The Viking viewer for connectomics: scalable multi-user annotation and summarization of large volume data sets

Synaptic Origins of the Complex Receptive Field Structure in Primate Smooth Monostratified Retinal Ganglion Cells

Considerable progress has been made in studying the receptive fields of the most common primate retinal ganglion cell (RGC) types, such as parasol RGCs. Much less is known about the rarer primate RGC types and the circuitry that gives rise to noncanonical receptive field structures. The goal of this study was to analyze synaptic inputs to smooth monostratified RGCs to determine the origins of their complex spatial receptive fields, which contain isolated regions of high sensitivity called "hotspots." Interestingly, smooth monostratified RGCs co-stratify with the well-studied parasol RGCs and are thus constrained to receiving input from bipolar and amacrine cells with processes sharing the same layer, raising the question of how their functional differences originate. Through 3D reconstructions of circuitry and synapses onto ON smooth monostratified and ON parasol RGCs from central macaque retina, we identified four distinct sampling strategies employed by smooth and parasol RGCs to extract diverse response properties from co-stratifying bipolar and amacrine cells. The two RGC types differed in the proportion of amacrine cell input, relative contributions of co-stratifying bipolar cell types, amount of synaptic input per bipolar cell, and spatial distribution of bipolar cell synapses. Our results indicate that the smooth RGC's complex receptive field structure arises through spatial asymmetries in excitatory bipolar cell input which formed several discrete clusters comparable with physiologically measured hotspots. Taken together, our results demonstrate how the striking differences between ON parasol and ON smooth monostratified RGCs arise from distinct strategies for sampling a common set of synaptic inputs.

CAVE: Connectome Annotation Versioning Engine

Advances in Electron Microscopy, image segmentation and computational infrastructure have given rise to large-scale and richly annotated connectomic datasets which are increasingly shared across communities. To enable collaboration, users need to be able to concurrently create new annotations and correct errors in the automated segmentation by proofreading. In large datasets, every proofreading edit relabels cell identities of millions of voxels and thousands of annotations like synapses. For analysis, users require immediate and reproducible access to this constantly changing and expanding data landscape. Here, we present the Connectome Annotation Versioning Engine (CAVE), a computational infrastructure for immediate and reproducible connectome analysis in up-to petascale datasets (~1mm3) while proofreading and annotating is ongoing. For segmentation, CAVE provides a distributed proofreading infrastructure for continuous versioning of large reconstructions. Annotations in CAVE are defined by locations such that they can be quickly assigned to the underlying segment which enables fast analysis queries of CAVE's data for arbitrary time points. CAVE supports schematized, extensible annotations, so that researchers can readily design novel annotation types. CAVE is already used for many connectomics datasets, including the largest datasets available to date.

Distinctive synaptic structural motifs link excitatory retinal interneurons to diverse postsynaptic partner types

Neurons make converging and diverging synaptic connections with distinct partner types. Whether synapses involving separate partners demonstrate similar or distinct structural motifs is not yet well understood. We thus used serial electron microscopy in mouse retina to map output synapses of cone bipolar cells (CBCs) and compare their structural arrangements across bipolar types and postsynaptic partners. Three presynaptic configurations emerge-single-ribbon, ribbonless, and multiribbon synapses. Each CBC type exploits these arrangements in a unique combination, a feature also found among rabbit ON CBCs. Though most synapses are dyads, monads and triads are also seen. Altogether, mouse CBCs exhibit at least six motifs, and each CBC type uses these in a stereotypic pattern. Moreover, synapses between CBCs and particular partner types appear biased toward certain motifs. Our observations reveal synaptic strategies that diversify the output within and across CBC types, potentially shaping the distinct functions of retinal microcircuits.

Trogocytosis of neurons and glial cells by microglia in a healthy adult macaque retina

Microglial cells are the primary resident immune cells in the retina. In healthy adults, they are ramified; that is, they have extensive processes that move continually. In adult retinas, microglia maintain the normal structure and function of neurons and other glial cells, but the mechanism underlying this process is not well-understood. In the mouse hippocampus, microglia engulf small pieces of axons and presynaptic terminals via a process called trogocytosis. Here we report that microglia in the adult macaque retina also engulf pieces of neurons and glial cells, but not at sites of synapses. We analyzed microglia in a volume of serial, ultrathin sections of central macaque retina in which many neurons that ramify in the inner plexiform layer (IPL) had been reconstructed previously. We surveyed the IPL and identified the somas of microglia by their small size and scant cytoplasm. We then reconstructed the microglia and studied their interactions with other cells. We found that ramified microglia frequently ingested small pieces of each major type of inner retinal neuron and Müller glial cells via trogocytosis. There were a few instances where the interactions took place near synapses, but the synapses, themselves, were never engulfed. If trogocytosis by retinal microglia plays a role in synaptic remodeling, it was not apparent from the ultrastructure. Instead, we propose that trogocytosis enables these microglia to present antigens derived from normal inner retinal cells and, when activated, they would promote antigen-specific tolerance.

Igneous: Distributed dense 3D segmentation meshing, neuron skeletonization, and hierarchical downsampling

Three-dimensional electron microscopy images of brain tissue and their dense segmentations are now petascale and growing. These volumes require the mass production of dense segmentation-derived neuron skeletons, multi-resolution meshes, image hierarchies (for both modalities) for visualization and analysis, and tools to manage the large amount of data. However, open tools for large-scale meshing, skeletonization, and data management have been missing. Igneous is a Python-based distributed computing framework that enables economical meshing, skeletonization, image hierarchy creation, and data management using cloud or cluster computing that has been proven to scale horizontally. We sketch Igneous's computing framework, show how to use it, and characterize its performance and data storage.

Synaptic inputs to displaced intrinsically-photosensitive ganglion cells in macaque retina

Ganglion cells are the projection neurons of the retina. Intrinsically photosensitive retinal ganglion cells (ipRGCs) express the photopigment melanopsin and also receive input from rods and cones via bipolar cells and amacrine cells. In primates, multiple types of ipRGCs have been identified. The ipRGCs with somas in the ganglion cell layer have been studied extensively, but less is known about those with somas in the inner nuclear layer, the "displaced" cells. To investigate their synaptic inputs, three sets of horizontal, ultrathin sections through central macaque retina were collected using serial block-face scanning electron microscopy. One displaced ipRGC received nearly all of its excitatory inputs from ON bipolar cells and would therefore be expected to have ON responses to light. In each of the three volumes, there was also at least one cell that had a large soma in the inner nuclear layer, varicose axons and dendrites with a large diameter that formed large, extremely sparse arbor in the outermost stratum of the inner plexiform layer. They were identified as the displaced M1 type of ipRGCs based on this morphology and on the high density of granules in their somas. They received extensive input from amacrine cells, including the dopaminergic type. The vast majority of their excitatory inputs were from OFF bipolar cells, including two subtypes with extensive input from the primary rod pathway. They would be expected to have OFF responses to light stimuli below the threshold for melanopsin or soon after the offset of a light stimulus.

