Ultrastructurally smooth thick partitioning and volume stitching for large-scale connectomics

Combining array tomography with electron tomography provides insights into leakiness of the blood-brain barrier in mouse cortex

Like other volume electron microscopy approaches, automated tape-collecting ultramicrotomy (ATUM) enables imaging of serial sections deposited on thick plastic tapes by scanning electron microscopy (SEM). ATUM is unique in enabling hierarchical imaging and thus efficient screening for target structures, as needed for correlative light and electron microscopy. However, SEM of sections on tape can only access the section surface, thereby limiting the axial resolution to the typical size of cellular vesicles with an order of magnitude lower than the acquired xy resolution. In contrast, serial-section electron tomography (ET), a transmission electron microscopy-based approach, yields isotropic voxels at full EM resolution, but requires deposition of sections on electron-stable thin and fragile films, thus making screening of large section libraries difficult and prone to section loss. To combine the strength of both approaches, we developed 'ATUM-Tomo, a hybrid method, where sections are first reversibly attached to plastic tape via a dissolvable coating, and after screening detached and transferred to the ET-compatible thin films. As a proof-of-principle, we applied correlative ATUM-Tomo to study ultrastructural features of blood-brain barrier (BBB) leakiness around microthrombi in a mouse model of traumatic brain injury. Microthrombi and associated sites of BBB leakiness were identified by confocal imaging of injected fluorescent and electron-dense nanoparticles, then relocalized by ATUM-SEM, and finally interrogated by correlative ATUM-Tomo. Overall, our new ATUM-Tomo approach will substantially advance ultrastructural analysis of biological phenomena that require cell- and tissue-level contextualization of the finest subcellular textures.

Connectome-driven neural inventory of a complete visual system

Vision provides animals with detailed information about their surroundings, conveying diverse features such as color, form, and movement across the visual scene. Computing these parallel spatial features requires a large and diverse network of neurons, such that in animals as distant as flies and humans, visual regions comprise half the brain's volume. These visual brain regions often reveal remarkable structure-function relationships, with neurons organized along spatial maps with shapes that directly relate to their roles in visual processing. To unravel the stunning diversity of a complex visual system, a careful mapping of the neural architecture matched to tools for targeted exploration of that circuitry is essential. Here, we report a new connectome of the right optic lobe from a male Drosophila central nervous system FIB-SEM volume and a comprehensive inventory of the fly's visual neurons. We developed a computational framework to quantify the anatomy of visual neurons, establishing a basis for interpreting how their shapes relate to spatial vision. By integrating this analysis with connectivity information, neurotransmitter identity, and expert curation, we classified the ~53,000 neurons into 727 types, about half of which are systematically described and named for the first time. Finally, we share an extensive collection of split-GAL4 lines matched to our neuron type catalog. Together, this comprehensive set of tools and data unlock new possibilities for systematic investigations of vision in Drosophila, a foundation for a deeper understanding of sensory processing.

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.

Three-dimensional architecture of granulosa cell derived from oocyte cumulus complex, revealed by FIB-SEM

The oocyte cumulus complex is mainly composed of an oocyte, the perivitelline space, zona pellucida and numerous granulosa cells. The cumulus granulosa cells (cGCs) provide a particularly important microenvironment for oocyte development, regulating its growth, maturation and meiosis. In this study, we studied the internal structures and cell-to-cell connections of mouse cGCs using focused ion beam scanning electron microscopy (FIB-SEM). We reconstructed three-dimensional models to display characteristic connections between the oocyte and cGCs, and to illustrate various main organelles in cGCs together with their interaction relationship. A special form of cilium identified in granulosa cell was never reported in previous literature.

Optical STEM detection for scanning electron microscopy

Recent advances in electron microscopy techniques have led to a significant scale up in volumetric imaging of biological tissue. The throughput of electron microscopes, however, remains a limiting factor for the volume that can be imaged in high resolution within reasonable time. Faster detection methods will improve throughput. Here, we have characterized and benchmarked a novel detection technique for scanning electron microscopy: optical scanning transmission electron microscopy (OSTEM). A qualitative and quantitative comparison was performed between OSTEM, secondary and backscattered electron detection and annular dark field detection in scanning transmission electron microscopy. Our analysis shows that OSTEM produces images similar to backscattered electron detection in terms of contrast, resolution and signal-to-noise ratio. OSTEM can complement large scale imaging with (scanning) transmission electron microscopy and has the potential to speed up imaging in single-beam scanning electron microscope.

mEMbrain: an interactive deep learning MATLAB tool for connectomic segmentation on commodity desktops

Connectomics is fundamental in propelling our understanding of the nervous system's organization, unearthing cells and wiring diagrams reconstructed from volume electron microscopy (EM) datasets. Such reconstructions, on the one hand, have benefited from ever more precise automatic segmentation methods, which leverage sophisticated deep learning architectures and advanced machine learning algorithms. On the other hand, the field of neuroscience at large, and of image processing in particular, has manifested a need for user-friendly and open source tools which enable the community to carry out advanced analyses. In line with this second vein, here we propose mEMbrain, an interactive MATLAB-based software which wraps algorithms and functions that enable labeling and segmentation of electron microscopy datasets in a user-friendly user interface compatible with Linux and Windows. Through its integration as an API to the volume annotation and segmentation tool VAST, mEMbrain encompasses functions for ground truth generation, image preprocessing, training of deep neural networks, and on-the-fly predictions for proofreading and evaluation. The final goals of our tool are to expedite manual labeling efforts and to harness MATLAB users with an array of semi-automatic approaches for instance segmentation. We tested our tool on a variety of datasets that span different species at various scales, regions of the nervous system and developmental stages. To further expedite research in connectomics, we provide an EM resource of ground truth annotation from four different animals and five datasets, amounting to around 180 h of expert annotations, yielding more than 1.2 GB of annotated EM images. In addition, we provide a set of four pre-trained networks for said datasets. All tools are available from https://lichtman.rc.fas.harvard.edu/mEMbrain/. With our software, our hope is to provide a solution for lab-based neural reconstructions which does not require coding by the user, thus paving the way to affordable connectomics.

