1
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Frank DD, Kronauer DJC. The Budding Neuroscience of Ant Social Behavior. Annu Rev Neurosci 2024; 47:167-185. [PMID: 38603564 DOI: 10.1146/annurev-neuro-083023-102101] [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] [Indexed: 04/13/2024]
Abstract
Ant physiology has been fashioned by 100 million years of social evolution. Ants perform many sophisticated social and collective behaviors yet possess nervous systems similar in schematic and scale to that of the fruit fly Drosophila melanogaster, a popular solitary model organism. Ants are thus attractive complementary subjects to investigate adaptations pertaining to complex social behaviors that are absent in flies. Despite research interest in ant behavior and the neurobiological foundations of sociality more broadly, our understanding of the ant nervous system is incomplete. Recent technical advances have enabled cutting-edge investigations of the nervous system in a fashion that is less dependent on model choice, opening the door for mechanistic social insect neuroscience. In this review, we revisit important aspects of what is known about the ant nervous system and behavior, and we look forward to how functional circuit neuroscience in ants will help us understand what distinguishes solitary animals from highly social ones.
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Affiliation(s)
- Dominic D Frank
- Laboratory of Social Evolution and Behavior, The Rockefeller University, New York, NY, USA; ,
| | - Daniel J C Kronauer
- Howard Hughes Medical Institute, New York, NY, USA
- Laboratory of Social Evolution and Behavior, The Rockefeller University, New York, NY, USA; ,
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2
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Ali MZ, Anushree, Ahsan A, Ola MS, Haque R, Ahsan J. Ionotropic receptors mediate olfactory learning and memory in Drosophila. INSECT SCIENCE 2024; 31:1249-1269. [PMID: 38114448 DOI: 10.1111/1744-7917.13308] [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: 07/12/2023] [Revised: 10/16/2023] [Accepted: 10/27/2023] [Indexed: 12/21/2023]
Abstract
Phenylacetaldehyde (PAH), an aromatic compound, is present in a diverse range of fruits including overripe bananas and prickly pear cactus, the two major host fruits for Drosophila melanogaster. PAH acts as a potent ligand for the ionotropic receptor 84a (IR84a) in the adult fruit fly and it is detected by the IR84a/IR8a heterotetrameric complex. Its role in the male courtship behavior through IR84a as an environmental aphrodisiac is of additional importance. In D. melanogaster, two distinct kinds of olfactory receptors, that is, odorant receptors (ORs) and ionotropic receptors (IRs), perceive the odorant stimuli. They display unique structural, molecular, and functional characteristics in addition to having different evolutionary origins. Traditionally, olfactory cues detected by the ORs such as ethyl acetate, 1-butanol, isoamyl acetate, 1-octanol, 4-methylcyclohexanol, etc. classified as aliphatic esters and alcohols have been employed in olfactory classical conditioning using fruit flies. This underlines the participation of OR-activated olfactory pathways in learning and memory formation. Our study elucidates that likewise ethyl acetate (EA) (an OR-responsive odorant), PAH (an IR-responsive aromatic compound) too can form learning and memory when associated with an appetitive gustatory reinforcer. The association of PAH with sucrose (PAH/SUC) led to learning and formation of the long-term memory (LTM). Additionally, the Orco1, Ir84aMI00501, and Ir8a1 mutant flies were used to confirm the exclusive participation of the IR84a/IR8a complex in PAH/SUC olfactory associative conditioning. These results highlight the involvement of IRs via an IR-activated pathway in facilitating robust olfactory behavior.
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Affiliation(s)
- Md Zeeshan Ali
- Department of Biotechnology, Central University of South Bihar, Gaya, Bihar, India
| | - Anushree
- Department of Biotechnology, Central University of South Bihar, Gaya, Bihar, India
| | - Aarif Ahsan
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, USA
| | - Mohammad Shamsul Ola
- Department of Biochemistry, College of Science, King Saud University, Riyadh, Saudi Arabia
| | - Rizwanul Haque
- Department of Biotechnology, Central University of South Bihar, Gaya, Bihar, India
| | - Jawaid Ahsan
- Department of Biotechnology, Central University of South Bihar, Gaya, Bihar, India
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3
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Zehtabian A, Fuchs J, Eickholt BJ, Ewers H. Automated Analysis of Neuronal Morphology in 2D Fluorescence Micrographs through an Unsupervised Semantic Segmentation of Neurons. Neuroscience 2024; 551:333-344. [PMID: 38838980 DOI: 10.1016/j.neuroscience.2024.05.024] [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: 12/19/2023] [Revised: 05/17/2024] [Accepted: 05/20/2024] [Indexed: 06/07/2024]
Abstract
Brain function emerges from a highly complex network of specialized cells that are interlinked by billions of synapses. The synaptic connectivity between neurons is established between the elongated processes of their axons and dendrites or, together, neurites. To establish these connections, cellular neurites have to grow in highly specialized, cell-type dependent patterns covering extensive distances and connecting with thousands of other neurons. The outgrowth and branching of neurites are tightly controlled during development and are a commonly used functional readout of imaging in the neurosciences. Manual analysis of neuronal morphology from microscopy images, however, is very time intensive and prone to bias. Most automated analyses of neurons rely on reconstruction of the neuron as a whole without a semantic analysis of each neurite. A fully-automated classification of all neurites still remains unavailable in open-source software. Here we present a standalone, GUI-based software for batch-quantification of neuronal morphology in two-dimensional fluorescence micrographs of cultured neurons with minimal requirements for user interaction. Single neurons are first reconstructed into binarized images using a Hessian-based segmentation algorithm to detect thin neurite structures combined with intensity- and shape-based reconstruction of the cell body. Neurites are then classified into axon, dendrites and their branches of increasing order using a geodesic distance transform of the cell skeleton. The software was benchmarked against a published dataset and reproduced the phenotype observed after manual annotation. Our tool promises accelerated and improved morphometric studies of neuronal morphology by allowing for consistent and automated analysis of large datasets.
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Affiliation(s)
- Amin Zehtabian
- Institute for Chemistry and Biochemistry, Freie Universität Berlin, Thielallee 63, 14195 Berlin, Germany.
| | - Joachim Fuchs
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Institute of Molecular Biology and Biochemistry, Virchowweg 6, 10117 Berlin, Germany
| | - Britta J Eickholt
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Institute of Molecular Biology and Biochemistry, Virchowweg 6, 10117 Berlin, Germany
| | - Helge Ewers
- Institute for Chemistry and Biochemistry, Freie Universität Berlin, Thielallee 63, 14195 Berlin, Germany
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4
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Thornton-Kolbe EM, Ahmed M, Gordon FR, Sieriebriennikov B, Williams DL, Kurmangaliyev YZ, Clowney EJ. Spatial constraints and cell surface molecule depletion structure a randomly connected learning circuit. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.17.603956. [PMID: 39071296 PMCID: PMC11275898 DOI: 10.1101/2024.07.17.603956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/30/2024]
Abstract
The brain can represent almost limitless objects to "categorize an unlabeled world" (Edelman, 1989). This feat is supported by expansion layer circuit architectures, in which neurons carrying information about discrete sensory channels make combinatorial connections onto much larger postsynaptic populations. Combinatorial connections in expansion layers are modeled as randomized sets. The extent to which randomized wiring exists in vivo is debated, and how combinatorial connectivity patterns are generated during development is not understood. Non-deterministic wiring algorithms could program such connectivity using minimal genomic information. Here, we investigate anatomic and transcriptional patterns and perturb partner availability to ask how Kenyon cells, the expansion layer neurons of the insect mushroom body, obtain combinatorial input from olfactory projection neurons. Olfactory projection neurons form their presynaptic outputs in an orderly, predictable, and biased fashion. We find that Kenyon cells accept spatially co-located but molecularly heterogeneous inputs from this orderly map, and ask how Kenyon cell surface molecule expression impacts partner choice. Cell surface immunoglobulins are broadly depleted in Kenyon cells, and we propose that this allows them to form connections with molecularly heterogeneous partners. This model can explain how developmentally identical neurons acquire diverse wiring identities.
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Affiliation(s)
- Emma M. Thornton-Kolbe
- Neurosciences Graduate Program, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Maria Ahmed
- Department of Molecular, Cellular, and Developmental Biology, University of Michigan, Ann Arbor, MI, USA
| | - Finley R. Gordon
- Department of Molecular, Cellular, and Developmental Biology, University of Michigan, Ann Arbor, MI, USA
| | | | - Donnell L. Williams
- Department of Molecular, Cellular, and Developmental Biology, University of Michigan, Ann Arbor, MI, USA
| | | | - E. Josephine Clowney
- Department of Molecular, Cellular, and Developmental Biology, University of Michigan, Ann Arbor, MI, USA
- Michigan Neuroscience Institute, Ann Arbor, MI, USA
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5
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Mobille Z, Sikandar UB, Sponberg S, Choi H. Temporal resolution of spike coding in feedforward networks with signal convergence and divergence. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.08.602598. [PMID: 39026834 PMCID: PMC11257569 DOI: 10.1101/2024.07.08.602598] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/20/2024]
Abstract
Convergent and divergent structures in the networks that make up biological brains are found universally across many species and brain regions at various scales. Neurons in these networks fire action potentials, or "spikes", whose precise timing is becoming increasingly appreciated as large sources of information about both sensory input and motor output. While previous theories on coding in convergent and divergent networks have largely neglected the role of precise spike timing, our model and analyses place this aspect at the forefront. For a suite of stimuli with different timescales, we demonstrate that structural bottlenecks (small groups of neurons) post-synaptic to network convergence have a stronger preference for spike timing codes than expansion layers created by structural divergence. Additionally, we found that a simple network model with similar convergence and divergence ratios to those found experimentally can reproduce the relative contribution of spike timing information about motor output in the hawkmoth Manduca sexta. Our simulations and analyses suggest a relationship between the level of convergent/divergent structure present in a feedforward network and the loss of stimulus information encoded by its population spike trains as their temporal resolution decreases, which could be confirmed experimentally across diverse neural systems in future studies. We further show that this relationship can be generalized across different models and measures, implying a potentially fundamental link between network structure and coding strategy using spikes.
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Affiliation(s)
- Zach Mobille
- School of Mathematics, Georgia Institute of Technology, Atlanta, GA 30332
- Quantitative Biosciences Program, Georgia Institute of Technology, Atlanta, GA 30332
| | - Usama Bin Sikandar
- School of Physics, Georgia Institute of Technology, Atlanta, GA 30332
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332
| | - Simon Sponberg
- Quantitative Biosciences Program, Georgia Institute of Technology, Atlanta, GA 30332
- School of Physics, Georgia Institute of Technology, Atlanta, GA 30332
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332
| | - Hannah Choi
- School of Mathematics, Georgia Institute of Technology, Atlanta, GA 30332
- Quantitative Biosciences Program, Georgia Institute of Technology, Atlanta, GA 30332
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6
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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. Nat Commun 2024; 15:5698. [PMID: 38972924 PMCID: PMC11228034 DOI: 10.1038/s41467-024-49616-z] [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: 01/24/2024] [Accepted: 06/11/2024] [Indexed: 07/09/2024] Open
Abstract
The arthropod mushroom body is well-studied as an expansion layer representing olfactory stimuli and linking them to contingent events. However, 8% of mushroom body Kenyon cells in Drosophila melanogaster receive predominantly visual input, and their function remains unclear. Here, we identify inputs to visual Kenyon cells using the FlyWire adult whole-brain connectome. Input repertoires are similar across hemispheres and connectomes with certain inputs highly overrepresented. Many visual neurons presynaptic to Kenyon cells have large receptive fields, while interneuron inputs receive spatially restricted signals that may be tuned to specific visual features. Individual visual Kenyon cells randomly sample sparse inputs from combinations of visual channels, including multiple optic lobe neuropils. These connectivity patterns suggest that visual coding in the mushroom body, like olfactory coding, is sparse, distributed, and combinatorial. However, the specific input repertoire to the smaller population of visual Kenyon cells suggests a constrained encoding of visual stimuli.
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Affiliation(s)
- Ishani Ganguly
- Department of Neuroscience, Columbia University, New York, NY, USA
- Center for Theoretical Neuroscience, Columbia University, New York, NY, USA
- Zuckerman Institute, Columbia University, New York, NY, USA
| | - Emily L Heckman
- Department of Molecular, Cellular, and Developmental Biology, University of Michigan, Ann Arbor, MI, USA
| | - Ashok Litwin-Kumar
- Department of Neuroscience, Columbia University, New York, NY, USA
- Center for Theoretical Neuroscience, Columbia University, New York, NY, USA
- Zuckerman Institute, Columbia University, New York, NY, USA
| | - E Josephine Clowney
- Department of Molecular, Cellular, and Developmental Biology, University of Michigan, Ann Arbor, MI, USA.
- Michigan Neuroscience Institute, University of Michigan, Ann Arbor, MI, USA.
| | - Rudy Behnia
- Department of Neuroscience, Columbia University, New York, NY, USA.
- Zuckerman Institute, Columbia University, New York, NY, USA.
- Kavli Institute for Brain Science, Columbia University, New York, NY, USA.
