Extensive soma-soma plate-like contact sites (ephapses) connect suprachiasmatic nucleus neurons.

The hypothalamic suprachiasmatic nucleus (SCN) is the central pacemaker for mammalian circadian rhythms. As such, this ensemble of cell-autonomous neuronal oscillators with divergent periods must maintain coordinated oscillations. To investigate ultrastructural features enabling such synchronization, 805 coronal ultrathin sections of mouse SCN tissue were imaged with electron microscopy and aligned into a volumetric stack, from which selected neurons within the SCN core were reconstructed in silico. We found that clustered SCN core neurons were physically connected to each other via multiple large soma-to-soma plate-like contacts. In some cases, a sliver of a glial process was interleaved. These contacts were large, covering on average ∼21% of apposing neuronal somata. It is possible that contacts may be the electrophysiological substrate for synchronization between SCN neurons. Such plate-like contacts may explain why the synchronization of SCN neurons is maintained even when chemical synaptic transmission or electrical synaptic transmission via gap junctions is blocked. Such ephaptic contact-mediated synchronization among nearby neurons may therefore contribute to the wave-like oscillations of circadian core clock genes and calcium signals observed in the SCN.

Three­dimensional reconstruction of SCN tissue via serial electron microscopy revealed a novel structural feature of SCN neurons that may account for interneuronal synchronization that persists even when the predominant mechanisms of neuronal communication are blocked. We found that SCN core neurons are connected by multiple soma­soma contact specializations, ultrastructural elements that could enable synchronization of tightly packed neurons organized in clustered networks. This extensive network of plate­like soma­soma contacts among clustered SCN neurons may provide insight into how ∼20,000 autonomous neuronal oscillators with a broad range of intrinsic periods remain synchronized in the absence of ordinary communication modalities, thereby conferring the resilience required for the SCN to function as the mammalian circadian pacemaker.

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.

Two-stage error detection to improve electron microscopy image mosaicking.

Large-scale electron microscopy (EM) has enabled the reconstruction of brain connectomes at the synaptic level by serially scanning over massive areas of sample sections. The acquired big EM data sets raise the great challenge of image mosaicking at high accuracy. Currently, it simply follows the conventional algorithms designed for natural images, which are usually composed of only a few tiles, using a single type of keypoint feature that would sacrifice speed for stronger performance. Even so, in the process of stitching hundreds of thousands of tiles for large EM data, errors are still inevitable and diverse. Moreover, there has not yet been an appropriate metric to quantitatively evaluate the stitching of biomedical EM images. Here we propose a two-stage error detection method to improve the EM image mosaicking. It firstly uses point-based error detection in combination with a hybrid feature framework to expedite the stitching computation while maintaining high accuracy. Following is the second detection of unresolved errors with a newly designed metric of EM stitched image quality assessment (EMSIQA). The novel detection-based mosaicking pipeline is tested on large EM data sets and proven to be more effective and as accurate when compared with existing methods.

A petavoxel fragment of human cerebral cortex reconstructed at nanoscale resolution.

To fully understand how the human brain works, knowledge of its structure at high resolution is needed. Presented here is a computationally intensive reconstruction of the ultrastructure of a cubic millimeter of human temporal cortex that was surgically removed to gain access to an underlying epileptic focus. It contains about 57,000 cells, about 230 millimeters of blood vessels, and about 150 million synapses and comprises 1.4 petabytes. Our analysis showed that glia outnumber neurons 2:1, oligodendrocytes were the most common cell, deep layer excitatory neurons could be classified on the basis of dendritic orientation, and among thousands of weak connections to each neuron, there exist rare powerful axonal inputs of up to 50 synapses. Further studies using this resource may bring valuable insights into the mysteries of the human brain.

Sample preparation methods for volume electron microscopy in mollusc Berghia stephanieae.

Creating a high-resolution brain atlas in diverse species offers crucial insights into general principles underlying brain function and development. A volume electron microscopy approach to generate such neural maps has been gaining importance due to advances in imaging, data storage capabilities, and data analysis protocols. Sample preparation remains challenging and is a crucial step to accelerate the imaging and data processing pipeline. Here, we introduce several replicable methods for processing the brains of the gastropod mollusc, Berghia stephanieae for volume electron microscopy. Although high-pressure freezing is the most reliable method, the depth of cryopreservation is a severe limitation for large tissue samples. We introduce a BROPA-based method using pyrogallol and methods to rapidly process samples that can save hours at the bench. This is the first report on sample preparation and imaging pipeline for volume electron microscopy in a gastropod mollusc, opening up the potential for connectomic analysis and comparisons with other phyla.