Conserved circuits for direction selectivity in the primate retina

The detection of motion direction is a fundamental visual function and a classic model for neural computation. In the non-primate retina, direction selectivity arises in starburst amacrine cell (SAC) dendrites, which provide selective inhibition to direction-selective retinal ganglion cells (dsRGCs). Although SACs are present in primates, their connectivity and the existence of dsRGCs remain open questions. Here, we present a connectomic reconstruction of the primate ON SAC circuit from a serial electron microscopy volume of the macaque central retina. We show that the structural basis for the SACs' ability to confer directional selectivity on postsynaptic neurons is conserved. SACs selectively target a candidate homolog to the mammalian ON-sustained dsRGCs that project to the accessory optic system (AOS) and contribute to gaze-stabilizing reflexes. These results indicate that the capacity to compute motion direction is present in the retina, which is earlier in the primate visual system than classically thought.

Automated Microscopy Image Segmentation and Analysis with Machine Learning

The development of automated quantitative image analysis pipelines requires thoughtful considerations to extract meaningful information. Commonly, extraction rules for quantitative parameters are defined and agreed beforehand to ensure repeatability between annotators. Machine/Deep Learning (ML/DL) now provides tools to automatically extract the set of rules to obtain quantitative information from the images (e.g. segmentation, enumeration, classification, etc.). Many parameters must be considered in the development of proper ML/DL pipelines. We herein present the important vocabulary, the necessary steps to create a thorough image segmentation pipeline, and also discuss technical aspects that should be considered in the development of automated image analysis pipelines through ML/DL.

Peptide-based immunotherapy against oxidized elastin ameliorates pathology in mouse model of smoke-induced ocular injury

PURPOSE

Age-related macular degeneration (AMD), the leading cause of blindness in western populations, is associated with an overactive complement system, and an increase in circulating antibodies against certain epitopes, including elastin. As loss of the elastin layer of Bruch's membrane (BrM) has been reported in aging and AMD, we previously showed that immunization with elastin peptide oxidatively modified by cigarette smoke (ox-elastin), exacerbated ocular pathology in the smoke-induced ocular pathology (SIOP) model. Here we asked whether ox-elastin peptide-based immunotherapy (PIT) ameliorates damage.

METHODS

C57BL/6J mice were injected with ox-elastin peptide at two doses via weekly subcutaneous administration, while exposed to cigarette smoke for 6 months. FcγR-/- and uninjected C57BL/6J mice served as controls. Retinal morphology was assessed by electron microscopy, and complement activation, antibody deposition and mechanisms of immunological tolerance were assessed by Western blotting and ELISA.

RESULTS

Elimination of Fcγ receptors, preventing antigen/antibody-dependent cytotoxicity, protected against SIOP. Mice receiving PIT with low dose ox-elastin (LD-PIT) exhibited reduced humoral immunity, reduced complement activation and IgG/IgM deposition in the RPE/choroid, and largely a preserved BrM. While there is no direct evidence of ox-elastin pathogenicity, LD-PIT reduced IFNγ and increased IL-4 within RPE/choroid. High dose PIT was not protective.

CONCLUSIONS

These data further support ox-elastin role in ocular damage in part via elastin-specific antibodies, and support the corollary that PIT with ox-elastin attenuates ocular pathology. Overall, damage is associated with complement activation, antibody-dependent cell-mediated cytotoxicity, and altered cytokine signature.

Primary Cilia in Amacrine Cells in Retinal Development

Purpose

Primary cilia are conserved organelles found in polarized cells within the eye that regulate cell growth, migration, and differentiation. Although the role of cilia in photoreceptors is well-studied, the formation of cilia in other retinal cell types has received little attention. In this study, we examined the ciliary profile focused on the inner nuclear layer of retinas in mice and rhesus macaque primates.

Methods

Retinal sections or flatmounts from Arl13b-Cetn2 tg transgenic mice were immunostained for cell markers (Pax6, Sox9, Chx10, Calbindin, Calretinin, ChaT, GAD67, Prox1, TH, and vGluT3) and analyzed by confocal microscopy. Primate retinal sections were immunostained for ciliary and cell markers (Pax6 and Arl13b). Optical coherence tomography (OCT) and ERGs were used to assess visual function of Vift88 mice.

Results

During different stages of mouse postnatal eye development, we found that cilia are present in Pax6-positive amacrine cells, which were also observed in primate retinas. The cilia of subtypes of amacrine cells in mice were shown by immunostaining and electron microscopy. We also removed primary cilia from vGluT3 amacrine cells in mouse and found no significant vision defects. In addition, cilia were present in the outer limiting membrane, suggesting that a population of Müller glial cells forms cilia.

Conclusions

We report that several subpopulations of amacrine cells in inner nuclear layers of the retina form cilia during early retinal development in mice and primates.

Synaptic inputs to broad thorny ganglion cells in macaque retina

In primates, broad thorny retinal ganglion cells are highly sensitive to small, moving stimuli. They have tortuous, fine dendrites with many short, spine-like branches that occupy three contiguous strata in the middle of the inner plexiform layer. The neural circuits that generate their responses to moving stimuli are not well-understood, and that was the goal of this study. A connectome from central macaque retina was generated by serial block-face scanning electron microscopy, a broad thorny cell was reconstructed, and its synaptic inputs were analyzed. It received fewer than 2% of its inputs from both ON and OFF types of bipolar cells; the vast majority of its inputs were from amacrine cells. The presynaptic amacrine cells were reconstructed, and seven types were identified based on their characteristic morphology. Two types of narrow-field cells, knotty bistratified Type 1 and wavy multistratified Type 2, were identified. Two types of medium-field amacrine cells, ON starburst and spiny, were also presynaptic to the broad thorny cell. Three types of wide-field amacrine cells, wiry Type 2, stellate wavy, and semilunar Type 2, also made synapses onto the broad thorny cell. Physiological experiments using a macaque retinal preparation in vitro confirmed that broad thorny cells received robust excitatory input from both the ON and the OFF pathways. Given the paucity of bipolar cell inputs, it is likely that amacrine cells provided much of the excitatory input, in addition to inhibitory input.

Model-based comparison of current flow in rod bipolar cells of healthy and early-stage degenerated retina

Retinal degenerative diseases, such as retinitis pigmentosa, are generally thought to initiate with the loss of photoreceptors, though recent work suggests that plasticity and remodeling occurs prior to photoreceptor cell loss. This degeneration subsequently leads to death of other retinal neurons, creating functional alterations and extensive remodeling of retinal networks. Retinal prosthetic devices stimulate the surviving retinal cells by applying external current using implanted electrodes. Although these devices restore partial vision, the quality of restored vision is limited. Further knowledge about the precise changes in degenerated retina as the disease progresses is essential to understand how current flows in retinas undergoing degenerative disease and to improve the performance of retinal prostheses. We developed computational models that describe current flow from rod photoreceptors to rod bipolar cells (RodBCs) in the healthy and early-stage degenerated retina. Morphologically accurate models of retinal cells with their synapses are constructed based on retinal connectome datasets, created using serial section transmission electron microscopy (TEM) images of 70 nm-thick slices of either healthy (RC1) or early-stage degenerated (RPC1) rabbit retina. The passive membrane and active ion currents of each cell are implemented using conductance-based models in the Neuron simulation environment. In response to photocurrent input at rod photoreceptors, the simulated membrane potential at RodBCs in early degenerate tissue is approximately 10-20 mV lower than that of RodBCs of that observed in wild type retina. Results presented here suggest that although RodBCs in RPC1 show early, altered morphology compared to RC1, the lower membrane potential is primarily a consequence of reduced rod photoreceptor input to RodBCs in the degenerated retina. Frequency response and step input analyses suggest that individual cell responses of RodBCs in either healthy or early-degenerated retina, prior to substantial photoreceptor cell loss, do not differ significantly.

