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Tao L, Ayembem D, Barranca VJ, Bhandawat V. Neurons underlying aggressive actions that are shared by both males and females in Drosophila. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.26.582148. [PMID: 38464020 PMCID: PMC10925114 DOI: 10.1101/2024.02.26.582148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Aggression involves both sexually monomorphic and dimorphic actions. How the brain implements these two types of actions is poorly understood. We found that a set of neurons, which we call CL062, previously shown to mediate male aggression also mediate female aggression. These neurons elicit aggression acutely and without the presence of a target. Although the same set of actions is elicited in males and females, the overall behavior is sexually dimorphic. The CL062 neurons do not express fruitless , a gene required for sexual dimorphism in flies, and expressed by most other neurons important for controlling fly aggression. Connectomic analysis suggests that these neurons have limited connections with fruitless expressing neurons that have been shown to be important for aggression, and signal to different descending neurons. Thus, CL062 is part of a monomorphic circuit for aggression that functions parallel to the known dimorphic circuits.
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Cornean J, Molina-Obando S, Gür B, Bast A, Ramos-Traslosheros G, Chojetzki J, Lörsch L, Ioannidou M, Taneja R, Schnaitmann C, Silies M. Heterogeneity of synaptic connectivity in the fly visual system. Nat Commun 2024; 15:1570. [PMID: 38383614 PMCID: PMC10882054 DOI: 10.1038/s41467-024-45971-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 02/08/2024] [Indexed: 02/23/2024] Open
Abstract
Visual systems are homogeneous structures, where repeating columnar units retinotopically cover the visual field. Each of these columns contain many of the same neuron types that are distinguished by anatomic, genetic and - generally - by functional properties. However, there are exceptions to this rule. In the 800 columns of the Drosophila eye, there is an anatomically and genetically identifiable cell type with variable functional properties, Tm9. Since anatomical connectivity shapes functional neuronal properties, we identified the presynaptic inputs of several hundred Tm9s across both optic lobes using the full adult female fly brain (FAFB) electron microscopic dataset and FlyWire connectome. Our work shows that Tm9 has three major and many sparsely distributed inputs. This differs from the presynaptic connectivity of other Tm neurons, which have only one major, and more stereotypic inputs than Tm9. Genetic synapse labeling showed that the heterogeneous wiring exists across individuals. Together, our data argue that the visual system uses heterogeneous, distributed circuit properties to achieve robust visual processing.
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Affiliation(s)
- Jacqueline Cornean
- Institute of Developmental Biology and Neurobiology, Johannes-Gutenberg University, 55128, Mainz, Germany
| | - Sebastian Molina-Obando
- Institute of Developmental Biology and Neurobiology, Johannes-Gutenberg University, 55128, Mainz, Germany
| | - Burak Gür
- Institute of Developmental Biology and Neurobiology, Johannes-Gutenberg University, 55128, Mainz, Germany
| | - Annika Bast
- Institute of Developmental Biology and Neurobiology, Johannes-Gutenberg University, 55128, Mainz, Germany
| | - Giordano Ramos-Traslosheros
- Institute of Developmental Biology and Neurobiology, Johannes-Gutenberg University, 55128, Mainz, Germany
- Department of Neurobiology, Harvard Medical School, Boston, MA, 02115, USA
| | - Jonas Chojetzki
- Institute of Developmental Biology and Neurobiology, Johannes-Gutenberg University, 55128, Mainz, Germany
| | - Lena Lörsch
- Institute of Developmental Biology and Neurobiology, Johannes-Gutenberg University, 55128, Mainz, Germany
| | - Maria Ioannidou
- Institute of Developmental Biology and Neurobiology, Johannes-Gutenberg University, 55128, Mainz, Germany
| | - Rachita Taneja
- Institute of Developmental Biology and Neurobiology, Johannes-Gutenberg University, 55128, Mainz, Germany
| | - Christopher Schnaitmann
- Institute of Developmental Biology and Neurobiology, Johannes-Gutenberg University, 55128, Mainz, Germany
| | - Marion Silies
- Institute of Developmental Biology and Neurobiology, Johannes-Gutenberg University, 55128, Mainz, Germany.
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53
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Pospisil DA, Aragon MJ, Dorkenwald S, Matsliah A, Sterling AR, Schlegel P, Yu SC, McKellar CE, Costa M, Eichler K, Jefferis GSXE, Murthy M, Pillow JW. From connectome to effectome: learning the causal interaction map of the fly brain. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.10.31.564922. [PMID: 37961285 PMCID: PMC10635032 DOI: 10.1101/2023.10.31.564922] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
A long-standing goal of neuroscience is to obtain a causal model of the nervous system. This would allow neuroscientists to explain animal behavior in terms of the dynamic interactions between neurons. The recently reported whole-brain fly connectome [1-7] specifies the synaptic paths by which neurons can affect each other but not whether, or how, they do affect each other in vivo. To overcome this limitation, we introduce a novel combined experimental and statistical strategy for efficiently learning a causal model of the fly brain, which we refer to as the "effectome". Specifically, we propose an estimator for a dynamical systems model of the fly brain that uses stochastic optogenetic perturbation data to accurately estimate causal effects and the connectome as a prior to drastically improve estimation efficiency. We then analyze the connectome to propose circuits that have the greatest total effect on the dynamics of the fly nervous system. We discover that, fortunately, the dominant circuits significantly involve only relatively small populations of neurons-thus imaging, stimulation, and neuronal identification are feasible. Intriguingly, we find that this approach also re-discovers known circuits and generates testable hypotheses about their dynamics. Overall, our analyses of the connectome provide evidence that global dynamics of the fly brain are generated by a large collection of small and often anatomically localized circuits operating, largely, independently of each other. This in turn implies that a causal model of a brain, a principal goal of systems neuroscience, can be feasibly obtained in the fly.
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Affiliation(s)
- Dean A Pospisil
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Max J Aragon
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Sven Dorkenwald
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Computer Science Department, Princeton University, Princeton, NJ, USA
| | - Arie Matsliah
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Amy R Sterling
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Philipp Schlegel
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Szi-Chieh Yu
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Claire E McKellar
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Marta Costa
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Katharina Eichler
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Gregory S X E Jefferis
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Mala Murthy
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Jonathan W Pillow
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
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54
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Tegethoff L, Briggman KL. Quantitative evaluation of embedding resins for volume electron microscopy. Front Neurosci 2024; 18:1286991. [PMID: 38406585 PMCID: PMC10884229 DOI: 10.3389/fnins.2024.1286991] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 01/29/2024] [Indexed: 02/27/2024] Open
Abstract
Optimal epoxy resin embedding is crucial for obtaining consistent serial sections from large tissue samples, especially for block faces spanning >1 mm2. We report a method to quantify non-uniformity in resin curing using block hardness measurements from block faces. We identify conditions that lead to non-uniform curing as well as a procedure to monitor the hardness of blocks for a wide range of common epoxy resins used for volume electron microscopy. We also assess cutting repeatability and uniformity by quantifying the transverse and sectional cutting forces during ultrathin sectioning using a sample-mounted force sensor. Our findings indicate that screening and optimizing resin formulations is required to achieve the best repeatability in terms of section thickness. Finally, we explore the encapsulation of irregularly shaped tissue samples in a gelatin matrix prior to epoxy resin embedding to yield more uniform sections.
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Affiliation(s)
| | - Kevin L. Briggman
- Max Planck Institute for Neurobiology of Behavior - caesar, Bonn, Germany
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55
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Ros IG, Omoto JJ, Dickinson MH. Descending control and regulation of spontaneous flight turns in Drosophila. Curr Biol 2024; 34:531-540.e5. [PMID: 38228148 PMCID: PMC10872223 DOI: 10.1016/j.cub.2023.12.047] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 12/12/2023] [Accepted: 12/14/2023] [Indexed: 01/18/2024]
Abstract
The clumped distribution of resources in the world has influenced the pattern of foraging behavior since the origins of locomotion, selecting for a common search motif in which straight movements through resource-poor regions alternate with zig-zag exploration in resource-rich domains. For example, during local search, flying flies spontaneously execute rapid flight turns, called body saccades, but suppress these maneuvers during long-distance dispersal or when surging upstream toward an attractive odor. Here, we describe the key cellular components of a neural network in flies that generate spontaneous turns as well as a specialized pair of neurons that inhibits the network and suppresses turning. Using 2-photon imaging, optogenetic activation, and genetic ablation, we show that only four descending neurons appear sufficient to generate the descending commands to execute flight saccades. The network is organized into two functional units-one for right turns and one for left-with each unit consisting of an excitatory (DNae014) and an inhibitory (DNb01) neuron that project to the flight motor neuropil within the ventral nerve cord. Using resources from recently published connectomes of the fly, we identified a pair of large, distinct interneurons (VES041) that form inhibitory connections to all four saccade command neurons and created specific genetic driver lines for this cell. As predicted by its connectivity, activation of VES041 strongly suppresses saccades, suggesting that it promotes straight flight to regulate the transition between local search and long-distance dispersal. These results thus identify the key elements of a network that may play a crucial role in foraging ecology.
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Affiliation(s)
- Ivo G Ros
- Division of Biology and Bioengineering, California Institute of Technology, 1200 E. California Blvd., Pasadena, CA 91125, USA
| | - Jaison J Omoto
- Division of Biology and Bioengineering, California Institute of Technology, 1200 E. California Blvd., Pasadena, CA 91125, USA
| | - Michael H Dickinson
- Division of Biology and Bioengineering, California Institute of Technology, 1200 E. California Blvd., Pasadena, CA 91125, USA.
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56
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McCafferty CL, Klumpe S, Amaro RE, Kukulski W, Collinson L, Engel BD. Integrating cellular electron microscopy with multimodal data to explore biology across space and time. Cell 2024; 187:563-584. [PMID: 38306982 DOI: 10.1016/j.cell.2024.01.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 01/03/2024] [Accepted: 01/03/2024] [Indexed: 02/04/2024]
Abstract
Biology spans a continuum of length and time scales. Individual experimental methods only glimpse discrete pieces of this spectrum but can be combined to construct a more holistic view. In this Review, we detail the latest advancements in volume electron microscopy (vEM) and cryo-electron tomography (cryo-ET), which together can visualize biological complexity across scales from the organization of cells in large tissues to the molecular details inside native cellular environments. In addition, we discuss emerging methodologies for integrating three-dimensional electron microscopy (3DEM) imaging with multimodal data, including fluorescence microscopy, mass spectrometry, single-particle analysis, and AI-based structure prediction. This multifaceted approach fills gaps in the biological continuum, providing functional context, spatial organization, molecular identity, and native interactions. We conclude with a perspective on incorporating diverse data into computational simulations that further bridge and extend length scales while integrating the dimension of time.
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Affiliation(s)
| | - Sven Klumpe
- Research Group CryoEM Technology, Max-Planck-Institute of Biochemistry, Am Klopferspitz 18, 82152 Martinsried, Germany.
| | - Rommie E Amaro
- Department of Molecular Biology, University of California, San Diego, La Jolla, CA 92093, USA.
| | - Wanda Kukulski
- Institute of Biochemistry and Molecular Medicine, University of Bern, Bühlstrasse 28, 3012 Bern, Switzerland.
| | - Lucy Collinson
- Electron Microscopy Science Technology Platform, Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK.
| | - Benjamin D Engel
- Biozentrum, University of Basel, Spitalstrasse 41, 4056 Basel, Switzerland.
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57
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Simpson JH. Descending control of motor sequences in Drosophila. Curr Opin Neurobiol 2024; 84:102822. [PMID: 38096757 PMCID: PMC11215313 DOI: 10.1016/j.conb.2023.102822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 11/22/2023] [Accepted: 11/22/2023] [Indexed: 02/18/2024]
Abstract
The descending neurons connecting the fly's brain to its ventral nerve cord respond to sensory stimuli and evoke motor programs of varying complexity. Anatomical characterization of the descending neurons and their synaptic connections suggests how these circuits organize movements, while optogenetic manipulation of their activity reveals what behaviors they can induce. Monitoring their responses to sensory stimuli or during behavior performance indicates what information they may encode. Recent advances in all three approaches make the descending neurons an excellent place to better understand the sensorimotor integration and transformation required for nervous systems to govern the motor sequences that constitute animal behavior.
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Affiliation(s)
- Julie H Simpson
- Dept. Molecular Cellular and Developmental Biology and Neuroscience Research Institute, University of California Santa Barbara, USA.
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58
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Hunter I, Coulson B, Pettini T, Davies JJ, Parkin J, Landgraf M, Baines RA. Balance of activity during a critical period tunes a developing network. eLife 2024; 12:RP91599. [PMID: 38193543 PMCID: PMC10945558 DOI: 10.7554/elife.91599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2024] Open
Abstract
Developing neural circuits are influenced by activity and are especially sensitive to changes in activity during critical periods (CPs) of development. Changes occurring during a CP often become 'locked in' so that they affect the mature network. Indeed, several neurodevelopmental disorders have been linked to excessive activity during such periods. It is, therefore, important to identify those aspects of neural circuit development that are influenced by neural activity during a CP. In this study, we take advantage of the genetic tractability of Drosophila to show that activity perturbation during an embryonic CP permanently alters properties of the locomotor circuit. Specific changes we identify include increased synchronicity of motoneuron activity and greater strengthening of excitatory over inhibitory synaptic drive to motoneurons. These changes are sufficient to reduce network robustness, evidenced by increased sensitivity to induced seizure. We also show that we can rescue these changes when increased activity is mitigated by inhibition provided by mechanosensory neurons. Similarly, we demonstrate a dose-dependent relationship between inhibition experienced during the CP and the extent to which it is possible to rescue the hyperexcitable phenotype characteristic of the parabss mutation. This suggests that developing circuits must be exposed to a properly balanced sum of excitation and inhibition during the CP to achieve normal mature network function. Our results, therefore, provide novel insight into how activity during a CP shapes specific elements of a circuit, and how activity during this period is integrated to tune neural circuits to the environment in which they will likely function.
