Author Archives: bwjones

Pathoconnectome Analysis of Müller Cells in Early Retinal Remodeling

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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.

Coupling Architecture Of The Aii/ON Cone Bipolar Cell Network In The Degenerate Retina

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Crystal Sigulinsky, a post-doc in the lab, presented her work on “coupling architecture of the
Aii/ON cone bipolar cell network in the degenerate retina” at the RD2018 meeting in Killarney, Ireland today.  Authors are: Crystal L Sigulinsky, Rebecca L Pfeiffer, James R Anderson, Jeebika Dahal, Hope Morrison, Daniel P. Emrich, Jessica C Garcia, Jia-Hui Yang, Carl B. Watt, Kevin D. Rapp, Mineo Kondo, Hiroko Terasaki, Robert E. Marc, and Bryan W. Jones.

Purpose: Retinal network hyperactivity within degenerative retinal networks is a component of the disease process with implications for therapeutic interventions for blinding diseases that depend upon the surviving retinal network. Connexin36-containing gap junctions centered on the Aii amacrine cell network appear to mediate the aberrant signaling observed in mouse models of retinal degeneration. However, it remains unclear whether this hyperactivity reflects changes in the underlying circuitry or dysfunction/dysregulation of the normative circuitry. Mapping retinal circuitry in the ultrastructural rabbit Retinal Connectome, RC1, has revealed Aii network topologies explicitly involving gap junctions. In addition to canonical Aii-to-Aii and Aii-to-ON cone bipolar cell (CBC) coupling, we describe pervasive in- and cross-class coupling motifs among ON CBCs that extend and dramatically expand the coupled Aii network topologies. Since virtually every gap junction in the inner plexiform layer contains Connexin36, these circuits likely participate in the aberrant signaling of degenerate retinas. This study examines these Aii and ON CBC coupling motifs in Retinal PathoConnectome 1 (RPC1), an ultrastructural pathoconnectome of a rabbit model of retinitis pigmentosa.

Approach: RPC1 is a 2nm/pixel resolution volume of retina from a 10 month old, transgenic P347L rabbit model of autosomal dominant retinitis pigmentosa in early phase 1 retinal remodeling, a time point where cone and rod photoreceptors are still present, albeit going through cell stress. RPC1 spans the vitreous to basal outer nuclear layer and was built by automated transmission electron microscopy and computational assembly. ON CBCs, Aii amacrine cells, and their coupling partners were annotated using the Viking application and explored with 3D rendering and graph visualization of connectivity. Gap junctions were validated by 0.25 nm resolution recapture with goniometric tilt when necessary. Motifs were compared to those discovered in RC1. RC1 is a 2 nm resolution, 0.25 mm diameter volume of a light-adapted adult female Dutch Belted rabbit retina spanning the ganglion cell through inner nuclear layers.

Conclusions: RPC1 shows degeneration of rod outer segments, Müller cell hypertrophy and neuronal sprouting, characteristic of early stage retinal degeneration and phase 1 remodeling, when retinal hyperactivity and its reliance on gap junctional coupling has likely already initiated and human patients would still have some vision. All major coupling motifs (Aii-to-Aii, Aii-to-ON CBC, and ON CBC-to-ON CBC) were observed. Preliminary examinations indicate that several ON CBC classes retained their class-specific coupling profiles, accepting and rejecting specific combinations of Aii and ON CBC class partnerships. However, recent findings reveal aberrant partnerships in the coupled network, including both loss of prominent motifs and acquisition of novel ones. Thus, clear aberrant morphological and synaptic changes have been identified in RPC1, including changes in the coupling specificity and gap junction distributions of both Aii amacrine cells and ON CBCs (Figure 6). This suggests that the Aii/ON CBC circuit topology is already altered during early phase 1 remodeling, with substantial implications for therapeutic interventions in human subjects. The full coupling network is actively being examined and progress has begun on RPC2, a second pathoconnectome for examining later, phase 2 remodeling in this same model.

An almost full size poster available here in pdf format.

Off to RD2018 and ISER 2018

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The Marclab for Connectomics is off to RD2018 and ISER 2018 in Killarney, Ireland and Belfast, Northern Ireland.  I’ll be organizing sessions on retinal degeneration, and I’m tremendously proud of the work Dr. Crystal Sigulinsky will be presenting from her work on gap junctional connectivity in retinal degenerations and the work Dr. Rebecca Pfeiffer (@BeccaPfeiffer19) will be presenting on her work on the retinal pathoconnectome in two talks on bipolar cells and Müller cells.

SEM vs. TEM

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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.

Nornir listed on NITRC

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Nornir is now registered with NeuroImaging Tools & Resources Collaboratory (NITRC).

Nornir’s takes large sets of overlapping images in 2D and produces registered (a.k.a. aligned) 2D mosaics and 3D volumes of any size and scale. Registered slices may be exported as a single large images or viewed/annoted with our Viking viewer.

Nornir has been used successfully on transmission electron microscopy, scanning electron microscopy images, and light microscopy images. Nornir supports interleaving different imaging methods into the same volume. Support for SerialEM, Objective Imaging, and Digital Micrograph (DM4) raw data is available. Adding formats is not complicated and the author will consider requests.

Nornir runs on fairly humble hardware for the task. A 32-core 64GB Xeon system built a ~60 TB 250um diameter 2.12nm/pixel volume from roughly 1400 slices. Nornir works incrementally, only updating data that has changed.

Installation is fairly simple and primarily uses Python’s PIP installer.

For further information: http://nornir.github.io/

Hitachi H-600 Transmission Electron Microscope Retired

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We are retiring our Hitachi H-600 Transmission Electron Microscope to make room for a new JEOL (@JEOLUSA) replacement to keep company with our other workhorse JEOL JEM-1400.  I have mixed feelings about retiring this microscope as this is the system we originally developed the first code to mosaic and register images and image slices for our connectomics work.

This fully functional and well cared for microscope will be made available through the University of Utah Surplus and Salvage as an auction if you are interested in bidding on it.  Contact me: bryan dot jones at m dot cc dot utah dot edu or @BWJones if you are interested in it.

Congratulations Dr. Kerzner

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Congratulations to Dr. Ethan Kerzner who successfully defended his PhD dissertation in the Viz Design Lab at the University of Utah’s Scientific Computing Institute.

Ethan’s work has been instrumental in helping us to understanding complex gap junctional networks in our retinal connectomics initiatives.  His Graffinity software package allowed us to explore multivariate graphs, and pull out complex relationships of neurons and gap junctions that would not have been easily possible with other approaches.

Ethan is now off to Google X, and we wish him the very best and look forward to many more interactions in the future.