Such a distortion could complicate the assignment of postsynaptic partners in polyadic synapses, but highlight gap junctions, which remain intact. Synapse annotation and connectome assembly were carried out cautiously and carefully with these caveats in mind White et al. Even with a well-preserved sample that has been fixed using high-pressure freezing and aligned well into a 3D volume, synapse annotation requires training, and includes of element of subjectivity see below; Figures 6 , 7.
For a compact nervous system such as C. Chemical synapses and gap junctions in C. Multiple chemical synapses are visible white arrows as well as a gap junction white flat-ended line. B Enlarged view of the chemical synapse highlighted with a dashed box in panel A.
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There is a presynaptic dense projection and a pool of synaptic vesicles, as well as some dense core vesicles further back in the neurite. This synapse is polyadic, releasing onto three neurons. C Enlarged view of the gap junction highlighted with a dashed box in panel A. There is a relatively flat area of close apposition between the membranes.
Examples of synapse annotation with different degrees of subjectivity. This synapse spans these three sections, and beyond not shown. The annotation of this synapse is less confident that the one presented in panel A. C Serial sections of a membrane swelling that is confidently annotated as not-a-synapse. A small density in the membrane of RIBL with sparse vesicles is not a presynaptic specialization. D Serial sections through a synapse showing the occasional subjectivity involved in defining postsynaptic partners. White arrowheads indicate the membrane of interest.
Below we describe the criteria used for synapse annotation in our high-pressure frozen and freeze substituted volumes of the C. Caenorhabditis elegans presynapses generally consist of a swelling in the neurite, with a visible electron-dense presynaptic density attached to the plasma membrane marking the active zone, with a cloud of vesicles adjacent to the presynaptic density Figures 6A,B , 7A.
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Vesicle clouds often consist of many clear core synaptic vesicles close to the active zone, and a small number of large, dense-core vesicles that reside more peripherally. Vesicle clouds can cover large areas with multiple small presynaptic dense projections, especially in the nerve ring. If the synapse is small, cut at an awkward angle, or if there are artifacts covering or interfering with the putative synapse, assigning whether it is a synapse or not can sometimes be a bit subjective Figures 7B,C. Many synapses are polyadic. Since most synapses in the C. To minimize the problem of subjectivity, our datasets are fully annotated by three independent annotators.
Using CATMAID one can assign confidence scores to synapses, with a score of 5 indicating a high level of confidence, and a score of 1 indicating very low confidence. The triplicate annotations are then merged, and every inconsistency between annotators is flagged for discussion. If agreement is not reached by the three annotators after debate, an average of the confidence scores is reported to allow subsequent data users to make their own judgments.
Gap junctions are notoriously difficult to identify in vEM.
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There are some morphological criteria that can help identify some with reasonable certainty. These features can be quite clear if cut at the perfect angle with thin 30—50 nm sections, but even in well-stained samples not all gap junctions can be marked unambiguously. Tomography, which acquires images of the same section at different tilt angles to generate a high-resolution 3D volume of the section, helps survey a putative gap junction, but it is unrealistic to apply such an approach to the entire series of the nervous system.
We corroborate our gap junction annotation by comparing patterns across our multiple new datasets and to the original datasets White et al. The slow chemical fixation protocol used for the original adult connectome, while distorting neurite morphology and pulling apart weaker contacts between neurites, allowed strong membrane connections such as gap junctions to be particularly well distinguished.
Some of the morphologically identified gap junctions have been functionally validated Chalfie et al. Comparing new and old datasets allows us to refine criteria for gap junction annotation in high-pressure frozen datasets. These criteria are validated by uncovering recurrent gap junction-like structures when comparing the same membranes between neuronal classes across datasets. Because in each sample, the junction between each neuron pair was sectioned from a different angle, stereotypic gap junctions can be confirmed in multiple views. Our approach will likely miss small or sparse gap junctions.