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Affiliation(s)
- Iain Hunter
- Division of Neuroscience, School of Biological Sciences, Faculty of Biology, Medicine and Health,University of ManchesterManchesterUnited Kingdom
| | - Bramwell Coulson
- Division of Neuroscience, School of Biological Sciences, Faculty of Biology, Medicine and Health,University of ManchesterManchesterUnited Kingdom
| | - Tom Pettini
- Department of Zoology, University of CambridgeCambridgeUnited Kingdom
| | - Jacob J Davies
- Division of Neuroscience, School of Biological Sciences, Faculty of Biology, Medicine and Health,University of ManchesterManchesterUnited Kingdom
| | - Jill Parkin
- Division of Neuroscience, School of Biological Sciences, Faculty of Biology, Medicine and Health,University of ManchesterManchesterUnited Kingdom
| | - Matthias Landgraf
- Department of Zoology, University of CambridgeCambridgeUnited Kingdom
| | - Richard A Baines
- Division of Neuroscience, School of Biological Sciences, Faculty of Biology, Medicine and Health,University of ManchesterManchesterUnited Kingdom
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59
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Schneider-Mizell CM, Bodor AL, Brittain D, Buchanan J, Bumbarger DJ, Elabbady L, Gamlin C, Kapner D, Kinn S, Mahalingam G, Seshamani S, Suckow S, Takeno M, Torres R, Yin W, Dorkenwald S, Bae JA, Castro MA, Halageri A, Jia Z, Jordan C, Kemnitz N, Lee K, Li K, Lu R, Macrina T, Mitchell E, Mondal SS, Mu S, Nehoran B, Popovych S, Silversmith W, Turner NL, Wong W, Wu J, Reimer J, Tolias AS, Seung HS, Reid RC, Collman F, Maçarico da Costa N. Cell-type-specific inhibitory circuitry from a connectomic census of mouse visual cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.01.23.525290. [PMID: 36747710 PMCID: PMC9900837 DOI: 10.1101/2023.01.23.525290] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Mammalian cortex features a vast diversity of neuronal cell types, each with characteristic anatomical, molecular and functional properties. Synaptic connectivity powerfully shapes how each cell type participates in the cortical circuit, but mapping connectivity rules at the resolution of distinct cell types remains difficult. Here, we used millimeter-scale volumetric electron microscopy1 to investigate the connectivity of all inhibitory neurons across a densely-segmented neuronal population of 1352 cells spanning all layers of mouse visual cortex, producing a wiring diagram of inhibitory connections with more than 70,000 synapses. Taking a data-driven approach inspired by classical neuroanatomy, we classified inhibitory neurons based on the relative targeting of dendritic compartments and other inhibitory cells and developed a novel classification of excitatory neurons based on the morphological and synaptic input properties. The synaptic connectivity between inhibitory cells revealed a novel class of disinhibitory specialist targeting basket cells, in addition to familiar subclasses. Analysis of the inhibitory connectivity onto excitatory neurons found widespread specificity, with many interneurons exhibiting differential targeting of certain subpopulations spatially intermingled with other potential targets. Inhibitory targeting was organized into "motif groups," diverse sets of cells that collectively target both perisomatic and dendritic compartments of the same excitatory targets. Collectively, our analysis identified new organizing principles for cortical inhibition and will serve as a foundation for linking modern multimodal neuronal atlases with the cortical wiring diagram.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Sam Kinn
- Allen Institute for Brain Science, Seattle, WA
| | | | | | | | - Marc Takeno
- Allen Institute for Brain Science, Seattle, WA
| | | | - Wenjing Yin
- Allen Institute for Brain Science, Seattle, WA
| | - Sven Dorkenwald
- Princeton Neuroscience Institute, Princeton University, NJ
- Computer Science Department, Princeton University
| | - J Alexander Bae
- Princeton Neuroscience Institute, Princeton University, NJ
- Electrical and Computer Engineering Department, Princeton University
| | | | | | - Zhen Jia
- Princeton Neuroscience Institute, Princeton University, NJ
- Computer Science Department, Princeton University
| | - Chris Jordan
- Princeton Neuroscience Institute, Princeton University, NJ
| | - Nico Kemnitz
- Princeton Neuroscience Institute, Princeton University, NJ
| | - Kisuk Lee
- Brain & Cognitive Sciences Department, Massachusetts Institute of Technology
| | - Kai Li
- Computer Science Department, Princeton University
| | - Ran Lu
- Princeton Neuroscience Institute, Princeton University, NJ
| | - Thomas Macrina
- Princeton Neuroscience Institute, Princeton University, NJ
- Computer Science Department, Princeton University
| | - Eric Mitchell
- Princeton Neuroscience Institute, Princeton University, NJ
| | - Shanka Subhra Mondal
- Princeton Neuroscience Institute, Princeton University, NJ
- Electrical and Computer Engineering Department, Princeton University
| | - Shang Mu
- Princeton Neuroscience Institute, Princeton University, NJ
| | - Barak Nehoran
- Princeton Neuroscience Institute, Princeton University, NJ
- Computer Science Department, Princeton University
| | - Sergiy Popovych
- Princeton Neuroscience Institute, Princeton University, NJ
- Computer Science Department, Princeton University
| | | | - Nicholas L Turner
- Princeton Neuroscience Institute, Princeton University, NJ
- Computer Science Department, Princeton University
| | - William Wong
- Princeton Neuroscience Institute, Princeton University, NJ
| | - Jingpeng Wu
- Princeton Neuroscience Institute, Princeton University, NJ
| | - Jacob Reimer
- Department of Neuroscience, Baylor College of Medicine, Houston, TX
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine
| | - Andreas S Tolias
- Department of Neuroscience, Baylor College of Medicine, Houston, TX
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine
- Department of Electrical and Computer Engineering, Rice University
| | - H Sebastian Seung
- Princeton Neuroscience Institute, Princeton University, NJ
- Computer Science Department, Princeton University
| | - R Clay Reid
- Allen Institute for Brain Science, Seattle, WA
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60
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Popovych S, Macrina T, Kemnitz N, Castro M, Nehoran B, Jia Z, Bae JA, Mitchell E, Mu S, Trautman ET, Saalfeld S, Li K, Seung HS. Petascale pipeline for precise alignment of images from serial section electron microscopy. Nat Commun 2024; 15:289. [PMID: 38177169 PMCID: PMC10767115 DOI: 10.1038/s41467-023-44354-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Accepted: 12/07/2023] [Indexed: 01/06/2024] Open
Abstract
The reconstruction of neural circuits from serial section electron microscopy (ssEM) images is being accelerated by automatic image segmentation methods. Segmentation accuracy is often limited by the preceding step of aligning 2D section images to create a 3D image stack. Precise and robust alignment in the presence of image artifacts is challenging, especially as datasets are attaining the petascale. We present a computational pipeline for aligning ssEM images with several key elements. Self-supervised convolutional nets are trained via metric learning to encode and align image pairs, and they are used to initialize iterative fine-tuning of alignment. A procedure called vector voting increases robustness to image artifacts or missing image data. For speedup the series is divided into blocks that are distributed to computational workers for alignment. The blocks are aligned to each other by composing transformations with decay, which achieves a global alignment without resorting to a time-consuming global optimization. We apply our pipeline to a whole fly brain dataset, and show improved accuracy relative to prior state of the art. We also demonstrate that our pipeline scales to a cubic millimeter of mouse visual cortex. Our pipeline is publicly available through two open source Python packages.
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Affiliation(s)
- Sergiy Popovych
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Computer Science Department, Princeton University, Princeton, NJ, USA
| | - Thomas Macrina
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Computer Science Department, Princeton University, Princeton, NJ, USA
| | - Nico Kemnitz
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Manuel Castro
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Barak Nehoran
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Zhen Jia
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Computer Science Department, Princeton University, Princeton, NJ, USA
| | - J Alexander Bae
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Electrical and Computer Engineering Department, Princeton University, Princeton, NJ, USA
| | - Eric Mitchell
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Shang Mu
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | | | | | - Kai Li
- Computer Science Department, Princeton University, Princeton, NJ, USA
| | - H Sebastian Seung
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.
- Computer Science Department, Princeton University, Princeton, NJ, USA.
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61
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Gebehart C, Büschges A. The processing of proprioceptive signals in distributed networks: insights from insect motor control. J Exp Biol 2024; 227:jeb246182. [PMID: 38180228 DOI: 10.1242/jeb.246182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2024]
Abstract
The integration of sensory information is required to maintain body posture and to generate robust yet flexible locomotion through unpredictable environments. To anticipate required adaptations in limb posture and enable compensation of sudden perturbations, an animal's nervous system assembles external (exteroception) and internal (proprioception) cues. Coherent neuronal representations of the proprioceptive context of the body and the appendages arise from the concerted action of multiple sense organs monitoring body kinetics and kinematics. This multimodal proprioceptive information, together with exteroceptive signals and brain-derived descending motor commands, converges onto premotor networks - i.e. the local neuronal circuitry controlling motor output and movements - within the ventral nerve cord (VNC), the insect equivalent of the vertebrate spinal cord. This Review summarizes existing knowledge and recent advances in understanding how local premotor networks in the VNC use convergent information to generate contextually appropriate activity, focusing on the example of posture control. We compare the role and advantages of distributed sensory processing over dedicated neuronal pathways, and the challenges of multimodal integration in distributed networks. We discuss how the gain of distributed networks may be tuned to enable the behavioral repertoire of these systems, and argue that insect premotor networks might compensate for their limited neuronal population size by, in comparison to vertebrate networks, relying more heavily on the specificity of their connections. At a time in which connectomics and physiological recording techniques enable anatomical and functional circuit dissection at an unprecedented resolution, insect motor systems offer unique opportunities to identify the mechanisms underlying multimodal integration for flexible motor control.
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Affiliation(s)
- Corinna Gebehart
- Champalimaud Foundation, Champalimaud Research, 1400-038 Lisbon, Portugal
| | - Ansgar Büschges
- Department of Animal Physiology, Institute of Zoology, Biocenter Cologne, University of Cologne, Zülpicher Strasse 47b, 50674 Cologne, Germany
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62
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Yadav RSP, Ansari F, Bera N, Kent C, Agrawal P. Lessons from lonely flies: Molecular and neuronal mechanisms underlying social isolation. Neurosci Biobehav Rev 2024; 156:105504. [PMID: 38061597 DOI: 10.1016/j.neubiorev.2023.105504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 12/01/2023] [Accepted: 12/04/2023] [Indexed: 12/26/2023]
Abstract
Animals respond to changes in the environment which affect their internal state by adapting their behaviors. Social isolation is a form of passive environmental stressor that alters behaviors across animal kingdom, including humans, rodents, and fruit flies. Social isolation is known to increase violence, disrupt sleep and increase depression leading to poor mental and physical health. Recent evidences from several model organisms suggest that social isolation leads to remodeling of the transcriptional and epigenetic landscape which alters behavioral outcomes. In this review, we explore how manipulating social experience of fruit fly Drosophila melanogaster can shed light on molecular and neuronal mechanisms underlying isolation driven behaviors. We discuss the recent advances made using the powerful genetic toolkit and behavioral assays in Drosophila to uncover role of neuromodulators, sensory modalities, pheromones, neuronal circuits and molecular mechanisms in mediating social isolation. The insights gained from these studies could be crucial for developing effective therapeutic interventions in future.
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Affiliation(s)
- R Sai Prathap Yadav
- Centre for Molecular Neurosciences, Kasturba Medical College, Manipal Academy of Higher Education, Karnataka 576104, India
| | - Faizah Ansari
- Centre for Molecular Neurosciences, Kasturba Medical College, Manipal Academy of Higher Education, Karnataka 576104, India
| | - Neha Bera
- Centre for Molecular Neurosciences, Kasturba Medical College, Manipal Academy of Higher Education, Karnataka 576104, India
| | - Clement Kent
- Department of Biology, York University, Toronto, ON M3J 1P3, Canada
| | - Pavan Agrawal
- Centre for Molecular Neurosciences, Kasturba Medical College, Manipal Academy of Higher Education, Karnataka 576104, India.
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63
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Eichler K, Hampel S, Alejandro-García A, Calle-Schuler SA, Santana-Cruz A, Kmecova L, Blagburn JM, Hoopfer ED, Seeds AM. Somatotopic organization among parallel sensory pathways that promote a grooming sequence in Drosophila. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.11.528119. [PMID: 36798384 PMCID: PMC9934617 DOI: 10.1101/2023.02.11.528119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/14/2023]
Abstract
Mechanosensory neurons located across the body surface respond to tactile stimuli and elicit diverse behavioral responses, from relatively simple stimulus location-aimed movements to complex movement sequences. How mechanosensory neurons and their postsynaptic circuits influence such diverse behaviors remains unclear. We previously discovered that Drosophila perform a body location-prioritized grooming sequence when mechanosensory neurons at different locations on the head and body are simultaneously stimulated by dust (Hampel et al., 2017; Seeds et al., 2014). Here, we identify nearly all mechanosensory neurons on the Drosophila head that individually elicit aimed grooming of specific head locations, while collectively eliciting a whole head grooming sequence. Different tracing methods were used to reconstruct the projections of these neurons from different locations on the head to their distinct arborizations in the brain. This provides the first synaptic resolution somatotopic map of a head, and defines the parallel-projecting mechanosensory pathways that elicit head grooming.