A pathoconnectome of early neurodegeneration: Network changes in retinal degeneration

Connectomics has demonstrated that synaptic networks and their topologies are precise and directly correlate with physiology and behavior. The next extension of connectomics is pathoconnectomics: to map neural network synaptology and circuit topologies corrupted by neurological disease in order to identify robust targets for therapeutics. In this report, we characterize a pathoconnectome of early retinal degeneration. This pathoconnectome was generated using serial section transmission electron microscopy to achieve an ultrastructural connectome with 2.18nm/px resolution for accurate identification of all chemical and gap junctional synapses. We observe aberrant connectivity in the rod-network pathway and novel synaptic connections deriving from neurite sprouting. These observations reveal principles of neuron responses to the loss of network components and can be extended to other neurodegenerative diseases.

Wide-field amacrine cell inputs to ON parasol ganglion cells in macaque retina

Parasol cells are one of the major types of primate retinal ganglion cells. The goal of this study was to describe the synaptic inputs that shape the light responses of the ON type of parasol cells, which are excited by increments in light intensity. A connectome from central macaque retina was generated by serial blockface scanning electron microscopy. Six neighboring ON parasol cells were reconstructed, and their synaptic inputs were analyzed. On average, they received 21% of their input from bipolar cells, excitatory local circuit neurons receiving input from cones. The majority of their input was from amacrine cells, local circuit neurons of the inner retina that are typically inhibitory. Their contributions to the neural circuit providing input to parasol cells are not well-understood, and the focus of this study was on the presynaptic wide-field amacrine cells, which provided 17% of the input to ON parasol cells. These are GABAergic amacrine cells with long, relatively straight dendrites, and sometimes also axons, that run in a single, narrow stratum of the inner plexiform layer. The presynaptic wide-field amacrine cells were reconstructed, and two types were identified based on their characteristic morphology. One presynaptic amacrine cell was identified as semilunar type 2, a polyaxonal cell that is electrically coupled to ON parasol cells. A second amacrine was identified as wiry type 2, a type known to be sensitive to motion. These inputs likely make ON parasol cells more sensitive to stimuli that are rapidly changing outside their classical receptive fields.

Network Architecture of Gap Junctional Coupling among Parallel Processing Channels in the Mammalian Retina

Gap junctions are ubiquitous throughout the nervous system, mediating critical signal transmission and integration, as well as emergent network properties. In mammalian retina, gap junctions within the Aii amacrine cell-ON cone bipolar cell (CBC) network are essential for night vision, modulation of day vision, and contribute to visual impairment in retinal degenerations, yet neither the extended network topology nor its conservation is well established. Here, we map the network contribution of gap junctions using a high-resolution connectomics dataset of an adult female rabbit retina. Gap junctions are prominent synaptic components of ON CBC classes, constituting 5%-25% of all axonal synaptic contacts. Many of these mediate canonical transfer of rod signals from Aii cells to ON CBCs for night vision, and we find that the uneven distribution of Aii signals to ON CBCs is conserved in rabbit, including one class entirely lacking direct Aii coupling. However, the majority of gap junctions formed by ON CBCs unexpectedly occur between ON CBCs, rather than with Aii cells. Such coupling is extensive, creating an interconnected network with numerous lateral paths both within, and particularly across, these parallel processing streams. Coupling patterns are precise with ON CBCs accepting and rejecting unique combinations of partnerships according to robust rulesets. Coupling specificity extends to both size and spatial topologies, thereby rivaling the synaptic specificity of chemical synapses. These ON CBC coupling motifs dramatically extend the coupled Aii-ON CBC network, with implications for signal flow in both scotopic and photopic retinal networks during visual processing and disease.SIGNIFICANCE STATEMENT Electrical synapses mediated by gap junctions are fundamental components of neural networks. In retina, coupling within the Aii-ON CBC network shapes visual processing in both the scotopic and photopic networks. In retinal degenerations, these same gap junctions mediate oscillatory activity that contributes to visual impairment. Here, we use high-resolution connectomics strategies to identify gap junctions and cellular partnerships. We describe novel, pervasive motifs both within and across classes of ON CBCs that dramatically extend the Aii-ON CBC network. These motifs are highly specific with implications for both signal processing within the retina and therapeutic interventions for blinding conditions. These findings highlight the underappreciated contribution of coupling motifs in retinal circuitry and the necessity of their detection in connectomics studies.

Optic cup morphogenesis requires neural crest-mediated basement membrane assembly

Organogenesis requires precise interactions between a developing tissue and its environment. In vertebrates, the developing eye is surrounded by a complex extracellular matrix as well as multiple mesenchymal cell populations. Disruptions to either the matrix or periocular mesenchyme can cause defects in early eye development, yet in many cases the underlying mechanism is unknown. Here, using multidimensional imaging and computational analyses in zebrafish, we establish that cell movements in the developing optic cup require neural crest. Ultrastructural analysis reveals that basement membrane formation around the developing eye is also dependent on neural crest, but only specifically around the retinal pigment epithelium. Neural crest cells produce the extracellular matrix protein nidogen: impairing nidogen function disrupts eye development, and, strikingly, expression of nidogen in the absence of neural crest partially restores optic cup morphogenesis. These results demonstrate that eye formation is regulated in part by extrinsic control of extracellular matrix assembly.This article has an associated 'The people behind the papers' interview.

A Color Vision Circuit for Non-Image-Forming Vision in the Primate Retina

Melanopsin-expressing, intrinsically photosensitive retinal ganglion cells (ipRGCs) synchronize our biological clocks with the external light/dark cycle [1]. In addition to photoentrainment, they mediate the effects of light experience as a central modulator of mood, learning, and health [2]. This makes a complete account of the circuity responsible for ipRGCs' light responses essential to understanding their diverse roles in our well-being. Considerable progress has been made in understanding ipRGCs' melanopsin-mediated responses in rodents [3-5]. However, in primates, ipRGCs also have a rare blue-OFF response mediated by an unknown short-wavelength-sensitive (S)-cone circuit [6]. Identifying this S-cone circuit is particularly important because ipRGCs mediate many of the wide-ranging effects of short-wavelength light on human biology. These effects are often attributed to melanopsin, but there is evidence for an S-cone contribution as well [7, 8]. Here, we tested the hypothesis that the S-OFF response is mediated by the S-ON pathway through inhibitory input from an undiscovered S-cone amacrine cell. Using serial electron microscopy in the macaque retina, we reconstructed the neurons and synapses of the S-cone connectome, revealing a novel inhibitory interneuron, an amacrine cell, receiving excitatory glutamatergic input exclusively from S-ON bipolar cells. This S-cone amacrine cell makes highly selective inhibitory synapses onto ipRGCs, resulting in a blue-OFF response. Identification of the S-cone amacrine cell provides the missing component of an evolutionarily ancient circuit using spectral information for non-image forming visual functions.

