NBLAST: Rapid, sensitive comparison of neuronal structure and construction of neuron family databases

Costa, Marta; Manton, James D.; Ostrovsky, Aaron D.; Prohaska, Steffen; Jefferis, Gregory S.X.E.

doi:10.1101/006346

Cited by 85 publications

(185 citation statements)

References 55 publications

(94 reference statements)

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“…This effort uses serial-section electron microscopy combined with cell-type specific genetic tools to map synaptic connections between morphologically identifiable cell types [204, 205]. Cell types identified morphologically can then be matched with driver lines using new bioinformatic tools [206]. This effort to map synaptic connections using large-scale electron microscopy has focused on the brain thus far, but it is currently being extended to the VNC as well.…”

Section: The Role Of Drosophila In the Study Of Mechanosensory Procesmentioning

confidence: 99%

Mechanosensation and Adaptive Motor Control in Insects

2016

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Summary The ability of animals to flexibly navigate through complex environments depends on the integration of sensory information with motor commands. The sensory modality most tightly linked to motor control is mechanosensation. Adaptive motor control depends critically on an animal’s ability to respond to mechanical forces generated both within and outside the body. The compact neural circuits of insects provide appealing systems to investigate how mechanical cues guide locomotion in rugged environments. Here, we review our current understanding of mechanosensation in insects and its role in adaptive motor control. We first examine the detection and encoding of mechanical forces by primary mechanoreceptor neurons. We then discuss how central circuits integrate and transform mechanosensory information to guide locomotion. Because most studies in this field have been performed in locusts, cockroaches, crickets, and stick insects, the examples we cite here are drawn mainly from these ‘big insects’. However, we also pay particular attention to the tiny fruit fly, Drosophila, where new tools are creating new opportunities, particularly for understanding central circuits. Our aim is to show how studies of big insects have yielded fundamental insights relevant to mechanosensation in all animals, and also to point out how the Drosophila toolkit can contribute to future progress in understanding mechanosensory processing.

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Section: The Role Of Drosophila In the Study Of Mechanosensory Procesmentioning

confidence: 99%

Mechanosensation and Adaptive Motor Control in Insects

2016

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“…All annotated synapses in this wiring diagram fulfill the four following criteria of mature synapses In this study we validate the reconstructions as previously described 11;55 , a method successfully employed in multiple studies 11;55-60 . Briefly, in Drosophila, as in other insects, the gross morphology of many neurons is stereotyped and individual neurons are uniquely identifiable based on morphology [61][62][63] . Furthermore, the nervous system in insects is largely bilaterally symmetric and homologous neurons are reproducibly found on the left and the right side of the animal.…”

Section: Circuit Mapping and Electron Microscopymentioning

confidence: 99%

The complete connectome of a learning and memory center in an insect brain

Eichler

Litwin-Kumar

Feng

et al. 2017

Preprint

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Associating stimuli with positive or negative reinforcement is essential for survival, but a complete wiring diagram of a higherorder circuit supporting associative memory has not been previously available. We reconstructed one such circuit at synaptic resolution, the Drosophila larval mushroom body, and found that most Kenyon cells integrate random combinations of inputs but a subset receives stereotyped inputs from single projection neurons. This organization maximizes performance of a model output neuron on a stimulus discrimination task. We also report a novel canonical circuit in each mushroom body compartment with previously unidentified connections: reciprocal Kenyon cell to modulatory neuron connections, modulatory neuron to output neuron connections, and a surprisingly high number of recurrent connections between Kenyon cells. Stereotyped connections between output neurons could enhance the selection of learned responses. The complete circuit map of the mushroom body should guide future functional studies of this learning and memory center.Massively parallel, higher-order neuronal circuits such as the cerebellum and insect mushroom body serve to form and retain associations between stimuli and reinforcement in vertebrates and higher invertebrates [1][2][3][4][5][6] . Although these systems provide a biological substrate for adaptive behavior, no complete synapseresolution wiring diagram of their connectivity has been available to guide analysis and inspire understanding. The mushroom body (MB) is a higher-order parallel fiber system in many invertebrate brains, including hemimetabolous as well as holometabolous insects and their larval stages 6 . MB function is essential for associative learning in adult insects 1;3-5 and in Drosophila larvae 1;7;8 , from the earliest larval stages onward 9 . Indeed, the basic organization of the adult and the larval MB and their afferent circuits is very similar; however, larvae have about an order of magnitude fewer neurons 7 . Thus, to systematically investigate the organizational logic of the MB, we used serial section electron microscopy (EM) to map with synaptic resolution the complete MB connectome in a first instar Drosophila larva (L1; Fig. 1a). L1 are foraging animals capable of all behaviors previously described in later larval stages 10 , including adaptive behaviors dependent on associative learning 7;9 (Fig. 1b). Their smaller neurons enable fast EM imaging of the entire nervous system and reconstruction of complete circuits 11;12 . Models of sensory processing in many neural circuits feature neurons that fire in response to combinations of sensory inputs, generating a high-dimensional representation of the sensory environment 13 . The intrinsic MB neurons, the Kenyon cells (KCs), integrate in their dendrites inputs from combinations of projection neurons (PNs) that encode various stimuli, predominantly olfactory in both adult 1;4-6 , and larva 12;14 , but also thermal, gustatory

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“…9, Supplementary Table 1, and Supplementary Data 1 and 2 ), PACT and PARS provide a means for efficiently acquiring information on the spatial position of neurons within large tissue volumes at high resolution. For this information to be applied to mapping the connectome 59 , however, these gigabytes or even terabytes of raw image data (e.g., for a whole mouse brain at 25× magnification) must be converted into a complex network of neuron projection pathways and neural contacts, a feat that poses substantial demands on both storage hardware and image analysis software. Many available software tools and image file formats were not designed with tera-scale data sets in mind and assume that entire image volumes fit in computer RAM.…”

Section: Introductionmentioning

confidence: 99%

Whole-body tissue stabilization and selective extractions via tissue-hydrogel hybrids for high-resolution intact circuit mapping and phenotyping

et al. 2015

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To facilitate fine-scale phenotyping of whole specimens, we describe here a set of tissue fixation-embedding, detergent-clearing and staining protocols that can be used to transform excised organs and whole organisms into optically transparent samples within 1–2 weeks without compromising their cellular architecture or endogenous fluorescence. PACT (passive CLARITY technique) and PARS (perfusion-assisted agent release in situ) use tissue-hydrogel hybrids to stabilize tissue biomolecules during selective lipid extraction, resulting in enhanced clearing efficiency and sample integrity. Furthermore, the macromolecule permeability of PACT- and PARS-processed tissue hybrids supports the diffusion of immunolabels throughout intact tissue, whereas RIMS (refractive index matching solution) grants high-resolution imaging at depth by further reducing light scattering in cleared and uncleared samples alike. These methods are adaptable to difficult-to-image tissues, such as bone (PACT-deCAL), and to magnified single-cell visualization (ePACT). Together, these protocols and solutions enable phenotyping of subcellular components and tracing cellular connectivity in intact biological networks.

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NBLAST: Rapid, sensitive comparison of neuronal structure and construction of neuron family databases

Cited by 85 publications

References 55 publications

Mechanosensation and Adaptive Motor Control in Insects

Mechanosensation and Adaptive Motor Control in Insects

The complete connectome of a learning and memory center in an insect brain

Whole-body tissue stabilization and selective extractions via tissue-hydrogel hybrids for high-resolution intact circuit mapping and phenotyping

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