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7
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Fulton KA, Zimmerman D, Samuel A, Vogt K, Datta SR. Common principles for odour coding across vertebrates and invertebrates. Nat Rev Neurosci 2024; 25:453-472. [PMID: 38806946 DOI: 10.1038/s41583-024-00822-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/30/2024] [Indexed: 05/30/2024]
Abstract
The olfactory system is an ideal and tractable system for exploring how the brain transforms sensory inputs into behaviour. The basic tasks of any olfactory system include odour detection, discrimination and categorization. The challenge for the olfactory system is to transform the high-dimensional space of olfactory stimuli into the much smaller space of perceived objects and valence that endows odours with meaning. Our current understanding of how neural circuits address this challenge has come primarily from observations of the mechanisms of the brain for processing other sensory modalities, such as vision and hearing, in which optimized deep hierarchical circuits are used to extract sensory features that vary along continuous physical dimensions. The olfactory system, by contrast, contends with an ill-defined, high-dimensional stimulus space and discrete stimuli using a circuit architecture that is shallow and parallelized. Here, we present recent observations in vertebrate and invertebrate systems that relate the statistical structure and state-dependent modulation of olfactory codes to mechanisms of perception and odour-guided behaviour.
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Affiliation(s)
- Kara A Fulton
- Department of Neuroscience, Harvard Medical School, Boston, MA, USA
| | - David Zimmerman
- Department of Physics, Harvard University, Cambridge, MA, USA
| | - Aravi Samuel
- Department of Physics, Harvard University, Cambridge, MA, USA
| | - Katrin Vogt
- Department of Physics, Harvard University, Cambridge, MA, USA.
- Department of Biology, University of Konstanz, Konstanz, Germany.
- Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Konstanz, Germany.
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8
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Xu L, Jiang HB, Yu JL, Wang JJ. Plasticity of the olfactory behaviors in Bactrocera dorsalis under various physiological states and environmental conditions. CURRENT OPINION IN INSECT SCIENCE 2024; 63:101196. [PMID: 38555081 DOI: 10.1016/j.cois.2024.101196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 03/24/2024] [Accepted: 03/25/2024] [Indexed: 04/02/2024]
Abstract
Insects rely heavily on their olfactory system for various behaviors, including foraging, mating, and oviposition. Numerous studies have demonstrated that insects can adjust their olfactory behaviors in response to different physiological states and environmental conditions. This flexibility allows them to perceive and process odorants according to different conditions. The Oriental fruit fly, Bactrocera dorsalis, is a highly destructive and invasive pest causing significant economic losses to fruit and vegetable crops worldwide. The olfactory behavior of B. dorsalis exhibits strong plasticity, resulting in its successful invasion. To enhance our understanding of B. dorsalis' olfactory behavior and explore potential strategies for behavior control, we have reviewed recent literature on its olfactory plasticity and potential molecular mechanisms.
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Affiliation(s)
- Li Xu
- Key Laboratory of Entomology and Pest Control Engineering, College of Plant Protection, Southwest University, Chongqing 400715, China; Key Laboratory of Agricultural Biosafety and Green Production of Upper Yangtze River (Ministry of Education), Academy of Agricultural Sciences, Southwest University, Chongqing 400715, China
| | - Hong-Bo Jiang
- Key Laboratory of Entomology and Pest Control Engineering, College of Plant Protection, Southwest University, Chongqing 400715, China; Key Laboratory of Agricultural Biosafety and Green Production of Upper Yangtze River (Ministry of Education), Academy of Agricultural Sciences, Southwest University, Chongqing 400715, China
| | - Jie-Ling Yu
- Key Laboratory of Entomology and Pest Control Engineering, College of Plant Protection, Southwest University, Chongqing 400715, China; Key Laboratory of Agricultural Biosafety and Green Production of Upper Yangtze River (Ministry of Education), Academy of Agricultural Sciences, Southwest University, Chongqing 400715, China
| | - Jin-Jun Wang
- Key Laboratory of Entomology and Pest Control Engineering, College of Plant Protection, Southwest University, Chongqing 400715, China; Key Laboratory of Agricultural Biosafety and Green Production of Upper Yangtze River (Ministry of Education), Academy of Agricultural Sciences, Southwest University, Chongqing 400715, China.
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9
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Puri P, Wu ST, Su CY, Aljadeff J. Peripheral preprocessing in Drosophila facilitates odor classification. Proc Natl Acad Sci U S A 2024; 121:e2316799121. [PMID: 38753511 PMCID: PMC11126917 DOI: 10.1073/pnas.2316799121] [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: 10/01/2023] [Accepted: 04/16/2024] [Indexed: 05/18/2024] Open
Abstract
The mammalian brain implements sophisticated sensory processing algorithms along multilayered ("deep") neural networks. Strategies that insects use to meet similar computational demands, while relying on smaller nervous systems with shallow architectures, remain elusive. Using Drosophila as a model, we uncover the algorithmic role of odor preprocessing by a shallow network of compartmentalized olfactory receptor neurons. Each compartment operates as a ratiometric unit for specific odor-mixtures. This computation arises from a simple mechanism: electrical coupling between two differently sized neurons. We demonstrate that downstream synaptic connectivity is shaped to optimally leverage amplification of a hedonic value signal in the periphery. Furthermore, peripheral preprocessing is shown to markedly improve novel odor classification in a higher brain center. Together, our work highlights a far-reaching functional role of the sensory periphery for downstream processing. By elucidating the implementation of powerful computations by a shallow network, we provide insights into general principles of efficient sensory processing algorithms.
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Affiliation(s)
- Palka Puri
- Department of Physics, University of California, San Diego, La Jolla, CA92093
| | - Shiuan-Tze Wu
- Department of Neurobiology, University of California, San Diego, La Jolla, CA92093
| | - Chih-Ying Su
- Department of Neurobiology, University of California, San Diego, La Jolla, CA92093
| | - Johnatan Aljadeff
- Department of Neurobiology, University of California, San Diego, La Jolla, CA92093
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10
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Eckstein N, Bates AS, Champion A, Du M, Yin Y, Schlegel P, Lu AKY, Rymer T, Finley-May S, Paterson T, Parekh R, Dorkenwald S, Matsliah A, Yu SC, McKellar C, Sterling A, Eichler K, Costa M, Seung S, Murthy M, Hartenstein V, Jefferis GSXE, Funke J. Neurotransmitter classification from electron microscopy images at synaptic sites in Drosophila melanogaster. Cell 2024; 187:2574-2594.e23. [PMID: 38729112 PMCID: PMC11106717 DOI: 10.1016/j.cell.2024.03.016] [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: 04/02/2023] [Revised: 10/04/2023] [Accepted: 03/13/2024] [Indexed: 05/12/2024]
Abstract
High-resolution electron microscopy of nervous systems has enabled the reconstruction of synaptic connectomes. However, we do not know the synaptic sign for each connection (i.e., whether a connection is excitatory or inhibitory), which is implied by the released transmitter. We demonstrate that artificial neural networks can predict transmitter types for presynapses from electron micrographs: a network trained to predict six transmitters (acetylcholine, glutamate, GABA, serotonin, dopamine, octopamine) achieves an accuracy of 87% for individual synapses, 94% for neurons, and 91% for known cell types across a D. melanogaster whole brain. We visualize the ultrastructural features used for prediction, discovering subtle but significant differences between transmitter phenotypes. We also analyze transmitter distributions across the brain and find that neurons that develop together largely express only one fast-acting transmitter (acetylcholine, glutamate, or GABA). We hope that our publicly available predictions act as an accelerant for neuroscientific hypothesis generation for the fly.
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Affiliation(s)
- Nils Eckstein
- HHMI Janelia Research Campus, Ashburn, VA, USA; Institute of Neuroinformatics UZH/ETHZ, Zurich, Switzerland
| | - Alexander Shakeel Bates
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK; Centre for Neural Circuits and Behaviour, The University of Oxford, Tinsley Building, Mansfield Road, Oxford OX1 3SR, UK; Department of Neurobiology and Howard Hughes Medical Institute, Harvard Medical School, Boston, MA, USA
| | - Andrew Champion
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Michelle Du
- HHMI Janelia Research Campus, Ashburn, VA, USA
| | - Yijie Yin
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Philipp Schlegel
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK; Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | | | | | | | | | | | - Sven Dorkenwald
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Arie Matsliah
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Szi-Chieh Yu
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Claire McKellar
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Amy Sterling
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Katharina Eichler
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Marta Costa
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Sebastian Seung
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Mala Murthy
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Volker Hartenstein
- Department of Molecular Cell and Developmental Biology, University of California, Los Angeles, Los Angeles, CA, 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.
| | - Jan Funke
- HHMI Janelia Research Campus, Ashburn, VA, USA.
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11
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Parnas M, Manoim JE, Lin AC. Sensory encoding and memory in the mushroom body: signals, noise, and variability. Learn Mem 2024; 31:a053825. [PMID: 38862174 PMCID: PMC11199953 DOI: 10.1101/lm.053825.123] [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: 09/10/2023] [Accepted: 11/21/2023] [Indexed: 06/13/2024]
Abstract
To survive in changing environments, animals need to learn to associate specific sensory stimuli with positive or negative valence. How do they form stimulus-specific memories to distinguish between positively/negatively associated stimuli and other irrelevant stimuli? Solving this task is one of the functions of the mushroom body, the associative memory center in insect brains. Here we summarize recent work on sensory encoding and memory in the Drosophila mushroom body, highlighting general principles such as pattern separation, sparse coding, noise and variability, coincidence detection, and spatially localized neuromodulation, and placing the mushroom body in comparative perspective with mammalian memory systems.
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Affiliation(s)
- Moshe Parnas
- Department of Physiology and Pharmacology, Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 69978, Israel
| | - Julia E Manoim
- Department of Physiology and Pharmacology, Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel
| | - Andrew C Lin
- School of Biosciences, University of Sheffield, Sheffield S10 2TN, United Kingdom
- Neuroscience Institute, University of Sheffield, Sheffield S10 2TN, United Kingdom
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12
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Sen E, El-Keredy A, Jacob N, Mancini N, Asnaz G, Widmann A, Gerber B, Thoener J. Cognitive limits of larval Drosophila: testing for conditioned inhibition, sensory preconditioning, and second-order conditioning. Learn Mem 2024; 31:a053726. [PMID: 38862170 PMCID: PMC11199949 DOI: 10.1101/lm.053726.122] [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: 08/21/2023] [Accepted: 01/18/2024] [Indexed: 06/13/2024]
Abstract
Drosophila larvae are an established model system for studying the mechanisms of innate and simple forms of learned behavior. They have about 10 times fewer neurons than adult flies, and it was the low total number of their neurons that allowed for an electron microscopic reconstruction of their brain at synaptic resolution. Regarding the mushroom body, a central brain structure for many forms of associative learning in insects, it turned out that more than half of the classes of synaptic connection had previously escaped attention. Understanding the function of these circuit motifs, subsequently confirmed in adult flies, is an important current research topic. In this context, we test larval Drosophila for their cognitive abilities in three tasks that are characteristically more complex than those previously studied. Our data provide evidence for (i) conditioned inhibition, as has previously been reported for adult flies and honeybees. Unlike what is described for adult flies and honeybees, however, our data do not provide evidence for (ii) sensory preconditioning or (iii) second-order conditioning in Drosophila larvae. We discuss the methodological features of our experiments as well as four specific aspects of the organization of the larval brain that may explain why these two forms of learning are observed in adult flies and honeybees, but not in larval Drosophila.
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Affiliation(s)
- Edanur Sen
- Department of Genetics, Leibniz Institute for Neurobiology, 39118 Magdeburg, Germany
| | - Amira El-Keredy
- Department of Genetics, Leibniz Institute for Neurobiology, 39118 Magdeburg, Germany
- Department of Genetics, Faculty of Agriculture, Tanta University, 31111 Tanta, Egypt
| | - Nina Jacob
- Department of Genetics, Leibniz Institute for Neurobiology, 39118 Magdeburg, Germany
| | - Nino Mancini
- Department of Genetics, Leibniz Institute for Neurobiology, 39118 Magdeburg, Germany
| | - Gülüm Asnaz
- Department of Molecular Neurobiology of Behavior, University of Göttingen, 37077 Göttingen, Germany
| | - Annekathrin Widmann
- Department of Molecular Neurobiology of Behavior, University of Göttingen, 37077 Göttingen, Germany
| | - Bertram Gerber
- Department of Genetics, Leibniz Institute for Neurobiology, 39118 Magdeburg, Germany
- Otto von Guericke University Magdeburg, Institute of Biology, 39106 Magdeburg, Germany
- Center for Behavioral Brain Sciences, 39106 Magdeburg, Germany
| | - Juliane Thoener
- Department of Genetics, Leibniz Institute for Neurobiology, 39118 Magdeburg, Germany
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13
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Lillvis JL, Wang K, Shiozaki HM, Xu M, Stern DL, Dickson BJ. Nested neural circuits generate distinct acoustic signals during Drosophila courtship. Curr Biol 2024; 34:808-824.e6. [PMID: 38295797 DOI: 10.1016/j.cub.2024.01.015] [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: 10/03/2023] [Revised: 01/02/2024] [Accepted: 01/08/2024] [Indexed: 02/29/2024]
Abstract
Many motor control systems generate multiple movements using a common set of muscles. How are premotor circuits able to flexibly generate diverse movement patterns? Here, we characterize the neuronal circuits that drive the distinct courtship songs of Drosophila melanogaster. Male flies vibrate their wings toward females to produce two different song modes-pulse and sine song-which signal species identity and male quality. Using cell-type-specific genetic reagents and the connectome, we provide a cellular and synaptic map of the circuits in the male ventral nerve cord that generate these songs and examine how activating or inhibiting each cell type within these circuits affects the song. Our data reveal that the song circuit is organized into two nested feedforward pathways with extensive reciprocal and feedback connections. The larger network produces pulse song, the more complex and ancestral song form. A subset of this network produces sine song, the simpler and more recent form. Such nested organization may be a common feature of motor control circuits in which evolution has layered increasing flexibility onto a basic movement pattern.