A Convenient All-Cell Optical Imaging Method Compatible with Serial SEM for Brain Mapping

The mammalian brain, with its complexity and intricacy, poses significant challenges for researchers aiming to understand its inner workings. Optical multilayer interference tomography (OMLIT) is a novel, promising imaging technique that enables the mapping and reconstruction of mesoscale all-cell brain atlases and is seamlessly compatible with tape-based serial scanning electron microscopy (SEM) for microscale mapping in the same tissue. However, currently, OMLIT suffers from imperfect coatings, leading to background noise and image contamination. In this study, we introduced a new imaging configuration using carbon spraying to eliminate the tape-coating step, resulting in reduced noise and enhanced imaging quality. We demonstrated the improved imaging quality and validated its applicability through a correlative light-electron imaging workflow. Our method successfully reconstructed all cells and vasculature within a large OMLIT dataset, enabling basic morphological classification and analysis. We also show that this approach can perform effectively on thicker sections, extending its applicability to sub-micron scale slices, saving sample preparation and imaging time, and increasing imaging throughput. Consequently, this method emerges as a promising candidate for high-speed, high-throughput brain tissue reconstruction and analysis. Our findings open new avenues for exploring the structure and function of the brain using OMLIT images.

mEMbrain: an interactive deep learning MATLAB tool for connectomic segmentation on commodity desktops

Connectomics is fundamental in propelling our understanding of the nervous system’s organization, unearthing cells and wiring diagrams reconstructed from volume electron microscopy (EM) datasets. Such reconstructions, on the one hand, have benefited from ever more precise automatic segmentation methods, which leverage sophisticated deep learning architectures and advanced machine learning algorithms. On the other hand, the field of neuroscience at large, and of image processing in particular, has manifested a need for user-friendly and open source tools which enable the community to carry out advanced analyses. In line with this second vein, here we propose mEMbrain, an interactive MATLAB-based software which wraps algorithms and functions that enable labeling and segmentation of electron microscopy datasets in a user-friendly user interface compatible with Linux and Windows. Through its integration as an API to the volume annotation and segmentation tool VAST, mEMbrain encompasses functions for ground truth generation, image preprocessing, training of deep neural networks, and on-the-fly predictions for proofreading and evaluation. The final goals of our tool are to expedite manual labeling efforts and to harness MATLAB users with an array of semi-automatic approaches for instance segmentation. We tested our tool on a variety of datasets that span different species at various scales, regions of the nervous system and developmental stages. To further expedite research in connectomics, we provide an EM resource of ground truth annotation from 4 different animals and 5 datasets, amounting to around 180 hours of expert annotations, yielding more than 1.2 GB of annotated EM images. In addition, we provide a set of 4 pre-trained networks for said datasets. All tools are available from https://lichtman.rc.fas.harvard.edu/mEMbrain/ . With our software, our hope is to provide a solution for lab-based neural reconstructions which does not require coding by the user, thus paving the way to affordable connectomics.

Methods of enhanced FIB-SEM sample preparation and image acquisition

The ability to view biomolecules in cells and measure changes in their structure, quantity, distribution, and interaction is fundamental to understanding biology. By coupling nano -scale resolution with meso and even macro scale volumes, the enhanced focused ion beam-scanning electron microscopy (FIB-SEM) pipeline has enabled numerous transformational discoveries in life science, many of which were major new landmarks in their fields. This pipeline consists of EM sample preparation, FIB-SEM sample preparation, FIB-SEM imaging, data alignment, and image analysis. While the EM sample preparation, data alignment, and image analysis are consistent with those from other volume Electron Microscopy (vEM) approaches, the enhanced FIB-SEM sample preparation and imaging are unique to the rest of comparable methods. We here illustrate the detailed methods of enhanced FIB-SEM sample preparation and image acquisition that have not been previously described. These methods can also be applied to the conventional FIB-SEM platforms for improved image acquisition quality and pipeline throughput.

Organizing principles of astrocytic nanoarchitecture in the mouse cerebral cortex

Astrocytes are increasingly understood to be important regulators of central nervous system (CNS) function in health and disease; yet, we have little quantitative understanding of their complex architecture. While broad categories of astrocytic structures are known, the discrete building blocks that compose them, along with their geometry and organizing principles, are poorly understood. Quantitative investigation of astrocytic complexity is impeded by the absence of high-resolution datasets and robust computational approaches to analyze these intricate cells. To address this, we produced four ultra-high-resolution datasets of mouse cerebral cortex using serial electron microscopy and developed astrocyte-tailored computer vision methods for accurate structural analysis. We unearthed specific anatomical building blocks, structural motifs, connectivity hubs, and hierarchical organizations of astrocytes. Furthermore, we found that astrocytes interact with discrete clusters of synapses and that astrocytic mitochondria are distributed to lie closer to larger clusters of synapses. Our findings provide a geometrically principled, quantitative understanding of astrocytic nanoarchitecture and point to an unexpected level of complexity in how astrocytes interact with CNS microanatomy.

Neurons on tape: Automated Tape Collecting Ultramicrotomy-mediated volume EM for targeting neuropathology

In this chapter, we review Automated Tape Collecting Ultramicrotomy (ATUM), which, among other array tomography methods, substantially simplified large-scale volume electron microscopy (vEM) projects. vEM reveals biological structures at nanometer resolution in three dimensions and resolves ambiguities of two-dimensional representations. However, as the structures of interest-like disease hallmarks emerging from neuropathology-are often rare but the field of view is small, this can easily turn a vEM project into a needle in a haystack problem. One solution for this is correlated light and electron microscopy (CLEM), providing tissue context, dynamic and molecular features before switching to targeted vEM to hone in on the object's ultrastructure. This requires precise coordinate transfer between the two imaging modalities (e.g., by micro computed tomography), especially for block face vEM which relies on physical destruction of sections. With array tomography methods, serial ultrathin sections are collected into a tissue library, thus allowing storage of precious samples like human biopsies and enabling repetitive imaging at different resolution levels for an SEM-based search strategy. For this, ATUM has been developed to reliably collect serial ultrathin sections via a conveyor belt onto a plastic tape that is later mounted onto silicon wafers for serial scanning EM (SEM). The ATUM-SEM procedure is highly modular and can be divided into sample preparation, serial ultramicrotomy onto tape, mounting, serial image acquisition-after which the acquired image stacks can be used for analysis. Here, we describe the steps of this workflow and how ATUM-SEM enables targeting and high resolution imaging of specific structures. ATUM-SEM is widely applicable. To illustrate this, we exemplify the approach by reconstructions of focal pathology in an Alzheimer mouse model and CLEM of a specific cortical synapse.