Synaptic inputs from identified bipolar and amacrine cells to a sparsely branched ganglion cell in rabbit retina

There are more than 30 distinct types of mammalian retinal ganglion cells, each sensitive to different features of the visual environment. In rabbit retina, they can be grouped into four classes according to their morphology and stratification of their dendrites in the inner plexiform layer (IPL). The goal of this study was to describe the synaptic inputs to one type of Class IV ganglion cell, the third member of the sparsely branched Class IV cells (SB3). One cell of this type was partially reconstructed in a retinal connectome developed using automated transmission electron microscopy (ATEM). It had slender, relatively straight dendrites that ramify in the sublamina a of the IPL. The dendrites of the SB3 cell were always postsynaptic in the IPL, supporting its identity as a ganglion cell. It received 29% of its input from bipolar cells, a value in the middle of the range for rabbit retinal ganglion cells studied previously. The SB3 cell typically received only one synapse per bipolar cell from multiple types of presumed OFF bipolar cells; reciprocal synapses from amacrine cells at the dyad synapses were infrequent. In a few instances, the bipolar cells presynaptic to the SB3 ganglion cell also provided input to an amacrine cell presynaptic to the ganglion cell. There was apparently no crossover inhibition from narrow-field ON amacrine cells. Most of the amacrine cell inputs were from axons and dendrites of GABAergic amacrine cells, likely providing inhibitory input from outside the classical receptive field.

Persistent remodeling and neurodegeneration in late-stage retinal degeneration

Retinal remodeling is a progressive series of negative plasticity revisions that arise from retinal degeneration, and are seen in retinitis pigmentosa, age-related macular degeneration and other forms of retinal disease. These processes occur regardless of the precipitating event leading to degeneration. Retinal remodeling then culminates in a late-stage neurodegeneration that is indistinguishable from progressive central nervous system (CNS) proteinopathies. Following long-term deafferentation from photoreceptor cell death in humans, and long-lived animal models of retinal degeneration, most retinal neurons reprogram, then die. Glial cells reprogram into multiple anomalous metabolic phenotypes. At the same time, survivor neurons display degenerative inclusions that appear identical to progressive CNS neurodegenerative disease, and contain aberrant α-synuclein (α-syn) and phosphorylated α-syn. In addition, ultrastructural analysis indicates a novel potential mechanism for misfolded protein transfer that may explain how proteinopathies spread. While neurodegeneration poses a barrier to prospective retinal interventions that target primary photoreceptor loss, understanding the progression and time-course of retinal remodeling will be essential for the establishment of windows of therapeutic intervention and appropriate tuning and design of interventions. Finally, the development of protein aggregates and widespread neurodegeneration in numerous retinal degenerative diseases positions the retina as a ideal platform for the study of proteinopathies, and mechanisms of neurodegeneration that drive devastating CNS diseases.

An S-cone circuit for edge detection in the primate retina

Midget retinal ganglion cells (RGCs) are the most common RGC type in the primate retina. Their responses have been proposed to mediate both color and spatial vision, yet the specific links between midget RGC responses and visual perception are unclear. Previous research on the dual roles of midget RGCs has focused on those comparing long (L) vs. middle (M) wavelength sensitive cones. However, there is evidence for several other rare midget RGC subtypes receiving S-cone input, but their role in color and spatial vision is uncertain. Here, we confirm the existence of the single S-cone center OFF midget RGC circuit in the central retina of macaque monkey both structurally and functionally. We investigated the receptive field properties of the S-OFF midget circuit with single cell electrophysiology and 3D electron microscopy reconstructions of the upstream circuitry. Like the well-studied L vs. M midget RGCs, the S-OFF midget RGCs have a center-surround receptive field consistent with a role in spatial vision. While spectral opponency in a primate RGC is classically assumed to contribute to hue perception, a role supporting edge detection is more consistent with the S-OFF midget RGC receptive field structure and studies of hue perception.

Heterocellular Coupling Between Amacrine Cells and Ganglion Cells

All superclasses of retinal neurons, including bipolar cells (BCs), amacrine cells (ACs) and ganglion cells (GCs), display gap junctional coupling. However, coupling varies extensively by class. Heterocellular AC coupling is common in many mammalian GC classes. Yet, the topology and functions of coupling networks remains largely undefined. GCs are the least frequent superclass in the inner plexiform layer and the gap junctions mediating GC-to-AC coupling (GC::AC) are sparsely arrayed amidst large cohorts of homocellular AC::AC, BC::BC, GC::GC and heterocellular AC::BC gap junctions. Here, we report quantitative coupling for identified GCs in retinal connectome 1 (RC1), a high resolution (2 nm) transmission electron microscopy-based volume of rabbit retina. These reveal that most GC gap junctions in RC1 are suboptical. GC classes lack direct cross-class homocellular coupling with other GCs, despite opportunities via direct membrane contact, while OFF alpha GCs and transient ON directionally selective (DS) GCs are strongly coupled to distinct AC cohorts. Integrated small molecule immunocytochemistry identifies these as GABAergic ACs (γ+ ACs). Multi-hop synaptic queries of RC1 connectome further profile these coupled γ+ ACs. Notably, OFF alpha GCs couple to OFF γ+ ACs and transient ON DS GCs couple to ON γ+ ACs, including a large interstitial amacrine cell, revealing matched ON/OFF photic drive polarities within coupled networks. Furthermore, BC input to these γ+ ACs is tightly matched to the GCs with which they couple. Evaluation of the coupled versus inhibitory targets of the γ+ ACs reveals that in both ON and OFF coupled GC networks these ACs are presynaptic to GC classes that are different than the classes with which they couple. These heterocellular coupling patterns provide a potential mechanism for an excited GC to indirectly inhibit nearby GCs of different classes. Similarly, coupled γ+ ACs engaged in feedback networks can leverage the additional gain of BC synapses in shaping the signaling of downstream targets based on their own selective coupling with GCs. A consequence of coupling is intercellular fluxes of small molecules. GC::AC coupling involves primarily γ+ cells, likely resulting in GABA diffusion into GCs. Surveying GABA signatures in the GC layer across diverse species suggests the majority of vertebrate retinas engage in GC::γ+ AC coupling.

Abstractocyte: A Visual Tool for Exploring Nanoscale Astroglial Cells

This paper presents Abstractocyte, a system for the visual analysis of astrocytes and their relation to neurons, in nanoscale volumes of brain tissue. Astrocytes are glial cells, i.e., non-neuronal cells that support neurons and the nervous system. The study of astrocytes has immense potential for understanding brain function. However, their complex and widely-branching structure requires high-resolution electron microscopy imaging and makes visualization and analysis challenging. Furthermore, the structure and function of astrocytes is very different from neurons, and therefore requires the development of new visualization and analysis tools. With Abstractocyte, biologists can explore the morphology of astrocytes using various visual abstraction levels, while simultaneously analyzing neighboring neurons and their connectivity. We define a novel, conceptual 2D abstraction space for jointly visualizing astrocytes and neurons. Neuroscientists can choose a specific joint visualization as a point in this space. Interactively moving this point allows them to smoothly transition between different abstraction levels in an intuitive manner. In contrast to simply switching between different visualizations, this preserves the visual context and correlations throughout the transition. Users can smoothly navigate from concrete, highly-detailed 3D views to simplified and abstracted 2D views. In addition to investigating astrocytes, neurons, and their relationships, we enable the interactive analysis of the distribution of glycogen, which is of high importance to neuroscientists. We describe the design of Abstractocyte, and present three case studies in which neuroscientists have successfully used our system to assess astrocytic coverage of synapses, glycogen distribution in relation to synapses, and astrocytic-mitochondria coverage.