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Affiliation(s)
- Joshua L Lillvis
- Janelia Research Campus, Howard Hughes Medical Institute, 19700 Helix Dr., Ashburn, VA 20147, USA.
| | - Kaiyu Wang
- Janelia Research Campus, Howard Hughes Medical Institute, 19700 Helix Dr., Ashburn, VA 20147, USA; Lingang Laboratory, Shanghai Center for Brain Science and Brain-Inspired Intelligence Technology, Shanghai 201602, China
| | - Hiroshi M Shiozaki
- Janelia Research Campus, Howard Hughes Medical Institute, 19700 Helix Dr., Ashburn, VA 20147, USA
| | - Min Xu
- Janelia Research Campus, Howard Hughes Medical Institute, 19700 Helix Dr., Ashburn, VA 20147, USA
| | - David L Stern
- Janelia Research Campus, Howard Hughes Medical Institute, 19700 Helix Dr., Ashburn, VA 20147, USA
| | - Barry J Dickson
- Janelia Research Campus, Howard Hughes Medical Institute, 19700 Helix Dr., Ashburn, VA 20147, USA; Queensland Brain Institute, University of Queensland, St. Lucia, QLD 4067, Australia.
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14
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Dorkenwald S, Li PH, Januszewski M, Berger DR, Maitin-Shepard J, Bodor AL, Collman F, Schneider-Mizell CM, da Costa NM, Lichtman JW, Jain V. Multi-layered maps of neuropil with segmentation-guided contrastive learning. Nat Methods 2023; 20:2011-2020. [PMID: 37985712 PMCID: PMC10703674 DOI: 10.1038/s41592-023-02059-8] [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: 11/18/2022] [Accepted: 10/02/2023] [Indexed: 11/22/2023]
Abstract
Maps of the nervous system that identify individual cells along with their type, subcellular components and connectivity have the potential to elucidate fundamental organizational principles of neural circuits. Nanometer-resolution imaging of brain tissue provides the necessary raw data, but inferring cellular and subcellular annotation layers is challenging. We present segmentation-guided contrastive learning of representations (SegCLR), a self-supervised machine learning technique that produces representations of cells directly from 3D imagery and segmentations. When applied to volumes of human and mouse cortex, SegCLR enables accurate classification of cellular subcompartments and achieves performance equivalent to a supervised approach while requiring 400-fold fewer labeled examples. SegCLR also enables inference of cell types from fragments as small as 10 μm, which enhances the utility of volumes in which many neurites are truncated at boundaries. Finally, SegCLR enables exploration of layer 5 pyramidal cell subtypes and automated large-scale analysis of synaptic partners in mouse visual cortex.
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Affiliation(s)
- Sven Dorkenwald
- Google Research, Mountain View, CA, USA
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Computer Science Department, Princeton University, Princeton, NJ, USA
| | | | | | - Daniel R Berger
- Department of Molecular and Cellular Biology, Center for Brain Science, Harvard, Cambridge, MA, USA
| | | | | | | | | | | | - Jeff W Lichtman
- Department of Molecular and Cellular Biology, Center for Brain Science, Harvard, Cambridge, MA, USA
| | - Viren Jain
- Google Research, Mountain View, CA, USA.
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15
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Jovanoski KD, Duquenoy L, Mitchell J, Kapoor I, Treiber CD, Croset V, Dempsey G, Parepalli S, Cognigni P, Otto N, Felsenberg J, Waddell S. Dopaminergic systems create reward seeking despite adverse consequences. Nature 2023; 623:356-365. [PMID: 37880370 PMCID: PMC10632144 DOI: 10.1038/s41586-023-06671-8] [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: 07/04/2022] [Accepted: 09/22/2023] [Indexed: 10/27/2023]
Abstract
Resource-seeking behaviours are ordinarily constrained by physiological needs and threats of danger, and the loss of these controls is associated with pathological reward seeking1. Although dysfunction of the dopaminergic valuation system of the brain is known to contribute towards unconstrained reward seeking2,3, the underlying reasons for this behaviour are unclear. Here we describe dopaminergic neural mechanisms that produce reward seeking despite adverse consequences in Drosophila melanogaster. Odours paired with optogenetic activation of a defined subset of reward-encoding dopaminergic neurons become cues that starved flies seek while neglecting food and enduring electric shock punishment. Unconstrained seeking of reward is not observed after learning with sugar or synthetic engagement of other dopaminergic neuron populations. Antagonism between reward-encoding and punishment-encoding dopaminergic neurons accounts for the perseverance of reward seeking despite punishment, whereas synthetic engagement of the reward-encoding dopaminergic neurons also impairs the ordinary need-dependent dopaminergic valuation of available food. Connectome analyses reveal that the population of reward-encoding dopaminergic neurons receives highly heterogeneous input, consistent with parallel representation of diverse rewards, and recordings demonstrate state-specific gating and satiety-related signals. We propose that a similar dopaminergic valuation system dysfunction is likely to contribute to maladaptive seeking of rewards by mammals.
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Affiliation(s)
| | - Lucille Duquenoy
- Centre for Neural Circuits and Behaviour, University of Oxford, Oxford, UK
| | - Jessica Mitchell
- Centre for Neural Circuits and Behaviour, University of Oxford, Oxford, UK
- Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland
| | - Ishaan Kapoor
- Centre for Neural Circuits and Behaviour, University of Oxford, Oxford, UK
| | | | - Vincent Croset
- Centre for Neural Circuits and Behaviour, University of Oxford, Oxford, UK
- Department of Biosciences, Durham University, Durham, UK
| | - Georgia Dempsey
- Centre for Neural Circuits and Behaviour, University of Oxford, Oxford, UK
| | - Sai Parepalli
- Centre for Neural Circuits and Behaviour, University of Oxford, Oxford, UK
| | - Paola Cognigni
- Centre for Neural Circuits and Behaviour, University of Oxford, Oxford, UK
- Northern Medical Physics and Clinical Engineering, Newcastle upon Tyne Hospitals NHS Trust, Newcastle upon Tyne, UK
| | - Nils Otto
- Centre for Neural Circuits and Behaviour, University of Oxford, Oxford, UK
- Institute of Anatomy and Molecular Neurobiology, Westfälische Wilhelms-University, Münster, Germany
| | - Johannes Felsenberg
- Centre for Neural Circuits and Behaviour, University of Oxford, Oxford, UK
- Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland
| | - Scott Waddell
- Centre for Neural Circuits and Behaviour, University of Oxford, Oxford, UK.
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16
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Tao L, Wechsler SP, Bhandawat V. Sensorimotor transformation underlying odor-modulated locomotion in walking Drosophila. Nat Commun 2023; 14:6818. [PMID: 37884581 PMCID: PMC10603174 DOI: 10.1038/s41467-023-42613-8] [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: 05/09/2022] [Accepted: 10/17/2023] [Indexed: 10/28/2023] Open
Abstract
Most real-world behaviors - such as odor-guided locomotion - are performed with incomplete information. Activity in olfactory receptor neuron (ORN) classes provides information about odor identity but not the location of its source. In this study, we investigate the sensorimotor transformation that relates ORN activation to locomotion changes in Drosophila by optogenetically activating different combinations of ORN classes and measuring the resulting changes in locomotion. Three features describe this sensorimotor transformation: First, locomotion depends on both the instantaneous firing frequency (f) and its change (df); the two together serve as a short-term memory that allows the fly to adapt its motor program to sensory context automatically. Second, the mapping between (f, df) and locomotor parameters such as speed or curvature is distinct for each pattern of activated ORNs. Finally, the sensorimotor mapping changes with time after odor exposure, allowing information integration over a longer timescale.
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Affiliation(s)
- Liangyu Tao
- School of Biomedical Engineering and Health Sciences, Drexel University, Philadelphia, PA, USA
| | - Samuel P Wechsler
- School of Biomedical Engineering and Health Sciences, Drexel University, Philadelphia, PA, USA
- Department of Neurobiology and Anatomy, Drexel University, Philadelphia, PA, USA
| | - Vikas Bhandawat
- School of Biomedical Engineering and Health Sciences, Drexel University, Philadelphia, PA, USA.
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17
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Muscinelli SP, Wagner MJ, Litwin-Kumar A. Optimal routing to cerebellum-like structures. Nat Neurosci 2023; 26:1630-1641. [PMID: 37604889 PMCID: PMC10506727 DOI: 10.1038/s41593-023-01403-7] [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: 03/28/2022] [Accepted: 07/12/2023] [Indexed: 08/23/2023]
Abstract
The vast expansion from mossy fibers to cerebellar granule cells (GrC) produces a neural representation that supports functions including associative and internal model learning. This motif is shared by other cerebellum-like structures and has inspired numerous theoretical models. Less attention has been paid to structures immediately presynaptic to GrC layers, whose architecture can be described as a 'bottleneck' and whose function is not understood. We therefore develop a theory of cerebellum-like structures in conjunction with their afferent pathways that predicts the role of the pontine relay to cerebellum and the glomerular organization of the insect antennal lobe. We highlight a new computational distinction between clustered and distributed neuronal representations that is reflected in the anatomy of these two brain structures. Our theory also reconciles recent observations of correlated GrC activity with theories of nonlinear mixing. More generally, it shows that structured compression followed by random expansion is an efficient architecture for flexible computation.
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Affiliation(s)
- Samuel P Muscinelli
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, NY, USA.
| | - Mark J Wagner
- National Institute of Neurological Disorders and Stroke, NIH, Bethesda, MD, USA
| | - Ashok Litwin-Kumar
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, NY, USA.
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18
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Sizemore TR, Jonaitis J, Dacks AM. Heterogeneous receptor expression underlies non-uniform peptidergic modulation of olfaction in Drosophila. Nat Commun 2023; 14:5280. [PMID: 37644052 PMCID: PMC10465596 DOI: 10.1038/s41467-023-41012-3] [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: 02/02/2023] [Accepted: 08/21/2023] [Indexed: 08/31/2023] Open
Abstract
Sensory systems are dynamically adjusted according to the animal's ongoing needs by neuromodulators, such as neuropeptides. Neuropeptides are often widely-distributed throughout sensory networks, but it is unclear whether such neuropeptides uniformly modulate network activity. Here, we leverage the Drosophila antennal lobe (AL) to resolve whether myoinhibitory peptide (MIP) uniformly modulates AL processing. Despite being uniformly distributed across the AL, MIP decreases olfactory input to some glomeruli, while increasing olfactory input to other glomeruli. We reveal that a heterogeneous ensemble of local interneurons (LNs) are the sole source of AL MIP, and show that differential expression of the inhibitory MIP receptor across glomeruli allows MIP to act on distinct intraglomerular substrates. Our findings demonstrate how even a seemingly simple case of modulation can have complex consequences on network processing by acting non-uniformly within different components of the overall network.
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Affiliation(s)
- Tyler R Sizemore
- Department of Biology, Life Sciences Building, West Virginia University, Morgantown, WV, 26506, USA.
- Department of Molecular, Cellular, and Developmental Biology, Yale Science Building, Yale University, New Haven, CT, 06520-8103, USA.
| | - Julius Jonaitis
- Department of Biology, Life Sciences Building, West Virginia University, Morgantown, WV, 26506, USA
| | - Andrew M Dacks
- Department of Biology, Life Sciences Building, West Virginia University, Morgantown, WV, 26506, USA.
- Department of Neuroscience, West Virginia University, Morgantown, WV, 26506, USA.
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19
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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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20
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Ahmed M, Rajagopalan AE, Pan Y, Li Y, Williams DL, Pedersen EA, Thakral M, Previero A, Close KC, Christoforou CP, Cai D, Turner GC, Clowney EJ. Input density tunes Kenyon cell sensory responses in the Drosophila mushroom body. Curr Biol 2023; 33:2742-2760.e12. [PMID: 37348501 PMCID: PMC10529417 DOI: 10.1016/j.cub.2023.05.064] [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: 02/05/2023] [Revised: 05/02/2023] [Accepted: 05/26/2023] [Indexed: 06/24/2023]
Abstract
The ability to discriminate sensory stimuli with overlapping features is thought to arise in brain structures called expansion layers, where neurons carrying information about sensory features make combinatorial connections onto a much larger set of cells. For 50 years, expansion coding has been a prime topic of theoretical neuroscience, which seeks to explain how quantitative parameters of the expansion circuit influence sensory sensitivity, discrimination, and generalization. Here, we investigate the developmental events that produce the quantitative parameters of the arthropod expansion layer, called the mushroom body. Using Drosophila melanogaster as a model, we employ genetic and chemical tools to engineer changes to circuit development. These allow us to produce living animals with hypothesis-driven variations on natural expansion layer wiring parameters. We then test the functional and behavioral consequences. By altering the number of expansion layer neurons (Kenyon cells) and their dendritic complexity, we find that input density, but not cell number, tunes neuronal odor selectivity. Simple odor discrimination behavior is maintained when the Kenyon cell number is reduced and augmented by Kenyon cell number expansion. Animals with increased input density to each Kenyon cell show increased overlap in Kenyon cell odor responses and become worse at odor discrimination tasks.