CLEMSite, a software for automated phenotypic screens using light microscopy and FIB-SEM

In recent years, Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) has emerged as a flexible method that enables semi-automated volume ultrastructural imaging. We present a toolset for adherent cells that enables tracking and finding cells, previously identified in light microscopy (LM), in the FIB-SEM, along with the automatic acquisition of high-resolution volume datasets. We detect the underlying grid pattern in both modalities (LM and EM), to identify common reference points. A combination of computer vision techniques enables complete automation of the workflow. This includes setting the coincidence point of both ion and electron beams, automated evaluation of the image quality and constantly tracking the sample position with the microscope's field of view reducing or even eliminating operator supervision. We show the ability to target the regions of interest in EM within 5 µm accuracy while iterating between different targets and implementing unattended data acquisition. Our results demonstrate that executing volume acquisition in multiple locations autonomously is possible in EM.

En bloc preparation of Drosophila brains enables high-throughput FIB-SEM connectomics

Deriving the detailed synaptic connections of an entire nervous system is the unrealized goal of the nascent field of connectomics. For the fruit fly Drosophila, in particular, we need to dissect the brain, connectives, and ventral nerve cord as a single continuous unit, fix and stain it, and undertake automated segmentation of neuron membranes. To achieve this, we designed a protocol using progressive lowering of temperature dehydration (PLT), a technique routinely used to preserve cellular structure and antigenicity. We combined PLT with low temperature en bloc staining (LTS) and recover fixed neurons as round profiles with darkly stained synapses, suitable for machine segmentation and automatic synapse detection. Here we report three different PLT-LTS methods designed to meet the requirements for FIB-SEM imaging of the Drosophila brain. These requirements include: good preservation of ultrastructural detail, high level of en bloc staining, artifact-free microdissection, and smooth hot-knife cutting to reduce the brain to dimensions suited to FIB-SEM. In addition to PLT-LTS, we designed a jig to microdissect and pre-fix the fly's delicate brain and central nervous system. Collectively these methods optimize morphological preservation, allow us to image the brain usually at 8 nm per voxel, and simultaneously speed the formerly slow rate of FIB-SEM imaging.

Proximity proteomics of synaptopodin provides insight into the molecular composition of the spine apparatus of dendritic spines

The spine apparatus is a specialized compartment of the neuronal smooth endoplasmic reticulum (ER) located in a subset of dendritic spines. It consists of stacks of ER cisterns that are interconnected by an unknown dense matrix and are continuous with each other and with the ER of the dendritic shaft. While this organelle was first observed over 60 y ago, its molecular organization remains a mystery. Here, we performed in vivo proximity proteomics to gain some insight into its molecular components. To do so, we used the only known spine apparatus-specific protein, synaptopodin, to target a biotinylating enzyme to this organelle. We validated the specific localization in dendritic spines of a small subset of proteins identified by this approach, and we further showed their colocalization with synaptopodin when expressed in nonneuronal cells. One such protein is Pdlim7, an actin binding protein not previously identified in spines. Pdlim7, which we found to interact with synaptopodin through multiple domains, also colocalizes with synaptopodin on the cisternal organelle, a peculiar stack of ER cisterns resembling the spine apparatus and found at axon initial segments of a subset of neurons. Moreover, Pdlim7 has an expression pattern similar to that of synaptopodin in the brain, highlighting a functional partnership between the two proteins. The components of the spine apparatus identified in this work will help elucidate mechanisms in the biogenesis and maintenance of this enigmatic structure with implications for the function of dendritic spines in physiology and disease.

How innovations in methodology offer new prospects for volume electron microscopy

Detailed knowledge of biological structure has been key in understanding biology at several levels of organisation, from organs to cells and proteins. Volume electron microscopy (volume EM) provides high resolution 3D structural information about tissues on the nanometre scale. However, the throughput rate of conventional electron microscopes has limited the volume size and number of samples that can be imaged. Recent improvements in methodology are currently driving a revolution in volume EM, making possible the structural imaging of whole organs and small organisms. In turn, these recent developments in image acquisition have created or stressed bottlenecks in other parts of the pipeline, like sample preparation, image analysis and data management. While the progress in image analysis is stunning due to the advent of automatic segmentation and server-based annotation tools, several challenges remain. Here we discuss recent trends in volume EM, emerging methods for increasing throughput and implications for sample preparation, image analysis and data management.

Volume electron microscopy

Life exists in three dimensions, but until the turn of the century most electron microscopy methods provided only 2D image data. Recently, electron microscopy techniques capable of delving deep into the structure of cells and tissues have emerged, collectively called volume electron microscopy (vEM). Developments in vEM have been dubbed a quiet revolution as the field evolved from established transmission and scanning electron microscopy techniques, so early publications largely focused on the bioscience applications rather than the underlying technological breakthroughs. However, with an explosion in the uptake of vEM across the biosciences and fast-paced advances in volume, resolution, throughput and ease of use, it is timely to introduce the field to new audiences. In this Primer, we introduce the different vEM imaging modalities, the specialized sample processing and image analysis pipelines that accompany each modality and the types of information revealed in the data. We showcase key applications in the biosciences where vEM has helped make breakthrough discoveries and consider limitations and future directions. We aim to show new users how vEM can support discovery science in their own research fields and inspire broader uptake of the technology, finally allowing its full adoption into mainstream biological imaging.