Mapping of neuronal and glial primary cilia contactome and connectome in the human cerebral cortex.

Primary cilia act as antenna receivers of environmental signals and enable effective neuronal or glial responses. Disruption of their function is associated with circuit disorders. To understand the signals these cilia receive, we comprehensively mapped cilia's contacts within the human cortical connectome using serial-section EM reconstruction of a 1 mm3 cortical volume, spanning the entire cortical thickness. We mapped the "contactome" of cilia emerging from neurons and astrocytes in every cortical layer. Depending on the layer and cell type, cilia make distinct patterns of contact. Primary cilia display cell-type- and layer-specific variations in size, shape, and microtubule axoneme core, which may affect their signaling competencies. Neuronal cilia are intrinsic components of a subset of cortical synapses and thus a part of the connectome. This diversity in the structure, contactome, and connectome of primary cilia endows each neuron or glial cell with a unique barcode of access to the surrounding neural circuitry.

Multi-layered maps of neuropil with segmentation-guided contrastive learning.

Maps of the nervous system that identify individual cells along with their type, subcellular components and connectivity have the potential to elucidate fundamental organizational principles of neural circuits. Nanometer-resolution imaging of brain tissue provides the necessary raw data, but inferring cellular and subcellular annotation layers is challenging. We present segmentation-guided contrastive learning of representations (SegCLR), a self-supervised machine learning technique that produces representations of cells directly from 3D imagery and segmentations. When applied to volumes of human and mouse cortex, SegCLR enables accurate classification of cellular subcompartments and achieves performance equivalent to a supervised approach while requiring 400-fold fewer labeled examples. SegCLR also enables inference of cell types from fragments as small as 10 µm, which enhances the utility of volumes in which many neurites are truncated at boundaries. Finally, SegCLR enables exploration of layer 5 pyramidal cell subtypes and automated large-scale analysis of synaptic partners in mouse visual cortex.

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-ß (Aß) 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, Aß, 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 Aß 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 Aß 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 Aß 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.

Probing Molecular Diversity and Ultrastructure of Brain Cells with Fluorescent Aptamers.

Detergent-free immunolabeling has been proven feasible for correlated light and electron microscopy, but its application is restricted by the availability of suitable affinity reagents. Here we introduce CAptVE, a method using slow off-rate modified aptamers for cell fluorescence labeling on ultrastructurally reconstructable electron micrographs. CAptVE provides labeling for a wide range of biomarkers, offering a pathway to integrate molecular analysis into recent approaches to delineate neural circuits via connectomics.

Vimo - Visual Analysis of Neuronal Connectivity Motifs.

Recent advances in high-resolution connectomics provide researchers with access to accurate petascale reconstructions of neuronal circuits and brain networks for the first time. Neuroscientists are analyzing these networks to better understand information processing in the brain. In particular, scientists are interested in identifying specific small network motifs, i.e., repeating subgraphs of the larger brain network that are believed to be neuronal building blocks. Although such motifs are typically small (e.g., 2 - 6 neurons), the vast data sizes and intricate data complexity present significant challenges to the search and analysis process. To analyze these motifs, it is crucial to review instances of a motif in the brain network and then map the graph structure to detailed 3D reconstructions of the involved neurons and synapses. We present Vimo, an interactive visual approach to analyze neuronal motifs and motif chains in large brain networks. Experts can sketch network motifs intuitively in a visual interface and specify structural properties of the involved neurons and synapses to query large connectomics datasets. Motif instances (MIs) can be explored in high-resolution 3D renderings. To simplify the analysis of MIs, we designed a continuous focus&context metaphor inspired by visual abstractions. This allows users to transition from a highly-detailed rendering of the anatomical structure to views that emphasize the underlying motif structure and synaptic connectivity. Furthermore, Vimo supports the identification of motif chains where a motif is used repeatedly (e.g., 2 - 4 times) to form a larger network structure. We evaluate Vimo in a user study and an in-depth case study with seven domain experts on motifs in a large connectome of the fruit fly, including more than 21,000 neurons and 20 million synapses. We find that Vimo enables hypothesis generation and confirmation through fast analysis iterations and connectivity highlighting.

A Scalable Staining Strategy for Whole-Brain Connectomics.