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Affiliation(s)
- Maria Ahmed
- Department of Molecular, Cellular and Developmental Biology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Adithya E Rajagopalan
- Howard Hughes Medical Institute, Janelia Research Campus, Ashburn, VA 20147, USA; The Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Yijie Pan
- Department of Molecular, Cellular and Developmental Biology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Ye Li
- Department of Cell and Developmental Biology, University of Michigan, Ann Arbor, MI 48104, USA
| | - Donnell L Williams
- Department of Molecular, Cellular and Developmental Biology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Erik A Pedersen
- Department of Molecular, Cellular and Developmental Biology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Manav Thakral
- Department of Molecular, Cellular and Developmental Biology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Angelica Previero
- Department of Molecular, Cellular and Developmental Biology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Kari C Close
- Howard Hughes Medical Institute, Janelia Research Campus, Ashburn, VA 20147, USA
| | | | - Dawen Cai
- Department of Cell and Developmental Biology, University of Michigan, Ann Arbor, MI 48104, USA; Biophysics LS&A, University of Michigan, Ann Arbor, MI 48109, USA; Michigan Neuroscience Institute Affiliate, University of Michigan, Ann Arbor, MI 48109, USA
| | - Glenn C Turner
- Howard Hughes Medical Institute, Janelia Research Campus, Ashburn, VA 20147, USA
| | - E Josephine Clowney
- Department of Molecular, Cellular and Developmental Biology, University of Michigan, Ann Arbor, MI 48109, USA; Michigan Neuroscience Institute Affiliate, University of Michigan, Ann Arbor, MI 48109, USA.
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21
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Steele TJ, Lanz AJ, Nagel KI. Olfactory navigation in arthropods. J Comp Physiol A Neuroethol Sens Neural Behav Physiol 2023; 209:467-488. [PMID: 36658447 PMCID: PMC10354148 DOI: 10.1007/s00359-022-01611-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2022] [Revised: 12/26/2022] [Accepted: 12/31/2022] [Indexed: 01/21/2023]
Abstract
Using odors to find food and mates is one of the most ancient and highly conserved behaviors. Arthropods from flies to moths to crabs use broadly similar strategies to navigate toward odor sources-such as integrating flow information with odor information, comparing odor concentration across sensors, and integrating odor information over time. Because arthropods share many homologous brain structures-antennal lobes for processing olfactory information, mechanosensors for processing flow, mushroom bodies (or hemi-ellipsoid bodies) for associative learning, and central complexes for navigation, it is likely that these closely related behaviors are mediated by conserved neural circuits. However, differences in the types of odors they seek, the physics of odor dispersal, and the physics of locomotion in water, air, and on substrates mean that these circuits must have adapted to generate a wide diversity of odor-seeking behaviors. In this review, we discuss common strategies and specializations observed in olfactory navigation behavior across arthropods, and review our current knowledge about the neural circuits subserving this behavior. We propose that a comparative study of arthropod nervous systems may provide insight into how a set of basic circuit structures has diversified to generate behavior adapted to different environments.
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Affiliation(s)
- Theresa J Steele
- Neuroscience Institute, NYU School of Medicine, 435 E 30th St., New York, NY, 10016, USA
| | - Aaron J Lanz
- Neuroscience Institute, NYU School of Medicine, 435 E 30th St., New York, NY, 10016, USA
| | - Katherine I Nagel
- Neuroscience Institute, NYU School of Medicine, 435 E 30th St., New York, NY, 10016, USA.
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22
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Singh P, Goyal S, Gupta S, Garg S, Tiwari A, Rajput V, Bates AS, Gupta AK, Gupta N. Combinatorial encoding of odors in the mosquito antennal lobe. Nat Commun 2023; 14:3539. [PMID: 37322224 PMCID: PMC10272161 DOI: 10.1038/s41467-023-39303-w] [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: 06/10/2022] [Accepted: 06/06/2023] [Indexed: 06/17/2023] Open
Abstract
Among the cues that a mosquito uses to find a host for blood-feeding, the smell of the host plays an important role. Previous studies have shown that host odors contain hundreds of chemical odorants, which are detected by different receptors on the peripheral sensory organs of mosquitoes. But how individual odorants are encoded by downstream neurons in the mosquito brain is not known. We developed an in vivo preparation for patch-clamp electrophysiology to record from projection neurons and local neurons in the antennal lobe of Aedes aegypti. Combining intracellular recordings with dye-fills, morphological reconstructions, and immunohistochemistry, we identify different sub-classes of antennal lobe neurons and their putative interactions. Our recordings show that an odorant can activate multiple neurons innervating different glomeruli, and that the stimulus identity and its behavioral preference are represented in the population activity of the projection neurons. Our results provide a detailed description of the second-order olfactory neurons in the central nervous system of mosquitoes and lay a foundation for understanding the neural basis of their olfactory behaviors.
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Affiliation(s)
- Pranjul Singh
- Department of Biological Sciences and Bioengineering, Indian Institute of Technology Kanpur, Kanpur, Uttar Pradesh, 208016, India
| | - Shefali Goyal
- Department of Biological Sciences and Bioengineering, Indian Institute of Technology Kanpur, Kanpur, Uttar Pradesh, 208016, India
| | - Smith Gupta
- Department of Biological Sciences and Bioengineering, Indian Institute of Technology Kanpur, Kanpur, Uttar Pradesh, 208016, India
| | - Sanket Garg
- Department of Biological Sciences and Bioengineering, Indian Institute of Technology Kanpur, Kanpur, Uttar Pradesh, 208016, India
- Department of Economic Sciences, Indian Institute of Technology Kanpur, Kanpur, Uttar Pradesh, 208016, India
| | - Abhinav Tiwari
- Department of Biological Sciences and Bioengineering, Indian Institute of Technology Kanpur, Kanpur, Uttar Pradesh, 208016, India
| | - Varad Rajput
- Department of Biological Sciences and Bioengineering, Indian Institute of Technology Kanpur, Kanpur, Uttar Pradesh, 208016, India
| | - Alexander Shakeel Bates
- Department of Neurobiology and Howard Hughes Medical Institute, Harvard Medical School, Boston, MA, USA
| | - Arjit Kant Gupta
- Department of Biological Sciences and Bioengineering, Indian Institute of Technology Kanpur, Kanpur, Uttar Pradesh, 208016, India
| | - Nitin Gupta
- Department of Biological Sciences and Bioengineering, Indian Institute of Technology Kanpur, Kanpur, Uttar Pradesh, 208016, India.
- Mehta Family Center for Engineering in Medicine, Indian Institute of Technology Kanpur, Kanpur, Uttar Pradesh, 208016, India.
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23
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Taisz I, Donà E, Münch D, Bailey SN, Morris BJ, Meechan KI, Stevens KM, Varela-Martínez I, Gkantia M, Schlegel P, Ribeiro C, Jefferis GSXE, Galili DS. Generating parallel representations of position and identity in the olfactory system. Cell 2023; 186:2556-2573.e22. [PMID: 37236194 PMCID: PMC10403364 DOI: 10.1016/j.cell.2023.04.038] [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: 05/27/2022] [Revised: 12/07/2022] [Accepted: 04/28/2023] [Indexed: 05/28/2023]
Abstract
In Drosophila, a dedicated olfactory channel senses a male pheromone, cis-vaccenyl acetate (cVA), promoting female courtship while repelling males. Here, we show that separate cVA-processing streams extract qualitative and positional information. cVA sensory neurons respond to concentration differences in a 5-mm range around a male. Second-order projection neurons encode the angular position of a male by detecting inter-antennal differences in cVA concentration, which are amplified through contralateral inhibition. At the third circuit layer, we identify 47 cell types with diverse input-output connectivity. One population responds tonically to male flies, a second is tuned to olfactory looming, while a third integrates cVA and taste to coincidentally promote female mating. The separation of olfactory features resembles the mammalian what and where visual streams; together with multisensory integration, this enables behavioral responses appropriate to specific ethological contexts.
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Affiliation(s)
- István Taisz
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK
| | - Erika Donà
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK
| | | | | | - Billy J Morris
- Department of Zoology, University of Cambridge, Cambridge, UK
| | | | - Katie M Stevens
- Department of Zoology, University of Cambridge, Cambridge, UK
| | | | - Marina Gkantia
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK
| | - Philipp Schlegel
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK; Department of Zoology, University of Cambridge, Cambridge, UK
| | | | - Gregory S X E Jefferis
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK; Department of Zoology, University of Cambridge, Cambridge, UK.
| | - Dana S Galili
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK.
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24
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Temporal control of neuronal wiring. Semin Cell Dev Biol 2023; 142:81-90. [PMID: 35644877 DOI: 10.1016/j.semcdb.2022.05.012] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 05/13/2022] [Accepted: 05/16/2022] [Indexed: 12/22/2022]
Abstract
Wiring an animal brain is a complex process involving a staggering number of cell-types born at different times and locations in the developing brain. Incorporation of these cells into precise circuits with high fidelity is critical for animal survival and behavior. Assembly of neuronal circuits is heavily dependent upon proper timing of wiring programs, requiring neurons to express specific sets of genes (sometimes transiently) at the right time in development. While cell-type specificity of genetic programs regulating wiring has been studied in detail, mechanisms regulating proper timing and coordination of these programs across cell-types are only just beginning to emerge. In this review, we discuss some temporal regulators of wiring programs and how their activity is controlled over time and space. A common feature emerges from these temporal regulators - they are induced by cell-extrinsic cues and control transcription factors capable of regulating a highly cell-type specific set of target genes. Target specificity in these contexts comes from cell-type specific transcription factors. We propose that the spatiotemporal specificity of wiring programs is controlled by the combinatorial activity of temporal programs and cell-type specific transcription factors. Going forward, a better understanding of temporal regulators will be key to understanding the mechanisms underlying brain wiring, and will be critical for the development of in vitro models like brain organoids.
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25
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Sorkaç A, Moșneanu RA, Crown AM, Savaş D, Okoro AM, Memiş E, Talay M, Barnea G. retro-Tango enables versatile retrograde circuit tracing in Drosophila. eLife 2023; 12:e85041. [PMID: 37166114 PMCID: PMC10208638 DOI: 10.7554/elife.85041] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Accepted: 05/11/2023] [Indexed: 05/12/2023] Open
Abstract
Transsynaptic tracing methods are crucial tools in studying neural circuits. Although a couple of anterograde tracing methods and a targeted retrograde tool have been developed in Drosophila melanogaster, there is still need for an unbiased, user-friendly, and flexible retrograde tracing system. Here, we describe retro-Tango, a method for transsynaptic, retrograde circuit tracing and manipulation in Drosophila. In this genetically encoded system, a ligand-receptor interaction at the synapse triggers an intracellular signaling cascade that results in reporter gene expression in presynaptic neurons. Importantly, panneuronal expression of the elements of the cascade renders this method versatile, enabling its use not only to test hypotheses but also to generate them. We validate retro-Tango in various circuits and benchmark it by comparing our findings with the electron microscopy reconstruction of the Drosophila hemibrain. Our experiments establish retro-Tango as a key method for circuit tracing in neuroscience research.
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Affiliation(s)
- Altar Sorkaç
- Department of Neuroscience, Brown UniversityProvidenceUnited States
- Carney Institute for Brain Science, Brown UniversityProvidenceUnited States
| | - Rareș A Moșneanu
- Department of Neuroscience, Brown UniversityProvidenceUnited States
- Carney Institute for Brain Science, Brown UniversityProvidenceUnited States
| | - Anthony M Crown
- Department of Neuroscience, Brown UniversityProvidenceUnited States
- Carney Institute for Brain Science, Brown UniversityProvidenceUnited States
| | - Doruk Savaş
- Department of Neuroscience, Brown UniversityProvidenceUnited States
- Carney Institute for Brain Science, Brown UniversityProvidenceUnited States
| | - Angel M Okoro
- Department of Neuroscience, Brown UniversityProvidenceUnited States
- Carney Institute for Brain Science, Brown UniversityProvidenceUnited States
| | - Ezgi Memiş
- Department of Neuroscience, Brown UniversityProvidenceUnited States
- Carney Institute for Brain Science, Brown UniversityProvidenceUnited States
| | - Mustafa Talay
- Department of Neuroscience, Brown UniversityProvidenceUnited States
- Carney Institute for Brain Science, Brown UniversityProvidenceUnited States
| | - Gilad Barnea
- Department of Neuroscience, Brown UniversityProvidenceUnited States
- Carney Institute for Brain Science, Brown UniversityProvidenceUnited States
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26
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Shiu PK, Sterne GR, Spiller N, Franconville R, Sandoval A, Zhou J, Simha N, Kang CH, Yu S, Kim JS, Dorkenwald S, Matsliah A, Schlegel P, Szi-chieh Y, McKellar CE, Sterling A, Costa M, Eichler K, Jefferis GS, Murthy M, Bates AS, Eckstein N, Funke J, Bidaye SS, Hampel S, Seeds AM, Scott K. A leaky integrate-and-fire computational model based on the connectome of the entire adult Drosophila brain reveals insights into sensorimotor processing. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.02.539144. [PMID: 37205514 PMCID: PMC10187186 DOI: 10.1101/2023.05.02.539144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
The forthcoming assembly of the adult Drosophila melanogaster central brain connectome, containing over 125,000 neurons and 50 million synaptic connections, provides a template for examining sensory processing throughout the brain. Here, we create a leaky integrate-and-fire computational model of the entire Drosophila brain, based on neural connectivity and neurotransmitter identity, to study circuit properties of feeding and grooming behaviors. We show that activation of sugar-sensing or water-sensing gustatory neurons in the computational model accurately predicts neurons that respond to tastes and are required for feeding initiation. Computational activation of neurons in the feeding region of the Drosophila brain predicts those that elicit motor neuron firing, a testable hypothesis that we validate by optogenetic activation and behavioral studies. Moreover, computational activation of different classes of gustatory neurons makes accurate predictions of how multiple taste modalities interact, providing circuit-level insight into aversive and appetitive taste processing. Our computational model predicts that the sugar and water pathways form a partially shared appetitive feeding initiation pathway, which our calcium imaging and behavioral experiments confirm. Additionally, we applied this model to mechanosensory circuits and found that computational activation of mechanosensory neurons predicts activation of a small set of neurons comprising the antennal grooming circuit that do not overlap with gustatory circuits, and accurately describes the circuit response upon activation of different mechanosensory subtypes. Our results demonstrate that modeling brain circuits purely from connectivity and predicted neurotransmitter identity generates experimentally testable hypotheses and can accurately describe complete sensorimotor transformations.