Smart imaging to empower brain-wide neuroscience at single-cell levels

A deep understanding of the neuronal connectivity and networks with detailed cell typing across brain regions is necessary to unravel the mechanisms behind the emotional and memorial functions as well as to find the treatment of brain impairment. Brain-wide imaging with single-cell resolution provides unique advantages to access morphological features of a neuron and to investigate the connectivity of neuron networks, which has led to exciting discoveries over the past years based on animal models, such as rodents. Nonetheless, high-throughput systems are in urgent demand to support studies of neural morphologies at larger scale and more detailed level, as well as to enable research on non-human primates (NHP) and human brains. The advances in artificial intelligence (AI) and computational resources bring great opportunity to 'smart' imaging systems, i.e., to automate, speed up, optimize and upgrade the imaging systems with AI and computational strategies. In this light, we review the important computational techniques that can support smart systems in brain-wide imaging at single-cell resolution.

Single-Neuron Labeling in Fixed Tissue and Targeted Volume Electron Microscopy

The structural complexity of nervous tissue makes it very difficult to unravel the connectivity between neural elements at different scales. Numerous methods are available to trace long-range projections at the light microscopic level, and to identify the actual synaptic connections at the electron microscopic level. However, correlating mesoscopic and nanoscopic scales in the same cell, cell population or brain region is a problematic, laborious and technically demanding task. Here we present an effective method for the 3D reconstruction of labeled subcellular structures at the ultrastructural level, after single-neuron labeling in fixed tissue. The brain is fixed by intracardial perfusion of aldehydes and thick vibratome sections (250 μm) are obtained. Single cells in these vibratome sections are intracellularly injected with horseradish peroxidase (HRP), so that the cell body and its processes can be identified. The thick sections are later flat-embedded in epoxy resin and re-sectioned into a series of thinner (7 μm) sections. The sections containing the regions of interest of the labeled cells are then imaged with automated focused ion beam milling and scanning electron microscopy (FIB-SEM), acquiring long series of high-resolution images that can be reconstructed, visualized, and analyzed in 3D. With this methodology, we can accurately select any cellular segment at the light microscopic level (e.g., proximal, intermediate or distal dendrites, collateral branches, axonal segments, etc.) and analyze its synaptic connections at the electron microscopic level, along with other ultrastructural features. Thus, this method not only facilitates the mapping of the synaptic connectivity of single-labeled neurons, but also the analysis of the surrounding neuropil. Since the labeled processes can be located at different layers or subregions, this method can also be used to obtain data on the differences in local synaptic organization that may exist at different portions of the labeled neurons.

Reconstruction of neocortex: Organelles, compartments, cells, circuits, and activity

We assembled a semi-automated reconstruction of L2/3 mouse primary visual cortex from ∼250 × 140 × 90 μm3 of electron microscopic images, including pyramidal and non-pyramidal neurons, astrocytes, microglia, oligodendrocytes and precursors, pericytes, vasculature, nuclei, mitochondria, and synapses. Visual responses of a subset of pyramidal cells are included. The data are publicly available, along with tools for programmatic and three-dimensional interactive access. Brief vignettes illustrate the breadth of potential applications relating structure to function in cortical circuits and neuronal cell biology. Mitochondria and synapse organization are characterized as a function of path length from the soma. Pyramidal connectivity motif frequencies are predicted accurately using a configuration model of random graphs. Pyramidal cells receiving more connections from nearby cells exhibit stronger and more reliable visual responses. Sample code shows data access and analysis.

High-throughput mapping of a whole rhesus monkey brain at micrometer resolution

Whole-brain mesoscale mapping in primates has been hindered by large brain sizes and the relatively low throughput of available microscopy methods. Here, we present an approach that combines primate-optimized tissue sectioning and clearing with ultrahigh-speed fluorescence microscopy implementing improved volumetric imaging with synchronized on-the-fly-scan and readout technique, and is capable of completing whole-brain imaging of a rhesus monkey at 1 × 1 × 2.5 µm³ voxel resolution within 100 h. We also developed a highly efficient method for long-range tracing of sparse axonal fibers in datasets numbering hundreds of terabytes. This pipeline, which we call serial sectioning and clearing, three-dimensional microscopy with semiautomated reconstruction and tracing (SMART), enables effective connectome-scale mapping of large primate brains. With SMART, we were able to construct a cortical projection map of the mediodorsal nucleus of the thalamus and identify distinct turning and routing patterns of individual axons in the cortical folds while approaching their arborization destinations.

Field-Emission Scanning Electron Microscope as a Tool for Large-Area and Large-Volume Ultrastructural Studies

Simple Summary

Ultrastructural studies of cells and tissues are usually performed using transmission electron microscopy (TEM), which enables imaging at the highest possible resolution. The weak point of TEM is the limited ability to analyze the ultrastructure of large areas and volumes of biological samples. This limitation can be overcome by using modern field-emission scanning electron microscopy (FE-SEM) with high-sensitivity detection, which enables the creation of TEM-like images from the flat surfaces of resin-embedded biological specimens. Several FE-SEM-based techniques for two- and three-dimensional ultrastructural studies of cells, tissues, organs, and organisms have been developed in the 21st century. These techniques have created a new era in structural biology and have changed the role of the scanning electron microscope (SEM) in biological and medical laboratories. Since the premiere of the first commercially available SEM in 1965, these instruments were used almost exclusively to obtain topographical information over a large range of magnifications. Currently, FE-SEM offers many attractive possibilities in the studies of cell and tissue ultrastructure, and they are presented in this review.