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Affiliation(s)
- Katharina Eichler
- Institute of Neurobiology, University of Puerto Rico-Medical Sciences Campus, San Juan, Puerto Rico
- Contributed equally
| | - Stefanie Hampel
- Institute of Neurobiology, University of Puerto Rico-Medical Sciences Campus, San Juan, Puerto Rico
- Contributed equally
| | - Adrián Alejandro-García
- Institute of Neurobiology, University of Puerto Rico-Medical Sciences Campus, San Juan, Puerto Rico
- Contributed equally
| | - Steven A Calle-Schuler
- Institute of Neurobiology, University of Puerto Rico-Medical Sciences Campus, San Juan, Puerto Rico
| | - Alexis Santana-Cruz
- Institute of Neurobiology, University of Puerto Rico-Medical Sciences Campus, San Juan, Puerto Rico
| | - Lucia Kmecova
- Institute of Neurobiology, University of Puerto Rico-Medical Sciences Campus, San Juan, Puerto Rico
- Neuroscience Program, Carleton College, Northfield, Minnesota
- Contributed equally
| | - Jonathan M Blagburn
- Institute of Neurobiology, University of Puerto Rico-Medical Sciences Campus, San Juan, Puerto Rico
| | - Eric D Hoopfer
- Neuroscience Program, Carleton College, Northfield, Minnesota
| | - Andrew M Seeds
- Institute of Neurobiology, University of Puerto Rico-Medical Sciences Campus, San Juan, Puerto Rico
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64
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Li M, Chen DS, Junker IP, Szorenyi F, Chen GH, Berger AJ, Comeault AA, Matute DR, Ding Y. Ancestral neural circuits potentiate the origin of a female sexual behavior. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.05.570174. [PMID: 38106147 PMCID: PMC10723342 DOI: 10.1101/2023.12.05.570174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Courtship interactions are remarkably diverse in form and complexity among species. How neural circuits evolve to encode new behaviors that are functionally integrated into these dynamic social interactions is unknown. Here we report a recently originated female sexual behavior in the island endemic Drosophila species D. santomea, where females signal receptivity to male courtship songs by spreading their wings, which in turn promotes prolonged songs in courting males. Copulation success depends on this female signal and correlates with males' ability to adjust his singing in such a social feedback loop. Functional comparison of sexual circuitry across species suggests that a pair of descending neurons, which integrates male song stimuli and female internal state to control a conserved female abdominal behavior, drives wing spreading in D. santomea. This co-option occurred through the refinement of a pre-existing, plastic circuit that can be optogenetically activated in an outgroup species. Combined, our results show that the ancestral potential of a socially-tuned key circuit node to engage the wing motor program facilitates the expression of a new female behavior in appropriate sensory and motivational contexts. More broadly, our work provides insights into the evolution of social behaviors, particularly female behaviors, and the underlying neural mechanisms.
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Affiliation(s)
- Minhao Li
- Department of Biology, University of Pennsylvania, Philadelphia, PA, USA
| | - Dawn S Chen
- Department of Biology, University of Pennsylvania, Philadelphia, PA, USA
| | - Ian P Junker
- Department of Biology, University of Pennsylvania, Philadelphia, PA, USA
| | - Fabianna Szorenyi
- Department of Biology, University of Pennsylvania, Philadelphia, PA, USA
| | - Guan Hao Chen
- Department of Biology, University of Pennsylvania, Philadelphia, PA, USA
| | - Arnold J Berger
- Department of Biology, University of Pennsylvania, Philadelphia, PA, USA
| | - Aaron A Comeault
- Department of Biology, University of North Carolina, Chapel Hill, NC, USA
- Current address: School of Environmental and Natural Sciences, Bangor University, Bangor, UK
| | - Daniel R Matute
- Department of Biology, University of North Carolina, Chapel Hill, NC, USA
| | - Yun Ding
- Department of Biology, University of Pennsylvania, Philadelphia, PA, USA
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65
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Barth-Maron A, D'Alessandro I, Wilson RI. Interactions between specialized gain control mechanisms in olfactory processing. Curr Biol 2023; 33:5109-5120.e7. [PMID: 37967554 DOI: 10.1016/j.cub.2023.10.041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 08/16/2023] [Accepted: 10/23/2023] [Indexed: 11/17/2023]
Abstract
Gain control is a process that adjusts a system's sensitivity when input levels change. Neural systems contain multiple mechanisms of gain control, but we do not understand why so many mechanisms are needed or how they interact. Here, we investigate these questions in the Drosophila antennal lobe, where we identify several types of inhibitory interneurons with specialized gain control functions. We find that some interneurons are nonspiking, with compartmentalized calcium signals, and they specialize in intra-glomerular gain control. Conversely, we find that other interneurons are recruited by strong and widespread network input; they specialize in global presynaptic gain control. Using computational modeling and optogenetic perturbations, we show how these mechanisms can work together to improve stimulus discrimination while also minimizing temporal distortions in network activity. Our results demonstrate how the robustness of neural network function can be increased by interactions among diverse and specialized mechanisms of gain control.
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Affiliation(s)
- Asa Barth-Maron
- Department of Neurobiology, Harvard Medical School, 220 Longwood Avenue, Boston, MA 02115, USA
| | - Isabel D'Alessandro
- Department of Neurobiology, Harvard Medical School, 220 Longwood Avenue, Boston, MA 02115, USA
| | - Rachel I Wilson
- Department of Neurobiology, Harvard Medical School, 220 Longwood Avenue, Boston, MA 02115, USA.
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66
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Micheva KD, Gong B, Collman F, Weinberg RJ, Smith SJ, Trimmer JS, Murray KD. Developing a Toolbox of Antibodies Validated for Array Tomography-Based Imaging of Brain Synapses. eNeuro 2023; 10:ENEURO.0290-23.2023. [PMID: 37945352 PMCID: PMC10748464 DOI: 10.1523/eneuro.0290-23.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 10/19/2023] [Accepted: 10/24/2023] [Indexed: 11/12/2023] Open
Abstract
Antibody (Ab)-based imaging techniques rely on reagents whose performance may be application specific. Because commercial antibodies are validated for only a few purposes, users interested in other applications may have to perform extensive in-house antibody testing. Here, we present a novel application-specific proxy screening step to efficiently identify candidate antibodies for array tomography (AT), a serial section volume microscopy technique for high-dimensional quantitative analysis of the cellular proteome. To identify antibodies suitable for AT-based analysis of synapses in mammalian brain, we introduce a heterologous cell-based assay that simulates characteristic features of AT, such as chemical fixation and resin embedding that are likely to influence antibody binding. The assay was included into an initial screening strategy to generate monoclonal antibodies that can be used for AT. This approach simplifies the screening of candidate antibodies and has high predictive value for identifying antibodies suitable for AT analyses. In addition, we have created a comprehensive database of AT-validated antibodies with a neuroscience focus and show that these antibodies have a high likelihood of success for postembedding applications in general, including immunogold electron microscopy. The generation of a large and growing toolbox of AT-compatible antibodies will further enhance the value of this imaging technique.
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Affiliation(s)
- Kristina D Micheva
- Department of Cellular and Molecular Physiology, Stanford School of Medicine, Stanford, 94305, CA
| | - Belvin Gong
- Department of Physiology and Membrane Biology, University of California Davis School of Medicine, Davis, 95618, CA
| | | | - Richard J Weinberg
- Department of Cell Biology and Physiology, University of North Carolina, Chapel Hill, 27514, NC
| | | | - James S Trimmer
- Department of Physiology and Membrane Biology, University of California Davis School of Medicine, Davis, 95618, CA
| | - Karl D Murray
- Department of Physiology and Membrane Biology, University of California Davis School of Medicine, Davis, 95618, CA
- Department of Psychiatry and Behavioral Sciences, University of California, Davis, Davis, 95618, CA
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67
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Garner D, Kind E, Nern A, Houghton L, Zhao A, Sancer G, Rubin GM, Wernet MF, Kim SS. Connectomic reconstruction predicts the functional organization of visual inputs to the navigation center of the Drosophila brain. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.29.569241. [PMID: 38076786 PMCID: PMC10705420 DOI: 10.1101/2023.11.29.569241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/22/2023]
Abstract
Many animals, including humans, navigate their surroundings by visual input, yet we understand little about how visual information is transformed and integrated by the navigation system. In Drosophila melanogaster, compass neurons in the donut-shaped ellipsoid body of the central complex generate a sense of direction by integrating visual input from ring neurons, a part of the anterior visual pathway (AVP). Here, we densely reconstruct all neurons in the AVP using FlyWire, an AI-assisted tool for analyzing electron-microscopy data. The AVP comprises four neuropils, sequentially linked by three major classes of neurons: MeTu neurons, which connect the medulla in the optic lobe to the small unit of anterior optic tubercle (AOTUsu) in the central brain; TuBu neurons, which connect the anterior optic tubercle to the bulb neuropil; and ring neurons, which connect the bulb to the ellipsoid body. Based on neuronal morphologies, connectivity between different neural classes, and the locations of synapses, we identified non-overlapping channels originating from four types of MeTu neurons, which we further divided into ten subtypes based on the presynaptic connections in medulla and postsynaptic connections in AOTUsu. To gain an objective measure of the natural variation within the pathway, we quantified the differences between anterior visual pathways from both hemispheres and between two electron-microscopy datasets. Furthermore, we infer potential visual features and the visual area from which any given ring neuron receives input by combining the connectivity of the entire AVP, the MeTu neurons' dendritic fields, and presynaptic connectivity in the optic lobes. These results provide a strong foundation for understanding how distinct visual features are extracted and transformed across multiple processing stages to provide critical information for computing the fly's sense of direction.
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Affiliation(s)
- Dustin Garner
- Molecular, Cellular, and Developmental Biology, University of California Santa Barbara, Santa Barbara, CA, USA
| | - Emil Kind
- Department of Biology, Freie Universität Berlin, Berlin, Germany
| | - Aljoscha Nern
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Lucy Houghton
- Molecular, Cellular, and Developmental Biology, University of California Santa Barbara, Santa Barbara, CA, USA
| | - Arthur Zhao
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Gizem Sancer
- Department of Biology, Freie Universität Berlin, Berlin, Germany
| | - Gerald M. Rubin
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | | | - Sung Soo Kim
- Molecular, Cellular, and Developmental Biology, University of California Santa Barbara, Santa Barbara, CA, USA
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68
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Amin H, Nolte SS, Swain B, von Philipsborn AC. GABAergic signaling shapes multiple aspects of Drosophila courtship motor behavior. iScience 2023; 26:108069. [PMID: 37860694 PMCID: PMC10583093 DOI: 10.1016/j.isci.2023.108069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 09/06/2023] [Accepted: 09/25/2023] [Indexed: 10/21/2023] Open
Abstract
Inhibitory neurons are essential for orchestrating and structuring behavior. We use one of the best studied behaviors in Drosophila, male courtship, to analyze how inhibitory, GABAergic neurons shape the different steps of this multifaceted motor sequence. RNAi-mediated knockdown of the GABA-producing enzyme GAD1 and the ionotropic receptor Rdl in sex specific, fruitless expressing neurons in the ventral nerve cord causes uncoordinated and futile copulation attempts, defects in wing extension choice and severe alterations of courtship song. Altered song of GABA depleted males fails to stimulate female receptivity, but rescue of song patterning alone is not sufficient to rescue male mating success. Knockdown of GAD1 and Rdl in male brain circuits abolishes courtship conditioning. We characterize the around 220 neurons coexpressing GAD1 and Fruitless in the Drosophila male nervous system and propose inhibitory circuit motifs underlying key features of courtship behavior based on the observed phenotypes.
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Affiliation(s)
- Hoger Amin
- Department of Molecular Biology and Genetics and Department of Biomedicine, Danish Research Institute of Translational Neuroscience (DANDRITE), Aarhus University, 8000 Aarhus, Denmark
| | - Stella S. Nolte
- Department of Molecular Biology and Genetics and Department of Biomedicine, Danish Research Institute of Translational Neuroscience (DANDRITE), Aarhus University, 8000 Aarhus, Denmark
| | - Bijayalaxmi Swain
- Department of Molecular Biology and Genetics and Department of Biomedicine, Danish Research Institute of Translational Neuroscience (DANDRITE), Aarhus University, 8000 Aarhus, Denmark
| | - Anne C. von Philipsborn
- Department of Molecular Biology and Genetics and Department of Biomedicine, Danish Research Institute of Translational Neuroscience (DANDRITE), Aarhus University, 8000 Aarhus, Denmark
- Department of Neuroscience and Movement Science, Medicine Section, University of Fribourg, 1700 Fribourg, Switzerland
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69
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Micheva KD, Gong B, Collman F, Weinberg RJ, Smith SJ, Trimmer JS, Murray KD. Developing a Toolbox of Antibodies Validated for Array Tomography-Based Imaging of Brain Synapses. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.28.546920. [PMID: 37425759 PMCID: PMC10327040 DOI: 10.1101/2023.06.28.546920] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
Antibody-based imaging techniques rely on reagents whose performance may be application-specific. Because commercial antibodies are validated for only a few purposes, users interested in other applications may have to perform extensive in-house antibody testing. Here we present a novel application-specific proxy screening step to efficiently identify candidate antibodies for array tomography (AT), a serial section volume microscopy technique for high-dimensional quantitative analysis of the cellular proteome. To identify antibodies suitable for AT-based analysis of synapses in mammalian brain, we introduce a heterologous cell-based assay that simulates characteristic features of AT, such as chemical fixation and resin embedding that are likely to influence antibody binding. The assay was included into an initial screening strategy to generate monoclonal antibodies that can be used for AT. This approach simplifies the screening of candidate antibodies and has high predictive value for identifying antibodies suitable for AT analyses. In addition, we have created a comprehensive database of AT-validated antibodies with a neuroscience focus and show that these antibodies have a high likelihood of success for postembedding applications in general, including immunogold electron microscopy. The generation of a large and growing toolbox of AT-compatible antibodies will further enhance the value of this imaging technique.
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Affiliation(s)
- Kristina D. Micheva
- Department of Cellular and Molecular Physiology, Stanford School of Medicine, Stanford, CA
| | - Belvin Gong
- Department of Physiology and Membrane Biology, University of California Davis School of Medicine, Davis, CA
| | | | - Richard J. Weinberg
- Department of Cell Biology & Physiology, University of North Carolina, Chapel Hill, NC
| | | | - James S. Trimmer
- Department of Physiology and Membrane Biology, University of California Davis School of Medicine, Davis, CA
| | - Karl D. Murray
- Department of Physiology and Membrane Biology, University of California Davis School of Medicine, Davis, CA
- Department of Psychiatry & Behavioral Sciences, University of California, Davis, CA
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70
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Chua NJ, Makarova AA, Gunn P, Villani S, Cohen B, Thasin M, Wu J, Shefter D, Pang S, Xu CS, Hess HF, Polilov AA, Chklovskii DB. A complete reconstruction of the early visual system of an adult insect. Curr Biol 2023; 33:4611-4623.e4. [PMID: 37774707 DOI: 10.1016/j.cub.2023.09.021] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 09/07/2023] [Accepted: 09/08/2023] [Indexed: 10/01/2023]
Abstract
For most model organisms in neuroscience, research into visual processing in the brain is difficult because of a lack of high-resolution maps that capture complex neuronal circuitry. The microinsect Megaphragma viggianii, because of its small size and non-trivial behavior, provides a unique opportunity for tractable whole-organism connectomics. We image its whole head using serial electron microscopy. We reconstruct its compound eye and analyze the optical properties of the ommatidia as well as the connectome of the first visual neuropil-the lamina. Compared with the fruit fly and the honeybee, Megaphragma visual system is highly simplified: it has 29 ommatidia per eye and 6 lamina neuron types. We report features that are both stereotypical among most ommatidia and specialized to some. By identifying the "barebones" circuits critical for flying insects, our results will facilitate constructing computational models of visual processing in insects.