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Affiliation(s)
- Philip K. Shiu
- Department of Molecular and Cell Biology and Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA
| | - Gabriella R. Sterne
- Department of Molecular and Cell Biology and Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA
- University of Rochester Medical Center, Department of Biomedical Genetics
| | - Nico Spiller
- Max Planck Florida Institute for Neuroscience, Jupiter, FL, USA
| | | | - Andrea Sandoval
- Department of Molecular and Cell Biology and Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA
| | - Joie Zhou
- Department of Molecular and Cell Biology and Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA
| | - Neha Simha
- Department of Molecular and Cell Biology and Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA
| | - Chan Hyuk Kang
- Department of Biological Sciences, Sungkyunkwan University, Suwon, 16419, South Korea
| | - Seongbong Yu
- Department of Biological Sciences, Sungkyunkwan University, Suwon, 16419, South Korea
| | - Jinseop S. Kim
- Department of Biological Sciences, Sungkyunkwan University, Suwon, 16419, South Korea
| | - 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
| | - Philipp Schlegel
- Department of Zoology, University of Cambridge
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge
| | - Yu Szi-chieh
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Claire E. McKellar
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Amy Sterling
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Marta Costa
- Department of Zoology, University of Cambridge
| | | | - Gregory S.X.E. Jefferis
- Department of Zoology, University of Cambridge
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge
| | - Mala Murthy
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Alexander Shakeel Bates
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge
- Centre for Neural Circuits and Behaviour, The University of Oxford
- Department of Neurobiology and Howard Hughes Medical Institute, Harvard Medical School, Boston, MA, USA
| | | | - Jan Funke
- HHMI Janelia Research Campus, Ashburn, USA
| | - Salil S. Bidaye
- Max Planck Florida Institute for Neuroscience, Jupiter, FL, USA
| | - Stefanie Hampel
- Institute of Neurobiology, University of Puerto Rico-Medical Sciences Campus, San Juan, Puerto Rico
| | - Andrew M. Seeds
- Institute of Neurobiology, University of Puerto Rico-Medical Sciences Campus, San Juan, Puerto Rico
| | - Kristin Scott
- Department of Molecular and Cell Biology and Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA
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27
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Pedigo BD, Powell M, Bridgeford EW, Winding M, Priebe CE, Vogelstein JT. Generative network modeling reveals quantitative definitions of bilateral symmetry exhibited by a whole insect brain connectome. eLife 2023; 12:e83739. [PMID: 36976249 PMCID: PMC10115445 DOI: 10.7554/elife.83739] [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: 09/27/2022] [Accepted: 03/27/2023] [Indexed: 03/29/2023] Open
Abstract
Comparing connectomes can help explain how neural connectivity is related to genetics, disease, development, learning, and behavior. However, making statistical inferences about the significance and nature of differences between two networks is an open problem, and such analysis has not been extensively applied to nanoscale connectomes. Here, we investigate this problem via a case study on the bilateral symmetry of a larval Drosophila brain connectome. We translate notions of 'bilateral symmetry' to generative models of the network structure of the left and right hemispheres, allowing us to test and refine our understanding of symmetry. We find significant differences in connection probabilities both across the entire left and right networks and between specific cell types. By rescaling connection probabilities or removing certain edges based on weight, we also present adjusted definitions of bilateral symmetry exhibited by this connectome. This work shows how statistical inferences from networks can inform the study of connectomes, facilitating future comparisons of neural structures.
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Affiliation(s)
- Benjamin D Pedigo
- Department of Biomedical Engineering, Johns Hopkins UniversityBaltimoreUnited States
| | - Mike Powell
- Department of Biomedical Engineering, Johns Hopkins UniversityBaltimoreUnited States
| | - Eric W Bridgeford
- Department of Biostatistics, Johns Hopkins UniversityBaltimoreUnited States
| | - Michael Winding
- Department of Zoology, University of CambridgeCambridgeUnited Kingdom
- Neurobiology Division, MRC Laboratory of Molecular BiologyCambridgeUnited Kingdom
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Carey E Priebe
- Department of Applied Mathematics and Statistics, Johns Hopkins UniversityBaltimoreUnited States
| | - Joshua T Vogelstein
- Department of Biomedical Engineering, Johns Hopkins UniversityBaltimoreUnited States
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28
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Winding M, Pedigo BD, Barnes CL, Patsolic HG, Park Y, Kazimiers T, Fushiki A, Andrade IV, Khandelwal A, Valdes-Aleman J, Li F, Randel N, Barsotti E, Correia A, Fetter RD, Hartenstein V, Priebe CE, Vogelstein JT, Cardona A, Zlatic M. The connectome of an insect brain. Science 2023; 379:eadd9330. [PMID: 36893230 PMCID: PMC7614541 DOI: 10.1126/science.add9330] [Citation(s) in RCA: 82] [Impact Index Per Article: 82.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2022] [Accepted: 02/07/2023] [Indexed: 03/11/2023]
Abstract
Brains contain networks of interconnected neurons and so knowing the network architecture is essential for understanding brain function. We therefore mapped the synaptic-resolution connectome of an entire insect brain (Drosophila larva) with rich behavior, including learning, value computation, and action selection, comprising 3016 neurons and 548,000 synapses. We characterized neuron types, hubs, feedforward and feedback pathways, as well as cross-hemisphere and brain-nerve cord interactions. We found pervasive multisensory and interhemispheric integration, highly recurrent architecture, abundant feedback from descending neurons, and multiple novel circuit motifs. The brain's most recurrent circuits comprised the input and output neurons of the learning center. Some structural features, including multilayer shortcuts and nested recurrent loops, resembled state-of-the-art deep learning architectures. The identified brain architecture provides a basis for future experimental and theoretical studies of neural circuits.
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Affiliation(s)
- Michael Winding
- University of Cambridge, Department of Zoology, Cambridge, UK
- MRC Laboratory of Molecular Biology, Neurobiology Division, Cambridge, UK
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Benjamin D. Pedigo
- Johns Hopkins University, Department of Biomedical Engineering, Baltimore, MD, USA
| | - Christopher L. Barnes
- MRC Laboratory of Molecular Biology, Neurobiology Division, Cambridge, UK
- University of Cambridge, Department of Physiology, Development, and Neuroscience, Cambridge, UK
| | - Heather G. Patsolic
- Johns Hopkins University, Department of Applied Mathematics and Statistics, Baltimore, MD, USA
- Accenture, Arlington, VA, USA
| | - Youngser Park
- Johns Hopkins University, Center for Imaging Science, Baltimore, MD, USA
| | - Tom Kazimiers
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
- kazmos GmbH, Dresden, Germany
| | - Akira Fushiki
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
| | - Ingrid V. Andrade
- University of California Los Angeles, Department of Molecular, Cell and Developmental Biology, Los Angeles, CA, USA
| | - Avinash Khandelwal
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Javier Valdes-Aleman
- University of Cambridge, Department of Zoology, Cambridge, UK
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Feng Li
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Nadine Randel
- University of Cambridge, Department of Zoology, Cambridge, UK
- MRC Laboratory of Molecular Biology, Neurobiology Division, Cambridge, UK
| | - Elizabeth Barsotti
- MRC Laboratory of Molecular Biology, Neurobiology Division, Cambridge, UK
- University of Cambridge, Department of Physiology, Development, and Neuroscience, Cambridge, UK
| | - Ana Correia
- MRC Laboratory of Molecular Biology, Neurobiology Division, Cambridge, UK
- University of Cambridge, Department of Physiology, Development, and Neuroscience, Cambridge, UK
| | - Richard D. Fetter
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
- Stanford University, Stanford, CA, USA
| | - Volker Hartenstein
- University of California Los Angeles, Department of Molecular, Cell and Developmental Biology, Los Angeles, CA, USA
| | - Carey E. Priebe
- Johns Hopkins University, Department of Applied Mathematics and Statistics, Baltimore, MD, USA
- Johns Hopkins University, Center for Imaging Science, Baltimore, MD, USA
| | - Joshua T. Vogelstein
- Johns Hopkins University, Department of Biomedical Engineering, Baltimore, MD, USA
- Johns Hopkins University, Center for Imaging Science, Baltimore, MD, USA
| | - Albert Cardona
- MRC Laboratory of Molecular Biology, Neurobiology Division, Cambridge, UK
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
- University of Cambridge, Department of Physiology, Development, and Neuroscience, Cambridge, UK
| | - Marta Zlatic
- University of Cambridge, Department of Zoology, Cambridge, UK
- MRC Laboratory of Molecular Biology, Neurobiology Division, Cambridge, UK
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
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29
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Fabian B, Sachse S. Experience-dependent plasticity in the olfactory system of Drosophila melanogaster and other insects. Front Cell Neurosci 2023; 17:1130091. [PMID: 36923450 PMCID: PMC10010147 DOI: 10.3389/fncel.2023.1130091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 02/07/2023] [Indexed: 02/24/2023] Open
Abstract
It is long known that the nervous system of vertebrates can be shaped by internal and external factors. On the other hand, the nervous system of insects was long assumed to be stereotypic, although evidence for plasticity effects accumulated for several decades. To cover the topic comprehensively, this review recapitulates the establishment of the term "plasticity" in neuroscience and introduces its original meaning. We describe the basic composition of the insect olfactory system using Drosophila melanogaster as a representative example and outline experience-dependent plasticity effects observed in this part of the brain in a variety of insects, including hymenopterans, lepidopterans, locusts, and flies. In particular, we highlight recent advances in the study of experience-dependent plasticity effects in the olfactory system of D. melanogaster, as it is the most accessible olfactory system of all insect species due to the genetic tools available. The partly contradictory results demonstrate that morphological, physiological and behavioral changes in response to long-term olfactory stimulation are more complex than previously thought. Different molecular mechanisms leading to these changes were unveiled in the past and are likely responsible for this complexity. We discuss common problems in the study of experience-dependent plasticity, ways to overcome them, and future directions in this area of research. In addition, we critically examine the transferability of laboratory data to natural systems to address the topic as holistically as possible. As a mechanism that allows organisms to adapt to new environmental conditions, experience-dependent plasticity contributes to an animal's resilience and is therefore a crucial topic for future research, especially in an era of rapid environmental changes.
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Affiliation(s)
| | - Silke Sachse
- Research Group Olfactory Coding, Max Planck Institute for Chemical Ecology, Jena, Germany
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30
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Court R, Costa M, Pilgrim C, Millburn G, Holmes A, McLachlan A, Larkin A, Matentzoglu N, Kir H, Parkinson H, Brown NH, O’Kane CJ, Armstrong JD, Jefferis GSXE, Osumi-Sutherland D. Virtual Fly Brain-An interactive atlas of the Drosophila nervous system. Front Physiol 2023; 14:1076533. [PMID: 36776967 PMCID: PMC9908962 DOI: 10.3389/fphys.2023.1076533] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 01/02/2023] [Indexed: 01/27/2023] Open
Abstract
As a model organism, Drosophila is uniquely placed to contribute to our understanding of how brains control complex behavior. Not only does it have complex adaptive behaviors, but also a uniquely powerful genetic toolkit, increasingly complete dense connectomic maps of the central nervous system and a rapidly growing set of transcriptomic profiles of cell types. But this also poses a challenge: Given the massive amounts of available data, how are researchers to Find, Access, Integrate and Reuse (FAIR) relevant data in order to develop an integrated anatomical and molecular picture of circuits, inform hypothesis generation, and find reagents for experiments to test these hypotheses? The Virtual Fly Brain (virtualflybrain.org) web application & API provide a solution to this problem, using FAIR principles to integrate 3D images of neurons and brain regions, connectomics, transcriptomics and reagent expression data covering the whole CNS in both larva and adult. Users can search for neurons, neuroanatomy and reagents by name, location, or connectivity, via text search, clicking on 3D images, search-by-image, and queries by type (e.g., dopaminergic neuron) or properties (e.g., synaptic input in the antennal lobe). Returned results include cross-registered 3D images that can be explored in linked 2D and 3D browsers or downloaded under open licenses, and extensive descriptions of cell types and regions curated from the literature. These solutions are potentially extensible to cover similar atlasing and data integration challenges in vertebrates.