Abstract

The development of field-emission scanning electron microscopes for high-resolution imaging at very low acceleration voltages and equipped with highly sensitive detectors of backscattered electrons (BSE) has enabled transmission electron microscopy (TEM)-like imaging of the cut surfaces of tissue blocks, which are impermeable to the electron beam, or tissue sections mounted on the solid substrates. This has resulted in the development of methods that simplify and accelerate ultrastructural studies of large areas and volumes of biological samples. This article provides an overview of these methods, including their advantages and disadvantages. The imaging of large sample areas can be performed using two methods based on the detection of transmitted electrons or BSE. Effective imaging using BSE requires special fixation and en bloc contrasting of samples. BSE imaging has resulted in the development of volume imaging techniques, including array tomography (AT) and serial block-face imaging (SBF-SEM). In AT, serial ultrathin sections are collected manually on a solid substrate such as a glass and silicon wafer or automatically on a tape using a special ultramicrotome. The imaging of serial sections is used to obtain three-dimensional (3D) information. SBF-SEM is based on removing the top layer of a resin-embedded sample using an ultramicrotome inside the SEM specimen chamber and then imaging the exposed surface with a BSE detector. The steps of cutting and imaging the resin block are repeated hundreds or thousands of times to obtain a z-stack for 3D analyses.

Niwaki Instead of Random Forests: Targeted Serial Sectioning Scanning Electron Microscopy With Reimaging Capabilities for Exploring Central Nervous System Cell Biology and Pathology

Ultrastructural analysis of discrete neurobiological structures by volume scanning electron microscopy (SEM) often constitutes a "needle-in-the-haystack" problem and therefore relies on sophisticated search strategies. The appropriate SEM approach for a given relocation task not only depends on the desired final image quality but also on the complexity and required accuracy of the screening process. Block-face SEM techniques like Focused Ion Beam or serial block-face SEM are "one-shot" imaging runs by nature and, thus, require precise relocation prior to acquisition. In contrast, "multi-shot" approaches conserve the sectioned tissue through the collection of serial sections onto solid support and allow reimaging. These tissue libraries generated by Array Tomography or Automated Tape Collecting Ultramicrotomy can be screened at low resolution to target high resolution SEM. This is particularly useful if a structure of interest is rare or has been predetermined by correlated light microscopy, which can assign molecular, dynamic and functional information to an ultrastructure. As such approaches require bridging mm to nm scales, they rely on tissue trimming at different stages of sample processing. Relocation is facilitated by endogenous or exogenous landmarks that are visible by several imaging modalities, combined with appropriate registration strategies that allow overlaying images of various sources. Here, we discuss the opportunities of using multi-shot serial sectioning SEM approaches, as well as suitable trimming and registration techniques, to slim down the high-resolution imaging volume to the actual structure of interest and hence facilitate ambitious targeted volume SEM projects.

Connectomics of Bone to Brain-Probing Physical Renderings of Cellular Experience

“Brainless” cells, the living constituents inhabiting all biological materials, exhibit remarkably smart, i.e., stimuli-responsive and adaptive, behavior. The emergent spatial and temporal patterns of adaptation, observed as changes in cellular connectivity and tissue remodeling by cells, underpin neuroplasticity, muscle memory, immunological imprinting, and sentience itself, in diverse physiological systems from brain to bone. Connectomics addresses the direct connectivity of cells and cells’ adaptation to dynamic environments through manufacture of extracellular matrix, forming tissues and architectures comprising interacting organs and systems of organisms. There is imperative to understand the physical renderings of cellular experience throughout life, from the time of emergence, to growth, adaptation and aging-associated degeneration of tissues. Here we address this need through development of technological approaches that incorporate cross length scale (nm to m) structural data, acquired via multibeam scanning electron microscopy, with machine learning and information transfer using network modeling approaches. This pilot case study uses cutting edge imaging methods for nano- to meso-scale study of cellular inhabitants within human hip tissue resected during the normal course of hip replacement surgery. We discuss the technical approach and workflow and identify the resulting opportunities as well as pitfalls to avoid, delineating a path for cellular connectomics studies in diverse tissue/organ environments and their interactions within organisms and across species. Finally, we discuss the implications of the outlined approach for neuromechanics and the control of physical behavior and neuromuscular training.

Methods for analyzing neuronal structure and activity in Caenorhabditis elegans

The model research animal Caenorhabditis elegans has unique properties making it particularly advantageous for studies of the nervous system. The nervous system is composed of a stereotyped complement of neurons connected in a consistent manner. Here, we describe methods for studying nervous system structure and function. The transparency of the animal makes it possible to visualize and identify neurons in living animals with fluorescent probes. These methods have been recently enhanced for the efficient use of neuron-specific reporter genes. Because of its simple structure, for a number of years, C. elegans has been at the forefront of connectomic studies defining synaptic connectivity by electron microscopy. This field is burgeoning with new, more powerful techniques, and recommended up-to-date methods are here described that encourage the possibility of new work in C. elegans. Fluorescent probes for single synapses and synaptic connections have allowed verification of the EM reconstructions and for experimental approaches to synapse formation. Advances in microscopy and in fluorescent reporters sensitive to Ca2+ levels have opened the way to observing activity within single neurons across the entire nervous system.

Permeabilization-free en bloc immunohistochemistry for correlative microscopy

A dense reconstruction of neuronal synaptic connectivity typically requires high-resolution 3D electron microscopy (EM) data, but EM data alone lacks functional information about neurons and synapses. One approach to augment structural EM datasets is with the fluorescent immunohistochemical (IHC) localization of functionally relevant proteins. We describe a protocol that obviates the requirement of tissue permeabilization in thick tissue sections, a major impediment for correlative pre-embedding IHC and EM. We demonstrate the permeabilization-free labeling of neuronal cell types, intracellular enzymes, and synaptic proteins in tissue sections hundreds of microns thick in multiple brain regions from mice while simultaneously retaining the ultrastructural integrity of the tissue. Finally, we explore the utility of this protocol by performing proof-of-principle correlative experiments combining two-photon imaging of protein distributions and 3D EM.

Optimization of negative stage bias potential for faster imaging in large-scale electron microscopy

•

The use of a negative bias potential was empirically optimized for tissue imaging with SEM.

•

Optimized bias potential leads to a factor 20 increase in imaging speeds as well as an order of magnitude improvement to SNR.

•

SNR increase results from a combination of BSE acceleration and detector response.

•

Similar increases to SNR can be obtained when a magnetic immersion field is combined with a negative bias potential.

•

Stage bias can be applied within an integrated fluorescence and electron microscope allowing for fast correlative imaging of tissue sections.

