Tag Archives: Crystal Sigulinsky

Synaptic Inputs To A Gamma Ganglion Cell In Rabbit Retina

This abstract was presented today, May 8th at the 2017 Association for Research in Vision and Opthalmology (ARVO) meetings in Baltimore, Maryland by Andrea Bordt, Diego Perez, Robert E. Marc, James R. Anderson, Carl B. Watt, Bryan W. Jones, Crystal Sigulinsky, James S. Lauritzen, Danny Emrich, Noah Nelson, Luke Tseng, Weiley Liu, and David W. Marshak. Full resolution version here.

Purpose: There are at least 30 distinct types of mammalian retinal ganglion cells, each sensitive to different features of the visual environment, and these can be grouped according to their morphology. One such group, the gamma cells, was identified more than 40 years ago, but their synaptic inputs have never been described. That was the goal of this study.

Methods: The synaptic inputs to a subtype of gamma cell with dendrites ramifying in the outer sublamina of the inner plexiform layer (IPL) of the rabbit retina were identified in a retinal connectome developed using automated transmission electron microscopy.

Results: The gamma cell was always postsynaptic in the IPL, confirming its identity as a ganglion cell. The local synaptic input should produce relatively weak OFF reposnses to stimuli confined to the center of the gamma cell’s receptive field. It typically received only one synapse per bipolar cell from at least 4 types of OFF bipolar cells. Because bipolar cells vary in their response kinetics and contrast sensitivity. each type would provide a small, asynchronous excitatory input. The amacrine cells at the dyad synapses provided only a small amount presynaptic inhibition; reciprocal synapses were observed in only 3 of the 18 ribbon synapses. There was no glycinergic crossover inhibition, another local interaction that would enhance light responses. Local postsynaptic inhibition was somewhat more common; in 6 instances, the bipolar cells presynaptic to the gamma cell or their electrically coupled neighbors also provided input to an amacrine cell that inhibited the gamma cell. The other amacrine cell inputs to the gamma cell should have a much greater impact on the light responses because they are far more numerous. These are from axons and long dendrites of GABAergic amacrine cells, and they provide 60% of all the input. This finding suggests that many types of stimuli in the receptive field surround or outside of the classical receptive field would provide potent inhibition to the gamma cell.

Conclusions: The synaptic inputs rsuggest that gamma cells in rabbit retina would have light responses like their homologs in mouse retina, OFF responses to small stimuli in the receptive field center that are suppressed by a variety of larger stimuli. Thus, they would signal object motion selectively.

Graffinity: Visualizing Connectivity in Large Graphs

We have a new publication out, (direct link)(Wiley link), Graffinity: Visualizing Connectivity in Large Graphs.  Authors are, Ethan Kerzner (@EthanKerzner), Alexander Lex, Crystal Sigulinsky, Timothy Urness, Bryan W. Jones,  Robert Marc, and Miriah Meyer.

Abstract:  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.

The Rod-Cone Crossover Connectome of Mammalian Bipolar Cells

We have a new publication out (direct link), The rod-cone crossover connectome of mammalian bipolar cells authored by Scott Lauritzen, Crystal Sigulinsky, James Anderson, Michael Kalloniatis, Noah Nelson, Danny Emrich, Chris Rapp, Nicolas McCarthy, Ethan Kerzner, Mariah Meyer, Bryan W. Jones, and Robert Marc.

Abstract: 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 3-4x larger than typical feedback synapses, which may be a signature for synaptic winner-take-all switches.

2-nm Resolution Anatomy of Retinal Neuro-Glial-Vascular Architecture

This abstract was presented today, May 2th at the 2016 Association for Research in Vision and Opthalmology (ARVO) meetings in Seattle, Washington by Jefferson R. Brown, Rebecca L. Pfeiffer, Crystal Sigulinsky, Felix Vazquez-Chona, Daniel Emrich, Bryan W. Jones, Robert E. Marc.

Abstract Number: 995

Author Block: Jefferson R. Brown, Rebecca L. Pfeiffer, Crystal Sigulinsky, Felix Vazquez-Chona, Daniel Emrich, Bryan W. Jones, Robert E. Marc
1 Dept of Ophthalmology, University of Utah, Salt Lake City, Utah, United States

Disclosure Block:Jefferson R. Brown, None; Rebecca L. Pfeiffer, None; Crystal Sigulinsky, None; Felix Vazquez-Chona, None; Daniel Emrich, None; Bryan W. Jones, None; Robert E. Marc, Signature Immunologics (Code I (Personal Financial Interest) )

Purpose:Retinal vasculature is strongly affected by degenerative pathologies and in turn, may also contribute to their progression. However, much of what we understand about the normal, healthy interaction between neurons, glia, and blood vessels at the ultrastructural level is limited to single section electron microscopy. The technology of serial section transmission electron microscopy (ssTEM) extends the high definition of TEM imaging into three dimensions to create volumes, allowing for more thorough visualization and analysis of the vascular-glial-neuronal complex.

Methods:RC2 is a 40TB ssTEM volume of over 1,400 horizontal sections of retinal tissue derived from an adult female C57BL/6J mouse. The tissue sample is 250 um in diameter and spans the outer nuclear layer to the vitreal surface. Baseline resolution is 2.18nm per pixel. Visualization, navigation and metadata annotations of the database are made via the Viking software suite.

Results:Much of the retinal vascular basement membrane directly contacts Muller cells. In the ganglion cell layer, direct basement membrane contact with astrocytes is frequent. Microglia commonly contact the basement membrane, and occasionally direct contact of neurons onto basement membrane was observed. Full 3D reconstruction of all vascular pathways with associated endothelia and pericytes within the volume was completed, demonstrating that all the retinal capillary layers are continuous with one another [Figure].

Conclusions:The presence of occasional direct neuronal contact onto vascular basement membrane supports earlier work by Ochs and colleagues (2000) and suggests the blood-retina barrier does not universally involve retinal glia. However, since such contacts are extremely sparse, it remains to be seen whether this finding has biologic significance, though their existence suggests significance. The RC2 volume is a valuable resource to aid in discovery of defining characteristics of wild type neurovascular architecture.

The intro figure is a side view of reconstruction of all vasculature within the RC2 volume. Vessels at the top of the figure correspond to the outer plexiform layer, while those at the bottom correspond to the ganglion cell layer. This capillary plexus is one continuous structure. Visualization by VikingView software.

The AII Amacrine Cell Connectome: A Dense Network Hub


We have a new publication in Frontiers in Neuroscience, The AII Amacrine Cell Connectome: A Dense Network Hub.  Authors are Robert E. MarcJames R. Anderson, Bryan W. Jones, Crystal Sigulinsky and J. Scott Lauritzen.

Abstract:  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.


Retinal Connectomics: Toward Complete, Accurate Networks

Retinal Connectomics_600

We have a new publication, Retinal connectomics: Toward complete, accurate networks in Progress in Retinal and Eye Research.  Authors are:  Robert E. Marc, Bryan W. JonesCarl B. Watt, Crystal Sigulinsky, James R. Anderson and J. Scott Lauritzen.

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 1012-1015 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 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.