1
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Seggewisse A, Winding M. Mapping the fly nerve cord. eLife 2024; 13:e99804. [PMID: 38979985 PMCID: PMC11233131 DOI: 10.7554/elife.99804] [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: 07/10/2024] Open
Abstract
The first neuronal wiring diagram of an insect nerve cord, which includes biological information on cell type and organisation, enables further investigation into premotor circuit function.
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2
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Moore JJ, Genkin A, Tournoy M, Pughe-Sanford JL, de Ruyter van Steveninck RR, Chklovskii DB. The neuron as a direct data-driven controller. Proc Natl Acad Sci U S A 2024; 121:e2311893121. [PMID: 38913890 PMCID: PMC11228465 DOI: 10.1073/pnas.2311893121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 04/12/2024] [Indexed: 06/26/2024] Open
Abstract
In the quest to model neuronal function amid gaps in physiological data, a promising strategy is to develop a normative theory that interprets neuronal physiology as optimizing a computational objective. This study extends current normative models, which primarily optimize prediction, by conceptualizing neurons as optimal feedback controllers. We posit that neurons, especially those beyond early sensory areas, steer their environment toward a specific desired state through their output. This environment comprises both synaptically interlinked neurons and external motor sensory feedback loops, enabling neurons to evaluate the effectiveness of their control via synaptic feedback. To model neurons as biologically feasible controllers which implicitly identify loop dynamics, infer latent states, and optimize control we utilize the contemporary direct data-driven control (DD-DC) framework. Our DD-DC neuron model explains various neurophysiological phenomena: the shift from potentiation to depression in spike-timing-dependent plasticity with its asymmetry, the duration and adaptive nature of feedforward and feedback neuronal filters, the imprecision in spike generation under constant stimulation, and the characteristic operational variability and noise in the brain. Our model presents a significant departure from the traditional, feedforward, instant-response McCulloch-Pitts-Rosenblatt neuron, offering a modern, biologically informed fundamental unit for constructing neural networks.
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Affiliation(s)
- Jason J Moore
- Neuroscience Institute, New York University Grossman School of Medicine, New York City, NY 10016
- Center for Computational Neuroscience, Flatiron Institute, New York City, NY 10010
| | - Alexander Genkin
- Center for Computational Neuroscience, Flatiron Institute, New York City, NY 10010
| | - Magnus Tournoy
- Center for Computational Neuroscience, Flatiron Institute, New York City, NY 10010
| | | | | | - Dmitri B Chklovskii
- Neuroscience Institute, New York University Grossman School of Medicine, New York City, NY 10016
- Center for Computational Neuroscience, Flatiron Institute, New York City, NY 10010
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3
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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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4
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Azevedo A, Lesser E, Phelps JS, Mark B, Elabbady L, Kuroda S, Sustar A, Moussa A, Khandelwal A, Dallmann CJ, Agrawal S, Lee SYJ, Pratt B, Cook A, Skutt-Kakaria K, Gerhard S, Lu R, Kemnitz N, Lee K, Halageri A, Castro M, Ih D, Gager J, Tammam M, Dorkenwald S, Collman F, Schneider-Mizell C, Brittain D, Jordan CS, Dickinson M, Pacureanu A, Seung HS, Macrina T, Lee WCA, Tuthill JC. Connectomic reconstruction of a female Drosophila ventral nerve cord. Nature 2024; 631:360-368. [PMID: 38926570 DOI: 10.1038/s41586-024-07389-x] [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: 06/02/2023] [Accepted: 04/04/2024] [Indexed: 06/28/2024]
Abstract
A deep understanding of how the brain controls behaviour requires mapping neural circuits down to the muscles that they control. Here, we apply automated tools to segment neurons and identify synapses in an electron microscopy dataset of an adult female Drosophila melanogaster ventral nerve cord (VNC)1, which functions like the vertebrate spinal cord to sense and control the body. We find that the fly VNC contains roughly 45 million synapses and 14,600 neuronal cell bodies. To interpret the output of the connectome, we mapped the muscle targets of leg and wing motor neurons using genetic driver lines2 and X-ray holographic nanotomography3. With this motor neuron atlas, we identified neural circuits that coordinate leg and wing movements during take-off. We provide the reconstruction of VNC circuits, the motor neuron atlas and tools for programmatic and interactive access as resources to support experimental and theoretical studies of how the nervous system controls behaviour.
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Affiliation(s)
- Anthony Azevedo
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Ellen Lesser
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Jasper S Phelps
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
- Neuroengineering Laboratory, Brain Mind Institute and Institute of Bioengineering, EPFL, Lausanne, Switzerland
| | - Brandon Mark
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Leila Elabbady
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Sumiya Kuroda
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Anne Sustar
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Anthony Moussa
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Avinash Khandelwal
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Chris J Dallmann
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Sweta Agrawal
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Su-Yee J Lee
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Brandon Pratt
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Andrew Cook
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | | | - Stephan Gerhard
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
- UniDesign Solutions, Zurich, Switzerland
| | - Ran Lu
- Zetta AI, Sherrill, NJ, USA
| | | | - Kisuk Lee
- Zetta AI, Sherrill, NJ, USA
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | | | | | | | | | | | - Sven Dorkenwald
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Computer Science Department, Princeton University, Princeton, NJ, USA
| | | | | | | | - Chris S Jordan
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | | | | | | | | | - Wei-Chung Allen Lee
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA.
- F. M. Kirby Neurobiology Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
| | - John C Tuthill
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA.
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5
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Haustein M, Blanke A, Bockemühl T, Büschges A. A leg model based on anatomical landmarks to study 3D joint kinematics of walking in Drosophila melanogaster. Front Bioeng Biotechnol 2024; 12:1357598. [PMID: 38988867 PMCID: PMC11233710 DOI: 10.3389/fbioe.2024.1357598] [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: 12/18/2023] [Accepted: 05/20/2024] [Indexed: 07/12/2024] Open
Abstract
Walking is the most common form of how animals move on land. The model organism Drosophila melanogaster has become increasingly popular for studying how the nervous system controls behavior in general and walking in particular. Despite recent advances in tracking and modeling leg movements of walking Drosophila in 3D, there are still gaps in knowledge about the biomechanics of leg joints due to the tiny size of fruit flies. For instance, the natural alignment of joint rotational axes was largely neglected in previous kinematic analyses. In this study, we therefore present a detailed kinematic leg model in which not only the segment lengths but also the main rotational axes of the joints were derived from anatomical landmarks, namely, the joint condyles. Our model with natural oblique joint axes is able to adapt to the 3D leg postures of straight and forward walking fruit flies with high accuracy. When we compared our model to an orthogonalized version, we observed that our model showed a smaller error as well as differences in the used range of motion (ROM), highlighting the advantages of modeling natural rotational axes alignment for the study of joint kinematics. We further found that the kinematic profiles of front, middle, and hind legs differed in the number of required degrees of freedom as well as their contributions to stepping, time courses of joint angles, and ROM. Our findings provide deeper insights into the joint kinematics of walking in Drosophila, and, additionally, will help to develop dynamical, musculoskeletal, and neuromechanical simulations.
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Affiliation(s)
- Moritz Haustein
- Institute of Zoology, Biocenter Cologne, University of Cologne, Cologne, Germany
| | - Alexander Blanke
- Bonn Institute for Organismic Biology (BIOB), Animal Biodiversity, University of Bonn, Bonn, Germany
| | - Till Bockemühl
- Institute of Zoology, Biocenter Cologne, University of Cologne, Cologne, Germany
| | - Ansgar Büschges
- Institute of Zoology, Biocenter Cologne, University of Cologne, Cologne, Germany
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6
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Qi C, Qian C, Steijvers E, Colvin RA, Lee D. Single dopaminergic neuron DAN-c1 in Drosophila larval brain mediates aversive olfactory learning through D2-like receptors. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.15.575767. [PMID: 38293177 PMCID: PMC10827047 DOI: 10.1101/2024.01.15.575767] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
The intricate relationship between the dopaminergic system and olfactory associative learning in Drosophila has been an intense scientific inquiry. Leveraging the formidable genetic tools, we conducted a screening of 57 dopaminergic drivers, leading to the discovery of DAN-c1 driver, uniquely targeting the single dopaminergic neuron (DAN) in each brain hemisphere. While the involvement of excitatory D1-like receptors is well-established, the role of D2-like receptors (D2Rs) remains underexplored. Our investigation reveals the expression of D2Rs in both DANs and the mushroom body (MB) of third instar larval brains. Silencing D2Rs in DAN-c1 via microRNA disrupts aversive learning, further supported by optogenetic activation of DAN-c1 during training, affirming the inhibitory role of D2R autoreceptor. Intriguingly, D2R knockdown in the MB impairs both appetitive and aversive learning. These findings elucidate the distinct contributions of D2Rs in diverse brain structures, providing novel insights into the molecular mechanisms governing associative learning in Drosophila larvae.
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Affiliation(s)
- Cheng Qi
- Department of Biological Sciences, Ohio University, Athens, OH 45701, USA
| | | | | | - Robert A. Colvin
- Department of Biological Sciences, Ohio University, Athens, OH 45701, USA
| | - Daewoo Lee
- Department of Biological Sciences, Ohio University, Athens, OH 45701, USA
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7
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Jonaitis J, Hibbard KL, McCafferty Layte K, Hiramoto A, Cardona A, Truman JW, Nose A, Zwart MF, Pulver SR. STEERING FROM THE REAR: COORDINATION OF CENTRAL PATTERN GENERATORS UNDERLYING NAVIGATION BY ASCENDING INTERNEURONS. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.17.598162. [PMID: 38948859 PMCID: PMC11212907 DOI: 10.1101/2024.06.17.598162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
Understanding how animals coordinate movements to achieve goals is a fundamental pursuit in neuroscience. Here we explore how neurons that reside in posterior lower-order regions of a locomotor system project to anterior higher-order regions to influence steering and navigation. We characterized the anatomy and functional role of a population of ascending interneurons in the ventral nerve cord of Drosophila larvae. Through electron microscopy reconstructions and light microscopy, we determined that the cholinergic 19f cells receive input primarily from premotor interneurons and synapse upon a diverse array of postsynaptic targets within the anterior segments including other 19f cells. Calcium imaging of 19f activity in isolated central nervous system (CNS) preparations in relation to motor neurons revealed that 19f neurons are recruited into most larval motor programmes. 19f activity lags behind motor neuron activity and as a population, the cells encode spatio-temporal patterns of locomotor activity in the larval CNS. Optogenetic manipulations of 19f cell activity in isolated CNS preparations revealed that they coordinate the activity of central pattern generators underlying exploratory headsweeps and forward locomotion in a context and location specific manner. In behaving animals, activating 19f cells suppressed exploratory headsweeps and slowed forward locomotion, while inhibition of 19f activity potentiated headsweeps, slowing forward movement. Inhibiting activity in 19f cells ultimately affected the ability of larvae to remain in the vicinity of an odor source during an olfactory navigation task. Overall, our findings provide insights into how ascending interneurons monitor motor activity and shape interactions amongst rhythm generators underlying complex navigational tasks.