Large-scale electron microscopy (EM) allows analysis of both tissues and macromolecules in a semi-automated manner, but acquisition rate forms a bottleneck. We reasoned that a negative bias potential may be used to enhance signal collection, allowing shorter dwell times and thus increasing imaging speed. Negative bias potential has previously been used to tune penetration depth in block-face imaging. However, optimization of negative bias potential for application in thin section imaging will be needed prior to routine use and application in large-scale EM. Here, we present negative bias potential optimized through a combination of simulations and empirical measurements. We find that the use of a negative bias potential generally results in improvement of image quality and signal-to-noise ratio (SNR). The extent of these improvements depends on the presence and strength of a magnetic immersion field. Maintaining other imaging conditions and aiming for the same image quality and SNR, the use of a negative stage bias can allow for a 20-fold decrease in dwell time, thus reducing the time for a week long acquisition to less than 8 h. We further show that negative bias potential can be applied in an integrated correlative light electron microscopy (CLEM) application, allowing fast acquisition of a high precision overlaid LM-EM dataset. Application of negative stage bias potential will thus help to solve the current bottleneck of image acquisition of large fields of view at high resolution in large-scale microscopy.

Protocol for preparation of heterogeneous biological samples for 3D electron microscopy: a case study for insects

Modern morphological and structural studies are coming to a new level by incorporating the latest methods of three-dimensional electron microscopy (3D-EM). One of the key problems for the wide usage of these methods is posed by difficulties with sample preparation, since the methods work poorly with heterogeneous (consisting of tissues different in structure and in chemical composition) samples and require expensive equipment and usually much time. We have developed a simple protocol allows preparing heterogeneous biological samples suitable for 3D-EM in a laboratory that has a standard supply of equipment and reagents for electron microscopy. This protocol, combined with focused ion-beam scanning electron microscopy, makes it possible to study 3D ultrastructure of complex biological samples, e.g., whole insect heads, over their entire volume at the cellular and subcellular levels. The protocol provides new opportunities for many areas of study, including connectomics.

Reconstruction of motor control circuits in adult Drosophila using automated transmission electron microscopy

To investigate circuit mechanisms underlying locomotor behavior, we used serial-section electron microscopy (EM) to acquire a synapse-resolution dataset containing the ventral nerve cord (VNC) of an adult female Drosophila melanogaster. To generate this dataset, we developed GridTape, a technology that combines automated serial-section collection with automated high-throughput transmission EM. Using this dataset, we studied neuronal networks that control leg and wing movements by reconstructing all 507 motor neurons that control the limbs. We show that a specific class of leg sensory neurons synapses directly onto motor neurons with the largest-caliber axons on both sides of the body, representing a unique pathway for fast limb control. We provide open access to the dataset and reconstructions registered to a standard atlas to permit matching of cells between EM and light microscopy data. We also provide GridTape instrumentation designs and software to make large-scale EM more accessible and affordable to the scientific community.

ATUM-FIB microscopy for targeting and multiscale imaging of rare events in mouse cortex

Here, we describe a detailed workflow for ATUM-FIB microscopy, a hybrid method that combines serial-sectioning scanning electron microscopy (SEM) with focused ion beam SEM (FIB-SEM). This detailed protocol is optimized for mouse cortex samples. The main processing steps include the generation of semi-thick sections from sequentially cured resin blocks using a heated microtomy approach. We demonstrate the different imaging modalities, including serial light and electron microscopy for target recognition and FIB-SEM for isotropic imaging of regions of interest.

For complete details on the use and execution of this protocol, please refer to Kislinger et al. (2020).

•

A protocol for serial semi-thick ultramicrotomy of resin-embedded neuronal tissue

•

Serial semi-thick sections on tape inspected by light and scanning electron microscopy

•

Targeting regions of interest by FIB-SEM with high resolution isotropic voxels

A petascale automated imaging pipeline for mapping neuronal circuits with high-throughput transmission electron microscopy

Electron microscopy (EM) is widely used for studying cellular structure and network connectivity in the brain. We have built a parallel imaging pipeline using transmission electron microscopes that scales this technology, implements 24/7 continuous autonomous imaging, and enables the acquisition of petascale datasets. The suitability of this architecture for large-scale imaging was demonstrated by acquiring a volume of more than 1 mm3 of mouse neocortex, spanning four different visual areas at synaptic resolution, in less than 6 months. Over 26,500 ultrathin tissue sections from the same block were imaged, yielding a dataset of more than 2 petabytes. The combined burst acquisition rate of the pipeline is 3 Gpixel per sec and the net rate is 600 Mpixel per sec with six microscopes running in parallel. This work demonstrates the feasibility of acquiring EM datasets at the scale of cortical microcircuits in multiple brain regions and species.

A connectome and analysis of the adult Drosophila central brain

The neural circuits responsible for animal behavior remain largely unknown. We summarize new methods and present the circuitry of a large fraction of the brain of the fruit fly Drosophila melanogaster. Improved methods include new procedures to prepare, image, align, segment, find synapses in, and proofread such large data sets. We define cell types, refine computational compartments, and provide an exhaustive atlas of cell examples and types, many of them novel. We provide detailed circuits consisting of neurons and their chemical synapses for most of the central brain. We make the data public and simplify access, reducing the effort needed to answer circuit questions, and provide procedures linking the neurons defined by our analysis with genetic reagents. Biologically, we examine distributions of connection strengths, neural motifs on different scales, electrical consequences of compartmentalization, and evidence that maximizing packing density is an important criterion in the evolution of the fly's brain.