Graffinity: Visualizing Connectivity in Large Graphs

Multivariate graphs are prolific across many fields, including transportation and neuroscience. A key task in graph analysis is the exploration of connectivity, to, for example, analyze how signals flow through neurons, or to explore how well different cities are connected by flights. While standard node-link diagrams are helpful in judging connectivity, they do not scale to large networks. Adjacency matrices also do not scale to large networks and are only suitable to judge connectivity of adjacent nodes. A key approach to realize scalable graph visualization are queries: instead of displaying the whole network, only a relevant subset is shown. Query-based techniques for analyzing connectivity in graphs, however, can also easily suffer from cluttering if the query result is big enough. To remedy this, we introduce techniques that provide an overview of the connectivity and reveal details on demand. We have two main contributions: (1) two novel visualization techniques that work in concert for summarizing graph connectivity; and (2) Graffinity, an open-source implementation of these visualizations supplemented by detail views to enable a complete analysis workflow. Graffinity was designed in a close collaboration with neuroscientists and is optimized for connectomics data analysis, yet the technique is applicable across domains. We validate the connectivity overview and our open-source tool with illustrative examples using flight and connectomics data.

Deformable Registration of Biomedical Images Using 2D Hidden Markov Models

Robust registration of unimodal and multimodal images is a key task in biomedical image analysis, and is often utilized as an initial step on which subsequent analysis techniques critically depend. We propose a novel probabilistic framework, based on a variant of the 2D hidden Markov model, namely, the turbo hidden Markov model, to capture the deformation between pairs of images. The hidden Markov model is tailored to capture spatial transformations across images via state transitions, and modality-specific data costs via emission probabilities. The method is derived for the unimodal setting (where simpler matching metrics may be used) as well as the multimodal setting, where different modalities may provide very different representations for a given class of objects, necessitating the use of advanced similarity measures. We utilize a rich model with hundreds of model parameters to describe the deformation relationships across such modalities. We also introduce a local edge-adaptive constraint to allow for varying degrees of smoothness between object boundaries and homogeneous regions. The parameters of the described method are estimated in a principled manner from training data via maximum likelihood learning, and the deformation is subsequently estimated using an efficient dynamic programming algorithm. Experimental results demonstrate the improved performance of the proposed approach over the state-of-the-art deformable registration techniques, on both unimodal and multimodal biomedical data sets.

Rod-cone crossover connectome of mammalian bipolar cells

The basis of cross-suppression between rod and cone channels has long been an enigma. Using rabbit retinal connectome RC1, we show that all cone bipolar cell (BC) classes inhibit rod BCs via amacrine cell (AC) motifs (C1-6); that all cone BC classes are themselves inhibited by AC motifs (R1-5, R25) driven by rod BCs. A sparse symmetric AC motif (CR) is presynaptic and postsynaptic to both rod and cone BCs. ON cone BCs of all classes drive inhibition of rod BCs via motif C1 wide-field GABAergic ACs (γACs) and motif C2 narrow field glycinergic ON ACs (GACs). Each rod BC receives ≈10 crossover AC synapses and each ON cone BC can target ≈10 or more rod BCs via separate AC processes. OFF cone BCs mediate monosynaptic inhibition of rod BCs via motif C3 driven by OFF γACs and GACs and disynaptic inhibition via motifs C4 and C5 driven by OFF wide-field γACs and narrow-field GACs, respectively. Motifs C4 and C5 form halos of 60-100 inhibitory synapses on proximal dendrites of AI γACs. Rod BCs inhibit surrounding arrays of cone BCs through AII GAC networks that access ON and OFF cone BC patches via motifs R1, R2, R4, R5 and a unique ON AC motif R3 that collects rod BC inputs and targets ON cone BCs. Crossover synapses for motifs C1, C4, C5, and R3 are 3-4× larger than typical feedback synapses, which may be a signature for synaptic winner-take-all switches. J. Comp. Neurol. 527:87-116, 2019. © 2016 The Authors The Journal of Comparative Neurology Published by Wiley Periodicals, Inc.

Store-Operated Calcium Entry in Müller Glia Is Controlled by Synergistic Activation of TRPC and Orai Channels

The endoplasmic reticulum (ER) is at the epicenter of astrocyte Ca(2+) signaling. We sought to identify the molecular mechanism underlying store-operated calcium entry that replenishes ER stores in mouse Müller cells. Store depletion, induced through blockade of sequestration transporters in Ca(2+)-free saline, induced synergistic activation of canonical transient receptor potential 1 (TRPC1) and Orai channels. Store-operated TRPC1 channels were identified by their electrophysiological properties, pharmacological blockers, and ablation of the Trpc1 gene. Ca(2+) release-activated currents (ICRAC) were identified by ion permeability, voltage dependence, and sensitivity to selective Orai antagonists Synta66 and GSK7975A. Depletion-evoked calcium influx was initiated at the Müller end-foot and apical process, triggering centrifugal propagation of Ca(2+) waves into the cell body. EM analysis of the end-foot compartment showed high-density ER cisternae that shadow retinal ganglion cell (RGC) somata and axons, protoplasmic astrocytes, vascular endothelial cells, and ER-mitochondrial contacts at the vitreal surface of the end-foot. The mouse retina expresses transcripts encoding both Stim and all known Orai genes; Müller glia predominantly express stromal interacting molecule 1 (STIM1), whereas STIM2 is mainly confined to the outer plexiform and RGC layers. Elimination of TRPC1 facilitated Müller gliosis induced by the elevation of intraocular pressure, suggesting that TRPC channels might play a neuroprotective role during mechanical stress. By characterizing the properties of store-operated signaling pathways in Müller cells, these studies expand the current knowledge about the functional roles these cells play in retinal physiology and pathology while also providing further evidence for the complexity of calcium signaling mechanisms in CNS astroglia.

SIGNIFICANCE STATEMENT

Store-operated Ca(2+) signaling represents a major signaling pathway and source of cytosolic Ca(2+) in astrocytes. Here, we show that the store-operated response in Müller cells, radial glia that perform key structural, signaling, osmoregulatory, and mechanosensory functions within the retina, is mediated through synergistic activation of transient receptor potential and Orai channels. The end-foot disproportionately expresses the depletion sensor stromal interacting molecule 1, which contains an extraordinarily high density of endoplasmic reticulum cisternae that shadow neuronal, astrocytic, vascular, and axonal structures; interface with mitochondria; but also originate store-operated Ca(2+) entry-induced transcellular Ca(2+) waves that propagate glial excitation into the proximal retina. These results identify a molecular mechanism that underlies complex interactions between the plasma membrane and calcium stores, and contributes to astroglial function, regulation, and response to mechanical stress.