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Affiliation(s)
- Robert Court
- School of Informatics, University of Edinburgh, Edinburgh, United Kingtom
| | - Marta Costa
- Department of Zoology, University of Cambridge, Cambridge, United Kingtom
- Department of Genetics, University of Cambridge, Cambridge, United Kingtom
| | - Clare Pilgrim
- Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, United Kingtom
| | - Gillian Millburn
- Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, United Kingtom
| | - Alex Holmes
- Department of Genetics, University of Cambridge, Cambridge, United Kingtom
| | - Alex McLachlan
- Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, United Kingtom
| | - Aoife Larkin
- Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, United Kingtom
| | | | - Huseyin Kir
- European Bioinformatics Institute (EMBL-EBI), Hinxton, United Kingtom
| | - Helen Parkinson
- European Bioinformatics Institute (EMBL-EBI), Hinxton, United Kingtom
| | - Nicolas H. Brown
- Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, United Kingtom
| | - Cahir J. O’Kane
- Department of Genetics, University of Cambridge, Cambridge, United Kingtom
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31
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Deere JU, Sarkissian AA, Yang M, Uttley HA, Martinez Santana N, Nguyen L, Ravi K, Devineni AV. Selective integration of diverse taste inputs within a single taste modality. eLife 2023; 12:84856. [PMID: 36692370 PMCID: PMC9873257 DOI: 10.7554/elife.84856] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Accepted: 01/10/2023] [Indexed: 01/25/2023] Open
Abstract
A fundamental question in sensory processing is how different channels of sensory input are processed to regulate behavior. Different input channels may converge onto common downstream pathways to drive the same behaviors, or they may activate separate pathways to regulate distinct behaviors. We investigated this question in the Drosophila bitter taste system, which contains diverse bitter-sensing cells residing in different taste organs. First, we optogenetically activated subsets of bitter neurons within each organ. These subsets elicited broad and highly overlapping behavioral effects, suggesting that they converge onto common downstream pathways, but we also observed behavioral differences that argue for biased convergence. Consistent with these results, transsynaptic tracing revealed that bitter neurons in different organs connect to overlapping downstream pathways with biased connectivity. We investigated taste processing in one type of downstream bitter neuron that projects to the higher brain. These neurons integrate input from multiple organs and regulate specific taste-related behaviors. We then traced downstream circuits, providing the first glimpse into taste processing in the higher brain. Together, these results reveal that different bitter inputs are selectively integrated early in the circuit, enabling the pooling of information, while the circuit then diverges into multiple pathways that may have different roles.
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Affiliation(s)
- Julia U Deere
- Zuckerman Mind Brain Behavior Institute, Columbia UniversityNew YorkUnited States
| | | | - Meifeng Yang
- Department of Biology, Emory UniversityAtlantaUnited States
| | - Hannah A Uttley
- Zuckerman Mind Brain Behavior Institute, Columbia UniversityNew YorkUnited States
| | | | - Lam Nguyen
- Department of Biology, Emory UniversityAtlantaUnited States
| | - Kaushiki Ravi
- Department of Biology, Emory UniversityAtlantaUnited States
| | - Anita V Devineni
- Zuckerman Mind Brain Behavior Institute, Columbia UniversityNew YorkUnited States
- Neuroscience Graduate Program, Emory UniversityAtlantaUnited States
- Department of Biology, Emory UniversityAtlantaUnited States
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32
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Sheridan A, Nguyen TM, Deb D, Lee WCA, Saalfeld S, Turaga SC, Manor U, Funke J. Local shape descriptors for neuron segmentation. Nat Methods 2023; 20:295-303. [PMID: 36585455 PMCID: PMC9911350 DOI: 10.1038/s41592-022-01711-z] [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: 07/13/2021] [Accepted: 11/01/2022] [Indexed: 12/31/2022]
Abstract
We present an auxiliary learning task for the problem of neuron segmentation in electron microscopy volumes. The auxiliary task consists of the prediction of local shape descriptors (LSDs), which we combine with conventional voxel-wise direct neighbor affinities for neuron boundary detection. The shape descriptors capture local statistics about the neuron to be segmented, such as diameter, elongation, and direction. On a study comparing several existing methods across various specimen, imaging techniques, and resolutions, auxiliary learning of LSDs consistently increases segmentation accuracy of affinity-based methods over a range of metrics. Furthermore, the addition of LSDs promotes affinity-based segmentation methods to be on par with the current state of the art for neuron segmentation (flood-filling networks), while being two orders of magnitudes more efficient-a critical requirement for the processing of future petabyte-sized datasets.
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Affiliation(s)
- Arlo Sheridan
- grid.443970.dHHMI Janelia, Ashburn, VA USA ,grid.250671.70000 0001 0662 7144Waitt Advanced Biophotonics Center, Salk Institute for Biological Studies, La Jolla, CA USA
| | - Tri M. Nguyen
- grid.38142.3c000000041936754XDepartment of Neurobiology, Harvard Medical School, Boston, MA USA
| | | | - Wei-Chung Allen Lee
- grid.38142.3c000000041936754XF.M. Kirby Neurobiology Center, Boston Children’s Hospital, Harvard Medical School, Boston, MA USA
| | | | | | - Uri Manor
- grid.250671.70000 0001 0662 7144Waitt Advanced Biophotonics Center, Salk Institute for Biological Studies, La Jolla, CA USA
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33
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Playing to the crowd: Using Drosophila to dissect mechanisms underlying plastic male strategies in sperm competition games. ADVANCES IN THE STUDY OF BEHAVIOR 2023. [DOI: 10.1016/bs.asb.2022.11.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
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34
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Wechsler SP, Bhandawat V. Behavioral algorithms and neural mechanisms underlying odor-modulated locomotion in insects. J Exp Biol 2023; 226:jeb200261. [PMID: 36637433 PMCID: PMC10086387 DOI: 10.1242/jeb.200261] [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: 01/14/2023]
Abstract
Odors released from mates and resources such as a host and food are often the first sensory signals that an animal can detect. Changes in locomotion in response to odors are an important mechanism by which animals access resources important to their survival. Odor-modulated changes in locomotion in insects constitute a whole suite of flexible behaviors that allow insects to close in on these resources from long distances and perform local searches to locate and subsequently assess them. Here, we review changes in odor-mediated locomotion across many insect species. We emphasize that changes in locomotion induced by odors are diverse. In particular, the olfactory stimulus is sporadic at long distances and becomes more continuous at short distances. This distance-dependent change in temporal profile produces a corresponding change in an insect's locomotory strategy. We also discuss the neural circuits underlying odor modulation of locomotion.
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Affiliation(s)
- Samuel P. Wechsler
- School of Biomedical Engineering, Sciences and Health Systems, Drexel University, Philadelphia, PA 19104, USA
| | - Vikas Bhandawat
- School of Biomedical Engineering, Sciences and Health Systems, Drexel University, Philadelphia, PA 19104, USA
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35
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Choi K, Kim WK, Hyeon C. Polymer Physics-Based Classification of Neurons. Neuroinformatics 2023; 21:177-193. [PMID: 36190621 DOI: 10.1007/s12021-022-09605-3] [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] [Accepted: 09/12/2022] [Indexed: 11/26/2022]
Abstract
Recognizing that diverse morphologies of neurons are reminiscent of structures of branched polymers, we put forward a principled and systematic way of classifying neurons that employs the ideas of polymer physics. In particular, we use 3D coordinates of individual neurons, which are accessible in recent neuron reconstruction datasets from electron microscope images. We numerically calculate the form factor, F(q), a Fourier transform of the distance distribution of particles comprising an object of interest, which is routinely measured in scattering experiments to quantitatively characterize the structure of materials. For a polymer-like object consisting of n monomers spanning over a length scale of r, F(q) scales with the wavenumber [Formula: see text] as [Formula: see text] at an intermediate range of q, where [Formula: see text] is the fractal dimension or the inverse scaling exponent ([Formula: see text]) characterizing the geometrical feature ([Formula: see text]) of the object. F(q) can be used to describe a neuron morphology in terms of its size ([Formula: see text]) and the extent of branching quantified by [Formula: see text]. By defining the distance between F(q)s as a measure of similarity between two neuronal morphologies, we tackle the neuron classification problem. In comparison with other existing classification methods for neuronal morphologies, our F(q)-based classification rests solely on 3D coordinates of neurons with no prior knowledge of morphological features. When applied to publicly available neuron datasets from three different organisms, our method not only complements other methods but also offers a physical picture of how the dendritic and axonal branches of an individual neuron fill the space of dense neural networks inside the brain.
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Affiliation(s)
- Kiri Choi
- School of Computational Sciences, Korea Institute for Advanced Study, Seoul, 02455, Korea
| | - Won Kyu Kim
- School of Computational Sciences, Korea Institute for Advanced Study, Seoul, 02455, Korea
| | - Changbong Hyeon
- School of Computational Sciences, Korea Institute for Advanced Study, Seoul, 02455, Korea.
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36
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Abstract
Among the many wonders of nature, the sense of smell of the fly Drosophila melanogaster might seem, at first glance, of esoteric interest. Nevertheless, for over a century, the 'nose' of this insect has been an extraordinary system to explore questions in animal behaviour, ecology and evolution, neuroscience, physiology and molecular genetics. The insights gained are relevant for our understanding of the sensory biology of vertebrates, including humans, and other insect species, encompassing those detrimental to human health. Here, I present an overview of our current knowledge of D. melanogaster olfaction, from molecules to behaviours, with an emphasis on the historical motivations of studies and illustration of how technical innovations have enabled advances. I also highlight some of the pressing and long-term questions.
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Affiliation(s)
- Richard Benton
- Center for Integrative Genomics, Faculty of Biology and Medicine, University of Lausanne, CH-1015 Lausanne, Switzerland
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37
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Galili DS, Jefferis GS, Costa M. Connectomics and the neural basis of behaviour. CURRENT OPINION IN INSECT SCIENCE 2022; 54:100968. [PMID: 36113710 PMCID: PMC7614087 DOI: 10.1016/j.cois.2022.100968] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 08/30/2022] [Accepted: 09/06/2022] [Indexed: 06/15/2023]
Abstract
Methods to acquire and process synaptic-resolution electron-microscopy datasets have progressed very rapidly, allowing production and annotation of larger, more complete connectomes. More accurate neuronal matching techniques are enriching cell type data with gene expression, neuron activity, behaviour and developmental information, providing ways to test hypotheses of circuit function. In a variety of behaviours such as learned and innate olfaction, navigation and sexual behaviour, connectomics has already revealed interconnected modules with a hierarchical structure, recurrence and integration of sensory streams. Comparing individual connectomes to determine which circuit features are robust and which are variable is one key research area; new work in comparative connectomics across development, experience, sex and species will establish strong links between neuronal connectivity and brain function.
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Affiliation(s)
- Dana S Galili
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge CB2 0QH, UK
| | - Gregory Sxe Jefferis
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge CB2 0QH, UK; Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Downing Street, Cambridge CB2 3EJ, UK
| | - Marta Costa
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Downing Street, Cambridge CB2 3EJ, UK.
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38
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Hildebrandt K, Klöppel C, Gogel J, Hartenstein V, Walldorf U. Orthopedia expression during Drosophila melanogaster nervous system development and its regulation by microRNA-252. Dev Biol 2022; 492:87-100. [PMID: 36179878 DOI: 10.1016/j.ydbio.2022.09.006] [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: 07/15/2022] [Revised: 09/05/2022] [Accepted: 09/19/2022] [Indexed: 11/03/2022]
Abstract
During brain development of Drosophila melanogaster many transcription factors are involved in regulating neural fate and morphogenesis. In our study we show that the transcription factor Orthopedia (Otp), a member of the 57B homeobox gene cluster, plays an important role in this process. Otp is expressed in a stable pattern in defined lineages from mid-embryonic stages into the adult brain and therefore a very stable marker for these lineages. We determined the abundance of the two different otp transcripts in the brain and hindgut during development using qPCR. CRISPR/Cas9 generated otp mutants of the longer protein form significantly affect the expression of Otp in specific areas. We generated an otp enhancer trap strain by gene targeting and reintegration of Gal4, which mimics the complete expression of otp during development except the embryonic hindgut expression. Since in the embryo, the expression of Otp is posttranscriptionally regulated, we looked for putative miRNAs interacting with the otp 3'UTR, and identified microRNA-252 as a candidate. Further analyses with mutated and deleted forms of the microRNA-252 interacting sequence in the otp 3'UTR demonstrate an in vivo interaction of microRNA-252 with the otp 3'UTR. An effect of this interaction is seen in the adult brain, where Otp expression is partially abolished in a knockout strain of microRNA-252. Our results show that Otp is another important factor for brain development in Drosophila melanogaster.
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Affiliation(s)
- Kirsten Hildebrandt
- Developmental Biology, Saarland University, Building 61, 66421, Homburg, Saar, Germany
| | - Christine Klöppel
- Developmental Biology, Saarland University, Building 61, 66421, Homburg, Saar, Germany
| | - Jasmin Gogel
- Developmental Biology, Saarland University, Building 61, 66421, Homburg, Saar, Germany
| | - Volker Hartenstein
- Department of Molecular Cell and Developmental Biology, University of California, Los Angeles, CA, 90095, USA
| | - Uwe Walldorf
- Developmental Biology, Saarland University, Building 61, 66421, Homburg, Saar, Germany.
