Crystal Sigulinsky

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.

Vsx2/Chx10 ensures the correct timing and magnitude of Hedgehog signaling in the mouse retina.

Vertebrate retinal progenitor cells (RPCs) undergo a robust proliferative expansion to produce enough cells for the retina to form appropriately. Vsx2 (formerly Chx10), a homeodomain protein expressed in RPCs, is required for sufficient proliferation to occur. Sonic Hedgehog protein (SHH), secreted by retinal ganglion cells (RGCs), activates Hedgehog (Hh) signaling in RPCs and is also required for sufficient proliferation to occur. Therefore, we sought to determine if reduced Hh signaling is a contributing factor to the proliferation changes that occur in the absence of Vsx2. To do this, we examined Shh expression and Hh signaling activity in the homozygous ocular retardation J (orJ) mouse, which harbors a recessive null allele in the Vsx2 gene. We found that Shh expression and Hh signaling activity are delayed during early retinal development in orJ mice and this correlates with a delay in the onset of RGC differentiation. At birth, reduced expression of genes regulated by Hh signaling was observed despite the production of SHH ligand. orJ RPCs respond to pre-processed recombinant SHH ligand (SHH-N) in explant culture as evidenced by increased proliferation and expression of Hh target genes. Interestingly, proliferation in the orJ retina is further inhibited by cyclopamine, an antagonist of Hh signaling. Our results suggest that reduced Hh signaling contributes to the reduced level of RPC proliferation in the orJ retina, thereby revealing a role for Vsx2 in mediating mitogen signaling.

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.

Preparation and characterization of 15N-enriched, size-defined heparan sulfate precursor oligosaccharides

We report the preparation of size-defined [(15)N]N-acetylheparosan oligosaccharides from Escherichia coli-derived (15)N-enriched N-acetylheparosan. Optimized growth conditions of E. coli in minimal media containing (15)NH(4)Cl yielded [(15)N]N-acetylheparosan on a preparative scale. Depolymerization of [(15)N]N-acetylheparosan by heparitinase I yielded resolvable, even-numbered oligosaccharides ranging from disaccharide to icosaccharide. Anion-exchange chromatography-assisted fractionation afforded size-defined [(15)N]N-acetylheparosan oligosaccharides identifiable by ESI-TOFMS. These isotopically labeled oligosaccharides will prove to be valuable research tools for the chemoenzymatic synthesis of heparin and heparan sulfate oligosaccharides and for the study of their structural biology.

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.

High-Resolution Synaptic Connectomics

High-speed, high-resolution connectomics enables unambiguous mapping of synapses, gap junctions, adherens junctions, and other forms of adjacency among neurons in complex neural systems such as brain and retina. This chapter reviews the motivations for generating complete network architectures; the technologies available for large-scale network acquisition, visualization, and analysis; the fusion of molecular markers with a high-resolution ultrastructure; new networks and organelles discovered by ultrastructural connectomics; and new technological advances needed to expand the applications of connectomics.

Heterocellular coupling between amacrine cell and ganglion cells

All superclasses of retinal neurons display some form of electrical coupling including the key neurons of the inner plexiform layer: bipolar cells (BCs), amacrine or axonal cells (ACs) and ganglion cells (GCs). However, coupling varies extensively by class. For example, mammalian rod bipolar cells form no gap junctions at all, while all cone bipolar cells form class-specific coupling arrays, many of them homocellular in-superclass arrays. Ganglion cells are unique in that classes with coupling predominantly form heterocellular cross-class arrays of ganglion cell::amacrine cell (GC::AC) coupling in the mammalian retina. Ganglion cells are the least frequent superclass in the inner plexiform layer and GC::AC gap junctions are sparsely arrayed amidst massive cohorts of AC::AC, bipolar cell BC::BC, and AC::BC gap junctions. Many of these gap junctions and most ganglion cell gap junctions are suboptical, complicating analysis of specific ganglion cells. High resolution 2 nm TEM analysis of rabbit retinal connectome RC1 allows quantitative GC::AC coupling maps of identified ganglion cells. Ganglion cells classes apparently avoid direct cross-class homocellular coupling altogether even though they have opportunities via direct membrane touches, while transient OFF alpha ganglion cells and transient ON directionally selective (DS) ganglion cells are strongly coupled to distinct amacrine / axonal cell cohorts. A key feature of coupled ganglion cells is intercellular metabolite flux. Most GC::AC coupling involves GABAergic cells (γ+ amacrine cells), which results in significant GABA flux into ganglion cells. Surveying GABA coupling signatures in the ganglion cell layer across species suggests that the majority of vertebrate retinas engage in GC::AC coupling. Multi-hop synaptic queries of the entire RC1 connectome clearly profiles the coupled amacrine and axonal cells. Photic drive polarities and source bipolar cell class selec-tivities are tightly matched across coupled cells. OFF alpha ganglion cells are coupled to OFF γ+ amacrine cells and transient ON DS ganglion cells are coupled to ON γ+ amacrine cells including a large interstitial axonal cell (IAC). Synaptic tabulations show close matches between the classes of bipolar cells sampled by the coupled amacrine and ganglion cells. Further, both ON and OFF coupling ganglion networks show a common theme: synaptic asymmetry whereby the coupled γ+ neurons are also presynaptic to ganglion cell dendrites from different classes of ganglion cells outside the coupled set. In effect, these heterocellular coupling patterns enable an excited ganglion cell to directly inhibit nearby ganglion cells of different classes. Similarly, coupled γ+ amacrine cells engaged in feedback networks can leverage the additional gain of bipolar cell synapses in shaping the signaling of a spectrum of downstream targets based on their own selective coupling with ganglion cells.

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.

Genetic chimeras reveal the autonomy requirements for Vsx2 in embryonic retinal progenitor cells

BackgroundVertebrate retinal development is a complex process, requiring the specification and maintenance of retinal identity, proliferative expansion of retinal progenitor cells (RPCs), and their differentiation into retinal neurons and glia. The homeobox gene Vsx2 is expressed in RPCs and required for the proper execution of this retinal program. However, our understanding of the mechanisms by which Vsx2 does this is still rudimentary. To define the autonomy requirements for Vsx2 in the regulation of RPC properties, we generated chimeric mouse embryos comprised of wild-type and Vsx2-deficient cells.ResultsWe show that Vsx2 maintains retinal identity in part through the cell-autonomous repression of the retinal pigment epithelium determinant Mitf, and that Lhx2 is required cell autonomously for the ectopic Mitf expression in Vsx2-deficient cells. We also found significant cell-nonautonomous contributions to Vsx2-mediated regulation of RPC proliferation, pointing to an important role for Vsx2 in establishing a growth-promoting extracellular environment. Additionally, we report a cell-autonomous requirement for Vsx2 in controlling when neurogenesis is initiated, indicating that Vsx2 is an important mediator of neurogenic competence. Finally, the distribution of wild-type cells shifted away from RPCs and toward retinal ganglion cell precursors in patches of high Vsx2-deficient cell density to potentially compensate for the lack of fated precursors in these areas.ConclusionsThrough the generation and analysis of genetic chimeras, we demonstrate that Vsx2 utilizes both cell-autonomous and cell-nonautonomous mechanisms to regulate progenitor properties in the embryonic retina. Importantly, Vsx2’s role in regulating Mitf is in part separable from its role in promoting proliferation, and proliferation is excluded as the intrinsic timer that determines when neurogenesis is initiated. These findings highlight the complexity of Vsx2 function during retinal development and provide a framework for identifying the molecular mechanisms mediating these functions.