Virtual electrode design for increasing spatial resolution in retinal prosthesis

Retinal prostheses systems are currently used to restore partial vision to patients blinded by degenerative diseases by electrically stimulating surviving retinal cells. To obtain likely maximum resolution, electrode size is minimised, allowing for a large quantity on an array and localised stimulation regions. Besides the small size leading to fabrication difficulties and higher electrochemical charge density, there are challenges associated with the number of drivers needed for a large electrode count as well as the strategies to deliver sufficient power to these drivers wirelessly. In hopes to increase electrode resolution while avoiding these issues, the authors propose a new 'virtual electrode' design to increase locations of likely stimulation. Passive metallisation strategically placed between disk electrodes, combined with alternating surrounding stimuli, channel current into a location between electrodes, producing a virtual stimulation site. A computational study was conducted to optimise the passive metal element geometry, quantify the expected current density output, and simulate retinal ganglion cell activity due to virtual electrode stimulation. Results show that this procedure leads to array geometry that focuses injected current and achieves retinal ganglion cell stimulation in a region beneath the 'virtual electrode,' creating an alternate stimulation site without additional drivers.

Astrocyte structural reactivity and plasticity in models of retinal detachment

Although retinal neurodegenerative conditions such as age-related macular degeneration, glaucoma, diabetic retinopathy, retinitis pigmentosa, and retinal detachment have different etiologies and pathological characteristics, they also have many responses in common at the cellular level, including neural and glial remodeling. Structural changes in Müller cells, the large radial glia of the retina in retinal disease and injury have been well described, that of the retinal astrocytes remains less so. Using modern imaging technology to describe the structural remodeling of retinal astrocytes after retinal detachment is the focus of this paper. We present both a review of critical literature as well as novel work focusing on the responses of astrocytes following rhegmatogenous and serous retinal detachment. The mouse presents a convenient model system in which to study astrocyte reactivity since the Mϋller cell response is muted in comparison to other species thereby allowing better visualization of the astrocytes. We also show data from rat, cat, squirrel, and human retina demonstrating similarities and differences across species. Our data from immunolabeling and dye-filling experiments demonstrate previously undescribed morphological characteristics of normal astrocytes and changes induced by detachment. Astrocytes not only upregulate GFAP, but structurally remodel, becoming increasingly irregular in appearance, and often penetrating deep into neural retina. Understanding these responses, their consequences, and what drives them may prove to be an important component in improving visual outcome in a variety of therapeutic situations. Our data further supports the concept that astrocytes are important players in the retina's overall response to injury and disease.

A platform for brain-wide imaging and reconstruction of individual neurons

The structure of axonal arbors controls how signals from individual neurons are routed within the mammalian brain. However, the arbors of very few long-range projection neurons have been reconstructed in their entirety, as axons with diameters as small as 100 nm arborize in target regions dispersed over many millimeters of tissue. We introduce a platform for high-resolution, three-dimensional fluorescence imaging of complete tissue volumes that enables the visualization and reconstruction of long-range axonal arbors. This platform relies on a high-speed two-photon microscope integrated with a tissue vibratome and a suite of computational tools for large-scale image data. We demonstrate the power of this approach by reconstructing the axonal arbors of multiple neurons in the motor cortex across a single mouse brain.

NeuroBlocks--Visual Tracking of Segmentation and Proofreading for Large Connectomics Projects

In the field of connectomics, neuroscientists acquire electron microscopy volumes at nanometer resolution in order to reconstruct a detailed wiring diagram of the neurons in the brain. The resulting image volumes, which often are hundreds of terabytes in size, need to be segmented to identify cell boundaries, synapses, and important cell organelles. However, the segmentation process of a single volume is very complex, time-intensive, and usually performed using a diverse set of tools and many users. To tackle the associated challenges, this paper presents NeuroBlocks, which is a novel visualization system for tracking the state, progress, and evolution of very large volumetric segmentation data in neuroscience. NeuroBlocks is a multi-user web-based application that seamlessly integrates the diverse set of tools that neuroscientists currently use for manual and semi-automatic segmentation, proofreading, visualization, and analysis. NeuroBlocks is the first system that integrates this heterogeneous tool set, providing crucial support for the management, provenance, accountability, and auditing of large-scale segmentations. We describe the design of NeuroBlocks, starting with an analysis of the domain-specific tasks, their inherent challenges, and our subsequent task abstraction and visual representation. We demonstrate the utility of our design based on two case studies that focus on different user roles and their respective requirements for performing and tracking the progress of segmentation and proofreading in a large real-world connectomics project.

A multi-scale computational model for the study of retinal prosthetic stimulation

An implantable retinal prosthesis has been developed to restore vision to patients who have been blinded by degenerative diseases that destroy photoreceptors. By electrically stimulating the surviving retinal cells, the damaged photoreceptors may be bypassed and limited vision can be restored. While this has been shown to restore partial vision, the understanding of how cells react to this systematic electrical stimulation is largely unknown. Better predictive models and a deeper understanding of neural responses to electrical stimulation is necessary for designing a successful prosthesis. In this work, a computational model of an epi-retinal implant was built and simulated, spanning multiple spatial scales, including a large-scale model of the retina and implant electronics, as well as underlying neuronal networks.

Object-based representation and analysis of light and electron microscopic volume data using Blender

Background

Rapid improvements in light and electron microscopy imaging techniques and the development of 3D anatomical atlases necessitate new approaches for the visualization and analysis of image data. Pixel-based representations of raw light microscopy data suffer from limitations in the number of channels that can be visualized simultaneously. Complex electron microscopic reconstructions from large tissue volumes are also challenging to visualize and analyze.

Results

Here we exploit the advanced visualization capabilities and flexibility of the open-source platform Blender to visualize and analyze anatomical atlases. We use light-microscopy-based gene expression atlases and electron microscopy connectome volume data from larval stages of the marine annelid Platynereis dumerilii. We build object-based larval gene expression atlases in Blender and develop tools for annotation and coexpression analysis. We also represent and analyze connectome data including neuronal reconstructions and underlying synaptic connectivity.

Conclusions

We demonstrate the power and flexibility of Blender for visualizing and exploring complex anatomical atlases. The resources we have developed for Platynereis will facilitate data sharing and the standardization of anatomical atlases for this species. The flexibility of Blender, particularly its embedded Python application programming interface, means that our methods can be easily extended to other organisms.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0652-7) contains supplementary material, which is available to authorized users.

Design and Evaluation of Interactive Proofreading Tools for Connectomics

Proofreading refers to the manual correction of automatic segmentations of image data. In connectomics, electron microscopy data is acquired at nanometer-scale resolution and results in very large image volumes of brain tissue that require fully automatic segmentation algorithms to identify cell boundaries. However, these algorithms require hundreds of corrections per cubic micron of tissue. Even though this task is time consuming, it is fairly easy for humans to perform corrections through splitting, merging, and adjusting segments during proofreading. In this paper we present the design and implementation of Mojo, a fully-featured single-user desktop application for proofreading, and Dojo, a multi-user web-based application for collaborative proofreading. We evaluate the accuracy and speed of Mojo, Dojo, and Raveler, a proofreading tool from Janelia Farm, through a quantitative user study. We designed a between-subjects experiment and asked non-experts to proofread neurons in a publicly available connectomics dataset. Our results show a significant improvement of corrections using web-based Dojo, when given the same amount of time. In addition, all participants using Dojo reported better usability. We discuss our findings and provide an analysis of requirements for designing visual proofreading software.