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Affiliation(s)
- Julius Jonaitis
- School of Psychology and Neuroscience, University of St Andrews, St Andrews, UK
| | | | | | - Atsuki Hiramoto
- Department of Complexity Science and Engineering, Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan
| | - Albert Cardona
- HHMI Janelia Research Campus, Ashburn, VA, USA
- Department of Physiology, Development, and Neuroscience, University of Cambridge, Cambridge, UK
- MRC Laboratory of Molecular Biology, Cambridge UK
| | - James W. Truman
- Friday Harbor Laboratories, University of Washington, Friday Harbor, WA, USA
| | - Akinao Nose
- Department of Complexity Science and Engineering, Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan
- Department of Physics, Graduate School of Science, The University of Tokyo, Tokyo, Japan
| | - Maarten F. Zwart
- School of Psychology and Neuroscience, University of St Andrews, St Andrews, UK
- Centre of Biophotonics, University of St Andrews, St Andrews, UK
- Institute for Behavioural and Neural Sciences, University of St Andrews, St Andrews, UK
| | - Stefan R. Pulver
- School of Psychology and Neuroscience, University of St Andrews, St Andrews, UK
- Centre of Biophotonics, University of St Andrews, St Andrews, UK
- Institute for Behavioural and Neural Sciences, University of St Andrews, St Andrews, UK
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8
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McNulty P, Wu R, Yamaguchi A, Heckscher ES, Haas A, Nwankpa A, Skanata MM, Gershow M. CRASH2p: Closed-loop Two Photon Imaging in Freely Moving Animals. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.22.595209. [PMID: 38826435 PMCID: PMC11142166 DOI: 10.1101/2024.05.22.595209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Direct measurement of neural activity in freely moving animals is essential for understanding how the brain controls and represents behaviors. Genetically encoded calcium indicators report neural activity as changes in fluorescence intensity, but brain motion confounds quantitative measurement of fluorescence. Translation, rotation, and deformation of the brain and the movements of intervening scattering or auto-fluorescent tissue all alter the amount of fluorescent light captured by a microscope. Compared to single-photon approaches, two photon microscopy is less sensitive to scattering and off-target fluorescence, but more sensitive to motion, and two photon imaging has always required anchoring the microscope to the brain. We developed a closed-loop resonant axial-scanning high-speed two photon (CRASH2p) microscope for real-time 3D motion correction in unrestrained animals, without implantation of reference markers. We complemented CRASH2p with a novel scanning strategy and a multistage registration pipeline. We performed volumetric ratiometrically corrected functional imaging in the CNS of freely moving Drosophila larvae and discovered previously unknown neural correlates of behavior.
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Affiliation(s)
- Paul McNulty
- Department of Physics,New York University, New York, USA
| | - Rui Wu
- Department of Physics,New York University, New York, USA
| | | | - Ellie S. Heckscher
- Department of Molecular Genetics and Cell Biology, University of Chicago, Chicago, IL
| | - Andrew Haas
- Department of Physics,New York University, New York, USA
| | | | | | - Marc Gershow
- Department of Physics,New York University, New York, USA
- Center for Neural Science,New York University, New York, USA
- Neuroscience Institute, New York University, New York, USA
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9
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Krauhausen I, Griggs S, McCulloch I, den Toonder JMJ, Gkoupidenis P, van de Burgt Y. Bio-inspired multimodal learning with organic neuromorphic electronics for behavioral conditioning in robotics. Nat Commun 2024; 15:4765. [PMID: 38834541 DOI: 10.1038/s41467-024-48881-2] [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: 01/19/2024] [Accepted: 05/13/2024] [Indexed: 06/06/2024] Open
Abstract
Biological systems interact directly with the environment and learn by receiving multimodal feedback via sensory stimuli that shape the formation of internal neuronal representations. Drawing inspiration from biological concepts such as exploration and sensory processing that eventually lead to behavioral conditioning, we present a robotic system handling objects through multimodal learning. A small-scale organic neuromorphic circuit locally integrates and adaptively processes multimodal sensory stimuli, enabling the robot to interact intelligently with its surroundings. The real-time handling of sensory stimuli via low-voltage organic neuromorphic devices with synaptic functionality forms multimodal associative connections that lead to behavioral conditioning, and thus the robot learns to avoid potentially dangerous objects. This work demonstrates that adaptive neuro-inspired circuitry with multifunctional organic materials, can accommodate locally efficient bio-inspired learning for advancing intelligent robotics.
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Affiliation(s)
- Imke Krauhausen
- Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, The Netherlands
- Microsystems, Department of Mechanical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Max Planck Institute for Polymer Research, Mainz, Germany
| | - Sophie Griggs
- Department of Chemistry, University of Oxford, Oxford, UK
| | - Iain McCulloch
- Department of Chemistry, University of Oxford, Oxford, UK
| | - Jaap M J den Toonder
- Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, The Netherlands
- Microsystems, Department of Mechanical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | | | - Yoeri van de Burgt
- Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, The Netherlands.
- Microsystems, Department of Mechanical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
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10
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Prince GS, Reynolds M, Martina V, Sun H. Gene-environmental regulation of the postnatal post-mitotic neuronal maturation. Trends Genet 2024; 40:480-494. [PMID: 38658255 PMCID: PMC11153025 DOI: 10.1016/j.tig.2024.03.006] [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: 01/30/2024] [Revised: 03/20/2024] [Accepted: 03/21/2024] [Indexed: 04/26/2024]
Abstract
Embryonic neurodevelopment, particularly neural progenitor differentiation into post-mitotic neurons, has been extensively studied. While the number and composition of post-mitotic neurons remain relatively constant from birth to adulthood, the brain undergoes significant postnatal maturation marked by major property changes frequently disrupted in neural diseases. This review first summarizes recent characterizations of the functional and molecular maturation of the postnatal nervous system. We then review regulatory mechanisms controlling the precise gene expression changes crucial for the intricate sequence of maturation events, highlighting experience-dependent versus cell-intrinsic genetic timer mechanisms. Despite significant advances in understanding of the gene-environmental regulation of postnatal neuronal maturation, many aspects remain unknown. The review concludes with our perspective on exciting future research directions in the next decade.
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Affiliation(s)
- Gabrielle S Prince
- Department of Cell, Developmental, and Integrative Biology, The University of Alabama at Birmingham, Birmingham, AL, USA
| | - Molly Reynolds
- Department of Cell, Developmental, and Integrative Biology, The University of Alabama at Birmingham, Birmingham, AL, USA
| | - Verdion Martina
- Department of Cell, Developmental, and Integrative Biology, The University of Alabama at Birmingham, Birmingham, AL, USA
| | - HaoSheng Sun
- Department of Cell, Developmental, and Integrative Biology, The University of Alabama at Birmingham, Birmingham, AL, USA; Freeman Hrabowski Scholar, Howard Hughes Medical Institute, Chevy Chase, MD, USA.
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11
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Liu J, Zheng Y, Lin L, Guo J, Lv Y, Yuan J, Zhai H, Chen X, Shen L, Li L, Bai S, Han H. A robust transformer-based pipeline of 3D cell alignment, denoise and instance segmentation on electron microscopy sequence images. JOURNAL OF PLANT PHYSIOLOGY 2024; 297:154236. [PMID: 38621330 DOI: 10.1016/j.jplph.2024.154236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Revised: 03/15/2024] [Accepted: 03/20/2024] [Indexed: 04/17/2024]
Abstract
Germline cells are critical for transmitting genetic information to subsequent generations in biological organisms. While their differentiation from somatic cells during embryonic development is well-documented in most animals, the regulatory mechanisms initiating plant germline cells are not well understood. To thoroughly investigate the complex morphological transformations of their ultrastructure over developmental time, nanoscale 3D reconstruction of entire plant tissues is necessary, achievable exclusively through electron microscopy imaging. This paper presents a full-process framework designed for reconstructing large-volume plant tissue from serial electron microscopy images. The framework ensures end-to-end direct output of reconstruction results, including topological networks and morphological analysis. The proposed 3D cell alignment, denoise, and instance segmentation pipeline (3DCADS) leverages deep learning to provide a cell instance segmentation workflow for electron microscopy image series, ensuring accurate and robust 3D cell reconstructions with high computational efficiency. The pipeline involves five stages: the registration of electron microscopy serial images; image enhancement and denoising; semantic segmentation using a Transformer-based neural network; instance segmentation through a supervoxel-based clustering algorithm; and an automated analysis and statistical assessment of the reconstruction results, with the mapping of topological connections. The 3DCADS model's precision was validated on a plant tissue ground-truth dataset, outperforming traditional baseline models and deep learning baselines in overall accuracy. The framework was applied to the reconstruction of early meiosis stages in the anthers of Arabidopsis thaliana, resulting in a topological connectivity network and analysis of morphological parameters and characteristics of cell distribution. The experiment underscores the 3DCADS model's potential for biological tissue identification and its significance in quantitative analysis of plant cell development, crucial for examining samples across different genetic phenotypes and mutations in plant development. Additionally, the paper discusses the regulatory mechanisms of Arabidopsis thaliana's germline cells and the development of stamen cells before meiosis, offering new insights into the transition from somatic to germline cell fate in plants.