Multiple approaches have been attempted to highlight gap junctions in EM volumes. CLEM c orrelative l ight and e lectron m icroscopy , where gap junctions are labeled by immunostaining against one of the C. This approach requires a weak fixation that compromises structural preservation, and it would be difficult to expand this approach to all 25 C. We and others are working to develop EM preservation protocols to improve gap junction annotation. In a large, good quality C. Each neuron class has been described in such superb detail in The Mind of a Worm White et al.
go to site WormAtlas hosts scanned copies of the neuron pages from The Mind of a Worm that are accessible through a drop-down menu in an internet browser Altun et al. Several features indicate neuron identity: cell body position, neurite trajectory, stereotypic neurite placement or morphology and stereotypic connectivity patterns.
We found that this stereotypy holds across postnatal developmental stages for most neurons, with a few exceptions.
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Among them, VD presynaptic swellings are large, face directly toward the muscle, most of the time without any neurons as dyadic postsynaptic partners Jin et al. Neurons can be identified from 3D volumes.
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Some other motor neurons are also labeled, to give a sense of the relative position within the nerve cord. D A cartoon of most of the commissure bundles in C. The positions, handedness and commissure bundle partners are known, and very stereotypic. Bundles of neuron processes are shown as red cables.
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The cell bodies are denoted with spheres, and also have stereotypic positions along the body of the worm and relative to each other. Neurite trajectory and process placement are used to further identify neurons. For example, VAs project axons anteriorly from the soma, whereas VB axons project posteriorly. VDs also project their axons anteriorly, but they send a dorsal-projecting commissure at the end of the axon regions. Commissure trajectory whether it exits the ventral nerve cord from the left or right side and partners in each commissure bundle further assist cell identification Figure 8D.
Similar observations and strategies apply to the other neuropils in the worm, such as the dorsal nerve cord, the nerve ring, and the other cords and ganglia of the worm, as well as across different stages of development. Some neurons are not born until later in development Sulston and Horvitz, , but most neurons have stereotypic features and connectivity across larval stages.
A notable exception is the DD motor neuron class, which exhibits extensive remodeling of connectivity during development White et al. After obtaining a connectome, we further assess pairwise connections to gauge confidence in biologically relevant connections. Connections between two neurons consisting of many synapses are considered high confidence. A connection is considered uncertain if it consists of very few synapses. When few synapses are observed between neurons, we often observe inconsistency in the existence of the connection across animals. From comparing multiple datasets that we have acquired for the C.
Even so, to minimize variability introduced by annotators, and assess true biological variability, acquiring connectomes from multiple animals is advisable.
The pipeline described above represents only a starting point for modern high throughput C. We should expect rapid and substantial improvement both in terms of throughput and quality. Future improvements will include automated image segmentation, synapse annotation and neuron and neurite identification. This will be facilitated by the generation of new C. Incorporating of these improvements will allow not only rapid reconstruction of connectomes from multiple animals, but also facilitate targeted reconstruction of specific segments of the nervous system by computer vision.
The delineation of the neurotransmitter type and receptor complement of each neuron Serrano-Saiz et al. Performing connectomics on animals with genetic mutations that affect diverse properties of neurons — neuronal fate, synaptic transmission, cell adhesion and signaling — holds the promise of identifying genetic and biochemical pathways that determine connectivity.
This system holds a promise to reveal insight on principles of how a connectome leads to hard-wired and flexible behaviors Johnson et al. The field of C. Modern techniques now allow us to use connectomics to address questions about the dynamic and comparative structures of complete nervous systems. How does a connectome remodel across development? What sexual dimorphisms are held within a connectome? How do mutations in genes that establish the trajectory of neurite growth, the specificity of synapse partners, and the molecular complement of the plasma membrane, change a connectome?
Does a connectome drift with age? How much inter-individual variability is there? Is learning and memory physically manifested within the connectome? What about the influence of environment?