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Affiliation(s)
- Nicholas J Chua
- Center for Computational Neuroscience, Flatiron Institute, New York, NY 10010, USA; Department of Biological Sciences, Columbia University, New York, NY 10027, USA
| | | | - Pat Gunn
- Center for Computational Neuroscience, Flatiron Institute, New York, NY 10010, USA
| | - Sonia Villani
- Center for Computational Neuroscience, Flatiron Institute, New York, NY 10010, USA
| | - Ben Cohen
- Center for Computational Neuroscience, Flatiron Institute, New York, NY 10010, USA
| | - Myisha Thasin
- Center for Computational Neuroscience, Flatiron Institute, New York, NY 10010, USA
| | - Jingpeng Wu
- Center for Computational Neuroscience, Flatiron Institute, New York, NY 10010, USA
| | - Deena Shefter
- Center for Computational Neuroscience, Flatiron Institute, New York, NY 10010, USA
| | - Song Pang
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - C Shan Xu
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Harald F Hess
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Alexey A Polilov
- Faculty of Biology, Lomonosov Moscow State University, Moscow 119991, Russia
| | - Dmitri B Chklovskii
- Center for Computational Neuroscience, Flatiron Institute, New York, NY 10010, USA; Neuroscience Institute, New York University Langone Medical Center, New York, NY 10016, USA.
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71
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Mancini N, Thoener J, Tafani E, Pauls D, Mayseless O, Strauch M, Eichler K, Champion A, Kobler O, Weber D, Sen E, Weiglein A, Hartenstein V, Chytoudis-Peroudis CC, Jovanic T, Thum AS, Rohwedder A, Schleyer M, Gerber B. Rewarding Capacity of Optogenetically Activating a Giant GABAergic Central-Brain Interneuron in Larval Drosophila. J Neurosci 2023; 43:7393-7428. [PMID: 37734947 PMCID: PMC10621887 DOI: 10.1523/jneurosci.2310-22.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2022] [Revised: 07/19/2023] [Accepted: 08/26/2023] [Indexed: 09/23/2023] Open
Abstract
Larvae of the fruit fly Drosophila melanogaster are a powerful study case for understanding the neural circuits underlying behavior. Indeed, the numerical simplicity of the larval brain has permitted the reconstruction of its synaptic connectome, and genetic tools for manipulating single, identified neurons allow neural circuit function to be investigated with relative ease and precision. We focus on one of the most complex neurons in the brain of the larva (of either sex), the GABAergic anterior paired lateral neuron (APL). Using behavioral and connectomic analyses, optogenetics, Ca2+ imaging, and pharmacology, we study how APL affects associative olfactory memory. We first provide a detailed account of the structure, regional polarity, connectivity, and metamorphic development of APL, and further confirm that optogenetic activation of APL has an inhibiting effect on its main targets, the mushroom body Kenyon cells. All these findings are consistent with the previously identified function of APL in the sparsening of sensory representations. To our surprise, however, we found that optogenetically activating APL can also have a strong rewarding effect. Specifically, APL activation together with odor presentation establishes an odor-specific, appetitive, associative short-term memory, whereas naive olfactory behavior remains unaffected. An acute, systemic inhibition of dopamine synthesis as well as an ablation of the dopaminergic pPAM neurons impair reward learning through APL activation. Our findings provide a study case of complex circuit function in a numerically simple brain, and suggest a previously unrecognized capacity of central-brain GABAergic neurons to engage in dopaminergic reinforcement.SIGNIFICANCE STATEMENT The single, identified giant anterior paired lateral (APL) neuron is one of the most complex neurons in the insect brain. It is GABAergic and contributes to the sparsening of neuronal activity in the mushroom body, the memory center of insects. We provide the most detailed account yet of the structure of APL in larval Drosophila as a neurogenetically accessible study case. We further reveal that, contrary to expectations, the experimental activation of APL can exert a rewarding effect, likely via dopaminergic reward pathways. The present study both provides an example of unexpected circuit complexity in a numerically simple brain, and reports an unexpected effect of activity in central-brain GABAergic circuits.
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Affiliation(s)
- Nino Mancini
- Leibniz Institute for Neurobiology, Department Genetics of Learning and Memory, Magdeburg, 39118, Germany
| | - Juliane Thoener
- Leibniz Institute for Neurobiology, Department Genetics of Learning and Memory, Magdeburg, 39118, Germany
| | - Esmeralda Tafani
- Leibniz Institute for Neurobiology, Department Genetics of Learning and Memory, Magdeburg, 39118, Germany
| | - Dennis Pauls
- Department of Animal Physiology, Institute of Biology, Leipzig University, Leipzig, 04103, Germany
| | - Oded Mayseless
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, 7610001, Israel
| | - Martin Strauch
- Institute of Imaging and Computer Vision, RWTH Aachen University, Aachen, 52074, Germany
| | - Katharina Eichler
- Institute of Neurobiology, University of Puerto Rico Medical Science Campus, Old San Juan, Puerto Rico, 00901
| | - Andrew Champion
- Department of Physiology, Development and Neuroscience, Cambridge University, Cambridge, CB2 3EL, United Kingdom
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, 20147, Virginia
| | - Oliver Kobler
- Leibniz Institute for Neurobiology, Combinatorial Neuroimaging Core Facility, Magdeburg, 39118, Germany
| | - Denise Weber
- Department of Genetics, Institute of Biology, Leipzig University, Leipzig, 04103, Germany
| | - Edanur Sen
- Leibniz Institute for Neurobiology, Department Genetics of Learning and Memory, Magdeburg, 39118, Germany
| | - Aliće Weiglein
- Leibniz Institute for Neurobiology, Department Genetics of Learning and Memory, Magdeburg, 39118, Germany
| | - Volker Hartenstein
- University of California, Department of Molecular, Cell and Developmental Biology, Los Angeles, California 90095-1606
| | | | - Tihana Jovanic
- Université Paris-Saclay, Centre National de la Recherche Scientifique, Institut des neurosciences Paris-Saclay, Saclay, 91400, France
| | - Andreas S Thum
- Department of Genetics, Institute of Biology, Leipzig University, Leipzig, 04103, Germany
| | - Astrid Rohwedder
- Department of Genetics, Institute of Biology, Leipzig University, Leipzig, 04103, Germany
| | - Michael Schleyer
- Leibniz Institute for Neurobiology, Department Genetics of Learning and Memory, Magdeburg, 39118, Germany
| | - Bertram Gerber
- Leibniz Institute for Neurobiology, Department Genetics of Learning and Memory, Magdeburg, 39118, Germany
- Center for Behavioral Brain Sciences, Magdeburg, 39106, Germany
- Institute for Biology, Otto von Guericke University, Magdeburg, 39120, Germany
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72
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Kato A, Ohta K, Okanoya K, Kazama H. Dopaminergic neurons dynamically update sensory values during olfactory maneuver. Cell Rep 2023; 42:113122. [PMID: 37757823 DOI: 10.1016/j.celrep.2023.113122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 07/29/2023] [Accepted: 08/25/2023] [Indexed: 09/29/2023] Open
Abstract
Dopaminergic neurons (DANs) drive associative learning to update the value of sensory cues, but their contribution to the assessment of sensory values outside the context of association remains largely unexplored. Here, we show in Drosophila that DANs in the mushroom body encode the innate value of odors and constantly update the current value by inducing plasticity during olfactory maneuver. Our connectome-based network model linking all the way from the olfactory neurons to DANs reproduces the characteristics of DAN responses, proposing a concrete circuit mechanism for computation. Downstream of DANs, odors alone induce value- and dopamine-dependent changes in the activity of mushroom body output neurons, which store the current value of odors. Consistent with this neural plasticity, specific sets of DANs bidirectionally modulate flies' steering in a virtual olfactory environment. Thus, the DAN circuit known for discrete, associative learning also continuously updates odor values in a nonassociative manner.
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Affiliation(s)
- Ayaka Kato
- RIKEN Center for Brain Science, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan; Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro-ku, Tokyo 153-8902, Japan
| | - Kazumi Ohta
- RIKEN Center for Brain Science, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan; RIKEN CBS-KAO Collaboration Center, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
| | - Kazuo Okanoya
- Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro-ku, Tokyo 153-8902, Japan
| | - Hokto Kazama
- RIKEN Center for Brain Science, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan; Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro-ku, Tokyo 153-8902, Japan; RIKEN CBS-KAO Collaboration Center, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan.
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73
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Baldenius M, Kautzmann S, Nanda S, Klämbt C. Signaling Pathways Controlling Axonal Wrapping in Drosophila. Cells 2023; 12:2553. [PMID: 37947631 PMCID: PMC10647682 DOI: 10.3390/cells12212553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 10/24/2023] [Accepted: 10/27/2023] [Indexed: 11/12/2023] Open
Abstract
The rapid transmission of action potentials is an important ability that enables efficient communication within the nervous system. Glial cells influence conduction velocity along axons by regulating the radial axonal diameter, providing electrical insulation as well as affecting the distribution of voltage-gated ion channels. Differentiation of these wrapping glial cells requires a complex set of neuron-glia interactions involving three basic mechanistic features. The glia must recognize the axon, grow around it, and eventually arrest its growth to form single or multiple axon wraps. This likely depends on the integration of numerous evolutionary conserved signaling and adhesion systems. Here, we summarize the mechanisms and underlying signaling pathways that control glial wrapping in Drosophila and compare those to the mechanisms that control glial differentiation in mammals. This analysis shows that Drosophila is a beneficial model to study the development of even complex structures like myelin.
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Affiliation(s)
| | | | | | - Christian Klämbt
- Institute for Neuro- and Behavioral Biology, Faculty of Biology, University of Münster, Röntgenstraße 16, D-48149 Münster, Germany; (M.B.)
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Yang HH, Brezovec LE, Capdevila LS, Vanderbeck QX, Adachi A, Mann RS, Wilson RI. Fine-grained descending control of steering in walking Drosophila. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.15.562426. [PMID: 37904997 PMCID: PMC10614758 DOI: 10.1101/2023.10.15.562426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/02/2023]
Abstract
Locomotion involves rhythmic limb movement patterns that originate in circuits outside the brain. Purposeful locomotion requires descending commands from the brain, but we do not understand how these commands are structured. Here we investigate this issue, focusing on the control of steering in walking Drosophila. First, we describe different limb "gestures" associated with different steering maneuvers. Next, we identify a set of descending neurons whose activity predicts steering. Focusing on two descending cell types downstream from distinct brain networks, we show that they evoke specific limb gestures: one lengthens strides on the outside of a turn, while the other attenuates strides on the inside of a turn. Notably, a single descending neuron can have opposite effects during different locomotor rhythm phases, and we identify networks positioned to implement this phase-specific gating. Together, our results show how purposeful locomotion emerges from brain cells that drive specific, coordinated modulations of low-level patterns.
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Affiliation(s)
- Helen H. Yang
- Department of Neurobiology, Harvard Medical School, Boston, MA 02115 USA
| | - Luke E. Brezovec
- Department of Neurobiology, Stanford University, Stanford, CA 94305 USA
| | | | | | - Atsuko Adachi
- Department of Biochemistry and Molecular Biophysics, Department of Neuroscience, Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027 USA
| | - Richard S. Mann
- Department of Biochemistry and Molecular Biophysics, Department of Neuroscience, Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027 USA
| | - Rachel I. Wilson
- Department of Neurobiology, Harvard Medical School, Boston, MA 02115 USA
- Lead contact
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75
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Kievits AJ, Duinkerken BHP, Fermie J, Lane R, Giepmans BNG, Hoogenboom JP. Optical STEM detection for scanning electron microscopy. Ultramicroscopy 2023; 256:113877. [PMID: 37931528 DOI: 10.1016/j.ultramic.2023.113877] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 10/13/2023] [Accepted: 10/21/2023] [Indexed: 11/08/2023]
Abstract
Recent advances in electron microscopy techniques have led to a significant scale up in volumetric imaging of biological tissue. The throughput of electron microscopes, however, remains a limiting factor for the volume that can be imaged in high resolution within reasonable time. Faster detection methods will improve throughput. Here, we have characterized and benchmarked a novel detection technique for scanning electron microscopy: optical scanning transmission electron microscopy (OSTEM). A qualitative and quantitative comparison was performed between OSTEM, secondary and backscattered electron detection and annular dark field detection in scanning transmission electron microscopy. Our analysis shows that OSTEM produces images similar to backscattered electron detection in terms of contrast, resolution and signal-to-noise ratio. OSTEM can complement large scale imaging with (scanning) transmission electron microscopy and has the potential to speed up imaging in single-beam scanning electron microscope.