Mapping the complete synaptic connectivity of a mammalian brain would be transformative, revealing the pathways underlying perception, behavior, and memory. Serial section electron microscopy, via membrane staining using osmium tetroxide, is ideal for visualizing cells and synaptic connections but, in whole brain samples, faces significant challenges related to chemical treatment and volume changes. These issues can adversely affect both the ultrastructural quality and macroscopic tissue integrity. By leveraging time-lapse X-ray imaging and brain proxies, we have developed a 12-step protocol, ODeCO, that effectively infiltrates osmium throughout an entire mouse brain while preserving ultrastructure without any cracks or fragmentation, a necessary prerequisite for constructing the first comprehensive mouse brain connectome.

Preserving extracellular space for high-quality optical and ultrastructural studies of whole mammalian brains.

Analysis of brain structure, connectivity, and molecular diversity relies on effective tissue fixation. Conventional tissue fixation causes extracellular space (ECS) loss, complicating the segmentation of cellular objects from electron microscopy datasets. Previous techniques for preserving ECS in mammalian brains utilizing high-pressure perfusion can give inconsistent results owing to variations in the hydrostatic pressure within the vasculature. A more reliable fixation protocol that uniformly preserves the ECS throughout whole brains would greatly benefit a wide range of neuroscience studies. Here, we report a straightforward transcardial perfusion strategy that preserves ECS throughout the whole rodent brain. No special setup is needed besides sequential solution changes, and the protocol offers excellent reproducibility. In addition to better capturing tissue ultrastructure, preservation of ECS has many downstream advantages such as accelerating heavy-metal staining for electron microscopy, improving detergent-free immunohistochemistry for correlated light and electron microscopy, and facilitating lipid removal for tissue clearing.

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.

Multiplexed volumetric CLEM enabled by antibody derivatives provides new insights into the cytology of the mouse cerebellar cortex.

Mapping neuronal networks that underlie behavior has become a central focus in neuroscience. While serial section electron microscopy (ssEM) can reveal the fine structure of neuronal networks (connectomics), it does not provide the molecular information that helps identify cell types or their functional properties. Volumetric correlated light and electron microscopy (vCLEM) combines ssEM and volumetric fluorescence microscopy to incorporate molecular labeling into ssEM datasets. We developed an approach that uses small fluorescent single-chain variable fragment (scFv) immuno-probes to perform multiplexed detergent-free immuno-labeling and ssEM on the same samples. We generated eight such fluorescent scFvs that targeted useful markers for brain studies (green fluorescent protein, glial fibrillary acidic protein, calbindin, parvalbumin, voltage-gated potassium channel subfamily A member 2, vesicular glutamate transporter 1, postsynaptic density protein 95, and neuropeptide Y). To test the vCLEM approach, six different fluorescent probes were imaged in a sample of the cortex of a cerebellar lobule (Crus 1), using confocal microscopy with spectral unmixing, followed by ssEM imaging of the same sample. The results show excellent ultrastructure with superimposition of the multiple fluorescence channels. Using this approach we could document a poorly described cell type in the cerebellum, 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.

Connectomics of the Octopus vulgaris vertical lobe provides insight into conserved and novel principles of a memory acquisition network.

Here, we present the first analysis of the connectome of a small volume of the Octopus vulgaris vertical lobe (VL), a brain structure mediating the acquisition of long-term memory in this behaviorally advanced mollusk. Serial section electron microscopy revealed new types of interneurons, cellular components of extensive modulatory systems, and multiple synaptic motifs. The sensory input to the VL is conveyed via~1.8 × 106 axons that sparsely innervate two parallel and interconnected feedforward networks formed by the two types of amacrine interneurons (AM), simple AMs (SAMs) and complex AMs (CAMs). SAMs make up 89.3% of the~25 × 106VL cells, each receiving a synaptic input from only a single input neuron on its non-bifurcating primary neurite, suggesting that each input neuron is represented in only~12 ± 3.4SAMs. This synaptic site is likely a 'memory site' as it is endowed with LTP. The CAMs, a newly described AM type, comprise 1.6% of the VL cells. Their bifurcating neurites integrate multiple inputs from the input axons and SAMs. While the SAM network appears to feedforward sparse 'memorizable' sensory representations to the VL output layer, the CAMs appear to monitor global activity and feedforward a balancing inhibition for 'sharpening' the stimulus-specific VL output. While sharing morphological and wiring features with circuits supporting associative learning in other animals, the VL has evolved a unique circuit that enables associative learning based on feedforward information flow.

