser capture microdissection, could further expand spatial imaging capabilities into high-resolution OMIC approaches such as proteomic, lipidomics, small molecule, and drug discovery. Here, we will describe a protocol to integrate the detection of rare viral reservoirs with imaging mass spectrometry.

Advanced Imaging Techniques for the Characterization of Subcellular Organelle Structure in Pancreatic Islet β Cells

Type 2 diabetes (T2D) affects more than 32.3 million individuals in the United States, creating an economic burden of nearly $966 billion in 2021. T2D results from a combination of insulin resistance and inadequate insulin secretion from the pancreatic β cell. However, genetic and physiologic data indicate that defects in β cell function are the chief determinant of whether an individual with insulin resistance will progress to a diagnosis of T2D. The subcellular organelles of the insulin secretory pathway, including the endoplasmic reticulum, Golgi apparatus, and secretory granules, play a critical role in maintaining the heavy biosynthetic burden of insulin production, processing, and secretion. In addition, the mitochondria enable the process of insulin release by integrating the metabolism of nutrients into energy output. Advanced imaging techniques are needed to determine how changes in the structure and composition of these organelles contribute to the loss of insulin secretory capacity in the β cell during T2D. Several microscopy techniques, including electron microscopy, fluorescence microscopy, and soft X-ray tomography, have been utilized to investigate the structure-function relationship within the β cell. In this overview article, we will detail the methodology, strengths, and weaknesses of each approach. © 2024 American Physiological Society. Compr Physiol 14:5243-5267, 2024.

Revealing the 3D Structure of Block Copolymers with Electron Microscopy: Current Status and Future Directions

Block copolymers (BCPs) are considered model systems for understanding and utilizing self-assembly in soft matter. Their tunable nanometric structure and composition enable comprehensive studies of self-assembly processes as well as make them relevant materials in diverse applications. A key step in developing and controlling BCP nanostructures is a full understanding of their three-dimensional (3D) structure and how this structure is affected by the BCP chemistry, confinement, boundary conditions, and the self-assembly evolution and dynamics. Electron microscopy (EM) is a leading method in BCP 3D characterization owing to its high resolution in imaging nanosized structures. Here we discuss the two main 3D EM methods: namely, transmission EM tomography and slice and view scanning EM tomography. We present each method's principles, examine their strengths and weaknesses, and discuss ways researchers have devised to overcome some of the challenges in BCP 3D characterization with EM- from specimen preparation to imaging radiation-sensitive materials. Importantly, we review current and new cutting-edge EM methods such as direct electron detectors, energy dispersive X-ray spectroscopy of soft matter, high temporal rate imaging, and single-particle analysis that have great potential for expanding the BCP understanding through EM in the future.

STED microscopy reveals dendrite-specificity of spines in turtle cortex

Dendritic spines are key structures for neural communication, learning and memory. Spine size and shape probably reflect synaptic strength and learning. Imaging with superresolution STED microscopy the detailed shape of the majority of the spines of individual neurons in turtle cortex (Trachemys scripta elegans) revealed several distinguishable shape classes. Dendritic spines of a given class were not distributed randomly, but rather decorated significantly more often some dendrites than others. The individuality of dendrites was corroborated by significant inter-dendrite differences in other parameters such as spine density and length. In addition, many spines were branched or possessed spinules. These findings may have implications for the role of individual dendrites in this cortex.

Studying zebrafish nervous system structure and function in health and disease with electron microscopy

Zebrafish (Danio rerio) is a well-established model for studying the nervous system. Findings in zebrafish often inform studies on human diseases of the nervous system and provide crucial insight into disease mechanisms. The functions of the nervous system often rely on communication between neurons. Signal transduction is achieved via release of signaling molecules in the form of neuropeptides or neurotransmitters at synapses. Snapshots of membrane dynamics of these processes are imaged by electron microscopy. Electron microscopy can reveal ultrastructure and thus synaptic processes. This is crucial both for mapping synaptic connections and for investigating synaptic functions. In addition, via volumetric electron microscopy, the overall architecture of the nervous system becomes accessible, where structure can inform function. Electron microscopy is thus of particular value for studying the nervous system. However, today a plethora of electron microscopy techniques and protocols exist. Which technique is most suitable highly depends on the research question and scope as well as on the type of tissue that is examined. This review gives an overview of the electron microcopy techniques used on the zebrafish nervous system. It aims to give researchers a guide on which techniques are suitable for their specific questions and capabilities as well as an overview of the capabilities of electron microscopy in neurobiological research in the zebrafish model.