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Affiliation(s)
- Jiazheng Liu
- School of Future Technology, University of Chinese Academy of Sciences, Beijing 101408, China; Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; Team of Microscale Reconstruction and Intelligent Analysis, Laboratory of Brain-AI, Institute of Automation, Chinese Academy of Sciences, Beijing 101499, China
| | - Yafeng Zheng
- College of Life Sciences, Peking University, Beijing 100871, China; State Key Laboratory of Protein and Plant Gene Research, Beijing 100871, China
| | - Limei Lin
- Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; Team of Microscale Reconstruction and Intelligent Analysis, Laboratory of Brain-AI, Institute of Automation, Chinese Academy of Sciences, Beijing 101499, China
| | - Jingyue Guo
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 101408, China; Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; Team of Microscale Reconstruction and Intelligent Analysis, Laboratory of Brain-AI, Institute of Automation, Chinese Academy of Sciences, Beijing 101499, China
| | - Yanan Lv
- Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; Team of Microscale Reconstruction and Intelligent Analysis, Laboratory of Brain-AI, Institute of Automation, Chinese Academy of Sciences, Beijing 101499, China
| | - Jingbin Yuan
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 101408, China; Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; Team of Microscale Reconstruction and Intelligent Analysis, Laboratory of Brain-AI, Institute of Automation, Chinese Academy of Sciences, Beijing 101499, China
| | - Hao Zhai
- School of Future Technology, University of Chinese Academy of Sciences, Beijing 101408, China; Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; Team of Microscale Reconstruction and Intelligent Analysis, Laboratory of Brain-AI, Institute of Automation, Chinese Academy of Sciences, Beijing 101499, China
| | - Xi Chen
- Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; Team of Microscale Reconstruction and Intelligent Analysis, Laboratory of Brain-AI, Institute of Automation, Chinese Academy of Sciences, Beijing 101499, China
| | - Lijun Shen
- Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; Team of Microscale Reconstruction and Intelligent Analysis, Laboratory of Brain-AI, Institute of Automation, Chinese Academy of Sciences, Beijing 101499, China
| | - LinLin Li
- Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; Team of Microscale Reconstruction and Intelligent Analysis, Laboratory of Brain-AI, Institute of Automation, Chinese Academy of Sciences, Beijing 101499, China.
| | - Shunong Bai
- College of Life Sciences, Peking University, Beijing 100871, China; State Key Laboratory of Protein and Plant Gene Research, Beijing 100871, China.
| | - Hua Han
- School of Future Technology, University of Chinese Academy of Sciences, Beijing 101408, China; Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; Team of Microscale Reconstruction and Intelligent Analysis, Laboratory of Brain-AI, Institute of Automation, Chinese Academy of Sciences, Beijing 101499, China.
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12
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Kramer TS, Flavell SW. Building and integrating brain-wide maps of nervous system function in invertebrates. Curr Opin Neurobiol 2024; 86:102868. [PMID: 38569231 DOI: 10.1016/j.conb.2024.102868] [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] [Revised: 02/13/2024] [Accepted: 03/07/2024] [Indexed: 04/05/2024]
Abstract
The selection and execution of context-appropriate behaviors is controlled by the integrated action of neural circuits throughout the brain. However, how activity is coordinated across brain regions, and how nervous system structure enables these functional interactions, remain open questions. Recent technical advances have made it feasible to build brain-wide maps of nervous system structure and function, such as brain activity maps, connectomes, and cell atlases. Here, we review recent progress in this area, focusing on C. elegans and D. melanogaster, as recent work has produced global maps of these nervous systems. We also describe neural circuit motifs elucidated in studies of specific networks, which highlight the complexities that must be captured to build accurate models of whole-brain function.
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Affiliation(s)
- Talya S Kramer
- Picower Institute for Learning and Memory, Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA; MIT Biology Graduate Program, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Steven W Flavell
- Picower Institute for Learning and Memory, Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA.
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13
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Nern A, Loesche F, Takemura SY, Burnett LE, Dreher M, Gruntman E, Hoeller J, Huang GB, Januszewski M, Klapoetke NC, Koskela S, Longden KD, Lu Z, Preibisch S, Qiu W, Rogers EM, Seenivasan P, Zhao A, Bogovic J, Canino BS, Clements J, Cook M, Finley-May S, Flynn MA, Hameed I, Fragniere AMC, Hayworth KJ, Hopkins GP, Hubbard PM, Katz WT, Kovalyak J, Lauchie SA, Leonard M, Lohff A, Maldonado CA, Mooney C, Okeoma N, Olbris DJ, Ordish C, Paterson T, Phillips EM, Pietzsch T, Salinas JR, Rivlin PK, Schlegel P, Scott AL, Scuderi LA, Takemura S, Talebi I, Thomson A, Trautman ET, Umayam L, Walsh C, Walsh JJ, Xu CS, Yakal EA, Yang T, Zhao T, Funke J, George R, Hess HF, Jefferis GSXE, Knecht C, Korff W, Plaza SM, Romani S, Saalfeld S, Scheffer LK, Berg S, Rubin GM, Reiser MB. Connectome-driven neural inventory of a complete visual system. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.16.589741. [PMID: 38659887 PMCID: PMC11042306 DOI: 10.1101/2024.04.16.589741] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
Vision provides animals with detailed information about their surroundings, conveying diverse features such as color, form, and movement across the visual scene. Computing these parallel spatial features requires a large and diverse network of neurons, such that in animals as distant as flies and humans, visual regions comprise half the brain's volume. These visual brain regions often reveal remarkable structure-function relationships, with neurons organized along spatial maps with shapes that directly relate to their roles in visual processing. To unravel the stunning diversity of a complex visual system, a careful mapping of the neural architecture matched to tools for targeted exploration of that circuitry is essential. Here, we report a new connectome of the right optic lobe from a male Drosophila central nervous system FIB-SEM volume and a comprehensive inventory of the fly's visual neurons. We developed a computational framework to quantify the anatomy of visual neurons, establishing a basis for interpreting how their shapes relate to spatial vision. By integrating this analysis with connectivity information, neurotransmitter identity, and expert curation, we classified the ~53,000 neurons into 727 types, about half of which are systematically described and named for the first time. Finally, we share an extensive collection of split-GAL4 lines matched to our neuron type catalog. Together, this comprehensive set of tools and data unlock new possibilities for systematic investigations of vision in Drosophila, a foundation for a deeper understanding of sensory processing.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Alexandra MC Fragniere
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Philipp Schlegel
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Gregory SXE Jefferis
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
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14
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Bullard MR, Cervantes JCM, Quaicoe NB, Jin A, Adams DA, Lin JM, Iliadis E, Seidler TM, Cervantes-Sandoval I, He HY. Accelerated protein retention expansion microscopy using microwave radiation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.11.593228. [PMID: 38766072 PMCID: PMC11100821 DOI: 10.1101/2024.05.11.593228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Protein retention expansion microscopy (ExM) retains genetically encoded fluorescent proteins or antibody-conjugated fluorescent probes in fixed tissue and isotropically expands the tissue through a swellable polymer network to allow nanoscale (<70 nm) resolution on diffraction-limited confocal microscopes. Despite numerous advantages ExM brings to biological studies, the full protocol is time-consuming and can take multiple days to complete. Here, we adapted the ExM protocol to the vibratome-sectioned brain tissue of Xenopus laevis tadpoles and implemented a microwave-assisted protocol to reduce the workflow from days to hours. In addition to the significantly accelerated processing time, our microwave-assisted ExM (M/WExM) protocol maintains the superior resolution and signal-to-noise ratio of the original ExM protocol. Furthermore, the M/WExM protocol yields higher magnitude of expansion, suggesting that in addition to accelerating the process through increased diffusion rate of reagents, microwave radiation may also facilitate the expansion process. To demonstrate the applicability of this method to other specimens and protocols, we adapted the microwave-accelerated protocol to whole mount adult brain tissue of Drosophila melanogaster fruit flies, and successfully reduced the total processing time of a widely-used Drosophila IHC-ExM protocol from 6 days to 2 days. Our results demonstrate that with appropriate adjustment of the microwave parameters (wattage, pulse duration, interval, and number of cycles), this protocol can be readily adapted to different model organisms and tissue types to greatly increase the efficiency of ExM experiments.
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Affiliation(s)
| | | | | | - Amanda Jin
- Department of Biology, Georgetown University, Washington, DC 20057
| | - Danya A. Adams
- Department of Biology, Georgetown University, Washington, DC 20057
| | - Jessica M. Lin
- Department of Biology, Georgetown University, Washington, DC 20057
| | - Elena Iliadis
- Department of Biology, Georgetown University, Washington, DC 20057
| | - Tess M. Seidler
- Department of Biology, Georgetown University, Washington, DC 20057
| | | | - Hai-yan He
- Department of Biology, Georgetown University, Washington, DC 20057
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15
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Lee SYJ, Dallmann CJ, Cook AP, Tuthill JC, Agrawal S. Divergent neural circuits for proprioceptive and exteroceptive sensing of the Drosophila leg. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.23.590808. [PMID: 38712128 PMCID: PMC11071415 DOI: 10.1101/2024.04.23.590808] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Somatosensory neurons provide the nervous system with information about mechanical forces originating inside and outside the body. Here, we use connectomics to reconstruct and analyze neural circuits downstream of the largest somatosensory organ in the Drosophila leg, the femoral chordotonal organ (FeCO). The FeCO has been proposed to support both proprioceptive sensing of the fly's femur-tibia joint and exteroceptive sensing of substrate vibrations, but it remains unknown which sensory neurons and central circuits contribute to each of these functions. We found that different subtypes of FeCO sensory neurons feed into distinct proprioceptive and exteroceptive pathways. Position- and movement-encoding FeCO neurons connect to local leg motor control circuits in the ventral nerve cord (VNC), indicating a proprioceptive function. In contrast, signals from the vibration-encoding FeCO neurons are integrated across legs and transmitted to auditory regions in the brain, indicating an exteroceptive function. Overall, our analyses reveal the structure of specialized circuits for processing proprioceptive and exteroceptive signals from the fly leg. They also demonstrate how analyzing patterns of synaptic connectivity can distill organizing principles from complex sensorimotor circuits.