Multiscale ATUM-FIB Microscopy Enables Targeted Ultrastructural Analysis at Isotropic Resolution

Volume electron microscopy enables the ultrastructural analysis of biological tissue. Currently, the techniques involving ultramicrotomy (ATUM, ssTEM) allow large fields of view but afford only limited z-resolution, whereas ion beam-milling approaches (FIB-SEM) yield isotropic voxels but are restricted in volume size. Now we present a hybrid method, named ATUM-FIB, which combines the advantages of both approaches. ATUM-FIB is based on serial sectioning of tissue into “semithick” (2–10 μm) sections collected onto tape. Serial light and electron microscopy allows the identification of regions of interest that are then directly accessible for targeted FIB-SEM. The set of semithick sections thus represents a tissue “library” which provides three-dimensional context information that can be probed “on demand” by local high-resolution analysis. We demonstrate the potential of this technique to reveal the ultrastructure of rare but pathologically important events by identifying microglia contact sites with amyloid plaques in a mouse model of familial Alzheimer's disease.

•

Fast nanometer-resolution relocation and 3D imaging of preselected structures

•

Transparent tape-based multiscale light and volume electron microscopy

•

Heated ultramicrotomy at 2–10 μm with precured epoxy resin

Massive Data Management and Sharing Module for Connectome Reconstruction

Recently, with the rapid development of electron microscopy (EM) technology and the increasing demand of neuron circuit reconstruction, the scale of reconstruction data grows significantly. This brings many challenges, one of which is how to effectively manage large-scale data so that researchers can mine valuable information. For this purpose, we developed a data management module equipped with two parts, a storage and retrieval module on the server-side and an image cache module on the client-side. On the server-side, Hadoop and HBase are introduced to resolve massive data storage and retrieval. The pyramid model is adopted to store electron microscope images, which represent multiresolution data of the image. A block storage method is proposed to store volume segmentation results. We design a spatial location-based retrieval method for fast obtaining images and segments by layers rapidly, which achieves a constant time complexity. On the client-side, a three-level image cache module is designed to reduce latency when acquiring data. Through theoretical analysis and practical tests, our tool shows excellent real-time performance when handling large-scale data. Additionally, the server-side can be used as a backend of other similar software or a public database to manage shared datasets, showing strong scalability.

Gas cluster ion beam SEM for imaging of large tissue samples with 10 nm isotropic resolution

We demonstrate gas cluster ion beam scanning electron microscopy (SEM), in which wide-area ion milling is performed on a series of thick tissue sections. This three-dimensional electron microscopy technique acquires datasets with <10 nm isotropic resolution of each section, and these can then be stitched together to span the sectioned volume. Incorporating gas cluster ion beam SEM into existing single-beam and multibeam SEM workflows should be straightforward, increasing reliability while improving z resolution by a factor of three or more.

Revealing the Synaptic Hodology of Mammalian Neural Circuits With Multiscale Neurocartography

The functional features of neural circuits are determined by a combination of properties that range in scale from projections systems across the whole brain to molecular interactions at the synapse. The burgeoning field of neurocartography seeks to map these relevant features of brain structure—spanning a volume ∼20 orders of magnitude—to determine how neural circuits perform computations supporting cognitive function and complex behavior. Recent technological breakthroughs in tissue sample preparation, high-throughput electron microscopy imaging, and automated image analyses have produced the first visualizations of all synaptic connections between neurons of invertebrate model systems. However, the sheer size of the central nervous system in mammals implies that reconstruction of the first full brain maps at synaptic scale may not be feasible for decades. In this review, we outline existing and emerging technologies for neurocartography that complement electron microscopy-based strategies and are beginning to derive some basic organizing principles of circuit hodology at the mesoscale, microscale, and nanoscale. Specifically, we discuss how a host of light microscopy techniques including array tomography have been utilized to determine both long-range and subcellular organizing principles of synaptic connectivity. In addition, we discuss how new techniques, such as two-photon serial tomography of the entire mouse brain, have become attractive approaches to dissect the potential connectivity of defined cell types. Ultimately, principles derived from these techniques promise to facilitate a conceptual understanding of how connectomes, and neurocartography in general, can be effectively utilized toward reaching a mechanistic understanding of circuit function.

The Fly Brain Atlas

The brain's synaptic networks endow an animal with powerfully adaptive biological behavior. Maps of such synaptic circuits densely reconstructed in those model brains that can be examined and manipulated by genetic means offer the best prospect for understanding the underlying biological bases of behavior. That prospect is now technologically feasible and a scientifically enabling possibility in neurobiology, much as genomics has been in molecular biology and genetics. In Drosophila, two major advances are in electron microscopic technology, using focused ion beam-scanning electron microscopy (FIB-SEM) milling to capture and align digital images, and in computer-aided reconstruction of neuron morphologies. The last decade has witnessed enormous progress in detailed knowledge of the actual synaptic circuits formed by real neurons. Advances in various brain regions that heralded identification of the motion-sensing circuits in the optic lobe are now extending to other brain regions, with the prospect of encompassing the fly's entire nervous system, both brain and ventral nerve cord.

Big data in nanoscale connectomics, and the greed for training labels

The neurosciences have developed methods that outpace most other biomedical fields in terms of acquired bytes. We review how the information content and analysis challenge of such data indicates that electron microscopy (EM)-based connectomics is an especially hard problem. Here, as in many other current machine learning applications, the need for excessive amounts of labelled data while utilizing only a small fraction of available raw image data for algorithm training illustrates the still fundamental gap between artificial and biological intelligence. Substantial improvements of label and energy efficiency in machine learning may be required to address the formidable challenge of acquiring the nanoscale connectome of a human brain.

Bright sub-20-nm cathodoluminescent nanoprobes for electron microscopy

Electron microscopy has been instrumental in our understanding of complex biological systems. Although electron microscopy reveals cellular morphology with nanoscale resolution, it does not provide information on the location of different types of proteins. An electron-microscopy-based bioimaging technology capable of localizing individual proteins and resolving protein-protein interactions with respect to cellular ultrastructure would provide important insights into the molecular biology of a cell. Here, we synthesize small lanthanide-doped nanoparticles and measure the absolute photon emission rate of individual nanoparticles resulting from a given electron excitation flux (cathodoluminescence). Our results suggest that the optimization of nanoparticle composition, synthesis protocols and electron imaging conditions can lead to sub-20-nm nanolabels that would enable high signal-to-noise localization of individual biomolecules within a cellular context. In ensemble measurements, these labels exhibit narrow spectra of nine distinct colours, so the imaging of biomolecules in a multicolour electron microscopy modality may be possible.

