Recent advancements in nano- and microfabrication techniques have led to the development of highly biomimetic patterned substrates able to guide neuronal sprouting, routing, elongation, and branching. Such substrates, recapitulating shapes and geometries found in the native brain, may pave the way toward the development of cell instructive paradigms able to guide morphogenesis at the neuron-material interface. In this scenario, high-resolution electron microscopy approaches, owing to their ability of discerning the details of neural morphogenesis at a nanoscale resolution, may play a crucial role in unravelling the fine ultrastructure of neurons interfacing with biomimetic structured substrates.

Biomolecular condensates are triggered by multivalent interactions conferred by the intrinsically disordered regions and/or interacting domains within the constituents. While light microscopy has provided powerful tools to study the dynamics of intracellular condensates, electron microscopy (EM) gives more detailed insights into their ultrastructure and spatial connectivity with membrane system. In this chapter, we describe the methods for detecting the membraneless condensates in plant cells by high-pressure freezing -based EM coupled with immuno-gold labeling and correlative light electron microscopy techniques, which may benefit researchers in future studies.

Macroautophagy, hereafter autophagy, plays a crucial role in the degradation of harmful or unwanted cellular components through a double-membrane autophagosome. Upon autophagosome fusion with the vacuole, the degraded materials are subsequently recycled to generate macromolecules, contributing to cellular homeostasis, metabolism, and stress tolerance in plants. A hallmark during autophagy is the formation of isolation membrane structure named as phagophore, which undergoes multiple steps to become as a complete double-membrane autophagosome. Methodologies have been developed in recent years to observe and quantify the autophagic process, which greatly advance knowledge of autophagosome biogenesis in plant cells. In this chapter, we will introduce two methods to dissect the autophagosome-related structures in the Arabidopsis plant cells, including the correlative light and electron microscopy, to map the ultrastructural feature of autophagosomal structures, and time-lapse imaging to monitor the temporal recruitment of autophagy machinery during autophagosome formation.

Mapping neuronal networks is a central focus in neuroscience. While volume electron microscopy (vEM) can reveal the fine structure of neuronal networks (connectomics), it does not provide molecular information to identify cell types or functions. We developed an approach that uses fluorescent single-chain variable fragments (scFvs) to perform multiplexed detergent-free immunolabeling and volumetric-correlated-light-and-electron-microscopy on the same sample. We generated eight fluorescent scFvs targeting brain markers. Six fluorescent probes were imaged in the cerebellum of a female mouse, using confocal microscopy with spectral unmixing, followed by vEM of the same sample. The results provide excellent ultrastructure superimposed with multiple fluorescence channels. Using this approach, we documented a poorly described cell type, two types of mossy fiber terminals, and the subcellular localization of one type of ion channel. Because scFvs can be derived from existing monoclonal antibodies, hundreds of such probes can be generated to enable molecular overlays for connectomic studies.

Different methods for fixing biofilms of Staphylococcus aureus and Salmonella enterica for light and electron microscopy were compared. Paraformaldehyde fixation did not preserve biofilm integrity during dehydration; Ito-Karnovsky fixation revealed cell morphology, but did not preserve the matrix. Ruthenium red combined with aldehydes allowed the matrix to be preserved and visualized. An analysis of the ultrastructure of S. aureus and S. enterica cells in biofilms and suspensions at various fixations is presented. The ultrastructure of the biofilm matrix has been described.

Membranoproliferative glomerulonephritis (MPGN) is no longer a disease but a pattern of injury in various diseases. Characterized by electron-dense deposits, mesangial proliferation, and duplication of the glomerular basement membrane, MPGN was previously classified by findings seen by electron microscopy. However, recognizing complement dysfunction in relation to cases with the MPGN pattern of injury substantially changed our view of its pathogenesis. A new classification, including immune complex-mediated and complement-mediated MPGN, has become preferable and has been adopted by international guidelines. Despite these advancements, accurate diagnosis of MPGN remains a clinical challenge, given the pathological and clinical similarities between immune complex-mediated and complement-mediated MPGN. Additional testing, such as molecular and genetic testing, is often necessary. Here, we will summarize our current understanding of the MPGN pattern of injury from a pathology perspective as an introductory article in the following chapters.

The success of in vitro cultivation, particularly for micropropagation purposes, depends on the efficient control of contaminants. In this context, the sterilization of plant material constitutes a fundamental step in initiating cultures. Microbial contaminants can be found either on the surface (epiphyte) or inside plant explants (endophyte). However, the latter is generally challenging to detect and may not always be eradicated through surface sterilization alone. Endophyte contaminants, such as bacteria, can persist within plant material over several cultivation cycles, potentially interfering with or inhibiting in vitro establishment, growth, or recovery of cryopreserved materials. Therefore, microscopy techniques, such as electron microscopy, can yield valuable insights into bacterial endophytes\' localization, tissue colonization patterns, and functions in in vitro plant culture. This information is essential for adopting effective strategies for eliminating, preventing, or harmonious coexistence with contaminants.

Skeletal muscle, the major processor of dietary glucose, stores it in myriad glycogen granules. Their numbers vary with cellular location and physiological and pathophysiological states. AI models were developed to derive granular glycogen content from electron-microscopic images of human muscle. Two UNet-type semantic segmentation models were built: \"Locations\" classified pixels as belonging to different regions in the cell; \"Granules\" identified pixels within granules. From their joint output, a pixel fraction pf was calculated for images from patients positive (MHS) or negative (MHN) to a test for malignant hyperthermia susceptibility. pf was used to derive vf, the volume fraction occupied by granules. The relationship vf (pf) was derived from a simulation of volumes (\"baskets\") containing virtual granules at realistic concentrations. The simulated granules had diameters matching the real ones, which were measured by adapting a utility devised for calcium sparks. Applying this relationship to the pf measured in images, vf was calculated for every region and patient, and from them a glycogen concentration. The intermyofibrillar spaces and the sarcomeric I band had the highest granular content. The measured glycogen concentration was low enough to allow for a substantial presence of non-granular glycogen. The MHS samples had an approximately threefold lower concentration (significant in a hierarchical test), consistent with earlier evidence of diminished glucose processing in MHS. The AI models and the approach to infer three-dimensional magnitudes from two-dimensional images should be adaptable to other tasks on a variety of images from patients and animal models and different disease conditions.

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.

A deep understanding of how the brain controls behaviour requires mapping neural circuits down to the muscles that they control. Here, we apply automated tools to segment neurons and identify synapses in an electron microscopy dataset of an adult female Drosophila melanogaster ventral nerve cord (VNC)1, which functions like the vertebrate spinal cord to sense and control the body. We find that the fly VNC contains roughly 45 million synapses and 14,600 neuronal cell bodies. To interpret the output of the connectome, we mapped the muscle targets of leg and wing motor neurons using genetic driver lines2 and X-ray holographic nanotomography3. With this motor neuron atlas, we identified neural circuits that coordinate leg and wing movements during take-off. We provide the reconstruction of VNC circuits, the motor neuron atlas and tools for programmatic and interactive access as resources to support experimental and theoretical studies of how the nervous system controls behaviour.