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Affiliation(s)
- Arent J Kievits
- Department of Imaging Physics, Delft University of Technology, Delft, The Netherlands.
| | - B H Peter Duinkerken
- Department of Biomedical Sciences of Cells and Systems, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | | | - Ryan Lane
- Department of Imaging Physics, Delft University of Technology, Delft, The Netherlands
| | - Ben N G Giepmans
- Department of Biomedical Sciences of Cells and Systems, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Jacob P Hoogenboom
- Department of Imaging Physics, Delft University of Technology, Delft, The Netherlands
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76
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Yan L, Wu L, Wiggin TD, Su X, Yan W, Li H, Li L, Lu Z, Meng Z, Guo F, Griffith LC, Li F, Liu C. Brief Change in Dopamine Activity during Consolidation Impairs Long-Term Memory via Sleep Disruption. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.23.563499. [PMID: 37961167 PMCID: PMC10634733 DOI: 10.1101/2023.10.23.563499] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Sleep disturbances are associated with poor long-term memory (LTM) formation, yet the underlying cell types and neural circuits involved have not been fully decoded. Dopamine neurons (DANs) are involved in memory processing at multiple stages. Here, we show that brief activation of protocerebral anterior medial DANs (PAM-DANs) or inhibition of a pair of dorsal posterior medial (DPM) neurons during the first few hours of memory consolidation impairs 24 h LTM. Interestingly, sleep deprivation elevates the neural activity of PAM-DANs and DPM neurons, and brief thermos-activation of PAM-DANs or inactivation of DPM neurons results in sleep loss and fragmentation. Pharmacological rescue of sleep after this manipulation restores LTM. A specific subset of PAM-DANs, PAM-α1 that synapse onto DPM neurons specify the microcircuit that links sleep and memory. PAM-DANs, including PAM-α1, form functional synapses with DPM neurons mainly via Dop1R1 receptor to inhibit DPM. Our data suggest that the post-training activity of PAM(-α1)-DPM microcircuit, especially during memory consolidation, plays an essential role in maintaining the sleep necessary for LTM consolidation, providing a new cellular and circuit basis for the complex relationship between sleep and memory.
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77
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Zhao A, Nern A, Koskela S, Dreher M, Erginkaya M, Laughland CW, Ludwigh H, Thomson A, Hoeller J, Parekh R, Romani S, Bock DD, Chiappe E, Reiser MB. A comprehensive neuroanatomical survey of the Drosophila Lobula Plate Tangential Neurons with predictions for their optic flow sensitivity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.16.562634. [PMID: 37904921 PMCID: PMC10614863 DOI: 10.1101/2023.10.16.562634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/01/2023]
Abstract
Flying insects exhibit remarkable navigational abilities controlled by their compact nervous systems. Optic flow, the pattern of changes in the visual scene induced by locomotion, is a crucial sensory cue for robust self-motion estimation, especially during rapid flight. Neurons that respond to specific, large-field optic flow patterns have been studied for decades, primarily in large flies, such as houseflies, blowflies, and hover flies. The best-known optic-flow sensitive neurons are the large tangential cells of the dipteran lobula plate, whose visual-motion responses, and to a lesser extent, their morphology, have been explored using single-neuron neurophysiology. Most of these studies have focused on the large, Horizontal and Vertical System neurons, yet the lobula plate houses a much larger set of 'optic-flow' sensitive neurons, many of which have been challenging to unambiguously identify or to reliably target for functional studies. Here we report the comprehensive reconstruction and identification of the Lobula Plate Tangential Neurons in an Electron Microscopy (EM) volume of a whole Drosophila brain. This catalog of 58 LPT neurons (per brain hemisphere) contains many neurons that are described here for the first time and provides a basis for systematic investigation of the circuitry linking self-motion to locomotion control. Leveraging computational anatomy methods, we estimated the visual motion receptive fields of these neurons and compared their tuning to the visual consequence of body rotations and translational movements. We also matched these neurons, in most cases on a one-for-one basis, to stochastically labeled cells in genetic driver lines, to the mirror-symmetric neurons in the same EM brain volume, and to neurons in an additional EM data set. Using cell matches across data sets, we analyzed the integration of optic flow patterns by neurons downstream of the LPTs and find that most central brain neurons establish sharper selectivity for global optic flow patterns than their input neurons. Furthermore, we found that self-motion information extracted from optic flow is processed in distinct regions of the central brain, pointing to diverse foci for the generation of visual behaviors.
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Affiliation(s)
- Arthur Zhao
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA USA
| | - Aljoscha Nern
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA USA
| | - Sanna Koskela
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA USA
| | - Marisa Dreher
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA USA
| | - Mert Erginkaya
- Champalimaud Neuroscience Programme, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Connor W Laughland
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA USA
| | - Henrique Ludwigh
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA USA
| | - Alex Thomson
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA USA
| | - Judith Hoeller
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA USA
| | - Ruchi Parekh
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA USA
| | - Sandro Romani
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA USA
| | - Davi D Bock
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA USA
- Department of Neurological Sciences, Larner College of Medicine, University of Vermont, USA
| | - Eugenia Chiappe
- Champalimaud Neuroscience Programme, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Michael B Reiser
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA USA
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78
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Ganguly I, Heckman EL, Litwin-Kumar A, Clowney EJ, Behnia R. Diversity of visual inputs to Kenyon cells of the Drosophila mushroom body. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.12.561793. [PMID: 37873086 PMCID: PMC10592809 DOI: 10.1101/2023.10.12.561793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
The arthropod mushroom body is well-studied as an expansion layer that represents olfactory stimuli and links them to contingent events. However, 8% of mushroom body Kenyon cells in Drosophila melanogaster receive predominantly visual input, and their tuning and function are poorly understood. Here, we use the FlyWire adult whole-brain connectome to identify inputs to visual Kenyon cells. The types of visual neurons we identify are similar across hemispheres and connectomes with certain inputs highly overrepresented. Many visual projection neurons presynaptic to Kenyon cells receive input from large swathes of visual space, while local visual interneurons, providing smaller fractions of input, receive more spatially restricted signals that may be tuned to specific features of the visual scene. Like olfactory Kenyon cells, visual Kenyon cells receive sparse inputs from different combinations of visual channels, including inputs from multiple optic lobe neuropils. The sets of inputs to individual visual Kenyon cells are consistent with random sampling of available inputs. These connectivity patterns suggest that visual coding in the mushroom body, like olfactory coding, is sparse, distributed, and combinatorial. However, the expansion coding properties appear different, with a specific repertoire of visual inputs projecting onto a relatively small number of visual Kenyon cells.
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Affiliation(s)
- Ishani Ganguly
- The Mortimer B. Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, NY 10027, USA
- Center for Theoretical Neuroscience, Columbia University, New York, NY 10027, USA
| | - Emily L Heckman
- Department of Molecular, Cellular, and Developmental Biology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Ashok Litwin-Kumar
- The Mortimer B. Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, NY 10027, USA
- Center for Theoretical Neuroscience, Columbia University, New York, NY 10027, USA
| | - E Josephine Clowney
- Department of Molecular, Cellular, and Developmental Biology, University of Michigan, Ann Arbor, MI 48109, USA
- Michigan Neuroscience Institute Affiliate
| | - Rudy Behnia
- The Mortimer B. Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, NY 10027, USA
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79
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Shen Y, Dasgupta S, Navlakha S. Reducing Catastrophic Forgetting With Associative Learning: A Lesson From Fruit Flies. Neural Comput 2023; 35:1797-1819. [PMID: 37725710 DOI: 10.1162/neco_a_01615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Accepted: 07/14/2023] [Indexed: 09/21/2023]
Abstract
Catastrophic forgetting remains an outstanding challenge in continual learning. Recently, methods inspired by the brain, such as continual representation learning and memory replay, have been used to combat catastrophic forgetting. Associative learning (retaining associations between inputs and outputs, even after good representations are learned) plays an important function in the brain; however, its role in continual learning has not been carefully studied. Here, we identified a two-layer neural circuit in the fruit fly olfactory system that performs continual associative learning between odors and their associated valences. In the first layer, inputs (odors) are encoded using sparse, high-dimensional representations, which reduces memory interference by activating nonoverlapping populations of neurons for different odors. In the second layer, only the synapses between odor-activated neurons and the odor's associated output neuron are modified during learning; the rest of the weights are frozen to prevent unrelated memories from being overwritten. We prove theoretically that these two perceptron-like layers help reduce catastrophic forgetting compared to the original perceptron algorithm, under continual learning. We then show empirically on benchmark data sets that this simple and lightweight architecture outperforms other popular neural-inspired algorithms when also using a two-layer feedforward architecture. Overall, fruit flies evolved an efficient continual associative learning algorithm, and circuit mechanisms from neuroscience can be translated to improve machine computation.
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Affiliation(s)
- Yang Shen
- Cold Spring Harbor Laboratory, Simons Center for Quantitative Biology, Cold Spring Harbor, NY 11724, U.S.A.
| | - Sanjoy Dasgupta
- Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA 92093, U.S.A.
| | - Saket Navlakha
- Cold Spring Harbor Laboratory, Simons Center for Quantitative Biology, Cold Spring Harbor, NY 11724, U.S.A.
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80
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Ros IG, Omoto JJ, Dickinson MH. Descending control and regulation of spontaneous flight turns in Drosophila. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.06.555791. [PMID: 37732262 PMCID: PMC10508747 DOI: 10.1101/2023.09.06.555791] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/22/2023]
Abstract
The clumped distribution of resources in the world has influenced the pattern of foraging behavior since the origins of life, selecting for a common locomotor search motif in which straight movements through resource-poor regions alternate with zig -zag exploration in resource-rich domains. For example, flies execute rapid changes in flight heading called body saccades during local search, but suppress these turns during long-distance dispersal or when surging upwind after encountering an attractive odor plume. Here, we describe the key cellular components of a neural network in flies that generates spontaneous turns as well as a specialized neuron that inhibits the network to promote straight flight. Using 2-photon imaging, optogenetic activation, and genetic ablation, we show that only four descending neurons appear sufficient to generate the descending commands to execute flight saccades. The network is organized into two functional couplets-one for right turns and one for left-with each couplet consisting of an excitatory (DNae014) and inhibitory (DNb01) neuron that project to the flight motor neuropil within the ventral nerve cord. Using resources from recently published connectomes of the fly brain, we identified a large, unique interneuron (VES041) that forms inhibitory connections to all four saccade command neurons and created specific genetic driver lines for this cell. As suggested by its connectivity, activation of VES041 strongly suppresses saccades, suggesting that it regulates the transition between local search and long-distance dispersal. These results thus identify the critical elements of a network that not only structures the locomotor behavior of flies, but may also play a crucial role in their natural foraging ecology.
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81
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Sanfilippo P, Kim AJ, Bhukel A, Yoo J, Mirshahidi PS, Pandey V, Bevir H, Yuen A, Mirshahidi PS, Guo P, Li HS, Wohlschlegel JA, Aso Y, Zipursky SL. Mapping of multiple neurotransmitter receptor subtypes and distinct protein complexes to the connectome. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.02.560011. [PMID: 37873314 PMCID: PMC10592863 DOI: 10.1101/2023.10.02.560011] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Neurons express different combinations of neurotransmitter receptor (NR) subunits and receive inputs from multiple neuron types expressing different neurotransmitters. Localizing NR subunits to specific synaptic inputs has been challenging. Here we use epitope tagged endogenous NR subunits, expansion light-sheet microscopy, and EM connectomics to molecularly characterize synapses in Drosophila. We show that in directionally selective motion sensitive neurons, different multiple NRs elaborated a highly stereotyped molecular topography with NR localized to specific domains receiving cell-type specific inputs. Developmental studies suggested that NRs or complexes of them with other membrane proteins determines patterns of synaptic inputs. In support of this model, we identify a transmembrane protein associated selectively with a subset of spatially restricted synapses and demonstrate through genetic analysis its requirement for synapse formation. We propose that mechanisms which regulate the precise spatial distribution of NRs provide a molecular cartography specifying the patterns of synaptic connections onto dendrites.
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Affiliation(s)
- Piero Sanfilippo
- Department of Biological Chemistry, University of California Los Angeles, Los Angeles, CA, USA
- Howard Hughes Medical Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Alexander J Kim
- Department of Biological Chemistry, University of California Los Angeles, Los Angeles, CA, USA
| | - Anuradha Bhukel
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Juyoun Yoo
- Neuroscience Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA
| | - Pegah S Mirshahidi
- Department of Biological Chemistry, University of California Los Angeles, Los Angeles, CA, USA
| | - Vijaya Pandey
- Department of Biological Chemistry, University of California Los Angeles, Los Angeles, CA, USA
| | - Harry Bevir
- Department of Biological Chemistry, University of California Los Angeles, Los Angeles, CA, USA
| | - Ashley Yuen
- Department of Biological Chemistry, University of California Los Angeles, Los Angeles, CA, USA
| | - Parmis S Mirshahidi
- Department of Biological Chemistry, University of California Los Angeles, Los Angeles, CA, USA
| | - Peiyi Guo
- Department of Neurobiology, University of Massachusetts Medical School, Worcester, MA, USA
| | - Hong-Sheng Li
- Department of Neurobiology, University of Massachusetts Medical School, Worcester, MA, USA
| | - James A Wohlschlegel
- Department of Biological Chemistry, University of California Los Angeles, Los Angeles, CA, USA
| | - Yoshinori Aso
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - S Lawrence Zipursky
- Department of Biological Chemistry, University of California Los Angeles, Los Angeles, CA, USA
- Howard Hughes Medical Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- Lead Contact
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82
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Srinivasan S, Daste S, Modi MN, Turner GC, Fleischmann A, Navlakha S. Effects of stochastic coding on olfactory discrimination in flies and mice. PLoS Biol 2023; 21:e3002206. [PMID: 37906721 PMCID: PMC10618007 DOI: 10.1371/journal.pbio.3002206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 08/21/2023] [Indexed: 11/02/2023] Open
Abstract
Sparse coding can improve discrimination of sensory stimuli by reducing overlap between their representations. Two factors, however, can offset sparse coding's benefits: similar sensory stimuli have significant overlap and responses vary across trials. To elucidate the effects of these 2 factors, we analyzed odor responses in the fly and mouse olfactory regions implicated in learning and discrimination-the mushroom body (MB) and the piriform cortex (PCx). We found that neuronal responses fall along a continuum from extremely reliable across trials to extremely variable or stochastic. Computationally, we show that the observed variability arises from noise within central circuits rather than sensory noise. We propose this coding scheme to be advantageous for coarse- and fine-odor discrimination. More reliable cells enable quick discrimination between dissimilar odors. For similar odors, however, these cells overlap and do not provide distinguishing information. By contrast, more unreliable cells are decorrelated for similar odors, providing distinguishing information, though these benefits only accrue with extended training with more trials. Overall, we have uncovered a conserved, stochastic coding scheme in vertebrates and invertebrates, and we identify a candidate mechanism, based on variability in a winner-take-all (WTA) inhibitory circuit, that improves discrimination with training.