Multiplexed volumetric CLEM enabled by antibody derivatives provides new insights into the cytology of the mouse cerebellar cortex.

Mapping neuronal networks that underlie behavior has become a central focus in neuroscience. While serial section electron microscopy (ssEM) can reveal the fine structure of neuronal networks (connectomics), it does not provide the molecular information that helps identify cell types or their functional properties. Volumetric correlated light and electron microscopy (vCLEM) combines ssEM and volumetric fluorescence microscopy to incorporate molecular labeling into ssEM datasets. We developed an approach that uses small fluorescent single-chain variable fragment (scFv) immuno-probes to perform multiplexed detergent-free immuno-labeling and ssEM on the same samples. We generated eight such fluorescent scFvs that targeted useful markers for brain studies (green fluorescent protein, glial fibrillary acidic protein, calbindin, parvalbumin, voltage-gated potassium channel subfamily A member 2, vesicular glutamate transporter 1, postsynaptic density protein 95, and neuropeptide Y). To test the vCLEM approach, six different fluorescent probes were imaged in a sample of the cortex of a cerebellar lobule (Crus 1), using confocal microscopy with spectral unmixing, followed by ssEM imaging of the same sample. The results show excellent ultrastructure with superimposition of the multiple fluorescence channels. Using this approach we could document a poorly described cell type in the cerebellum, 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.

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.

3D reconstruction of the cerebellar germinal layer reveals tunneling connections between developing granule cells.

The difficulty of retrieving high-resolution, in vivo evidence of the proliferative and migratory processes occurring in neural germinal zones has limited our understanding of neurodevelopmental mechanisms. Here, we used a connectomic approach using a high-resolution, serial-sectioning scanning electron microscopy volume to investigate the laminar cytoarchitecture of the transient external granular layer (EGL) of the developing cerebellum, where granule cells coordinate a series of mitotic and migratory events. By integrating image segmentation, three-dimensional reconstruction, and deep-learning approaches, we found and characterized anatomically complex intercellular connections bridging pairs of cerebellar granule cells throughout the EGL. Connected cells were either mitotic, migratory, or transitioning between these two cell stages, displaying a chronological continuum of proliferative and migratory events never previously observed in vivo at this resolution. This unprecedented ultrastructural characterization poses intriguing hypotheses about intercellular connectivity between developing progenitors and its possible role in the development of the central nervous system.

Immersion Fixation and Staining of Multicubic Millimeter Volumes for Electron Microscopy-Based Connectomics of Human Brain Biopsies.

Connectomics allows mapping of cells and their circuits at the nanometer scale in volumes of approximately 1 mm3. Given that the human cerebral cortex can be 3 mm in thickness, larger volumes are required. Larger-volume circuit reconstructions of human brain are limited by 1) the availability of fresh biopsies; 2) the need for excellent preservation of ultrastructure, including extracellular space; and 3) the requirement of uniform staining throughout the sample, among other technical challenges. Cerebral cortical samples from neurosurgical patients are available owing to lead placement for deep brain stimulation. Described here is an immersion fixation, heavy metal staining, and tissue processing method that consistently provides excellent ultrastructure throughout human and rodent surgical brain samples of volumes 2 × 2 × 2 mm3 and up to 37 mm3 with one dimension ≤2 mm. This method should allow synapse-level circuit analysis in samples from patients with psychiatric and neurologic disorders.

Post-embryonic remodeling of the C. elegans motor circuit.

During development, animals can maintain behavioral output even as underlying circuitry structurally remodels. After hatching, C. elegans undergoes substantial motor neuron expansion and synapse rewiring while the animal continuously moves with an undulatory pattern. To understand how the circuit transitions from its juvenile to mature configuration without interrupting functional output, we reconstructed the C. elegans motor circuit by electron microscopy across larval development. We observed the following: First, embryonic motor neurons transiently interact with the developing post-embryonic motor neurons prior to remodeling of their juvenile wiring. Second, post-embryonic neurons initiate synapse development with their future partners as their neurites navigate through the juvenile nerve cords. Third, embryonic and post-embryonic neurons sequentially build structural machinery needed for the adult circuit before the embryonic neurons relinquish their roles to post-embryonic neurons. Fourth, this transition is repeated region by region along the body in an anterior-to-posterior sequence, following the birth order of neurons. Through this orchestrated and programmed rewiring, the motor circuit gradually transforms from asymmetric to symmetric wiring. These maturation strategies support the continuous maintenance of motor patterns as the juvenile circuit develops into the adult configuration.