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16
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Ke M, Hou Y, Zhang L, Liu G. Brain functional network changes in patients with juvenile myoclonic epilepsy: a study based on graph theory and Granger causality analysis. Front Neurosci 2024; 18:1363255. [PMID: 38774788 PMCID: PMC11106382 DOI: 10.3389/fnins.2024.1363255] [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: 12/30/2023] [Accepted: 04/04/2024] [Indexed: 05/24/2024] Open
Abstract
Many resting-state functional magnetic resonance imaging (rs-fMRI) studies have shown that the brain networks are disrupted in adolescent patients with juvenile myoclonic epilepsy (JME). However, previous studies have mainly focused on investigating brain connectivity disruptions from the perspective of static functional connections, overlooking the dynamic causal characteristics between brain network connections. In our study involving 37 JME patients and 35 Healthy Controls (HC), we utilized rs-fMRI to construct whole-brain functional connectivity network. By applying graph theory, we delved into the altered topological structures of the brain functional connectivity network in JME patients and identified abnormal regions as key regions of interest (ROIs). A novel aspect of our research was the application of a combined approach using the sliding window technique and Granger causality analysis (GCA). This method allowed us to delve into the dynamic causal relationships between these ROIs and uncover the intricate patterns of dynamic effective connectivity (DEC) that pervade various brain functional networks. Graph theory analysis revealed significant deviations in JME patients, characterized by abnormal increases or decreases in metrics such as nodal betweenness centrality, degree centrality, and efficiency. These findings underscore the presence of widespread disruptions in the topological features of the brain. Further, clustering analysis of the time series data from abnormal brain regions distinguished two distinct states indicative of DEC patterns: a state of strong connectivity at a lower frequency (State 1) and a state of weak connectivity at a higher frequency (State 2). Notably, both states were associated with connectivity abnormalities across different ROIs, suggesting the disruption of local properties within the brain functional connectivity network and the existence of widespread multi-functional brain functional networks damage in JME patients. Our findings elucidate significant disruptions in the local properties of whole-brain functional connectivity network in patients with JME, revealing causal impairments across multiple functional networks. These findings collectively suggest that JME is a generalized epilepsy with localized abnormalities. Such insights highlight the intricate network dysfunctions characteristic of JME, thereby enriching our understanding of its pathophysiological features.
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Affiliation(s)
- Ming Ke
- School of Computer and Communication, Lanzhou University of Technology, Lanzhou, China
| | - Yaru Hou
- School of Computer and Communication, Lanzhou University of Technology, Lanzhou, China
| | - Li Zhang
- Hospital of Lanzhou University of Technology, Lanzhou University of Technology, Lanzhou, China
| | - Guangyao Liu
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China
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17
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Nguyen TH, Vicidomini R, Choudhury SD, Han TH, Maric D, Brody T, Serpe M. scRNA-seq data from the larval Drosophila ventral cord provides a resource for studying motor systems function and development. Dev Cell 2024; 59:1210-1230.e9. [PMID: 38569548 PMCID: PMC11078614 DOI: 10.1016/j.devcel.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: 06/27/2023] [Revised: 12/05/2023] [Accepted: 03/06/2024] [Indexed: 04/05/2024]
Abstract
The Drosophila larval ventral nerve cord (VNC) shares many similarities with the spinal cord of vertebrates and has emerged as a major model for understanding the development and function of motor systems. Here, we use high-quality scRNA-seq, validated by anatomical identification, to create a comprehensive census of larval VNC cell types. We show that the neural lineages that comprise the adult VNC are already defined, but quiescent, at the larval stage. Using fluorescence-activated cell sorting (FACS)-enriched populations, we separate all motor neuron bundles and link individual neuron clusters to morphologically characterized known subtypes. We discovered a glutamate receptor subunit required for basal neurotransmission and homeostasis at the larval neuromuscular junction. We describe larval glia and endorse the general view that glia perform consistent activities throughout development. This census represents an extensive resource and a powerful platform for future discoveries of cellular and molecular mechanisms in repair, regeneration, plasticity, homeostasis, and behavioral coordination.
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Affiliation(s)
| | | | | | | | - Dragan Maric
- Flow and Imaging Cytometry Core, NINDS, NIH, Bethesda, MD 20892, USA
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18
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Avila B, Augusto P, Zimmer M, Serafino M, Makse HA. Fibration symmetries and cluster synchronization in the Caenorhabditis elegans connectome. ARXIV 2024:arXiv:2305.19367v2. [PMID: 37396607 PMCID: PMC10312817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Capturing how the Caenorhabditis elegans connectome structure gives rise to its neuron functionality remains unclear. It is through fiber symmetries found in its neuronal connectivity that synchronization of a group of neurons can be determined. To understand these we investigate graph symmetries and search for such in the symmetrized versions of the forward and backward locomotive sub-networks of the Caenorhabditi elegans worm neuron network. The use of ordinarily differential equations simulations admissible to these graphs are used to validate the predictions of these fiber symmetries and are compared to the more restrictive orbit symmetries. Additionally fibration symmetries are used to decompose these graphs into their fundamental building blocks which reveal units formed by nested loops or multilayered fibers. It is found that fiber symmetries of the connectome can accurately predict neuronal synchronization even under not idealized connectivity as long as the dynamics are within stable regimes of simulations.
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Affiliation(s)
- Bryant Avila
- Levich Institute, Physics Department, City College of New York, New York, NY, USA
| | - Pedro Augusto
- Vienna Biocenter PhD Program, Doctoral School of the University of Vienna and Medical University of Vienna, Vienna, Austria
- Department of Neuroscience and Developmental Biology, Vienna Biocenter (VBC), University of Vienna, Vienna, Austria
| | - Manuel Zimmer
- Research Institute of Molecular Pathology (IMP), Vienna Biocenter (VBC), University of Vienna, Vienna, Austria
- Department of Neuroscience and Developmental Biology, Vienna Biocenter (VBC), University of Vienna, Vienna, Austria
| | - Matteo Serafino
- Levich Institute, Physics Department, City College of New York, New York, NY, USA
| | - Hernán A. Makse
- Levich Institute, Physics Department, City College of New York, New York, NY, USA
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19
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Fiala A, Kaun KR. What do the mushroom bodies do for the insect brain? Twenty-five years of progress. Learn Mem 2024; 31:a053827. [PMID: 38862175 PMCID: PMC11199942 DOI: 10.1101/lm.053827.123] [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: 04/21/2024] [Accepted: 04/22/2024] [Indexed: 06/13/2024]
Abstract
In 1998, a special edition of Learning & Memory was published with a discrete focus of synthesizing the state of the field to provide an overview of the function of the insect mushroom body. While molecular neuroscience and optical imaging of larger brain areas were advancing, understanding the basic functioning of neuronal circuits, particularly in the context of the mushroom body, was rudimentary. In the past 25 years, technological innovations have allowed researchers to map and understand the in vivo function of the neuronal circuits of the mushroom body system, making it an ideal model for investigating the circuit basis of sensory encoding, memory formation, and behavioral decisions. Collaborative efforts within the community have played a crucial role, leading to an interactive connectome of the mushroom body and accessible genetic tools for studying mushroom body circuit function. Looking ahead, continued technological innovation and collaborative efforts are likely to further advance our understanding of the mushroom body and its role in behavior and cognition, providing insights that generalize to other brain structures and species.
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Affiliation(s)
- André Fiala
- Department of Molecular Neurobiology of Behaviour, University of Göttingen, Göttingen 37077, Germany
| | - Karla R Kaun
- Department of Neuroscience, Brown University, Providence, Rhode Island 02806, USA
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20
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Chan ICW, Chen N, Hernandez J, Meltzer H, Park A, Stahl A. Future avenues in Drosophila mushroom body research. Learn Mem 2024; 31:a053863. [PMID: 38862172 PMCID: PMC11199946 DOI: 10.1101/lm.053863.123] [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: 11/02/2023] [Accepted: 03/27/2024] [Indexed: 06/13/2024]
Abstract
How does the brain translate sensory information into complex behaviors? With relatively small neuronal numbers, readable behavioral outputs, and an unparalleled genetic toolkit, the Drosophila mushroom body (MB) offers an excellent model to address this question in the context of associative learning and memory. Recent technological breakthroughs, such as the freshly completed full-brain connectome, multiomics approaches, CRISPR-mediated gene editing, and machine learning techniques, led to major advancements in our understanding of the MB circuit at the molecular, structural, physiological, and functional levels. Despite significant progress in individual MB areas, the field still faces the fundamental challenge of resolving how these different levels combine and interact to ultimately control the behavior of an individual fly. In this review, we discuss various aspects of MB research, with a focus on the current knowledge gaps, and an outlook on the future methodological developments required to reach an overall view of the neurobiological basis of learning and memory.
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Affiliation(s)
- Ivy Chi Wai Chan
- Dynamics of Neuronal Circuits Group, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Developmental Biology, RWTH Aachen University, Aachen, Germany
| | - Nannan Chen
- School of Life Science and Technology, Key Laboratory of Developmental Genes and Human Disease, Southeast University, Nanjing 210096, China
| | - John Hernandez
- Neuroscience Department, Brown University, Providence, Rhode Island 02906, USA
| | - Hagar Meltzer
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 7610001, Israel
- Department of Molecular Neuroscience, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Annie Park
- Department of Physiology, Anatomy and Genetics, Centre for Neural Circuits and Behaviour, University of Oxford, Oxford, United Kingdom
| | - Aaron Stahl
- Neuroscience and Pharmacology, University of Iowa, Iowa City, Iowa 52242, USA
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21
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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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22
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Lesser E, Azevedo AW, Phelps JS, Elabbady L, Cook A, Sakeena Syed D, Mark B, Kuroda S, Sustar A, Moussa A, Dallmann CJ, Agrawal S, Lee SYJ, Pratt B, Skutt-Kakaria K, Gerhard S, Lu R, Kemnitz N, Lee K, Halageri A, Castro M, Ih D, Gager J, Tammam M, Dorkenwald S, Collman F, Schneider-Mizell C, Brittain D, Jordan CS, Macrina T, Dickinson M, Lee WCA, Tuthill JC. Synaptic architecture of leg and wing premotor control networks in Drosophila. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.05.30.542725. [PMID: 37398440 PMCID: PMC10312524 DOI: 10.1101/2023.05.30.542725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Animal movement is controlled by motor neurons (MNs), which project out of the central nervous system to activate muscles. MN activity is coordinated by complex premotor networks that allow individual muscles to contribute to many different behaviors. Here, we use connectomics to analyze the wiring logic of premotor circuits controlling the Drosophila leg and wing. We find that both premotor networks cluster into modules that link MNs innervating muscles with related functions. Within most leg motor modules, the synaptic weights of each premotor neuron are proportional to the size of their target MNs, establishing a circuit basis for hierarchical MN recruitment. In contrast, wing premotor networks lack proportional synaptic connectivity, which may allow wing steering muscles to be recruited with different relative timing. By comparing the architecture of distinct limb motor control systems within the same animal, we identify common principles of premotor network organization and specializations that reflect the unique biomechanical constraints and evolutionary origins of leg and wing motor control.