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Affiliation(s)
- Shyam Srinivasan
- Kavli Institute for Brain and Mind, University of California, San Diego, California, United States of America
- Salk Institute for Biological Studies, La Jolla, California, United States of America
| | - Simon Daste
- Department of Neuroscience, Division of Biology and Medicine, Brown University, Providence, Rhode Island, United States of America
- Robert J. and Nancy D. Carney Institute for Brain Science, Brown University, Providence, Rhode Island, United States of America
| | - Mehrab N. Modi
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, United States of America
| | - Glenn C. Turner
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, United States of America
| | - Alexander Fleischmann
- Department of Neuroscience, Division of Biology and Medicine, Brown University, Providence, Rhode Island, United States of America
- Robert J. and Nancy D. Carney Institute for Brain Science, Brown University, Providence, Rhode Island, United States of America
| | - Saket Navlakha
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America
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83
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Aswath A, Alsahaf A, Giepmans BNG, Azzopardi G. Segmentation in large-scale cellular electron microscopy with deep learning: A literature survey. Med Image Anal 2023; 89:102920. [PMID: 37572414 DOI: 10.1016/j.media.2023.102920] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 07/05/2023] [Accepted: 07/31/2023] [Indexed: 08/14/2023]
Abstract
Electron microscopy (EM) enables high-resolution imaging of tissues and cells based on 2D and 3D imaging techniques. Due to the laborious and time-consuming nature of manual segmentation of large-scale EM datasets, automated segmentation approaches are crucial. This review focuses on the progress of deep learning-based segmentation techniques in large-scale cellular EM throughout the last six years, during which significant progress has been made in both semantic and instance segmentation. A detailed account is given for the key datasets that contributed to the proliferation of deep learning in 2D and 3D EM segmentation. The review covers supervised, unsupervised, and self-supervised learning methods and examines how these algorithms were adapted to the task of segmenting cellular and sub-cellular structures in EM images. The special challenges posed by such images, like heterogeneity and spatial complexity, and the network architectures that overcame some of them are described. Moreover, an overview of the evaluation measures used to benchmark EM datasets in various segmentation tasks is provided. Finally, an outlook of current trends and future prospects of EM segmentation is given, especially with large-scale models and unlabeled images to learn generic features across EM datasets.
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Affiliation(s)
- Anusha Aswath
- Bernoulli Institute of Mathematics, Computer Science and Artificial Intelligence, University Groningen, Groningen, The Netherlands; Department of Biomedical Sciences of Cells and Systems, University Groningen, University Medical Center Groningen, Groningen, The Netherlands.
| | - Ahmad Alsahaf
- Department of Biomedical Sciences of Cells and Systems, University Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Ben N G Giepmans
- Department of Biomedical Sciences of Cells and Systems, University Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - George Azzopardi
- Bernoulli Institute of Mathematics, Computer Science and Artificial Intelligence, University Groningen, Groningen, The Netherlands
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84
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Pérez-Garza J, Parrish-Mulliken E, Deane Z, Ostroff LE. Rehydration of Freeze Substituted Brain Tissue for Pre-embedding Immunoelectron Microscopy. MICROSCOPY AND MICROANALYSIS : THE OFFICIAL JOURNAL OF MICROSCOPY SOCIETY OF AMERICA, MICROBEAM ANALYSIS SOCIETY, MICROSCOPICAL SOCIETY OF CANADA 2023; 29:1694-1704. [PMID: 37584524 PMCID: PMC10541149 DOI: 10.1093/micmic/ozad077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 05/27/2023] [Accepted: 07/16/2023] [Indexed: 08/17/2023]
Abstract
Electron microscopy (EM) volume reconstruction is a powerful tool for investigating the fundamental structure of brain circuits, but the full potential of this technique is limited by the difficulty of integrating molecular information. High quality ultrastructural preservation is necessary for EM reconstruction, and intact, highly contrasted cell membranes are essential for following small neuronal processes through serial sections. Unfortunately, the antibody labeling methods used to identify most endogenous molecules result in compromised morphology, especially of membranes. Cryofixation can produce superior morphological preservation and has the additional advantage of allowing indefinite storage of valuable samples. We have developed a method based on cryofixation that allows sensitive immunolabeling of endogenous molecules, preserves excellent ultrastructure, and is compatible with high-contrast staining for serial EM reconstruction.
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Affiliation(s)
- Janeth Pérez-Garza
- Department of Physiology and Neurobiology, University of Connecticut, 75 North Eagleville Rd. Unit 3156, Storrs, CT 06269-3156, USA
| | - Emily Parrish-Mulliken
- Department of Physiology and Neurobiology, University of Connecticut, 75 North Eagleville Rd. Unit 3156, Storrs, CT 06269-3156, USA
| | - Zachary Deane
- Department of Physiology and Neurobiology, University of Connecticut, 75 North Eagleville Rd. Unit 3156, Storrs, CT 06269-3156, USA
| | - Linnaea E Ostroff
- Department of Physiology and Neurobiology, University of Connecticut, 75 North Eagleville Rd. Unit 3156, Storrs, CT 06269-3156, USA
- Connecticut Institute for the Brain and Cognitive Sciences, University of Connecticut, 337 Mansfield Rd. Unit 1272, Storrs, CT 06269-1272, USA
- Institute of Materials Science, University of Connecticut, 25 King Hill Rd. Unit 3136, Storrs, CT 06269-3136, USA
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85
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Lu X, Wu Y, Schalek RL, Meirovitch Y, Berger DR, Lichtman JW. A Scalable Staining Strategy for Whole-Brain Connectomics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.26.558265. [PMID: 37808722 PMCID: PMC10557665 DOI: 10.1101/2023.09.26.558265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
Mapping the complete synaptic connectivity of a mammalian brain would be transformative, revealing the pathways underlying perception, behavior, and memory. Serial section electron microscopy, via membrane staining using osmium tetroxide, is ideal for visualizing cells and synaptic connections but, in whole brain samples, faces significant challenges related to chemical treatment and volume changes. These issues can adversely affect both the ultrastructural quality and macroscopic tissue integrity. By leveraging time-lapse X-ray imaging and brain proxies, we have developed a 12-step protocol, ODeCO, that effectively infiltrates osmium throughout an entire mouse brain while preserving ultrastructure without any cracks or fragmentation, a necessary prerequisite for constructing the first comprehensive mouse brain connectome.
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Affiliation(s)
- Xiaotang Lu
- Department of Molecular and Cellular Biology and The Center for Brain Science, Harvard University, Cambridge, Massachusetts, 02138, USA
| | - Yuelong Wu
- Department of Molecular and Cellular Biology and The Center for Brain Science, Harvard University, Cambridge, Massachusetts, 02138, USA
| | - Richard L. Schalek
- Department of Molecular and Cellular Biology and The Center for Brain Science, Harvard University, Cambridge, Massachusetts, 02138, USA
| | - Yaron Meirovitch
- Department of Molecular and Cellular Biology and The Center for Brain Science, Harvard University, Cambridge, Massachusetts, 02138, USA
| | - Daniel R. Berger
- Department of Molecular and Cellular Biology and The Center for Brain Science, Harvard University, Cambridge, Massachusetts, 02138, USA
| | - Jeff W. Lichtman
- Department of Molecular and Cellular Biology and The Center for Brain Science, Harvard University, Cambridge, Massachusetts, 02138, USA
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86
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Rajagopalan AE, Darshan R, Hibbard KL, Fitzgerald JE, Turner GC. Reward expectations direct learning and drive operant matching in Drosophila. Proc Natl Acad Sci U S A 2023; 120:e2221415120. [PMID: 37733736 PMCID: PMC10523640 DOI: 10.1073/pnas.2221415120] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Accepted: 08/11/2023] [Indexed: 09/23/2023] Open
Abstract
Foraging animals must use decision-making strategies that dynamically adapt to the changing availability of rewards in the environment. A wide diversity of animals do this by distributing their choices in proportion to the rewards received from each option, Herrnstein's operant matching law. Theoretical work suggests an elegant mechanistic explanation for this ubiquitous behavior, as operant matching follows automatically from simple synaptic plasticity rules acting within behaviorally relevant neural circuits. However, no past work has mapped operant matching onto plasticity mechanisms in the brain, leaving the biological relevance of the theory unclear. Here, we discovered operant matching in Drosophila and showed that it requires synaptic plasticity that acts in the mushroom body and incorporates the expectation of reward. We began by developing a dynamic foraging paradigm to measure choices from individual flies as they learn to associate odor cues with probabilistic rewards. We then built a model of the fly mushroom body to explain each fly's sequential choice behavior using a family of biologically realistic synaptic plasticity rules. As predicted by past theoretical work, we found that synaptic plasticity rules could explain fly matching behavior by incorporating stimulus expectations, reward expectations, or both. However, by optogenetically bypassing the representation of reward expectation, we abolished matching behavior and showed that the plasticity rule must specifically incorporate reward expectations. Altogether, these results reveal the first synapse-level mechanisms of operant matching and provide compelling evidence for the role of reward expectation signals in the fly brain.
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Affiliation(s)
- Adithya E. Rajagopalan
- Janelia Research Campus, HHMI, Ashburn, VA20147
- Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD21205
| | - Ran Darshan
- Janelia Research Campus, HHMI, Ashburn, VA20147
- Department of Physiology and Pharmacology, Sackler Faculty of Medicine, Sagol School of Neuroscience, The School of Physics and Astronomy, Tel Aviv University, Tel Aviv6997801, Israel
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87
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Mabuchi Y, Cui X, Xie L, Kim H, Jiang T, Yapici N. Visual feedback neurons fine-tune Drosophila male courtship via GABA-mediated inhibition. Curr Biol 2023; 33:3896-3910.e7. [PMID: 37673068 PMCID: PMC10529139 DOI: 10.1016/j.cub.2023.08.034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 06/27/2023] [Accepted: 08/11/2023] [Indexed: 09/08/2023]
Abstract
Many species of animals use vision to regulate their social behaviors. However, the molecular and circuit mechanisms underlying visually guided social interactions remain largely unknown. Here, we show that the Drosophila ortholog of the human GABAA-receptor-associated protein (GABARAP) is required in a class of visual feedback neurons, lamina tangential (Lat) cells, to fine-tune male courtship. GABARAP is a ubiquitin-like protein that maintains cell-surface levels of GABAA receptors. We demonstrate that knocking down GABARAP or GABAAreceptors in Lat neurons or hyperactivating them induces male courtship toward other males. Inhibiting Lat neurons, on the other hand, delays copulation by impairing the ability of males to follow females. Remarkably, the fly GABARAP protein and its human ortholog share a strong sequence identity, and the fly GABARAP function in Lat neurons can be rescued by its human ortholog. Using in vivo two-photon imaging and optogenetics, we reveal that Lat neurons are functionally connected to neural circuits that mediate visually guided courtship pursuits in males. Our work identifies a novel physiological function for GABARAP in regulating visually guided courtship pursuits in Drosophila males. Reduced GABAA signaling has been linked to social deficits observed in the autism spectrum and bipolar disorders. The functional similarity between the human and the fly GABARAP raises the possibility of a conserved role for this gene in regulating social behaviors across insects and mammals.
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Affiliation(s)
- Yuta Mabuchi
- Department of Neurobiology and Behavior, Cornell University, Ithaca, NY 14853, USA
| | - Xinyue Cui
- Department of Neurobiology and Behavior, Cornell University, Ithaca, NY 14853, USA
| | - Lily Xie
- Department of Neurobiology and Behavior, Cornell University, Ithaca, NY 14853, USA
| | - Haein Kim
- Department of Neurobiology and Behavior, Cornell University, Ithaca, NY 14853, USA
| | - Tianxing Jiang
- Department of Neurobiology and Behavior, Cornell University, Ithaca, NY 14853, USA
| | - Nilay Yapici
- Department of Neurobiology and Behavior, Cornell University, Ithaca, NY 14853, USA.
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88
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Yoo J, Dombrovski M, Mirshahidi P, Nern A, LoCascio SA, Zipursky SL, Kurmangaliyev YZ. Brain wiring determinants uncovered by integrating connectomes and transcriptomes. Curr Biol 2023; 33:3998-4005.e6. [PMID: 37647901 DOI: 10.1016/j.cub.2023.08.020] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 07/12/2023] [Accepted: 08/04/2023] [Indexed: 09/01/2023]
Abstract
Advances in brain connectomics have demonstrated the extraordinary complexity of neural circuits.1,2,3,4,5 Developing neurons encounter the axons and dendrites of many different neuron types and form synapses with only a subset of them. During circuit assembly, neurons express cell-type-specific repertoires comprising many cell adhesion molecules (CAMs) that can mediate interactions between developing neurites.6,7,8 Many CAM families have been shown to contribute to brain wiring in different ways.9,10 It has been challenging, however, to identify receptor-ligand pairs directly matching neurons with their synaptic targets. Here, we integrated the synapse-level connectome of the neural circuit11,12 with the developmental expression patterns7 and binding specificities of CAMs6,13 on pre- and postsynaptic neurons in the Drosophila visual system. To overcome the complexity of neural circuits, we focus on pairs of genetically related neurons that make differential wiring choices. In the motion detection circuit,14 closely related subtypes of T4/T5 neurons choose between alternative synaptic targets in adjacent layers of neuropil.12 This choice correlates with the matching expression in synaptic partners of different receptor-ligand pairs of the Beat and Side families of CAMs. Genetic analysis demonstrated that presynaptic Side-II and postsynaptic Beat-VI restrict synaptic partners to the same layer. Removal of this receptor-ligand pair disrupts layers and leads to inappropriate targeting of presynaptic sites and postsynaptic dendrites. We propose that different Side/Beat receptor-ligand pairs collaborate with other recognition molecules to determine wiring specificities in the fly brain. Combining transcriptomes, connectomes, and protein interactome maps allow unbiased identification of determinants of brain wiring.
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Affiliation(s)
- Juyoun Yoo
- Department of Biological Chemistry, Howard Hughes Medical Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Neuroscience Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Mark Dombrovski
- Department of Biological Chemistry, Howard Hughes Medical Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Parmis Mirshahidi
- Department of Biological Chemistry, Howard Hughes Medical Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Aljoscha Nern
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Samuel A LoCascio
- Department of Biological Chemistry, Howard Hughes Medical Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - S Lawrence Zipursky
- Department of Biological Chemistry, Howard Hughes Medical Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA.
| | - Yerbol Z Kurmangaliyev
- Department of Biological Chemistry, Howard Hughes Medical Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA.
