It is time to say goodbye to a workhorse that has been a part of a tremendous amount of retinal science to make room for a new instrument that will help us expand our workflow. See more over on Jonesblog.
Our paper Rod-cone crossover connectome of mammalian bipolar cells has been republished in a special issue of The Journal Of Comparative Neurology, Retinal Special Issue I: Mammals.
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.
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.
Rebecca Pfeiffer, a post-doc in the laboratory presented her work on “Rod Bipolar Cell Networks in Early Retinal Remodeling” as a platform presentation at the ISER 2018 meeting in Belfast, Northern 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, Jessica C Garcia, Mineo Kondo, Hiroko Terasaki, Robert E. Marc, and Bryan W. Jones.
Abstract: Retinal remodeling is a form of negative plasticity that occurs as a consequence of retinal degenerative diseases. Part of retinal remodeling involves anomalous sprouting of processes, termed neurites. The synaptic structures and partners of the neurites are not yet defined, leading to uncertainty about the consistency of network motifs between healthy and degenerate retina. Our goal is to map out the identities and network relationships of bipolar cell networks using a connectomics strategy. Retinal connectomes or ultrastructural maps of neuronal connectivity have substantially contributed to our understanding of retinal network topology, providing ground truth against which pathological network topologies can be evaluated. We have generated the first pathoconnectome (RPC1), or connectome of pathological tissues, of early retinal remodeling at 2nm/pixel, and are currently investigating the impact of remodeling on network architecture.
The tissue for RPC1 was obtained from a 10mo transgenic P347L rabbit model of autosomal dominant retinitis pigmentosa. Tissue was fixed in mixed aldehydes, osmicated, dehydrated, embedded in epon resin, and sectioned at 70nm. Serial sections were placed on grids, stained, and imaged using a JEOL JEM-1400 TEM using SerialEM software. Every 30th section was reserved for computational molecular phenotyping (CMP), and probed for small molecules: glutamate, glutamine, glycine, GABA, taurine, glutathione; or TEM compatible proteins GFAP and GS. The pathoconnectome volume is explored and annotated using the Viking software suite.
RPC1 was selected as an example of early retinal remodeling, demonstrating Muller cell hypertrophy, metabolic dysregulation, and degeneration of rod outer segments, indicating phase 1 remodeling and neuronal sprouting. We have observed the presence of both cone pedicles and rod spherules within the OPL to be synaptically active with neurites from some rod bipolar cells forming functional synapses with both rod spherules and cone pedicles. These rod bipolar cells also exhibit structurally altered ribbon synapses. We are currently evaluating network motifs and comparing them to networks established from our previous connectome, RC1, generated from a healthy rabbit.
These findings allow us to evaluate and analyze the impact of retinal remodeling on retinal networks which may have important implications for therapeutic interventions being developed which rely on inner retina network integrity.
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.
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.
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 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’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/