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Affiliation(s)
- Ellen Lesser
- Department of Physiology and Biophysics, University of Washington, WA, USA
| | - Anthony W. Azevedo
- Department of Physiology and Biophysics, University of Washington, WA, USA
| | - Jasper S. Phelps
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Leila Elabbady
- Department of Physiology and Biophysics, University of Washington, WA, USA
| | - Andrew Cook
- Department of Physiology and Biophysics, University of Washington, WA, USA
| | | | - Brandon Mark
- Department of Physiology and Biophysics, University of Washington, WA, USA
| | - Sumiya Kuroda
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Anne Sustar
- Department of Physiology and Biophysics, University of Washington, WA, USA
| | - Anthony Moussa
- Department of Physiology and Biophysics, University of Washington, WA, USA
| | - Chris J. Dallmann
- Department of Physiology and Biophysics, University of Washington, WA, USA
| | - Sweta Agrawal
- Department of Physiology and Biophysics, University of Washington, WA, USA
| | - Su-Yee J. Lee
- Department of Physiology and Biophysics, University of Washington, WA, USA
| | - Brandon Pratt
- Department of Physiology and Biophysics, University of Washington, WA, USA
| | | | - Stephan Gerhard
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
- UniDesign Solutions LLC, Switzerland
| | | | | | - Kisuk Lee
- Zetta AI, LLC, USA
- Princeton Neuroscience Institute, Princeton University, NJ, USA
| | | | | | | | | | | | - Sven Dorkenwald
- Princeton Neuroscience Institute, Princeton University, NJ, USA
- Computer Science Department, Princeton University, NJ, USA
| | | | | | | | - Chris S. Jordan
- Princeton Neuroscience Institute, Princeton University, NJ, USA
| | | | | | - Wei-Chung Allen Lee
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
- F.M. Kirby Neurobiology Center, Boston Children’s Hospital, Harvard Medical School, MA, USA
| | - John C. Tuthill
- Department of Physiology and Biophysics, University of Washington, WA, USA
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23
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Uytiepo M, Zhu Y, Bushong E, Polli F, Chou K, Zhao E, Kim C, Luu D, Chang L, Quach T, Haberl M, Patapoutian L, Beutter E, Zhang W, Dong B, McCue E, Ellisman M, Maximov A. Synaptic architecture of a memory engram in the mouse hippocampus. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.23.590812. [PMID: 38712256 PMCID: PMC11071366 DOI: 10.1101/2024.04.23.590812] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Memory engrams are formed through experience-dependent remodeling of neural circuits, but their detailed architectures have remained unresolved. Using 3D electron microscopy, we performed nanoscale reconstructions of the hippocampal CA3-CA1 pathway following chemogenetic labeling of cellular ensembles with a remote history of correlated excitation during associative learning. Projection neurons involved in memory acquisition expanded their connectomes via multi-synaptic boutons without altering the numbers and spatial arrangements of individual axonal terminals and dendritic spines. This expansion was driven by presynaptic activity elicited by specific negative valence stimuli, regardless of the co-activation state of postsynaptic partners. The rewiring of initial ensembles representing an engram coincided with local, input-specific changes in the shapes and organelle composition of glutamatergic synapses, reflecting their weights and potential for further modifications. Our findings challenge the view that the connectivity among neuronal substrates of memory traces is governed by Hebbian mechanisms, and offer a structural basis for representational drifts.
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24
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Bui KC, Kamiyama D. Adjacent Neuronal Fascicle Guides Motoneuron 24 Dendritic Branching and Axonal Routing Decisions through Dscam1 Signaling. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.08.588591. [PMID: 38645010 PMCID: PMC11030417 DOI: 10.1101/2024.04.08.588591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
The formation and precise positioning of axons and dendrites are crucial for the development of neural circuits. Although juxtracrine signaling via cell-cell contact is known to influence these processes, the specific structures and mechanisms regulating neuronal process positioning within the central nervous system (CNS) remain to be fully identified. Our study investigates motoneuron 24 (MN24) in the Drosophila embryonic CNS, which is characterized by a complex yet stereotyped axon projection pattern, known as 'axonal routing.' In this motoneuron, the primary dendritic branches project laterally toward the midline, specifically emerging at the sites where axons turn. We observed that Scp2-positive neurons contribute to the lateral fascicle structure in the ventral nerve cord (VNC) near MN24 dendrites. Notably, the knockout of the Down syndrome cell adhesion molecule (dscam1) results in the loss of dendrites and disruption of proper axonal routing in MN24, while not affecting the formation of the fascicle structure. Through cell-type specific knockdown and rescue experiments of dscam1, we have determined that the interaction between MN24 and Scp2-positive fascicle, mediated by Dscam1, promotes the development of both dendrites and axonal routing. Our findings demonstrate that the holistic configuration of neuronal structures, such as axons and dendrites, within single motoneurons can be governed by local contact with the adjacent neuron fascicle, a novel reference structure for neural circuitry wiring.
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Affiliation(s)
- Kathy Clara Bui
- Department of Cellular Biology, University of Georgia, Athens, GA 30605, USA
| | - Daichi Kamiyama
- Department of Cellular Biology, University of Georgia, Athens, GA 30605, USA
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25
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Avila B, Serafino M, Augusto P, Zimmer M, Makse HA. Fibration symmetries and cluster synchronization in the Caenorhabditis elegans connectome. PLoS One 2024; 19:e0297669. [PMID: 38598455 PMCID: PMC11006206 DOI: 10.1371/journal.pone.0297669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Accepted: 01/11/2024] [Indexed: 04/12/2024] Open
Abstract
Capturing how the Caenorhabditis elegans connectome structure gives rise to its neuron functionality remains unclear. It is through fiber symmetries found in its neuronal connectivity that synchronization of a group of neurons can be determined. To understand these we investigate graph symmetries and search for such in the symmetrized versions of the forward and backward locomotive sub-networks of the Caenorhabditi elegans worm neuron network. The use of ordinarily differential equations simulations admissible to these graphs are used to validate the predictions of these fiber symmetries and are compared to the more restrictive orbit symmetries. Additionally fibration symmetries are used to decompose these graphs into their fundamental building blocks which reveal units formed by nested loops or multilayered fibers. It is found that fiber symmetries of the connectome can accurately predict neuronal synchronization even under not idealized connectivity as long as the dynamics are within stable regimes of simulations.
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Affiliation(s)
- Bryant Avila
- Physics Department, Levich Institute, City College of New York, New York, NY, United Stated of America
| | - Matteo Serafino
- Physics Department, Levich Institute, City College of New York, New York, NY, United Stated of America
| | - Pedro Augusto
- Vienna Biocenter PhD Program, Doctoral School of the University of Vienna and Medical University of Vienna, Vienna, Austria
- Department of Neuroscience and Developmental Biology, Vienna Biocenter (VBC), University of Vienna, Vienna, Austria
| | - Manuel Zimmer
- Research Institute of Molecular Pathology (IMP), Vienna Biocenter (VBC), University of Vienna, Vienna, Austria
- Department of Neuroscience and Developmental Biology, Vienna Biocenter (VBC), University of Vienna, Vienna, Austria
| | - Hernán A. Makse
- Physics Department, Levich Institute, City College of New York, New York, NY, United Stated of America
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26
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Liao A, Qian C, Abdi S, Yee P, Cursain SM, Condron N, Condron B. Population parameters of Drosophila larval cooperative foraging. J Comp Physiol A Neuroethol Sens Neural Behav Physiol 2024:10.1007/s00359-024-01701-w. [PMID: 38594346 DOI: 10.1007/s00359-024-01701-w] [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: 02/16/2024] [Revised: 03/28/2024] [Accepted: 03/30/2024] [Indexed: 04/11/2024]
Abstract
Cooperative foraging behavior can be advantageous when there is a common exploitable resource. By cooperating, members of the group can take advantage of the potential of increased efficiency of working together as well as equitable distribution of the product. An experimental signature of cooperative foraging is an Allee effect where at a certain number of individuals, there is a peak of fitness. What happens when there are intruders especially ones that do not contribute to any work required for foraging? Drosophila larvae secrete digestive enzymes and exodigest food. Under crowded conditions in liquid food these larvae form synchronized feeding clusters which provides a fitness benefit. A key for this synchronized feeding behavior is the visually guided alignment between adjacent larvae in a feeding cluster. Larvae who do not align their movements are excluded from the groups and subsequently lose the benefit. This may be a way of editing the group to include only known members. To test the model, the fitness benefit from cooperative behavior was further investigated to establish an Allee effect for a number of strains including those who cannot exodigest or cluster. In a standard lab vial, about 40 larvae is the optimal number for fitness. Combinations of these larvae were also examined. The expectation was that larvae who do not contribute to exodigestion are obligate cheaters and would be expelled. Indeed, obligate cheaters gain greatly from the hosts but paradoxically, so do the hosts. Clusters that include cheaters are more stable. Therefore, clustering and the benefits from it are dependent on more than just the contribution to exodigestion. This experimental system should provide a rich future model to understand the metrics of cooperative behavior.
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Affiliation(s)
- Amy Liao
- Department of Biology, University of Virginia, Charlottesville, VA, 22901, USA
| | - Christy Qian
- Department of Biology, University of Virginia, Charlottesville, VA, 22901, USA
| | - Sepideh Abdi
- Department of Biology, University of Virginia, Charlottesville, VA, 22901, USA
| | - Peyton Yee
- Department of Biology, University of Virginia, Charlottesville, VA, 22901, USA
| | | | - Niav Condron
- Department of Biology, University of Virginia, Charlottesville, VA, 22901, USA
| | - Barry Condron
- Department of Biology, University of Virginia, Charlottesville, VA, 22901, USA.