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89
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González Segarra AJ, Pontes G, Jourjine N, Del Toro A, Scott K. Hunger- and thirst-sensing neurons modulate a neuroendocrine network to coordinate sugar and water ingestion. eLife 2023; 12:RP88143. [PMID: 37732734 PMCID: PMC10513480 DOI: 10.7554/elife.88143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/22/2023] Open
Abstract
Consumption of food and water is tightly regulated by the nervous system to maintain internal nutrient homeostasis. Although generally considered independently, interactions between hunger and thirst drives are important to coordinate competing needs. In Drosophila, four neurons called the interoceptive subesophageal zone neurons (ISNs) respond to intrinsic hunger and thirst signals to oppositely regulate sucrose and water ingestion. Here, we investigate the neural circuit downstream of the ISNs to examine how ingestion is regulated based on internal needs. Utilizing the recently available fly brain connectome, we find that the ISNs synapse with a novel cell-type bilateral T-shaped neuron (BiT) that projects to neuroendocrine centers. In vivo neural manipulations revealed that BiT oppositely regulates sugar and water ingestion. Neuroendocrine cells downstream of ISNs include several peptide-releasing and peptide-sensing neurons, including insulin producing cells (IPCs), crustacean cardioactive peptide (CCAP) neurons, and CCHamide-2 receptor isoform RA (CCHa2R-RA) neurons. These neurons contribute differentially to ingestion of sugar and water, with IPCs and CCAP neurons oppositely regulating sugar and water ingestion, and CCHa2R-RA neurons modulating only water ingestion. Thus, the decision to consume sugar or water occurs via regulation of a broad peptidergic network that integrates internal signals of nutritional state to generate nutrient-specific ingestion.
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Affiliation(s)
| | - Gina Pontes
- University of California, BerkeleyBerkeleyUnited States
| | | | | | - Kristin Scott
- University of California, BerkeleyBerkeleyUnited States
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90
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Aso Y, Yamada D, Bushey D, Hibbard KL, Sammons M, Otsuna H, Shuai Y, Hige T. Neural circuit mechanisms for transforming learned olfactory valences into wind-oriented movement. eLife 2023; 12:e85756. [PMID: 37721371 PMCID: PMC10588983 DOI: 10.7554/elife.85756] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 09/07/2023] [Indexed: 09/19/2023] Open
Abstract
How memories are used by the brain to guide future action is poorly understood. In olfactory associative learning in Drosophila, multiple compartments of the mushroom body act in parallel to assign a valence to a stimulus. Here, we show that appetitive memories stored in different compartments induce different levels of upwind locomotion. Using a photoactivation screen of a new collection of split-GAL4 drivers and EM connectomics, we identified a cluster of neurons postsynaptic to the mushroom body output neurons (MBONs) that can trigger robust upwind steering. These UpWind Neurons (UpWiNs) integrate inhibitory and excitatory synaptic inputs from MBONs of appetitive and aversive memory compartments, respectively. After formation of appetitive memory, UpWiNs acquire enhanced response to reward-predicting odors as the response of the inhibitory presynaptic MBON undergoes depression. Blocking UpWiNs impaired appetitive memory and reduced upwind locomotion during retrieval. Photoactivation of UpWiNs also increased the chance of returning to a location where activation was terminated, suggesting an additional role in olfactory navigation. Thus, our results provide insight into how learned abstract valences are gradually transformed into concrete memory-driven actions through divergent and convergent networks, a neuronal architecture that is commonly found in the vertebrate and invertebrate brains.
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Affiliation(s)
- Yoshinori Aso
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Daichi Yamada
- Department of Biology, University of North Carolina at Chapel HillChapel HillUnited States
| | - Daniel Bushey
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Karen L Hibbard
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Megan Sammons
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Hideo Otsuna
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Yichun Shuai
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Toshihide Hige
- Department of Biology, University of North Carolina at Chapel HillChapel HillUnited States
- Department of Cell Biology and Physiology, University of North Carolina at Chapel HillChapel HillUnited States
- Integrative Program for Biological and Genome Sciences, University of North Carolina at Chapel HillChapel HillUnited States
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91
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Hayashi S, Ohno N, Knott G, Molnár Z. Correlative light and volume electron microscopy to study brain development. Microscopy (Oxf) 2023; 72:279-286. [PMID: 36620906 DOI: 10.1093/jmicro/dfad002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 01/06/2023] [Indexed: 01/10/2023] Open
Abstract
Recent advances in volume electron microscopy (EM) have been driving our thorough understanding of the brain architecture. Volume EM becomes increasingly powerful when cells and their subcellular structures that are imaged in light microscopy are correlated to those in ultramicrographs obtained with EM. This correlative approach, called correlative light and volume electron microscopy (vCLEM), is used to link three-dimensional ultrastructural information with physiological data such as intracellular Ca2+ dynamics. Genetic tools to express fluorescent proteins and/or an engineered form of a soybean ascorbate peroxidase allow us to perform vCLEM using natural landmarks including blood vessels without immunohistochemical staining. This immunostaining-free vCLEM has been successfully employed in two-photon Ca2+ imaging in vivo as well as in studying complex synaptic connections in thalamic neurons that receive a variety of specialized inputs from the cerebral cortex. In this mini-review, we overview how volume EM and vCLEM have contributed to studying the developmental processes of the brain. We also discuss potential applications of genetic manipulation of target cells using clustered regularly interspaced short palindromic repeats-associated protein 9 and subsequent volume EM to the analysis of protein localization as well as to loss-of-function studies of genes regulating brain development. We give examples for the combinatorial usage of genetic tools with vCLEM that will further enhance our understanding of regulatory mechanisms underlying brain development.
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Affiliation(s)
- Shuichi Hayashi
- Department of Anatomy, Kawasaki Medical School, 577 Matsushima, Kurashiki, Okayama 701-0192, Japan
| | - Nobuhiko Ohno
- Department of Anatomy, Division of Histology and Cell Biology, School of Medicine, Jichi Medical University, 3311-1 Yakushiji, Shimotsuke, Tochigi 329-0498, Japan
- Division of Ultrastructural Research, National Institute for Physiological Sciences, 5-1 Higashiyama Myodaiji, Okazaki, Aichi 444-8787, Japan
| | - Graham Knott
- Biological Electron Microscopy Facility, Faculty of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Route Cantonale, Lausanne CH-1015, Switzerland
| | - Zoltán Molnár
- Department of Physiology, Anatomy and Genetics, University of Oxford, Sherrington Building, Parks Road, Oxford OX1 3PT, UK
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92
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Patel AA, Cardona A, Cox DN. Neural substrates of cold nociception in Drosophila larva. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.31.551339. [PMID: 37577520 PMCID: PMC10418107 DOI: 10.1101/2023.07.31.551339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
Metazoans detect and differentiate between innocuous (non-painful) and/or noxious (harmful) environmental cues using primary sensory neurons, which serve as the first node in a neural network that computes stimulus specific behaviors to either navigate away from injury-causing conditions or to perform protective behaviors that mitigate extensive injury. The ability of an animal to detect and respond to various sensory stimuli depends upon molecular diversity in the primary sensors and the underlying neural circuitry responsible for the relevant behavioral action selection. Recent studies in Drosophila larvae have revealed that somatosensory class III multidendritic (CIII md) neurons function as multimodal sensors regulating distinct behavioral responses to innocuous mechanical and nociceptive thermal stimuli. Recent advances in circuit bases of behavior have identified and functionally validated Drosophila larval somatosensory circuitry involved in innocuous (mechanical) and noxious (heat and mechanical) cues. However, central processing of cold nociceptive cues remained unexplored. We implicate multisensory integrators (Basins), premotor (Down-and-Back) and projection (A09e and TePns) neurons as neural substrates required for cold-evoked behavioral and calcium responses. Neural silencing of cell types downstream of CIII md neurons led to significant reductions in cold-evoked behaviors and neural co-activation of CIII md neurons plus additional cell types facilitated larval contraction (CT) responses. We further demonstrate that optogenetic activation of CIII md neurons evokes calcium increases in these neurons. Collectively, we demonstrate how Drosophila larvae process cold stimuli through functionally diverse somatosensory circuitry responsible for generating stimulus specific behaviors.
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Affiliation(s)
- Atit A. Patel
- Neuroscience Institute, Georgia State University, Atlanta, GA, USA
| | - Albert Cardona
- HHMI Janelia Research Campus, Ashburn, VA, USA
- MRC Laboratory of Molecular Biology, Cambridge, UK
- Department of Physiology, Development, and Neuroscience, University of Cambridge, UK
| | - Daniel N. Cox
- Neuroscience Institute, Georgia State University, Atlanta, GA, USA
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93
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Xin T, Lv Y, Chen H, Li L, Shen L, Shan G, Chen X, Han H. A novel registration method for long-serial section images of EM with a serial split technique based on unsupervised optical flow network. Bioinformatics 2023; 39:btad436. [PMID: 37462605 PMCID: PMC10403427 DOI: 10.1093/bioinformatics/btad436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 06/06/2023] [Accepted: 07/17/2023] [Indexed: 08/06/2023] Open
Abstract
MOTIVATION The registration of serial section electron microscope images is a critical step in reconstructing biological tissue volumes, and it aims to eliminate complex nonlinear deformations from sectioning and replicate the correct neurite structure. However, due to the inherent properties of biological structures and the challenges posed by section preparation of biological tissues, achieving an accurate registration of serial sections remains a significant challenge. Conventional nonlinear registration techniques, which are effective in eliminating nonlinear deformation, can also eliminate the natural morphological variation of neurites across sections. Additionally, accumulation of registration errors alters the neurite structure. RESULTS This article proposes a novel method for serial section registration that utilizes an unsupervised optical flow network to measure feature similarity rather than pixel similarity to eliminate nonlinear deformation and achieve pairwise registration between sections. The optical flow network is then employed to estimate and compensate for cumulative registration error, thereby allowing for the reconstruction of the structure of biological tissues. Based on the novel serial section registration method, a serial split technique is proposed for long-serial sections. Experimental results demonstrate that the state-of-the-art method proposed here effectively improves the spatial continuity of serial sections, leading to more accurate registration and improved reconstruction of the structure of biological tissues. AVAILABILITY AND IMPLEMENTATION The source code and data are available at https://github.com/TongXin-CASIA/EFSR.
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Affiliation(s)
- Tong Xin
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100190, China
- State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190,China
| | - Yanan Lv
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100190, China
- Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Haoran Chen
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100190, China
- State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190,China
| | - Linlin Li
- Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Lijun Shen
- Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Guangcun Shan
- School of Instrumentation Science and Opto-electronics Engineering & Beijing Advanced Innovation Center for Big Data-based Precision Medicine, Beihang University, Beijing 100083, China
| | - Xi Chen
- Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Hua Han
- State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190,China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing 100190, China
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94
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Chiappe ME. Circuits for self-motion estimation and walking control in Drosophila. Curr Opin Neurobiol 2023; 81:102748. [PMID: 37453230 DOI: 10.1016/j.conb.2023.102748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 06/11/2023] [Accepted: 06/13/2023] [Indexed: 07/18/2023]
Abstract
The brain's evolution and operation are inextricably linked to animal movement, and critical functions, such as motor control, spatial perception, and navigation, rely on precise knowledge of body movement. Such internal estimates of self-motion emerge from the integration of mechanosensory and visual feedback with motor-related signals. Thus, this internal representation likely depends on the activity of circuits distributed across the central nervous system. However, the circuits responsible for self-motion estimation, and the exact mechanisms by which motor-sensory coordination occurs within these circuits remain poorly understood. Recent technological advances have positioned Drosophila melanogaster as an advantageous model for investigating the emergence, maintenance, and utilization of self-motion representations during naturalistic walking behaviors. In this review, I will illustrate how the adult fly is providing insights into the fundamental problems of self-motion computations and walking control, which have relevance for all animals.
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Affiliation(s)
- M Eugenia Chiappe
- Champalimaud Neuroscience Programme, Champalimaud Centre for the Unknown, Lisbon, Portugal.
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95
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Dorkenwald S, Schneider-Mizell CM, Brittain D, Halageri A, Jordan C, Kemnitz N, Castro MA, Silversmith W, Maitin-Shephard J, Troidl J, Pfister H, Gillet V, Xenes D, Bae JA, Bodor AL, Buchanan J, Bumbarger DJ, Elabbady L, Jia Z, Kapner D, Kinn S, Lee K, Li K, Lu R, Macrina T, Mahalingam G, Mitchell E, Mondal SS, Mu S, Nehoran B, Popovych S, Takeno M, Torres R, Turner NL, Wong W, Wu J, Yin W, Yu SC, Reid RC, da Costa NM, Seung HS, Collman F. CAVE: Connectome Annotation Versioning Engine. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.26.550598. [PMID: 37546753 PMCID: PMC10402030 DOI: 10.1101/2023.07.26.550598] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
Advances in Electron Microscopy, image segmentation and computational infrastructure have given rise to large-scale and richly annotated connectomic datasets which are increasingly shared across communities. To enable collaboration, users need to be able to concurrently create new annotations and correct errors in the automated segmentation by proofreading. In large datasets, every proofreading edit relabels cell identities of millions of voxels and thousands of annotations like synapses. For analysis, users require immediate and reproducible access to this constantly changing and expanding data landscape. Here, we present the Connectome Annotation Versioning Engine (CAVE), a computational infrastructure for immediate and reproducible connectome analysis in up-to petascale datasets (~1mm3) while proofreading and annotating is ongoing. For segmentation, CAVE provides a distributed proofreading infrastructure for continuous versioning of large reconstructions. Annotations in CAVE are defined by locations such that they can be quickly assigned to the underlying segment which enables fast analysis queries of CAVE's data for arbitrary time points. CAVE supports schematized, extensible annotations, so that researchers can readily design novel annotation types. CAVE is already used for many connectomics datasets, including the largest datasets available to date.