NeuroLines: A Subway Map Metaphor for Visualizing Nanoscale Neuronal Connectivity

We present NeuroLines, a novel visualization technique designed for scalable detailed analysis of neuronal connectivity at the nanoscale level. The topology of 3D brain tissue data is abstracted into a multi-scale, relative distance-preserving subway map visualization that allows domain scientists to conduct an interactive analysis of neurons and their connectivity. Nanoscale connectomics aims at reverse-engineering the wiring of the brain. Reconstructing and analyzing the detailed connectivity of neurons and neurites (axons, dendrites) will be crucial for understanding the brain and its development and diseases. However, the enormous scale and complexity of nanoscale neuronal connectivity pose big challenges to existing visualization techniques in terms of scalability. NeuroLines offers a scalable visualization framework that can interactively render thousands of neurites, and that supports the detailed analysis of neuronal structures and their connectivity. We describe and analyze the design of NeuroLines based on two real-world use-cases of our collaborators in developmental neuroscience, and investigate its scalability to large-scale neuronal connectivity data.

Input clustering and the microscale structure of local circuits

The recent development of powerful tools for high-throughput mapping of synaptic networks promises major advances in understanding brain function. One open question is how circuits integrate and store information. Competing models based on random vs. structured connectivity make distinct predictions regarding the dendritic addressing of synaptic inputs. In this article we review recent experimental tests of one of these models, the input clustering hypothesis. Across circuits, brain regions and species, there is growing evidence of a link between synaptic co-activation and dendritic location, although this finding is not universal. The functional implications of input clustering and future challenges are discussed.

The AII amacrine cell connectome: a dense network hub

The mammalian AII retinal amacrine cell is a narrow-field, multistratified glycinergic neuron best known for its role in collecting scotopic signals from rod bipolar cells and distributing them to ON and OFF cone pathways in a crossover network via a combination of inhibitory synapses and heterocellular AII::ON cone bipolar cell gap junctions. Long considered a simple cell, a full connectomics analysis shows that AII cells possess the most complex interaction repertoire of any known vertebrate neuron, contacting at least 28 different cell classes, including every class of retinal bipolar cell. Beyond its basic role in distributing rod signals to cone pathways, the AII cell may also mediate narrow-field feedback and feedforward inhibition for the photopic OFF channel, photopic ON-OFF inhibitory crossover signaling, and serves as a nexus for a collection of inhibitory networks arising from cone pathways that likely negotiate fast switching between cone and rod vision. Further analysis of the complete synaptic counts for five AII cells shows that (1) synaptic sampling is normalized for anatomic target encounter rates; (2) qualitative targeting is specific and apparently errorless; and (3) that AII cells strongly differentiate partner cohorts by synaptic and/or coupling weights. The AII network is a dense hub connecting all primary retinal excitatory channels via precisely weighted drive and specific polarities. Homologs of AII amacrine cells have yet to be identified in non-mammalians, but we propose that such homologs should be narrow-field glycinergic amacrine cells driving photopic ON-OFF crossover via heterocellular coupling with ON cone bipolar cells and glycinergic synapses on OFF cone bipolar cells. The specific evolutionary event creating the mammalian AII scotopic-photopic hub would then simply be the emergence of large numbers of pure rod bipolar cells.

Automated detection of synapses in serial section transmission electron microscopy image stacks

We describe a method for fully automated detection of chemical synapses in serial electron microscopy images with highly anisotropic axial and lateral resolution, such as images taken on transmission electron microscopes. Our pipeline starts from classification of the pixels based on 3D pixel features, which is followed by segmentation with an Ising model MRF and another classification step, based on object-level features. Classifiers are learned on sparse user labels; a fully annotated data subvolume is not required for training. The algorithm was validated on a set of 238 synapses in 20 serial 7197×7351 pixel images (4.5×4.5×45 nm resolution) of mouse visual cortex, manually labeled by three independent human annotators and additionally re-verified by an expert neuroscientist. The error rate of the algorithm (12% false negative, 7% false positive detections) is better than state-of-the-art, even though, unlike the state-of-the-art method, our algorithm does not require a prior segmentation of the image volume into cells. The software is based on the ilastik learning and segmentation toolkit and the vigra image processing library and is freely available on our website, along with the test data and gold standard annotations (http://www.ilastik.org/synapse-detection/sstem).

ConnectomeExplorer: query-guided visual analysis of large volumetric neuroscience data

This paper presents ConnectomeExplorer, an application for the interactive exploration and query-guided visual analysis of large volumetric electron microscopy (EM) data sets in connectomics research. Our system incorporates a knowledge-based query algebra that supports the interactive specification of dynamically evaluated queries, which enable neuroscientists to pose and answer domain-specific questions in an intuitive manner. Queries are built step by step in a visual query builder, building more complex queries from combinations of simpler queries. Our application is based on a scalable volume visualization framework that scales to multiple volumes of several teravoxels each, enabling the concurrent visualization and querying of the original EM volume, additional segmentation volumes, neuronal connectivity, and additional meta data comprising a variety of neuronal data attributes. We evaluate our application on a data set of roughly one terabyte of EM data and 750 GB of segmentation data, containing over 4,000 segmented structures and 1,000 synapses. We demonstrate typical use-case scenarios of our collaborators in neuroscience, where our system has enabled them to answer specific scientific questions using interactive querying and analysis on the full-size data for the first time.

Robust segmentation based tracing using an adaptive wrapper for inducing priors

Segmentation based tracing algorithms detect the extent and borders of an object in a given frame IZ by propagating results from frames I1 ≤ z < Z. Although application specific tracers have been forthcoming, techniques that automatically adapt across applications have been less explored. We approach this problem by learning a prior model on topological dynamics that encourages segmentation transitions across frames that are most likely for a given application. Further, we augment a generic tracing technique with a locality sensitive prior derived from dense optic flow fields for deformation guidance. The proposed approach comprises two stages where the generic tracer initially yields multiple segmentation transitions when its parameters are perturbed, and the learnt topology prior subsequently propagates high scoring segmentations. Because the learnt topology model wraps around a generic tracer and adapts it by setting its free parameters, the need for careful parameter tuning is completely obviated. Through extensive experimental validation in surveillance, biological and medical image datasets, we verify the applicability of the proposed model while demonstrating good tracing performance under severe clutter.