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27
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Gowda SBM, Banu A, Hussain S, Mohammad F. Neuronal mechanisms regulating locomotion in adult Drosophila. J Neurosci Res 2024; 102:e25332. [PMID: 38646942 DOI: 10.1002/jnr.25332] [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: 02/26/2024] [Revised: 04/01/2024] [Accepted: 04/03/2024] [Indexed: 04/25/2024]
Abstract
The coordinated action of multiple leg joints and muscles is required even for the simplest movements. Understanding the neuronal circuits and mechanisms that generate precise movements is essential for comprehending the neuronal basis of the locomotion and to infer the neuronal mechanisms underlying several locomotor-related diseases. Drosophila melanogaster provides an excellent model system for investigating the neuronal circuits underlying motor behaviors due to its simple nervous system and genetic accessibility. This review discusses current genetic methods for studying locomotor circuits and their function in adult Drosophila. We highlight recently identified neuronal pathways that modulate distinct forward and backward locomotion and describe the underlying neuronal control of leg swing and stance phases in freely moving flies. We also report various automated leg tracking methods to measure leg motion parameters and define inter-leg coordination, gait and locomotor speed of freely moving adult flies. Finally, we emphasize the role of leg proprioceptive signals to central motor circuits in leg coordination. Together, this review highlights the utility of adult Drosophila as a model to uncover underlying motor circuitry and the functional organization of the leg motor system that governs correct movement.
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Affiliation(s)
- Swetha B M Gowda
- Division of Biological and Biomedical Sciences (BBS), College of Health and Life Sciences (CHLS), Hamad Bin Khalifa University (HBKU), Doha, Qatar
| | - Ayesha Banu
- Division of Biological and Biomedical Sciences (BBS), College of Health and Life Sciences (CHLS), Hamad Bin Khalifa University (HBKU), Doha, Qatar
| | - Sadam Hussain
- Division of Biological and Biomedical Sciences (BBS), College of Health and Life Sciences (CHLS), Hamad Bin Khalifa University (HBKU), Doha, Qatar
| | - Farhan Mohammad
- Division of Biological and Biomedical Sciences (BBS), College of Health and Life Sciences (CHLS), Hamad Bin Khalifa University (HBKU), Doha, Qatar
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28
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Boergens KM, Wildenberg G, Li R, Lambert L, Moradi A, Stam G, Tromp R, van der Molen SJ, King SB, Kasthuri N. Photoemission electron microscopy for connectomics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.09.05.556423. [PMID: 37771915 PMCID: PMC10525389 DOI: 10.1101/2023.09.05.556423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/30/2023]
Abstract
Detailing the physical basis of neural circuits with large-volume serial electron microscopy (EM), 'connectomics', has emerged as an invaluable tool in the neuroscience armamentarium. However, imaging synaptic resolution connectomes is currently limited to either transmission electron microscopy (TEM) or scanning electron microscopy (SEM). Here, we describe a third way, using photoemission electron microscopy (PEEM) which illuminates ultra-thin brain slices collected on solid substrates with UV light and images the photoelectron emission pattern with a wide-field electron microscope. PEEM works with existing sample preparations for EM and routinely provides sufficient resolution and contrast to reveal myelinated axons, somata, dendrites, and sub-cellular organelles. Under optimized conditions, PEEM provides synaptic resolution; and simulation and experiments show that PEEM can be transformatively fast, at Gigahertz pixel rates. We conclude that PEEM imaging leverages attractive aspects of SEM and TEM, namely reliable sample collection on robust substrates combined with fast wide-field imaging, and could enable faster data acquisition for next-generation circuit mapping.
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29
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Sanfilippo P, Kim AJ, Bhukel A, Yoo J, Mirshahidi PS, Pandey V, Bevir H, Yuen A, Mirshahidi PS, Guo P, Li HS, Wohlschlegel JA, Aso Y, Zipursky SL. Mapping of multiple neurotransmitter receptor subtypes and distinct protein complexes to the connectome. Neuron 2024; 112:942-958.e13. [PMID: 38262414 PMCID: PMC10957333 DOI: 10.1016/j.neuron.2023.12.014] [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: 08/28/2023] [Revised: 12/03/2023] [Accepted: 12/20/2023] [Indexed: 01/25/2024]
Abstract
Neurons express various combinations of neurotransmitter receptor (NR) subunits and receive inputs from multiple neuron types expressing different neurotransmitters. Localizing NR subunits to specific synaptic inputs has been challenging. Here, we use epitope-tagged endogenous NR subunits, expansion light-sheet microscopy, and electron microscopy (EM) connectomics to molecularly characterize synapses in Drosophila. We show that in directionally selective motion-sensitive neurons, different multiple NRs elaborated a highly stereotyped molecular topography with NR localized to specific domains receiving cell-type-specific inputs. Developmental studies suggested that NRs or complexes of them with other membrane proteins determine patterns of synaptic inputs. In support of this model, we identify a transmembrane protein selectively associated with a subset of spatially restricted synapses and demonstrate its requirement for synapse formation through genetic analysis. We propose that mechanisms that regulate the precise spatial distribution of NRs provide a molecular cartography specifying the patterns of synaptic connections onto dendrites.
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Affiliation(s)
- Piero Sanfilippo
- Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Howard Hughes Medical Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Alexander J Kim
- Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Anuradha Bhukel
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Juyoun Yoo
- Neuroscience Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Pegah S Mirshahidi
- Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Vijaya Pandey
- Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Harry Bevir
- Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Ashley Yuen
- Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Parmis S Mirshahidi
- Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Peiyi Guo
- Department of Neurobiology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA
| | - Hong-Sheng Li
- Department of Neurobiology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA
| | - James A Wohlschlegel
- Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Yoshinori Aso
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - S Lawrence Zipursky
- Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Howard Hughes Medical Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA.
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30
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Draper IR, Roberts MA, Gailloud M, Jackson FR. Drosophila noktochor regulates night sleep via a local mushroom body circuit. iScience 2024; 27:109106. [PMID: 38380256 PMCID: PMC10877950 DOI: 10.1016/j.isci.2024.109106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 11/22/2023] [Accepted: 01/31/2024] [Indexed: 02/22/2024] Open
Abstract
We show that a sleep-regulating, Ig-domain protein (NKT) is secreted from Drosophila mushroom body (MB) α'/β' neurons to act locally on other MB cell types. Pan-neuronal or broad MB expression of membrane-tethered NKT (tNkt) protein reduced sleep, like that of an NKT null mutant, suggesting blockade of a receptor mediating endogenous NKT action. In contrast, expression in neurons requiring NKT (the MB α'/β' cells), or non-MB sleep-regulating centers, did not reduce night sleep, indicating the presence of a local MB sleep-regulating circuit consisting of communicating neural subtypes. We suggest that the leucocyte-antigen-related like (Lar) transmembrane receptor may mediate NKT action. Knockdown or overexpression of Lar in the MB increased or decreased sleep, respectively, indicating the receptor promotes wakefulness. Surprisingly, selective expression of tNkt or knockdown of Lar in MB wake-promoting cells increased rather than decreased sleep, suggesting that NKT acts on wake- as well as sleep-promoting cell types to regulate sleep.
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Affiliation(s)
- Isabelle R. Draper
- Department of Neuroscience, Tufts University School of Medicine, 136 Harrison Avenue, Boston, MA 02111, USA
- Department of Medicine, Molecular Cardiology Research Institute, Tufts Medical Center, 800 Washington Street, Boston, MA 02111, USA
| | - Mary A. Roberts
- Department of Neuroscience, Tufts University School of Medicine, 136 Harrison Avenue, Boston, MA 02111, USA
| | - Matthew Gailloud
- Department of Neuroscience, Tufts University School of Medicine, 136 Harrison Avenue, Boston, MA 02111, USA
| | - F. Rob Jackson
- Department of Neuroscience, Tufts University School of Medicine, 136 Harrison Avenue, Boston, MA 02111, USA
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31
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Berni J. How larvae feel the world around them. eLife 2024; 13:e96708. [PMID: 38456840 PMCID: PMC10923558 DOI: 10.7554/elife.96708] [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: 03/09/2024] Open
Abstract
A complete map of the external sense organs shows how fruit fly larvae detect different aspects of their environment.
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Affiliation(s)
- Jimena Berni
- Department of Neuroscience, Brighton and Sussex Medical School, University of SussexBrightonUnited Kingdom
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32
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Riboul DV, Crill S, Oliva CD, Restifo MG, Joseph R, Joseph K, Nguyen KC, Hall DH, Fily Y, Macleod GT. Ultrastructural analysis reveals mitochondrial placement independent of synapse placement in fine caliber C. elegans neurons. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.05.30.542959. [PMID: 37398149 PMCID: PMC10312582 DOI: 10.1101/2023.05.30.542959] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Neurons rely on mitochondria for an efficient supply of ATP and other metabolites. However, while neurons are highly elongated, mitochondria are discrete and limited in number. Due to the slow rates of diffusion over long distances it follows that neurons would benefit from an ability to control the distribution of mitochondria to sites of high metabolic activity, such as synapses. It is assumed that neurons' possess this capacity, but ultrastructural data over substantial portions of a neuron's extent that would allow for tests of such hypotheses are scarce. Here, we mined the Caenorhabditis elegans electron micrographs of John White and Sydney Brenner and found systematic differences in average mitochondrial length (ranging from 1.3 to 2.4 μm), volume density (3.7% to 6.5%) and diameter (0.18 to 0.24 μm) between neurons of different neurotransmitter type and function, but found limited differences in mitochondrial morphometrics between axons and dendrites of the same neurons. Analyses of distance intervals found mitochondria to be distributed randomly with respect to presynaptic specializations, and an indication that mitochondria were displaced from postsynaptic specializations. Presynaptic specializations were primarily localized to varicosities, but mitochondria were no more likely to be found in synaptic varicosities than non-synaptic varicosities. Consistently, mitochondrial volume density was no greater in varicosities with synapses. Therefore, beyond the capacity to disperse mitochondria throughout their length, at least in C. elegans, fine caliber neurons manifest limited sub-cellular control of mitochondrial size and distribution.
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33
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Lin A, Yang R, Dorkenwald S, Matsliah A, Sterling AR, Schlegel P, Yu SC, McKellar CE, Costa M, Eichler K, Bates AS, Eckstein N, Funke J, Jefferis GSXE, Murthy M. Network Statistics of the Whole-Brain Connectome of Drosophila. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.07.29.551086. [PMID: 37547019 PMCID: PMC10402125 DOI: 10.1101/2023.07.29.551086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
Brains comprise complex networks of neurons and connections. Network analysis applied to the wiring diagrams of brains can offer insights into how brains support computations and regulate information flow. The completion of the first whole-brain connectome of an adult Drosophila, the largest connectome to date, containing 130,000 neurons and millions of connections, offers an unprecedented opportunity to analyze its network properties and topological features. To gain insights into local connectivity, we computed the prevalence of two- and three-node network motifs, examined their strengths and neurotransmitter compositions, and compared these topological metrics with wiring diagrams of other animals. We discovered that the network of the fly brain displays rich club organization, with a large population (30% percent of the connectome) of highly connected neurons. We identified subsets of rich club neurons that may serve as integrators or broadcasters of signals. Finally, we examined subnetworks based on 78 anatomically defined brain regions or neuropils. These data products are shared within the FlyWire Codex and will serve as a foundation for models and experiments exploring the relationship between neural activity and anatomical structure.