Structure-Based Intensity Propagation for 3-D Brain Reconstruction With Multilayer Section Microscopy

Microscopy is widely used for brain research because of its high resolution and ability to stain for many different biomarkers. Since whole brains are usually sectioned for tissue staining and imaging, reconstruction of 3D brain volumes from these sections is important for visualization and analysis. Recently developed tissue clearing techniques and advanced confocal microscopy enable multilayer sections to be imaged without compromising the resolution. However, noticeable structure inconsistence occurs if surface layers are used to align these sections. In this paper, a structure-based intensity propagation method is designed for the robust representation of multilayer sections. The 3D structures in reconstructed brains are more consistent using the proposed methods. Experiments are conducted on 367 multilayer sections from 20 mouse brains. The average reconstruction quality measured by the structure consistence index increases by 45% with the tissue flattening method and 29% further with the structure-based intensity propagation.

The Worm Connectome: Back to the Future

Large-scale efforts are underway to map the neuronal connectomes of various animal species. In 1986 White et al. reported the first complete reconstruction of the nervous system connectome of an animal. Largely through manual reconstruction of serial electron micrographs, the authors characterized the morphology of each of the 302 neurons of the adult nematode C. elegans, and mapped the chemical and electrical synapses that connect them. This wiring diagram has profoundly influenced behavioral neurobiology and network science, and it continues to offer a guide for the impact of connectomics on neurobiology.

Changes of Synaptic Structures Associated with Learning, Memory and Diseases

Synaptic plasticity is widely believed to be the cellular basis of learning and memory. It is influenced by various factors including development, sensory experiences, and brain disorders. Long-term synaptic plasticity is accompanied by protein synthesis and trafficking, leading to structural changes of the synapse. In this review, we focus on the synaptic structural plasticity, which has mainly been studied with in vivo two-photon laser scanning microscopy. We also discuss how a special type of synapses, the multi-contact synapses (including those formed by multi-synaptic boutons and multi-synaptic spines), are associated with experience and learning.

Cortical column and whole-brain imaging with molecular contrast and nanoscale resolution

Optical and electron microscopy have made tremendous inroads toward understanding the complexity of the brain. However, optical microscopy offers insufficient resolution to reveal subcellular details, and electron microscopy lacks the throughput and molecular contrast to visualize specific molecular constituents over millimeter-scale or larger dimensions. We combined expansion microscopy and lattice light-sheet microscopy to image the nanoscale spatial relationships between proteins across the thickness of the mouse cortex or the entire Drosophila brain. These included synaptic proteins at dendritic spines, myelination along axons, and presynaptic densities at dopaminergic neurons in every fly brain region. The technology should enable statistically rich, large-scale studies of neural development, sexual dimorphism, degree of stereotypy, and structural correlations to behavior or neural activity, all with molecular contrast.

Multi-Beam Scanning Electron Microscopy for High-Throughput Imaging in Connectomics Research

Major progress has been achieved in recent years in three-dimensional microscopy techniques. This applies to the life sciences in general, but specifically the neuroscientific field has been a main driver for developments regarding volume imaging. In particular, scanning electron microscopy offers new insights into the organization of cells and tissues by volume imaging methods, such as serial section array tomography, serial block-face imaging or focused ion beam tomography. However, most of these techniques are restricted to relatively small tissue volumes due to the limited acquisition throughput of most standard imaging techniques. Recently, a novel multi-beam scanning electron microscope technology optimized to the imaging of large sample areas has been developed. Complemented by the commercialization of automated sample preparation robots, the mapping of larger, cubic millimeter range tissue volumes at high-resolution is now within reach. This Mini Review will provide a brief overview of the various approaches to electron microscopic volume imaging, with an emphasis on serial section array tomography and multi-beam scanning electron microscopic imaging.

NeuTu: Software for Collaborative, Large-Scale, Segmentation-Based Connectome Reconstruction

Reconstructing a connectome from an EM dataset often requires a large effort of proofreading automatically generated segmentations. While many tools exist to enable tracing or proofreading, recent advances in EM imaging and segmentation quality suggest new strategies and pose unique challenges for tool design to accelerate proofreading. Namely, we now have access to very large multi-TB EM datasets where (1) many segments are largely correct, (2) segments can be very large (several GigaVoxels), and where (3) several proofreaders and scientists are expected to collaborate simultaneously. In this paper, we introduce NeuTu as a solution to efficiently proofread large, high-quality segmentation in a collaborative setting. NeuTu is a client program of our high-performance, scalable image database called DVID so that it can easily be scaled up. Besides common features of typical proofreading software, NeuTu tames unprecedentedly large data with its distinguishing functions, including: (1) low-latency 3D visualization of large mutable segmentations; (2) interactive splitting of very large false merges with highly optimized semi-automatic segmentation; (3) intuitive user operations for investigating or marking interesting points in 3D visualization; (4) visualizing proofreading history of a segmentation; and (5) real-time collaborative proofreading with lock-based concurrency control. These unique features have allowed us to manage the workflow of proofreading a large dataset smoothly without dividing them into subsets as in other segmentation-based tools. Most importantly, NeuTu has enabled some of the largest connectome reconstructions as well as interesting discoveries in the fly brain.

Large Volume Electron Microscopy and Neural Microcircuit Analysis

One recent technical innovation in neuroscience is microcircuit analysis using three-dimensional reconstructions of neural elements with a large volume Electron microscopy (EM) data set. Large-scale data sets are acquired with newly-developed electron microscope systems such as automated tape-collecting ultramicrotomy (ATUM) with scanning EM (SEM), serial block-face EM (SBEM) and focused ion beam-SEM (FIB-SEM). Currently, projects are also underway to develop computer applications for the registration and segmentation of the serially-captured electron micrographs that are suitable for analyzing large volume EM data sets thoroughly and efficiently. The analysis of large volume data sets can bring innovative research results. These recently available techniques promote our understanding of the functional architecture of the brain.