Retinal connectomics: towards complete, accurate networks

Connectomics is a strategy for mapping complex neural networks based on high-speed automated electron optical imaging, computational assembly of neural data volumes, web-based navigational tools to explore 10(12)-10(15) byte (terabyte to petabyte) image volumes, and annotation and markup tools to convert images into rich networks with cellular metadata. These collections of network data and associated metadata, analyzed using tools from graph theory and classification theory, can be merged with classical systems theory, giving a more completely parameterized view of how biologic information processing systems are implemented in retina and brain. Networks have two separable features: topology and connection attributes. The first findings from connectomics strongly validate the idea that the topologies of complete retinal networks are far more complex than the simple schematics that emerged from classical anatomy. In particular, connectomics has permitted an aggressive refactoring of the retinal inner plexiform layer, demonstrating that network function cannot be simply inferred from stratification; exposing the complex geometric rules for inserting different cells into a shared network; revealing unexpected bidirectional signaling pathways between mammalian rod and cone systems; documenting selective feedforward systems, novel candidate signaling architectures, new coupling motifs, and the highly complex architecture of the mammalian AII amacrine cell. This is but the beginning, as the underlying principles of connectomics are readily transferrable to non-neural cell complexes and provide new contexts for assessing intercellular communication.

Beyond counts and shapes: studying pathology of dendritic spines in the context of the surrounding neuropil through serial section electron microscopy

Because dendritic spines are the sites of excitatory synapses, pathological changes in spine morphology should be considered as part of pathological changes in neuronal circuitry in the forms of synaptic connections and connectivity strength. In the past, spine pathology has usually been measured by changes in their number or shape. A more complete understanding of spine pathology requires visualization at the nanometer level to analyze how the changes in number and size affect their presynaptic partners and associated astrocytic processes, as well as organelles and other intracellular structures. Currently, serial section electron microscopy (ssEM) offers the best approach to address this issue because of its ability to image the volume of brain tissue at the nanometer resolution. Renewed interest in ssEM has led to recent technological advances in imaging techniques and improvements in computational tools indispensable for three-dimensional analyses of brain tissue volumes. Here we consider the small but growing literature that has used ssEM analysis to unravel ultrastructural changes in neuropil including dendritic spines. These findings have implications in altered synaptic connectivity and cell biological processes involved in neuropathology, and serve as anatomical substrates for understanding changes in network activity that may underlie clinical symptoms.

ON cone bipolar cell axonal synapses in the OFF inner plexiform layer of the rabbit retina

Analysis of the rabbit retinal connectome RC1 reveals that the division between the ON and the OFF inner plexiform layer (IPL) is not structurally absolute. ON cone bipolar cells make noncanonical axonal synapses onto specific targets and receive amacrine cell synapses in the nominal OFF layer, creating novel motifs, including inhibitory crossover networks. Automated transmission electron microscopic imaging, molecular tagging, tracing, and rendering of ~400 bipolar cells reveals axonal ribbons in 36% of ON cone bipolar cells, throughout the OFF IPL. The targets include γ-aminobutyrate (GABA)-positive amacrine cells (γACs), glycine-positive amacrine cells (GACs), and ganglion cells. Most ON cone bipolar cell axonal contacts target GACs driven by OFF cone bipolar cells, forming new architectures for generating ON-OFF amacrine cells. Many of these ON-OFF GACs target ON cone bipolar cell axons, ON γACs, and/or ON-OFF ganglion cells, representing widespread mechanisms for OFF to ON crossover inhibition. Other targets include OFF γACs presynaptic to OFF bipolar cells, forming γAC-mediated crossover motifs. ON cone bipolar cell axonal ribbons drive bistratified ON-OFF ganglion cells in the OFF layer and provide ON drive to polarity-appropriate targets such as bistratified diving ganglion cells (bsdGCs). The targeting precision of ON cone bipolar cell axonal synapses shows that this drive incidence is necessarily a joint distribution of cone bipolar cell axonal frequency and target cell trajectories through a given volume of the OFF layer. Such joint distribution sampling is likely common when targets are sparser than sources and when sources are coupled, as are ON cone bipolar cells.

System-wide rewiring underlies behavioral differences in predatory and bacterial-feeding nematodes

The relationship between neural circuit function and patterns of synaptic connectivity is poorly understood, in part due to a lack of comparative data for larger complete systems. We compare system-wide maps of synaptic connectivity generated from serial transmission electron microscopy for the pharyngeal nervous systems of two nematodes with divergent feeding behavior: the microbivore Caenorhabditis elegans and the predatory nematode Pristionchus pacificus. We uncover a massive rewiring in a complex system of identified neurons, all of which are homologous based on neurite anatomy and cell body position. Comparative graph theoretical analysis reveals a striking pattern of neuronal wiring with increased connectional complexity in the anterior pharynx correlating with tooth-like denticles, a morphological feature in the mouth of P. pacificus. We apply focused centrality methods to identify neurons I1 and I2 as candidates for regulating predatory feeding and predict substantial divergence in the function of pharyngeal glands.

Large-volume reconstruction of brain tissue from high-resolution serial section images acquired by SEM-based scanning transmission electron microscopy

With recent improvements in instrumentation and computational tools, serial section electron microscopy has become increasingly straightforward. A new method for imaging ultrathin serial sections is developed based on a field emission scanning electron microscope fitted with a transmitted electron detector. This method is capable of automatically acquiring high-resolution serial images with a large field size and very little optical and physical distortions. In this chapter, we describe the procedures leading to the generation and analyses of a large-volume stack of high-resolution images (64 μm × 64 μm × 10 μm, or larger, at 2 nm pixel size), including how to obtain large-area serial sections of uniform thickness from well-preserved brain tissue that is rapidly perfusion-fixed with mixed aldehydes, processed with a microwave-enhanced method, and embedded into epoxy resin.

Building retinal connectomes

Understanding vertebrate vision depends on knowing, in part, the complete network graph of at least one representative retina. Acquiring such graphs is the business of synaptic connectomics, emerging as a practical technology due to improvements in electron imaging platform control, management software for large-scale datasets, and availability of data storage. The optimal strategy for building complete connectomes uses transmission electron imaging with 2 nm or better resolution, molecular tags for cell identification, open-access data volumes for navigation, and annotation with open-source tools to build 3D cell libraries, complete network diagrams and connectivity databases. The first forays into retinal connectomics have shown that even nominally well-studied cells have much richer connection graphs than expected.

Neuronal tracing for connectomic studies

Reconstruction of the complete wiring diagram, or connectome, of a neural circuit provides an alternative approach to conventional circuit analysis. One major obstacle of connectomics lies in segmenting and tracing neuronal processes from the vast number of images obtained with optical or electron microscopy. Here I review recent progress in automated tracing algorithms for connectomic reconstruction with fluorescence and electron microscopy, and discuss the challenges to image analysis posed by novel optical imaging techniques.

Scalable tracing of electron micrographs by fusing top down and bottom up cues using hypergraph diffusion

A novel framework for robust 3D tracing in electron micrographs is presented. The proposed framework is built using ideas from hypergraph diffusion, and achieves two main objectives. Firstly, the approach scales to trace hundreds of targets without noticeable increase in runtime complexity. Secondly, the framework yields flexibility to fuse top down (global cues as hyperedges) and bottom up (local superpixels as nodes) information. Subsequently, a procedure for auto-seeding to initialize the tracing procedure is proposed. The paper concludes with experimental validation on a challenging large scale tracing problem for simultaneously tracing 95 structures, illustrating applicability of the proposed algorithm.