3D reconstructions of parasite development and the intracellular niche of the microsporidian pathogen Encephalitozoon intestinalis

Microsporidia are an early-diverging group of fungal pathogens with a wide host range. Several microsporidian species cause opportunistic infections in humans that can be fatal. As obligate intracellular parasites with highly reduced genomes, microsporidia are dependent on host metabolites for successful replication and development. Our knowledge of microsporidian intracellular development remains rudimentary, and our understanding of the intracellular niche occupied by microsporidia has relied on 2D TEM images and light microscopy. Here, we use serial block-face scanning electron microscopy (SBF-SEM) to capture 3D snapshots of the human-infecting species, Encephalitozoon intestinalis, within host cells. We track E. intestinalis development through its life cycle, which allows us to propose a model for how its infection organelle, the polar tube, is assembled de novo in developing spores. 3D reconstructions of parasite-infected cells provide insights into the physical interactions between host cell organelles and parasitophorous vacuoles, which contain the developing parasites. The host cell mitochondrial network is substantially remodeled during E. intestinalis infection, leading to mitochondrial fragmentation. SBF-SEM analysis shows changes in mitochondrial morphology in infected cells, and live-cell imaging provides insights into mitochondrial dynamics during infection. Our data provide insights into parasite development, polar tube assembly, and microsporidia-induced host mitochondria remodeling.

A large-scale volumetric correlated light and electron microscopy study localizes Alzheimer's disease-related molecules in the hippocampus

Connectomics is a nascent neuroscience field to map and analyze neuronal networks. It provides a new way to investigate abnormalities in brain tissue, including in models of Alzheimer's disease (AD). This age-related disease is associated with alterations in amyloid-β (Aβ) and phosphorylated tau (pTau). These alterations correlate with AD's clinical manifestations, but causal links remain unclear. Therefore, studying these molecular alterations within the context of the local neuronal and glial milieu may provide insight into disease mechanisms. Volume electron microscopy (vEM) is an ideal tool for performing connectomics studies at the ultrastructural level, but localizing specific biomolecules within large-volume vEM data has been challenging. Here we report a volumetric correlated light and electron microscopy (vCLEM) approach using fluorescent nanobodies as immuno-probes to localize Alzheimer's disease-related molecules in a large vEM volume. Three molecules (pTau, Aβ, and a marker for activated microglia (CD11b)) were labeled without the need for detergents by three nanobody probes in a sample of the hippocampus of the 3xTg Alzheimer's disease model mouse. Confocal microscopy followed by vEM imaging of the same sample allowed for registration of the location of the molecules within the volume. This dataset revealed several ultrastructural abnormalities regarding the localizations of Aβ and pTau in novel locations. For example, two pTau-positive post-synaptic spine-like protrusions innervated by axon terminals were found projecting from the axon initial segment of a pyramidal cell. Three pyramidal neurons with intracellular Aβ or pTau were 3D reconstructed. Automatic synapse detection, which is necessary for connectomics analysis, revealed the changes in density and volume of synapses at different distances from an Aβ plaque. This vCLEM approach is useful to uncover molecular alterations within large-scale volume electron microscopy data, opening a new connectomics pathway to study Alzheimer's disease and other types of dementia.

Electrical synapses for a pooling layer of the convolutional neural network in retinas

We have an example of a synergetic effect between neuroscience and connectome via artificial intelligence. The invention of Neocognitron, a machine learning algorithm, was inspired by the visual cortical circuitry for complex cells to be made by combinations of simple cells, which uses a hierarchical convolutional neural network (CNN). The CNN machine learning algorithm is powerful in classifying neuron borderlines on electron micrograph images for automatized connectomic analysis. CNN is also useful as a functional framework to analyze the neurocircuitry of the visual system. The visual system encodes visual patterns in the retina and decodes them in the corresponding cortical areas. The knowledge of evolutionarily chosen mechanisms in retinas may help the innovation of new algorithms. Since over a half-century ago, a classical style of serial section transmission electron microscopy has vastly contributed to cell biology. It is still useful to comprehensively analyze the small area of retinal neurocircuitry that is rich in natural intelligence of pattern recognition. I discuss the perspective of our study on the primary rod signal pathway in mouse and macaque retinas with special reference to electrical synapses. Photon detection under the scotopic condition needs absolute sensitivity but no intricate pattern recognition. This extreme case is regarded as the most simplified pattern recognition of the input with no autocorrelation. A comparative study of mouse and macaque retinas, where exists the 7-fold difference in linear size, may give us the underlying principle with quantitative verification of their adaptational designs of neurocircuitry.