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Affiliation(s)
- Albert Lin
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Center for the Physics of Biological Function, Princeton University, Princeton, NJ, USA
| | - Runzhe Yang
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Computer Science Department, Princeton University, Princeton, NJ, USA
| | - Sven Dorkenwald
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Computer Science Department, Princeton University, Princeton, NJ, USA
| | - Arie Matsliah
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Amy R Sterling
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Philipp Schlegel
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Szi-Chieh Yu
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Claire E McKellar
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Marta Costa
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Katharina Eichler
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Alexander Shakeel Bates
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
- Centre for Neural Circuits and Behaviour, 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
| | - Gregory S X E Jefferis
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Mala Murthy
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
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34
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Caznok Silveira AC, Antunes ASLM, Athié MCP, da Silva BF, Ribeiro dos Santos JV, Canateli C, Fontoura MA, Pinto A, Pimentel-Silva LR, Avansini SH, de Carvalho M. Between neurons and networks: investigating mesoscale brain connectivity in neurological and psychiatric disorders. Front Neurosci 2024; 18:1340345. [PMID: 38445254 PMCID: PMC10912403 DOI: 10.3389/fnins.2024.1340345] [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: 11/17/2023] [Accepted: 01/29/2024] [Indexed: 03/07/2024] Open
Abstract
The study of brain connectivity has been a cornerstone in understanding the complexities of neurological and psychiatric disorders. It has provided invaluable insights into the functional architecture of the brain and how it is perturbed in disorders. However, a persistent challenge has been achieving the proper spatial resolution, and developing computational algorithms to address biological questions at the multi-cellular level, a scale often referred to as the mesoscale. Historically, neuroimaging studies of brain connectivity have predominantly focused on the macroscale, providing insights into inter-regional brain connections but often falling short of resolving the intricacies of neural circuitry at the cellular or mesoscale level. This limitation has hindered our ability to fully comprehend the underlying mechanisms of neurological and psychiatric disorders and to develop targeted interventions. In light of this issue, our review manuscript seeks to bridge this critical gap by delving into the domain of mesoscale neuroimaging. We aim to provide a comprehensive overview of conditions affected by aberrant neural connections, image acquisition techniques, feature extraction, and data analysis methods that are specifically tailored to the mesoscale. We further delineate the potential of brain connectivity research to elucidate complex biological questions, with a particular focus on schizophrenia and epilepsy. This review encompasses topics such as dendritic spine quantification, single neuron morphology, and brain region connectivity. We aim to showcase the applicability and significance of mesoscale neuroimaging techniques in the field of neuroscience, highlighting their potential for gaining insights into the complexities of neurological and psychiatric disorders.
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Affiliation(s)
- Ana Clara Caznok Silveira
- National Laboratory of Biosciences, Brazilian Center for Research in Energy and Materials, Campinas, Brazil
- School of Electrical and Computer Engineering, University of Campinas, Campinas, Brazil
| | | | - Maria Carolina Pedro Athié
- National Laboratory of Biosciences, Brazilian Center for Research in Energy and Materials, Campinas, Brazil
| | - Bárbara Filomena da Silva
- National Laboratory of Biosciences, Brazilian Center for Research in Energy and Materials, Campinas, Brazil
| | | | - Camila Canateli
- National Laboratory of Biosciences, Brazilian Center for Research in Energy and Materials, Campinas, Brazil
| | - Marina Alves Fontoura
- National Laboratory of Biosciences, Brazilian Center for Research in Energy and Materials, Campinas, Brazil
| | - Allan Pinto
- Brazilian Synchrotron Light Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, Brazil
| | | | - Simoni Helena Avansini
- National Laboratory of Biosciences, Brazilian Center for Research in Energy and Materials, Campinas, Brazil
| | - Murilo de Carvalho
- National Laboratory of Biosciences, Brazilian Center for Research in Energy and Materials, Campinas, Brazil
- Brazilian Synchrotron Light Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, Brazil
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35
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Goulard Coderc de Lacam E, Roux B, Chipot C. Classifying Protein-Protein Binding Affinity with Free-Energy Calculations and Machine Learning Approaches. J Chem Inf Model 2024; 64:1081-1091. [PMID: 38272021 DOI: 10.1021/acs.jcim.3c01586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2024]
Abstract
Understanding the intricate phenomenon of neuronal wiring in the brain is of great interest in neuroscience. In the fruit fly, Drosophila melanogaster, the Dpr-DIP interactome has been identified to play an important role in this process. However, experimental data suggest that a merely limited subset of complexes, essentially 57 out of a total of 231, exhibit strong binding affinity. In this work, we sought to identify the residue-level molecular basis underlying the difference in binding affinity using a state-of-the-art methodology consisting of standard binding free-energy calculations with a geometrical route and machine learning (ML) techniques. We determined the binding affinity for two complexes using statistical mechanics simulations, achieving an excellent reproduction of the experimental data. Moreover, we predicted the binding free energy for two additional low-affinity complexes, devoid of experimental estimation, while simultaneously identifying key residues for the binding. Furthermore, through the use of ML algorithms, linear discriminant analysis, and random forest, we achieved remarkable accuracy, as high as 0.99, in discerning between strong (cognate) and weak (noncognate) binders. The presented ML approach encompasses easily transferable input features, enabling its broad application to any interactome while facilitating the identification of pivotal residues critical for binding interactions. The predictive power of the generated model was probed on similar protein families from 13 diverse species. Our ML model exhibited commendable performance on these additional data sets, showcasing its reliability and robustness across the species barrier.
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Affiliation(s)
- Emma Goulard Coderc de Lacam
- Laboratoire International Associé Centre National de la Recherche Scientifique et University of Illinois at Urbana-Champaign, Unité Mixte de Recherche no. 7019, Université de Lorraine, B.P. 70239, 54506 Vandœuvre-lès-Nancy Cedex, France
| | - Benoît Roux
- Department of Biochemistry and Molecular Biology, The University of Chicago, 929 E. 57th Street W225, Chicago, Illinois 60637, United States
- Department of Chemistry, The University of Chicago, 5735 S Ellis Avenue, Chicago, Illinois 60637, United States
| | - Christophe Chipot
- Laboratoire International Associé Centre National de la Recherche Scientifique et University of Illinois at Urbana-Champaign, Unité Mixte de Recherche no. 7019, Université de Lorraine, B.P. 70239, 54506 Vandœuvre-lès-Nancy Cedex, France
- Department of Biochemistry and Molecular Biology, The University of Chicago, 929 E. 57th Street W225, Chicago, Illinois 60637, United States
- Theoretical and Computational Biophysics Group, Beckman Institute, and Department of Physics, University of Illinois at Urbana-Champaign, Urbana, Illinois 61820, United States
- Department of Chemistry, The University of Hawai'i at Ma̅noa, 2545 McCarthy Mall, Honolulu, Hawaii 96822, United States
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36
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Bontonou G, Saint-Leandre B, Kafle T, Baticle T, Hassan A, Sánchez-Alcañiz JA, Arguello JR. Evolution of chemosensory tissues and cells across ecologically diverse Drosophilids. Nat Commun 2024; 15:1047. [PMID: 38316749 PMCID: PMC10844241 DOI: 10.1038/s41467-023-44558-4] [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: 05/10/2023] [Accepted: 12/19/2023] [Indexed: 02/07/2024] Open
Abstract
Chemosensory tissues exhibit significant between-species variability, yet the evolution of gene expression and cell types underlying this diversity remain poorly understood. To address these questions, we conducted transcriptomic analyses of five chemosensory tissues from six Drosophila species and integrated the findings with single-cell datasets. While stabilizing selection predominantly shapes chemosensory transcriptomes, thousands of genes in each tissue have evolved expression differences. Genes that have changed expression in one tissue have often changed in multiple other tissues but at different past epochs and are more likely to be cell type-specific than unchanged genes. Notably, chemosensory-related genes have undergone widespread expression changes, with numerous species-specific gains/losses including novel chemoreceptors expression patterns. Sex differences are also pervasive, including a D. melanogaster-specific excess of male-biased expression in sensory and muscle cells in its forelegs. Together, our analyses provide new insights for understanding evolutionary changes in chemosensory tissues at both global and individual gene levels.
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Affiliation(s)
- Gwénaëlle Bontonou
- Department of Ecology & Evolution, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland.
- Swiss Institute of Bioinformatics, Lausanne, Switzerland.
| | - Bastien Saint-Leandre
- Department of Ecology & Evolution, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland.
- Swiss Institute of Bioinformatics, Lausanne, Switzerland.
| | - Tane Kafle
- Department of Ecology & Evolution, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Tess Baticle
- Department of Ecology & Evolution, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Afrah Hassan
- Department of Ecology & Evolution, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | | | - J Roman Arguello
- Department of Ecology & Evolution, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland.
- Swiss Institute of Bioinformatics, Lausanne, Switzerland.
- School of Biological and Behavioural Sciences, Queen Mary University of London, London, UK.
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37
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Wolterhoff N, Hiesinger PR. Synaptic promiscuity in brain development. Curr Biol 2024; 34:R102-R116. [PMID: 38320473 PMCID: PMC10849093 DOI: 10.1016/j.cub.2023.12.037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2024]
Abstract
Precise synaptic connectivity is a prerequisite for the function of neural circuits, yet individual neurons, taken out of their developmental context, readily form unspecific synapses. How does the genome encode brain wiring in light of this apparent contradiction? Synaptic specificity is the outcome of a long series of developmental processes and mechanisms before, during and after synapse formation. How much promiscuity is permissible or necessary at the moment of synaptic partner choice depends on the extent to which prior development restricts available partners or subsequent development corrects initially made synapses. Synaptic promiscuity at the moment of choice can thereby play important roles in the development of precise connectivity, but also facilitate developmental flexibility and robustness. In this review, we assess the experimental evidence for the prevalence and roles of promiscuous synapse formation during brain development. Many well-established experimental approaches are based on developmental genetic perturbation and an assessment of synaptic connectivity only in the adult; this can make it difficult to pinpoint when a given defect or mechanism occurred. In many cases, such studies reveal mechanisms that restrict partner availability already prior to synapse formation. Subsequently, at the moment of choice, factors including synaptic competency, interaction dynamics and molecular recognition further restrict synaptic partners. The discussion of the development of synaptic specificity through the lens of synaptic promiscuity suggests an algorithmic process based on neurons capable of promiscuous synapse formation that are continuously prevented from making the wrong choices, with no single mechanism or developmental time point sufficient to explain the outcome.