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39
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Hayashi TT, MacKenzie AJ, Ganguly I, Ellis KE, Smihula HM, Jacob MS, Litwin-Kumar A, Caron SJC. Mushroom body input connections form independently of sensory activity in Drosophila melanogaster. Curr Biol 2022; 32:4000-4012.e5. [PMID: 35977547 PMCID: PMC9533768 DOI: 10.1016/j.cub.2022.07.055] [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: 11/05/2021] [Revised: 05/04/2022] [Accepted: 07/21/2022] [Indexed: 11/19/2022]
Abstract
Associative brain centers, such as the insect mushroom body, need to represent sensory information in an efficient manner. In Drosophila melanogaster, the Kenyon cells of the mushroom body integrate inputs from a random set of olfactory projection neurons, but some projection neurons-namely those activated by a few ethologically meaningful odors-connect to Kenyon cells more frequently than others. This biased and random connectivity pattern is conceivably advantageous, as it enables the mushroom body to represent a large number of odors as unique activity patterns while prioritizing the representation of a few specific odors. How this connectivity pattern is established remains largely unknown. Here, we test whether the mechanisms patterning the connections between Kenyon cells and projection neurons depend on sensory activity or whether they are hardwired. We mapped a large number of mushroom body input connections in partially anosmic flies-flies lacking the obligate odorant co-receptor Orco-and in wild-type flies. Statistical analyses of these datasets reveal that the random and biased connectivity pattern observed between Kenyon cells and projection neurons forms normally in the absence of most olfactory sensory activity. This finding supports the idea that even comparatively subtle, population-level patterns of neuronal connectivity can be encoded by fixed genetic programs and are likely to be the result of evolved prioritization of ecologically and ethologically salient stimuli.
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Affiliation(s)
- Tatsuya Tatz Hayashi
- School of Biological Sciences, University of Utah, Aline Skaggs Wilmot Biology Building, 257 South 1400 East, Salt Lake City, UT 84112, USA; Neuroscience Program, University of Utah, Salt Lake City, UT 84112, USA
| | - Alexander John MacKenzie
- School of Biological Sciences, University of Utah, Aline Skaggs Wilmot Biology Building, 257 South 1400 East, Salt Lake City, UT 84112, USA; Neuroscience Program, University of Utah, Salt Lake City, UT 84112, USA
| | - Ishani Ganguly
- Center for Theoretical Neuroscience, Columbia University, Jerome L Greene Science Center, 3227 Broadway, New York, NY 10027, USA
| | - Kaitlyn Elizabeth Ellis
- School of Biological Sciences, University of Utah, Aline Skaggs Wilmot Biology Building, 257 South 1400 East, Salt Lake City, UT 84112, USA
| | - Hayley Marie Smihula
- School of Biological Sciences, University of Utah, Aline Skaggs Wilmot Biology Building, 257 South 1400 East, Salt Lake City, UT 84112, USA
| | - Miles Solomon Jacob
- School of Biological Sciences, University of Utah, Aline Skaggs Wilmot Biology Building, 257 South 1400 East, Salt Lake City, UT 84112, USA
| | - Ashok Litwin-Kumar
- Center for Theoretical Neuroscience, Columbia University, Jerome L Greene Science Center, 3227 Broadway, New York, NY 10027, USA
| | - Sophie Jeanne Cécile Caron
- School of Biological Sciences, University of Utah, Aline Skaggs Wilmot Biology Building, 257 South 1400 East, Salt Lake City, UT 84112, USA; Neuroscience Program, University of Utah, Salt Lake City, UT 84112, USA.
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40
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Zheng Z, Li F, Fisher C, Ali IJ, Sharifi N, Calle-Schuler S, Hsu J, Masoodpanah N, Kmecova L, Kazimiers T, Perlman E, Nichols M, Li PH, Jain V, Bock DD. Structured sampling of olfactory input by the fly mushroom body. Curr Biol 2022; 32:3334-3349.e6. [PMID: 35797998 PMCID: PMC9413950 DOI: 10.1016/j.cub.2022.06.031] [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: 06/05/2020] [Revised: 02/07/2022] [Accepted: 06/10/2022] [Indexed: 11/17/2022]
Abstract
Associative memory formation and recall in the fruit fly Drosophila melanogaster is subserved by the mushroom body (MB). Upon arrival in the MB, sensory information undergoes a profound transformation from broadly tuned and stereotyped odorant responses in the olfactory projection neuron (PN) layer to narrowly tuned and nonstereotyped responses in the Kenyon cells (KCs). Theory and experiment suggest that this transformation is implemented by random connectivity between KCs and PNs. However, this hypothesis has been challenging to test, given the difficulty of mapping synaptic connections between large numbers of brain-spanning neurons. Here, we used a recent whole-brain electron microscopy volume of the adult fruit fly to map PN-to-KC connectivity at synaptic resolution. The PN-KC connectome revealed unexpected structure, with preponderantly food-responsive PN types converging at above-chance levels on downstream KCs. Axons of the overconvergent PN types tended to arborize near one another in the MB main calyx, making local KC dendrites more likely to receive input from those types. Overconvergent PN types preferentially co-arborize and connect with dendrites of αβ and α'β' KC subtypes. Computational simulation of the observed network showed degraded discrimination performance compared with a random network, except when all signal flowed through the overconvergent, primarily food-responsive PN types. Additional theory and experiment will be needed to fully characterize the impact of the observed non-random network structure on associative memory formation and recall.
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Affiliation(s)
- Zhihao Zheng
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA; The Solomon H. Snyder Department of Neuroscience, The Johns Hopkins University, Baltimore, MD 21205, USA
| | - Feng Li
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Corey Fisher
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Iqbal J Ali
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Nadiya Sharifi
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Steven Calle-Schuler
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Joseph Hsu
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Najla Masoodpanah
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Lucia Kmecova
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Tom Kazimiers
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA; Kazmos GmbH, Dresden, Germany
| | - Eric Perlman
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA; Yikes LLC, Baltimore, MD, USA
| | - Matthew Nichols
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | | | | | - Davi D Bock
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA; Department of Neurological Sciences, University of Vermont, Burlington, VT 05405, USA.
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41
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Matheson AMM, Lanz AJ, Medina AM, Licata AM, Currier TA, Syed MH, Nagel KI. A neural circuit for wind-guided olfactory navigation. Nat Commun 2022; 13:4613. [PMID: 35941114 PMCID: PMC9360402 DOI: 10.1038/s41467-022-32247-7] [Citation(s) in RCA: 37] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 07/22/2022] [Indexed: 11/10/2022] Open
Abstract
To navigate towards a food source, animals frequently combine odor cues about source identity with wind direction cues about source location. Where and how these two cues are integrated to support navigation is unclear. Here we describe a pathway to the Drosophila fan-shaped body that encodes attractive odor and promotes upwind navigation. We show that neurons throughout this pathway encode odor, but not wind direction. Using connectomics, we identify fan-shaped body local neurons called h∆C that receive input from this odor pathway and a previously described wind pathway. We show that h∆C neurons exhibit odor-gated, wind direction-tuned activity, that sparse activation of h∆C neurons promotes navigation in a reproducible direction, and that h∆C activity is required for persistent upwind orientation during odor. Based on connectome data, we develop a computational model showing how h∆C activity can promote navigation towards a goal such as an upwind odor source. Our results suggest that odor and wind cues are processed by separate pathways and integrated within the fan-shaped body to support goal-directed navigation.
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Affiliation(s)
- Andrew M M Matheson
- Neuroscience Institute, NYU Medical Center, 435 E 30th St., New York, NY, 10016, USA
- Department of Biological Sciences, Columbia University, 600 Sherman Fairchild Center, New York, NY, 10027, USA
| | - Aaron J Lanz
- Neuroscience Institute, NYU Medical Center, 435 E 30th St., New York, NY, 10016, USA
| | - Ashley M Medina
- Neuroscience Institute, NYU Medical Center, 435 E 30th St., New York, NY, 10016, USA
| | - Al M Licata
- Neuroscience Institute, NYU Medical Center, 435 E 30th St., New York, NY, 10016, USA
| | - Timothy A Currier
- Neuroscience Institute, NYU Medical Center, 435 E 30th St., New York, NY, 10016, USA
- Center for Neural Science, NYU, New York, NY, 4 Washington Place, New York, NY, 10003, USA
- Department of Neurobiology, Stanford University, 299W. Campus Drive, Stanford, CA, 94305, USA
| | - Mubarak H Syed
- Department of Biology, 219 Yale Blvd NE, University of New Mexico, Albuquerque, NM, 87131, USA
| | - Katherine I Nagel
- Neuroscience Institute, NYU Medical Center, 435 E 30th St., New York, NY, 10016, USA.
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42
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Shiu PK, Sterne GR, Engert S, Dickson BJ, Scott K. Taste quality and hunger interactions in a feeding sensorimotor circuit. eLife 2022; 11:e79887. [PMID: 35791902 PMCID: PMC9292995 DOI: 10.7554/elife.79887] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 06/27/2022] [Indexed: 11/30/2022] Open
Abstract
Taste detection and hunger state dynamically regulate the decision to initiate feeding. To study how context-appropriate feeding decisions are generated, we combined synaptic resolution circuit reconstruction with targeted genetic access to specific neurons to elucidate a gustatory sensorimotor circuit for feeding initiation in adult Drosophila melanogaster. This circuit connects gustatory sensory neurons to proboscis motor neurons through three intermediate layers. Most neurons in this pathway are necessary and sufficient for proboscis extension, a feeding initiation behavior, and respond selectively to sugar taste detection. Pathway activity is amplified by hunger signals that act at select second-order neurons to promote feeding initiation in food-deprived animals. In contrast, the feeding initiation circuit is inhibited by a bitter taste pathway that impinges on premotor neurons, illuminating a local motif that weighs sugar and bitter taste detection to adjust the behavioral outcomes. Together, these studies reveal central mechanisms for the integration of external taste detection and internal nutritive state to flexibly execute a critical feeding decision.
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Affiliation(s)
- Philip K Shiu
- University of California, BerkeleyBerkeleyUnited States
| | - Gabriella R Sterne
- University of California, BerkeleyBerkeleyUnited States
- Janelia Research Campus, Howard Hughes Medical InstituteChevy ChaseUnited States
| | | | - Barry J Dickson
- Janelia Research Campus, Howard Hughes Medical InstituteChevy ChaseUnited States
- Queensland Brain Institute, University of QueenslandBrisbaneAustralia
| | - Kristin Scott
- University of California, BerkeleyBerkeleyUnited States
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43
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Andreani T, Rosensweig C, Sisobhan S, Ogunlana E, Kath W, Allada R. Circadian programming of the ellipsoid body sleep homeostat in Drosophila. eLife 2022; 11:e74327. [PMID: 35735904 PMCID: PMC9270026 DOI: 10.7554/elife.74327] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 06/23/2022] [Indexed: 11/18/2022] Open
Abstract
Homeostatic and circadian processes collaborate to appropriately time and consolidate sleep and wake. To understand how these processes are integrated, we scheduled brief sleep deprivation at different times of day in Drosophila and find elevated morning rebound compared to evening. These effects depend on discrete morning and evening clock neurons, independent of their roles in circadian locomotor activity. In the R5 ellipsoid body sleep homeostat, we identified elevated morning expression of activity dependent and presynaptic gene expression as well as the presynaptic protein BRUCHPILOT consistent with regulation by clock circuits. These neurons also display elevated calcium levels in response to sleep loss in the morning, but not the evening consistent with the observed time-dependent sleep rebound. These studies reveal the circuit and molecular mechanisms by which discrete circadian clock neurons program a homeostatic sleep center.
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Affiliation(s)
- Tomas Andreani
- Department of Neurobiology, Northwestern UniversityEvanstonUnited States
| | - Clark Rosensweig
- Department of Neurobiology, Northwestern UniversityEvanstonUnited States
| | - Shiju Sisobhan
- Department of Neurobiology, Northwestern UniversityEvanstonUnited States
| | - Emmanuel Ogunlana
- Department of Neurobiology, Northwestern UniversityEvanstonUnited States
| | - William Kath
- Department of Engineering Sciences and Applied Mathematics, Northwestern UniversityEvanstonUnited States
| | - Ravi Allada
- Department of Neurobiology, Northwestern UniversityChicagoUnited States
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44
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Das Chakraborty S, Chang H, Hansson BS, Sachse S. Higher-order olfactory neurons in the lateral horn support odor valence and odor identity coding in Drosophila. eLife 2022; 11:74637. [PMID: 35621267 PMCID: PMC9142144 DOI: 10.7554/elife.74637] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 05/03/2022] [Indexed: 11/15/2022] Open
Abstract
Understanding neuronal representations of odor-evoked activities and their progressive transformation from the sensory level to higher brain centers features one of the major aims in olfactory neuroscience. Here, we investigated how odor information is transformed and represented in higher-order neurons of the lateral horn, one of the higher olfactory centers implicated in determining innate behavior, using Drosophila melanogaster. We focused on a subset of third-order glutamatergic lateral horn neurons (LHNs) and characterized their odor coding properties in relation to their presynaptic partner neurons, the projection neurons (PNs) by two-photon functional imaging. We show that odors evoke reproducible, stereotypic, and odor-specific response patterns in LHNs. Notably, odor-evoked responses in these neurons are valence-specific in a way that their response amplitude is positively correlated with innate odor preferences. We postulate that this valence-specific activity is the result of integrating inputs from multiple olfactory channels through second-order neurons. GRASP and micro-lesioning experiments provide evidence that glutamatergic LHNs obtain their major excitatory input from uniglomerular PNs, while they receive an odor-specific inhibition through inhibitory multiglomerular PNs. In summary, our study indicates that odor representations in glutamatergic LHNs encode hedonic valence and odor identity and primarily retain the odor coding properties of second-order neurons.