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Affiliation(s)
- Sven Dorkenwald
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Computer Science Department, Princeton University, Princeton, USA
| | | | | | - Akhilesh Halageri
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Chris Jordan
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Nico Kemnitz
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Manual A. Castro
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | | | | | - Jakob Troidl
- School of Engineering and Applied Sciences, Harvard University, Boston, USA
| | - Hanspeter Pfister
- School of Engineering and Applied Sciences, Harvard University, Boston, USA
| | - Valentin Gillet
- Lund University, Department of Biology, Lund Vision Group, Lund, Sweden
| | - Daniel Xenes
- Research & Exploratory Development Department, Johns Hopkins University Applied Physics Laboratory, Laurel, United States
| | - J. Alexander Bae
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Electrical and Computer Engineering Department, Princeton University, Princeton, USA
| | | | | | | | | | - Zhen Jia
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Computer Science Department, Princeton University, Princeton, USA
| | | | - Sam Kinn
- Allen Institute for Brain Science, Seattle, USA
| | - Kisuk Lee
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Brain & Cognitive Sciences Department, Massachusetts Institute of Technology, Cambridge, USA
| | - Kai Li
- Computer Science Department, Princeton University, Princeton, USA
| | - Ran Lu
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Thomas Macrina
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Computer Science Department, Princeton University, Princeton, USA
| | | | - Eric Mitchell
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Shanka Subhra Mondal
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Electrical and Computer Engineering Department, Princeton University, Princeton, USA
| | - Shang Mu
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Barak Nehoran
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Computer Science Department, Princeton University, Princeton, USA
| | - Sergiy Popovych
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Computer Science Department, Princeton University, Princeton, USA
| | - Marc Takeno
- Allen Institute for Brain Science, Seattle, USA
| | | | - Nicholas L. Turner
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Computer Science Department, Princeton University, Princeton, USA
| | - William Wong
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Jingpeng Wu
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Wenjing Yin
- Allen Institute for Brain Science, Seattle, USA
| | - Szi-chieh Yu
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | | | | | - H. Sebastian Seung
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Computer Science Department, Princeton University, Princeton, USA
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96
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Zhang SX, Kim A, Madara JC, Zhu PK, Christenson LF, Lutas A, Kalugin PN, Jin Y, Pal A, Tian L, Lowell BB, Andermann ML. Competition between stochastic neuropeptide signals calibrates the rate of satiation. RESEARCH SQUARE 2023:rs.3.rs-3185572. [PMID: 37546985 PMCID: PMC10402269 DOI: 10.21203/rs.3.rs-3185572/v1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
We investigated how transmission of hunger- and satiety-promoting neuropeptides, NPY and αMSH, is integrated at the level of intracellular signaling to control feeding. Receptors for these peptides use the second messenger cAMP. How cAMP integrates opposing peptide signals to regulate energy balance, and the in vivo spatiotemporal dynamics of endogenous peptidergic signaling, remain largely unknown. We show that AgRP axon stimulation in the paraventricular hypothalamus evokes probabilistic NPY release that triggers stochastic cAMP decrements in downstream MC4R-expressing neurons (PVHMC4R). Meanwhile, POMC axon stimulation triggers stochastic, αMSH-dependent cAMP increments. Release of either peptide impacts a ~100 μm diameter region, and when these peptide signals overlap, they compete to control cAMP. The competition is reflected by hunger-state-dependent differences in the amplitude and persistence of cAMP transients: hunger peptides are more efficacious in the fasted state, satiety peptides in the fed state. Feeding resolves the competition by simultaneously elevating αMSH release and suppressing NPY release, thereby sustaining elevated cAMP in PVHMC4R neurons. In turn, cAMP potentiates feeding-related excitatory inputs and promotes satiation across minutes. Our findings highlight how biochemical integration of opposing, quantal peptide signals during energy intake orchestrates a gradual transition between stable states of hunger and satiety.
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Affiliation(s)
- Stephen X Zhang
- Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA
- Co-corresponding authors
| | - Angela Kim
- Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA
| | - Joseph C Madara
- Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA
| | - Paula K Zhu
- Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA
| | - Lauren F Christenson
- Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA
| | - Andrew Lutas
- Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA
- Present address: Diabetes, Endocrinology, and Obesity Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Peter N Kalugin
- Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA
- Program in Neuroscience, Harvard University, Cambridge, MA 02138, USA
| | - Yihan Jin
- Department of Biochemistry and Molecular Medicine, School of Medicine, University of California, Davis, Sacramento, CA 95817, USA
| | - Akash Pal
- Department of Biochemistry and Molecular Medicine, School of Medicine, University of California, Davis, Sacramento, CA 95817, USA
| | - Lin Tian
- Department of Biochemistry and Molecular Medicine, School of Medicine, University of California, Davis, Sacramento, CA 95817, USA
| | - Bradford B Lowell
- Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA
| | - Mark L Andermann
- Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA
- Program in Neuroscience, Harvard University, Cambridge, MA 02138, USA
- Department of Neurobiology, Harvard Medical School, Boston, MA 02115, USA
- Co-corresponding authors
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Schlegel P, Yin Y, Bates AS, Dorkenwald S, Eichler K, Brooks P, Han DS, Gkantia M, Dos Santos M, Munnelly EJ, Badalamente G, Capdevila LS, Sane VA, Pleijzier MW, Tamimi IFM, Dunne CR, Salgarella I, Javier A, Fang S, Perlman E, Kazimiers T, Jagannathan SR, Matsliah A, Sterling AR, Yu SC, McKellar CE, Costa M, Seung HS, Murthy M, Hartenstein V, Bock DD, Jefferis GSXE. Whole-brain annotation and multi-connectome cell typing quantifies circuit stereotypy in Drosophila. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.27.546055. [PMID: 37425808 PMCID: PMC10327018 DOI: 10.1101/2023.06.27.546055] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
The fruit fly Drosophila melanogaster combines surprisingly sophisticated behaviour with a highly tractable nervous system. A large part of the fly's success as a model organism in modern neuroscience stems from the concentration of collaboratively generated molecular genetic and digital resources. As presented in our FlyWire companion paper 1 , this now includes the first full brain connectome of an adult animal. Here we report the systematic and hierarchical annotation of this ~130,000-neuron connectome including neuronal classes, cell types and developmental units (hemilineages). This enables any researcher to navigate this huge dataset and find systems and neurons of interest, linked to the literature through the Virtual Fly Brain database 2 . Crucially, this resource includes 4,552 cell types. 3,094 are rigorous consensus validations of cell types previously proposed in the hemibrain connectome 3 . In addition, we propose 1,458 new cell types, arising mostly from the fact that the FlyWire connectome spans the whole brain, whereas the hemibrain derives from a subvolume. Comparison of FlyWire and the hemibrain showed that cell type counts and strong connections were largely stable, but connection weights were surprisingly variable within and across animals. Further analysis defined simple heuristics for connectome interpretation: connections stronger than 10 unitary synapses or providing >1% of the input to a target cell are highly conserved. Some cell types showed increased variability across connectomes: the most common cell type in the mushroom body, required for learning and memory, is almost twice as numerous in FlyWire as the hemibrain. We find evidence for functional homeostasis through adjustments of the absolute amount of excitatory input while maintaining the excitation-inhibition ratio. Finally, and surprisingly, about one third of the cell types proposed in the hemibrain connectome could not yet be reliably identified in the FlyWire connectome. We therefore suggest that cell types should be defined to be robust to inter-individual variation, namely as groups of cells that are quantitatively more similar to cells in a different brain than to any other cell in the same brain. Joint analysis of the FlyWire and hemibrain connectomes demonstrates the viability and utility of this new definition. Our work defines a consensus cell type atlas for the fly brain and provides both an intellectual framework and open source toolchain for brain-scale comparative connectomics.
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98
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Christenson MP, Díez ÁS, Heath SL, Saavedra-Weisenhaus M, Adachi A, Abbott LF, Behnia R. Hue selectivity from recurrent circuitry in Drosophila. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.12.548573. [PMID: 37502934 PMCID: PMC10369983 DOI: 10.1101/2023.07.12.548573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
A universal principle of sensory perception is the progressive transformation of sensory information from broad non-specific signals to stimulus-selective signals that form the basis of perception. To perceive color, our brains must transform the wavelengths of light reflected off objects into the derived quantities of brightness, saturation and hue. Neurons responding selectively to hue have been reported in primate cortex, but it is unknown how their narrow tuning in color space is produced by upstream circuit mechanisms. To enable circuit level analysis of color perception, we here report the discovery of neurons in the Drosophila optic lobe with hue selective properties. Using the connectivity graph of the fly brain, we construct a connectomics-constrained circuit model that accounts for this hue selectivity. Unexpectedly, our model predicts that recurrent connections in the circuit are critical for hue selectivity. Experiments using genetic manipulations to perturb recurrence in adult flies confirms this prediction. Our findings reveal the circuit basis for hue selectivity in color vision.
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99
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Zhang SX, Kim A, Madara JC, Zhu PK, Christenson LF, Lutas A, Kalugin PN, Jin Y, Pal A, Tian L, Lowell BB, Andermann ML. Competition between stochastic neuropeptide signals calibrates the rate of satiation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.11.548551. [PMID: 37503012 PMCID: PMC10369917 DOI: 10.1101/2023.07.11.548551] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
We investigated how transmission of hunger- and satiety-promoting neuropeptides, NPY and αMSH, is integrated at the level of intracellular signaling to control feeding. Receptors for these peptides use the second messenger cAMP, but the messenger's spatiotemporal dynamics and role in energy balance are controversial. We show that AgRP axon stimulation in the paraventricular hypothalamus evokes probabilistic and spatially restricted NPY release that triggers stochastic cAMP decrements in downstream MC4R-expressing neurons (PVH MC4R ). Meanwhile, POMC axon stimulation triggers stochastic, αMSH-dependent cAMP increments. NPY and αMSH competitively control cAMP, as reflected by hunger-state-dependent differences in the amplitude and persistence of cAMP transients evoked by each peptide. During feeding bouts, elevated αMSH release and suppressed NPY release cooperatively sustain elevated cAMP in PVH MC4R neurons, thereby potentiating feeding-related excitatory inputs and promoting satiation across minutes. Our findings highlight how state-dependent integration of opposing, quantal peptidergic events by a common biochemical target calibrates energy intake.
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100
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Dorkenwald S, Matsliah A, Sterling AR, Schlegel P, Yu SC, McKellar CE, Lin A, Costa M, Eichler K, Yin Y, Silversmith W, Schneider-Mizell C, Jordan CS, Brittain D, Halageri A, Kuehner K, Ogedengbe O, Morey R, Gager J, Kruk K, Perlman E, Yang R, Deutsch D, Bland D, Sorek M, Lu R, Macrina T, Lee K, Bae JA, Mu S, Nehoran B, Mitchell E, Popovych S, Wu J, Jia Z, Castro M, Kemnitz N, Ih D, Bates AS, Eckstein N, Funke J, Collman F, Bock DD, Jefferis GS, Seung HS, Murthy M. Neuronal wiring diagram of an adult brain. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.27.546656. [PMID: 37425937 PMCID: PMC10327113 DOI: 10.1101/2023.06.27.546656] [Citation(s) in RCA: 33] [Impact Index Per Article: 33.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
Connections between neurons can be mapped by acquiring and analyzing electron microscopic (EM) brain images. In recent years, this approach has been applied to chunks of brains to reconstruct local connectivity maps that are highly informative, yet inadequate for understanding brain function more globally. Here, we present the first neuronal wiring diagram of a whole adult brain, containing 5×107 chemical synapses between ~130,000 neurons reconstructed from a female Drosophila melanogaster. The resource also incorporates annotations of cell classes and types, nerves, hemilineages, and predictions of neurotransmitter identities. Data products are available by download, programmatic access, and interactive browsing and made interoperable with other fly data resources. We show how to derive a projectome, a map of projections between regions, from the connectome. We demonstrate the tracing of synaptic pathways and the analysis of information flow from inputs (sensory and ascending neurons) to outputs (motor, endocrine, and descending neurons), across both hemispheres, and between the central brain and the optic lobes. Tracing from a subset of photoreceptors all the way to descending motor pathways illustrates how structure can uncover putative circuit mechanisms underlying sensorimotor behaviors. The technologies and open ecosystem of the FlyWire Consortium set the stage for future large-scale connectome projects in other species.
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Affiliation(s)
- Sven Dorkenwald
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Computer Science Department, Princeton University, Princeton, USA
| | - Arie Matsliah
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Amy R Sterling
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Eyewire, Boston, USA
| | - Philipp Schlegel
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Szi-chieh Yu
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | | | - Albert Lin
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Center for the Physics of Biological Function, Princeton University, Princeton, USA
| | - Marta Costa
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Katharina Eichler
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Yijie Yin
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Will Silversmith
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | | | - Chris S. Jordan
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | | | - Akhilesh Halageri
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Kai Kuehner
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | | | - Ryan Morey
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Jay Gager
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | | | | | - Runzhe Yang
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Computer Science Department, Princeton University, Princeton, USA
| | - David Deutsch
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Department of Neurobiology, University of Haifa, Haifa, Israel
| | - Doug Bland
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Marissa Sorek
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Eyewire, Boston, USA
| | - Ran Lu
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Thomas Macrina
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Computer Science Department, Princeton University, Princeton, USA
| | - Kisuk Lee
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Brain & Cognitive Sciences Department, Massachusetts Institute of Technology, Cambridge, USA
| | - J. Alexander Bae
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Electrical and Computer Engineering Department, Princeton University, Princeton, USA
| | - Shang Mu
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Barak Nehoran
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Computer Science Department, Princeton University, Princeton, USA
| | - Eric Mitchell
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Sergiy Popovych
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Computer Science Department, Princeton University, Princeton, USA
| | - Jingpeng Wu
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Zhen Jia
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Manuel Castro
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Nico Kemnitz
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Dodam Ih
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Alexander Shakeel Bates
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
- Harvard Medical School, Boston, USA
- Centre for Neural Circuits and Behaviour, The University of Oxford, Oxford, UK
| | - Nils Eckstein
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, USA
| | - Jan Funke
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, USA
| | | | - Davi D. Bock
- Department of Neurological Sciences, Larner College of Medicine, University of Vermont, Burlington, USA
| | - Gregory S.X.E Jefferis
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - H. Sebastian Seung
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Computer Science Department, Princeton University, Princeton, USA
| | - Mala Murthy
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
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