Category Archives: Ultrastructure

Heterocellular Coupling Between Amacrine Cells and Ganglion Cells

We have a new paper out In Frontiers in Neural Circuits, Heterocellular Coupling Between Amacrine Cells and Ganglion Cells. This manuscript preprint was published in BioRxiv.

Authors: Robert E. Marc, Crystal Lynn Sigulinsky, Rebecca L. Pfeiffer, Daniel Emrich, James Russel Anderson and Bryan William Jones.

Abstract: 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.

Pathoconnectome Analysis of Müller Cells in Early Retinal Remodeling

Rebecca Pfeiffer, a post-doc in the laboratory presented her work on “Pathoconnectome Analysis of Müller Cells in Early Retinal Remodeling” as a platform presentation at the RD2018 meeting in Killarney, Ireland.

Authors: Rebecca Pfeiffer, James R. Anderson, Daniel P. Emrich, Jeebika Dahal, Crystal L Sigulinsky, Hope AB Morrison, Jia-Hui Yang, Carl B. Watt, Kevin D. Rapp, Mineo Kondo, Hiroko Terasaki, Jessica C Garcia, Robert E. Marc, and Bryan W. Jones.

Purpose: Glia play important roles in neural system function. These roles include, but are not limited to: amino acid recycling, ion homeostasis, glucose transport, and removal of waste. During retinal degeneration, Muller cells, the primary macroglia of the retina, are one of the first cells to show metabolic and morphological alterations in response to retinal stress. The metabolic alterations observed in Muller cells appear to manifest in regions of photoreceptor degeneration; however, the precise mechanisms that govern these alterations in response to neuronal stress, synapse maintenance, or glia-glia interactions is currently unknown.  This project aims to reconstruct Muller cells from a pathoconnectome of early retinal remodeling at 2nm/pixel with ultrastructural metabolic data to determine the relationship of structural and metabolic phenotypes between neighboring neurons and glia.

Methods:  Retinal pathoconnectome 1 (RPC1) is the first connectome to be assembled from pathologic neural tissue (a pathoconnectome). The tissue selected for RPC1 was collected post mortem from a 10 month transgenic P347L rabbit model of autosomal dominant retinitis pigmentosa, fixed in 1% formaldehyde, 2.5% glutaraldehyde, 3% sucrose, and 1mM MgSO4 in cacodylate buffer (pH 7.4). The tissue was subsequently osmicated, dehydrated, resin embedded, and sectioned at 70nm. Sections were placed on formvar grids, stained, and imaged at 2nm/pixel on a JEOL JEM-1400 TEM using SerialEM software. 1 section was reserved from every 30 sections for CMP, where it was placed on a slide and probed for small molecules: glutamate, glutamine, glycine, GABA, taurine, glutathione; or TEM compatible proteins GFAP and GS. The pathoconnectome volume was evaluated and annotated using the Viking software suite.

Results: RPC1 demonstrates hallmarks of early retinal degeneration and remodeling, including the glial phenotypes of hypertrophy and metabolic variation between neighboring Muller cells. Early evaluation of these glia demonstrates variations in osmication in Muller cells as well as apparent encroachment of glial end-feet on one another.  We are currently in the process of reconstructing multiple Muller cells within RPC1 and their neighboring neurons.  Once complete, we will assess the relationship between Muller cell phenotype and the phenotypes of contacted neuronal and glial neighbors.

Conclusions: How neural-glial relationships are affected by retinal remodeling may help us understand why remodeling and neurodegeneration follow photoreceptor degeneration. In addition, determining these relationships during remodeling will be crucial to developing therapeutics with long-term success. RPC1 provides a framework to analyze these relationships in early retinal remodeling through ultrastructural reconstructions of all neurons and glia in an intact retina. These reconstructions, informed by quantitative metabolite labeling, will allow us to evaluate these neural-glial interactions more comprehensively than other techniques have previously allowed.


SEM vs. TEM is a tradeoff of convenience, resolution, cost and speed. The very physics of SEM signal integration means that the fundamental acquisition time for large canonical volume datasets are incompatible with 5 year grant cycles. SEM based approaches can potentially rival TEM, but dwell time/pixel increases logarithmically with resolution.

To give you some idea for the resolution differences at routine capture speeds, both of these above images capture a region within the inner plexiform layer of retina, looking at bipolar cell terminals. The TEM image was captured at a standard operating resolution of 2nm/pixel. The SEM image was captured at 16nm/pixel. You cannot see any gap junctions that might be present in the SEM image and you can only infer or guess at synaptic ribbons. And look at the texture!

You *can* get better resolution with SEM, but as I said before, the capture time increases logarithmically. To accomplish what we perform in 8-10 hours with a TEM, would take 108-115 hours on a current, cutting edge multi beam SEM. There are many other advantages of TEM including the ability to capture higher resolution images faster, be able to re-image in goniometric tilt series, be able to integrate molecular markers inside connectome volumes, and a TEM is about 1/3rd the cost of an SEM. Also, SEM images tend to be texturally poor as they are made from capturing electron backscatter of surfaces rather than made by projection of electrons through a small volume, and there is tremendous value in the texture of ultrastructural images. Ergo, this is why we use TEM.

This is not to say that SEM is not a great tool. It is just not the best tool for large scale connectomics where you have to have the resolution to capture all synapses and gap junctions, over large areas. For smaller volumes that do not require a canonical sampling of cell classes, SEM is absolutely an appropriate tool.

This content was originally published on Jonesblog.