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Affiliation(s)
| | - Hetan Chang
- Department of Evolutionary Neuroethology, Max Planck Institute for Chemical Ecology, Jena, Germany
| | - Bill S Hansson
- Department of Evolutionary Neuroethology, Max Planck Institute for Chemical Ecology, Jena, Germany
| | - Silke Sachse
- Research Group Olfactory Coding, Max Planck Institute for Chemical Ecology, Jena, Germany
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45
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Engert S, Sterne GR, Bock DD, Scott K. Drosophila gustatory projections are segregated by taste modality and connectivity. eLife 2022; 11:78110. [PMID: 35611959 PMCID: PMC9170244 DOI: 10.7554/elife.78110] [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: 02/23/2022] [Accepted: 05/24/2022] [Indexed: 11/13/2022] Open
Abstract
Gustatory sensory neurons detect caloric and harmful compounds in potential food and convey this information to the brain to inform feeding decisions. To examine the signals that gustatory neurons transmit and receive, we reconstructed gustatory axons and their synaptic sites in the adult Drosophila melanogaster brain, utilizing a whole-brain electron microscopy volume. We reconstructed 87 gustatory projections from the proboscis labellum in the right hemisphere and 57 from the left, representing the majority of labellar gustatory axons. Gustatory neurons contain a nearly equal number of interspersed pre-and post-synaptic sites, with extensive synaptic connectivity among gustatory axons. Morphology- and connectivity-based clustering revealed six distinct groups, likely representing neurons recognizing different taste modalities. The vast majority of synaptic connections are between neurons of the same group. This study resolves the anatomy of labellar gustatory projections, reveals that gustatory projections are segregated based on taste modality, and uncovers synaptic connections that may alter the transmission of gustatory signals.
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Affiliation(s)
- Stefanie Engert
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, United States
| | - Gabriella R Sterne
- Department of Molecular and Cell Biology, University of California Berkeley, Berkeley, United States
| | - Davi D Bock
- Department of Neurological Sciences, University of Vermont, Burlington, United States
| | - Kristin Scott
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, United States
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46
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Tao L, Bhandawat V. Mechanisms of Variability Underlying Odor-Guided Locomotion. Front Behav Neurosci 2022; 16:871884. [PMID: 35600988 PMCID: PMC9115574 DOI: 10.3389/fnbeh.2022.871884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 03/14/2022] [Indexed: 11/17/2022] Open
Abstract
Changes in locomotion mediated by odors (odor-guided locomotion) are an important mechanism by which animals discover resources important to their survival. Odor-guided locomotion, like most other behaviors, is highly variable. Variability in behavior can arise at many nodes along the circuit that performs sensorimotor transformation. We review these sources of variability in the context of the Drosophila olfactory system. While these sources of variability are important, using a model for locomotion, we show that another important contributor to behavioral variability is the stochastic nature of decision-making during locomotion as well as the persistence of these decisions: Flies choose the speed and curvature stochastically from a distribution and locomote with the same speed and curvature for extended periods. This stochasticity in locomotion will result in variability in behavior even if there is no noise in sensorimotor transformation. Overall, the noise in sensorimotor transformation is amplified by mechanisms of locomotion making odor-guided locomotion in flies highly variable.
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47
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Rihani K, Sachse S. Shedding Light on Inter-Individual Variability of Olfactory Circuits in Drosophila. Front Behav Neurosci 2022; 16:835680. [PMID: 35548690 PMCID: PMC9084309 DOI: 10.3389/fnbeh.2022.835680] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Accepted: 03/29/2022] [Indexed: 12/25/2022] Open
Abstract
Inter-individual differences in behavioral responses, anatomy or functional properties of neuronal populations of animals having the same genotype were for a long time disregarded. The majority of behavioral studies were conducted at a group level, and usually the mean behavior of all individuals was considered. Similarly, in neurophysiological studies, data were pooled and normalized from several individuals. This approach is mostly suited to map and characterize stereotyped neuronal properties between individuals, but lacks the ability to depict inter-individual variability regarding neuronal wiring or physiological characteristics. Recent studies have shown that behavioral biases and preferences to olfactory stimuli can vary significantly among individuals of the same genotype. The origin and the benefit of these diverse “personalities” is still unclear and needs to be further investigated. A perspective taken into account the inter-individual differences is needed to explore the cellular mechanisms underlying this phenomenon. This review focuses on olfaction in the vinegar fly Drosophila melanogaster and summarizes previous and recent studies on odor-guided behavior and the underlying olfactory circuits in the light of inter-individual variability. We address the morphological and physiological variabilities present at each layer of the olfactory circuitry and attempt to link them to individual olfactory behavior. Additionally, we discuss the factors that might influence individuality with regard to olfactory perception.
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Affiliation(s)
- Karen Rihani
- Research Group Olfactory Coding, Max Planck Institute for Chemical Ecology, Jena, Germany
- Max Planck Center Next Generation Insect Chemical Ecology, Jena, Germany
| | - Silke Sachse
- Research Group Olfactory Coding, Max Planck Institute for Chemical Ecology, Jena, Germany
- Max Planck Center Next Generation Insect Chemical Ecology, Jena, Germany
- *Correspondence: Silke Sachse,
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48
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Task D, Lin CC, Vulpe A, Afify A, Ballou S, Brbic M, Schlegel P, Raji J, Jefferis GSXE, Li H, Menuz K, Potter CJ. Chemoreceptor co-expression in Drosophila melanogaster olfactory neurons. eLife 2022; 11:e72599. [PMID: 35442190 PMCID: PMC9020824 DOI: 10.7554/elife.72599] [Citation(s) in RCA: 49] [Impact Index Per Article: 24.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 03/07/2022] [Indexed: 12/20/2022] Open
Abstract
Drosophila melanogaster olfactory neurons have long been thought to express only one chemosensory receptor gene family. There are two main olfactory receptor gene families in Drosophila, the odorant receptors (ORs) and the ionotropic receptors (IRs). The dozens of odorant-binding receptors in each family require at least one co-receptor gene in order to function: Orco for ORs, and Ir25a, Ir8a, and Ir76b for IRs. Using a new genetic knock-in strategy, we targeted the four co-receptors representing the main chemosensory families in D. melanogaster (Orco, Ir8a, Ir76b, Ir25a). Co-receptor knock-in expression patterns were verified as accurate representations of endogenous expression. We find extensive overlap in expression among the different co-receptors. As defined by innervation into antennal lobe glomeruli, Ir25a is broadly expressed in 88% of all olfactory sensory neuron classes and is co-expressed in 82% of Orco+ neuron classes, including all neuron classes in the maxillary palp. Orco, Ir8a, and Ir76b expression patterns are also more expansive than previously assumed. Single sensillum recordings from Orco-expressing Ir25a mutant antennal and palpal neurons identify changes in olfactory responses. We also find co-expression of Orco and Ir25a in Drosophila sechellia and Anopheles coluzzii olfactory neurons. These results suggest that co-expression of chemosensory receptors is common in insect olfactory neurons. Together, our data present the first comprehensive map of chemosensory co-receptor expression and reveal their unexpected widespread co-expression in the fly olfactory system.
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Affiliation(s)
- Darya Task
- The Solomon H. Snyder Department of Neuroscience, Center for Sensory Biology, Johns Hopkins University School of MedicineBaltimoreUnited States
| | - Chun-Chieh Lin
- The Solomon H. Snyder Department of Neuroscience, Center for Sensory Biology, Johns Hopkins University School of MedicineBaltimoreUnited States
- Mortimer B. Zuckermann Mind Brain Behavior Institute, Columbia UniversityNew YorkUnited States
| | - Alina Vulpe
- Physiology & Neurobiology Department, University of ConnecticutMansfieldUnited States
| | - Ali Afify
- The Solomon H. Snyder Department of Neuroscience, Center for Sensory Biology, Johns Hopkins University School of MedicineBaltimoreUnited States
| | - Sydney Ballou
- Physiology & Neurobiology Department, University of ConnecticutMansfieldUnited States
| | - Maria Brbic
- Department of Computer Science, Stanford UniversityStanfordUnited States
| | - Philipp Schlegel
- Drosophila Connectomics Group, Department of Zoology, University of CambridgeCambridgeUnited Kingdom
| | - Joshua Raji
- The Solomon H. Snyder Department of Neuroscience, Center for Sensory Biology, Johns Hopkins University School of MedicineBaltimoreUnited States
| | - Gregory SXE Jefferis
- Drosophila Connectomics Group, Department of Zoology, University of CambridgeCambridgeUnited Kingdom
- Neurobiology Division, MRC Laboratory of Molecular BiologyCambridgeUnited Kingdom
| | - Hongjie Li
- Department of Biology, Howard Hughes Medical Institute, Stanford UniversityStanfordUnited States
| | - Karen Menuz
- Physiology & Neurobiology Department, University of ConnecticutMansfieldUnited States
| | - Christopher J Potter
- The Solomon H. Snyder Department of Neuroscience, Center for Sensory Biology, Johns Hopkins University School of MedicineBaltimoreUnited States
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49
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Yang K, Liu T, Wang Z, Liu J, Shen Y, Pan X, Wen R, Xie H, Ruan Z, Tan Z, Chen Y, Guo A, Liu H, Han H, Di Z, Zhang K. Classifying Drosophila Olfactory Projection Neuron Boutons by Quantitative Analysis of Electron Microscopic Reconstruction. iScience 2022; 25:104180. [PMID: 35494235 PMCID: PMC9038572 DOI: 10.1016/j.isci.2022.104180] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 01/25/2022] [Accepted: 03/29/2022] [Indexed: 11/29/2022] Open
Affiliation(s)
- Kai Yang
- School of Basic Medicine, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong 510006, China
- BNU-BUCM Hengqin Innovation Institute of Science and Technology, Zhuhai, Guangdong 518057, China
| | - Tong Liu
- International Academic Center of Complex Systems, Advanced Institute of Natural Sciences, Beijing Normal University at Zhuhai, Zhuhai, Guangdong 519087, China
| | - Ze Wang
- International Academic Center of Complex Systems, Advanced Institute of Natural Sciences, Beijing Normal University at Zhuhai, Zhuhai, Guangdong 519087, China
| | - Jing Liu
- Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Yuxinyao Shen
- Huitong College, Beijing Normal University at Zhuhai, Zhuhai, Guangdong 519087, China
| | - Xinyi Pan
- Huitong College, Beijing Normal University at Zhuhai, Zhuhai, Guangdong 519087, China
| | - Ruyi Wen
- Huitong College, Beijing Normal University at Zhuhai, Zhuhai, Guangdong 519087, China
| | - Haotian Xie
- Huitong College, Beijing Normal University at Zhuhai, Zhuhai, Guangdong 519087, China
| | - Zhaoxuan Ruan
- Huitong College, Beijing Normal University at Zhuhai, Zhuhai, Guangdong 519087, China
| | - Zixiao Tan
- Huitong College, Beijing Normal University at Zhuhai, Zhuhai, Guangdong 519087, China
| | - Yingying Chen
- Huitong College, Beijing Normal University at Zhuhai, Zhuhai, Guangdong 519087, China
| | - Aike Guo
- International Academic Center of Complex Systems, Advanced Institute of Natural Sciences, Beijing Normal University at Zhuhai, Zhuhai, Guangdong 519087, China
- Huitong College, Beijing Normal University at Zhuhai, Zhuhai, Guangdong 519087, China
- School of Life Sciences, Shanghai University, Shanghai 200444, China
| | - He Liu
- International Academic Center of Complex Systems, Advanced Institute of Natural Sciences, Beijing Normal University at Zhuhai, Zhuhai, Guangdong 519087, China
| | - Hua Han
- Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Zengru Di
- International Academic Center of Complex Systems, Advanced Institute of Natural Sciences, Beijing Normal University at Zhuhai, Zhuhai, Guangdong 519087, China
| | - Ke Zhang
- International Academic Center of Complex Systems, Advanced Institute of Natural Sciences, Beijing Normal University at Zhuhai, Zhuhai, Guangdong 519087, China
- Corresponding author
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50
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Olfactory stimuli and moonwalker SEZ neurons can drive backward locomotion in Drosophila. Curr Biol 2022; 32:1131-1149.e7. [PMID: 35139358 PMCID: PMC8926844 DOI: 10.1016/j.cub.2022.01.035] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2021] [Revised: 10/31/2021] [Accepted: 01/12/2022] [Indexed: 01/05/2023]
Abstract
How different sensory stimuli are collected, processed, and further transformed into a coordinated motor response is a fundamental question in neuroscience. In particular, the internal and external conditions that drive animals to switch to backward walking and the mechanisms by which the nervous system supports such behavior are still unknown. In fruit flies, moonwalker descending neurons (MDNs) are considered command-type neurons for backward locomotion as they receive visual and mechanosensory inputs and transmit motor-related signals to downstream neurons to elicit backward locomotion. Whether other modalities converge onto MDNs, which central brain neurons activate MDNs, and whether other retreat-driving pathways exist is currently unknown. Here, we show that olfactory stimulation can elicit MDN-mediated backward locomotion. Moreover, we identify the moonwalker subesophageal zone neurons (MooSEZs), a pair of bilateral neurons, which can trigger straight and rotational backward locomotion. MooSEZs act via postsynaptic MDNs and via other descending neurons. Although they respond to olfactory input, they are not required for odor-induced backward walking. Thus, this work reveals an important modality input to MDNs, a novel set of neurons presynaptic to MDNs driving backward locomotion and an MDN-independent backward locomotion pathway. MooSEZs elicit backward locomotion via MDN-dependent and MDN-independent pathways MooSEZs are connected to MDNs and other descending neurons MooSEZs and MDNs both respond to olfactory input MooSEZs can trigger rotational backward locomotion
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