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Affiliation(s)
- Neele Wolterhoff
- Division of Neurobiology, Free University Berlin, 14195 Berlin, Germany
| | - P Robin Hiesinger
- Division of Neurobiology, Free University Berlin, 14195 Berlin, Germany.
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38
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McCafferty CL, Klumpe S, Amaro RE, Kukulski W, Collinson L, Engel BD. Integrating cellular electron microscopy with multimodal data to explore biology across space and time. Cell 2024; 187:563-584. [PMID: 38306982 DOI: 10.1016/j.cell.2024.01.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 01/03/2024] [Accepted: 01/03/2024] [Indexed: 02/04/2024]
Abstract
Biology spans a continuum of length and time scales. Individual experimental methods only glimpse discrete pieces of this spectrum but can be combined to construct a more holistic view. In this Review, we detail the latest advancements in volume electron microscopy (vEM) and cryo-electron tomography (cryo-ET), which together can visualize biological complexity across scales from the organization of cells in large tissues to the molecular details inside native cellular environments. In addition, we discuss emerging methodologies for integrating three-dimensional electron microscopy (3DEM) imaging with multimodal data, including fluorescence microscopy, mass spectrometry, single-particle analysis, and AI-based structure prediction. This multifaceted approach fills gaps in the biological continuum, providing functional context, spatial organization, molecular identity, and native interactions. We conclude with a perspective on incorporating diverse data into computational simulations that further bridge and extend length scales while integrating the dimension of time.
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Affiliation(s)
| | - Sven Klumpe
- Research Group CryoEM Technology, Max-Planck-Institute of Biochemistry, Am Klopferspitz 18, 82152 Martinsried, Germany.
| | - Rommie E Amaro
- Department of Molecular Biology, University of California, San Diego, La Jolla, CA 92093, USA.
| | - Wanda Kukulski
- Institute of Biochemistry and Molecular Medicine, University of Bern, Bühlstrasse 28, 3012 Bern, Switzerland.
| | - Lucy Collinson
- Electron Microscopy Science Technology Platform, Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK.
| | - Benjamin D Engel
- Biozentrum, University of Basel, Spitalstrasse 41, 4056 Basel, Switzerland.
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39
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Simpson JH. Descending control of motor sequences in Drosophila. Curr Opin Neurobiol 2024; 84:102822. [PMID: 38096757 PMCID: PMC11215313 DOI: 10.1016/j.conb.2023.102822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 11/22/2023] [Accepted: 11/22/2023] [Indexed: 02/18/2024]
Abstract
The descending neurons connecting the fly's brain to its ventral nerve cord respond to sensory stimuli and evoke motor programs of varying complexity. Anatomical characterization of the descending neurons and their synaptic connections suggests how these circuits organize movements, while optogenetic manipulation of their activity reveals what behaviors they can induce. Monitoring their responses to sensory stimuli or during behavior performance indicates what information they may encode. Recent advances in all three approaches make the descending neurons an excellent place to better understand the sensorimotor integration and transformation required for nervous systems to govern the motor sequences that constitute animal behavior.
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Affiliation(s)
- Julie H Simpson
- Dept. Molecular Cellular and Developmental Biology and Neuroscience Research Institute, University of California Santa Barbara, USA.
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40
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Jürgensen AM, Sakagiannis P, Schleyer M, Gerber B, Nawrot MP. Prediction error drives associative learning and conditioned behavior in a spiking model of Drosophila larva. iScience 2024; 27:108640. [PMID: 38292165 PMCID: PMC10824792 DOI: 10.1016/j.isci.2023.108640] [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: 03/01/2023] [Revised: 11/10/2023] [Accepted: 12/01/2023] [Indexed: 02/01/2024] Open
Abstract
Predicting reinforcement from sensory cues is beneficial for goal-directed behavior. In insect brains, underlying associations between cues and reinforcement, encoded by dopaminergic neurons, are formed in the mushroom body. We propose a spiking model of the Drosophila larva mushroom body. It includes a feedback motif conveying learned reinforcement expectation to dopaminergic neurons, which can compute prediction error as the difference between expected and present reinforcement. We demonstrate that this can serve as a driving force in learning. When combined with synaptic homeostasis, our model accounts for theoretically derived features of acquisition and loss of associations that depend on the intensity of the reinforcement and its temporal proximity to the cue. From modeling olfactory learning over the time course of behavioral experiments and simulating the locomotion of individual larvae toward or away from odor sources in a virtual environment, we conclude that learning driven by prediction errors can explain larval behavior.
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Affiliation(s)
- Anna-Maria Jürgensen
- Computational Systems Neuroscience, Institute of Zoology, University of Cologne, 50674 Cologne, Germany
| | - Panagiotis Sakagiannis
- Computational Systems Neuroscience, Institute of Zoology, University of Cologne, 50674 Cologne, Germany
| | - Michael Schleyer
- Leibniz Institute for Neurobiology (LIN), Department of Genetics, 39118 Magdeburg, Germany
- Institute for the Advancement of Higher Education, Faculty of Science, Hokkaido University, Sapporo 060-08080, Japan
| | - Bertram Gerber
- Leibniz Institute for Neurobiology (LIN), Department of Genetics, 39118 Magdeburg, Germany
- Institute for Biology, Otto-von-Guericke University, 39120 Magdeburg, Germany
- Center for Brain and Behavioral Sciences (CBBS), Otto-von-Guericke University, 39118 Magdeburg, Germany
| | - Martin Paul Nawrot
- Computational Systems Neuroscience, Institute of Zoology, University of Cologne, 50674 Cologne, Germany
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41
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Hegedűs D, Grolmusz V. Robust circuitry-based scores of structural importance of human brain areas. PLoS One 2024; 19:e0292613. [PMID: 38232101 DOI: 10.1371/journal.pone.0292613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 09/25/2023] [Indexed: 01/19/2024] Open
Abstract
We consider the 1015-vertex human consensus connectome computed from the diffusion MRI data of 1064 subjects. We define seven different orders on these 1015 graph vertices, where the orders depend on parameters derived from the brain circuitry, that is, from the properties of the edges (or connections) incident to the vertices ordered. We order the vertices according to their degree, the sum, the maximum, and the average of the fiber counts on the incident edges, and the sum, the maximum and the average length of the fibers in the incident edges. We analyze the similarities of these seven orders by the Spearman correlation coefficient and by their inversion numbers and have found that all of these seven orders have great similarities. In other words, if we interpret the orders as scoring of the importance of the vertices in the consensus connectome, then the scores of the vertices will be similar in all seven orderings. That is, important vertices of the human connectome typically have many neighbors connected with long and thick axonal fibers (where thickness is measured by fiber numbers), and their incident edges have high maximum and average values of length and fiber-number parameters, too. Therefore, these parameters may yield robust ways of deciding which vertices are more important in the anatomy of our brain circuitry than the others.
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Affiliation(s)
- Dániel Hegedűs
- PIT Bioinformatics Group, Eötvös University, Budapest, Hungary
| | - Vince Grolmusz
- PIT Bioinformatics Group, Eötvös University, Budapest, Hungary
- Uratim Ltd., Budapest, Hungary
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42
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Calissano A, Papadopoulo T, Pennec X, Deslauriers‐Gauthier S. Graph alignment exploiting the spatial organization improves the similarity of brain networks. Hum Brain Mapp 2024; 45:e26554. [PMID: 38224543 PMCID: PMC10789220 DOI: 10.1002/hbm.26554] [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: 04/26/2023] [Revised: 10/19/2023] [Accepted: 11/22/2023] [Indexed: 01/17/2024] Open
Abstract
Every brain is unique, having its structural and functional organization shaped by both genetic and environmental factors over the course of its development. Brain image studies tend to produce results by averaging across a group of subjects, under the common assumption that it is possible to subdivide the cortex into homogeneous areas while maintaining a correspondence across subjects. We investigate this assumption: can the structural properties of a specific region of an atlas be assumed to be the same across subjects? This question is addressed by looking at the network representation of the brain, with nodes corresponding to brain regions and edges to their structural relationships. Using an unsupervised graph matching strategy, we align the structural connectomes of a set of healthy subjects, considering parcellations of different granularity, to understand the connectivity misalignment between regions. First, we compare the obtained permutations with four different algorithm initializations: Spatial Adjacency, Identity, Barycenter, and Random. Our results suggest that applying an alignment strategy improves the similarity across subjects when the number of parcels is above 100 and when using Spatial Adjacency and Identity initialization (the most plausible priors). Second, we characterize the obtained permutations, revealing that the majority of permutations happens between neighbors parcels. Lastly, we study the spatial distribution of the permutations. By visualizing the results on the cortex, we observe no clear spatial patterns on the permutations and all the regions across the context are mostly permuted with first and second order neighbors.
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Affiliation(s)
- Anna Calissano
- Inria Center at Université Côte d'AzurValbonneFrance
- Present address:
Imperial College London
| | | | - Xavier Pennec
- Inria Center at Université Côte d'AzurValbonneFrance
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43
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Gebehart C, Büschges A. The processing of proprioceptive signals in distributed networks: insights from insect motor control. J Exp Biol 2024; 227:jeb246182. [PMID: 38180228 DOI: 10.1242/jeb.246182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2024]
Abstract
The integration of sensory information is required to maintain body posture and to generate robust yet flexible locomotion through unpredictable environments. To anticipate required adaptations in limb posture and enable compensation of sudden perturbations, an animal's nervous system assembles external (exteroception) and internal (proprioception) cues. Coherent neuronal representations of the proprioceptive context of the body and the appendages arise from the concerted action of multiple sense organs monitoring body kinetics and kinematics. This multimodal proprioceptive information, together with exteroceptive signals and brain-derived descending motor commands, converges onto premotor networks - i.e. the local neuronal circuitry controlling motor output and movements - within the ventral nerve cord (VNC), the insect equivalent of the vertebrate spinal cord. This Review summarizes existing knowledge and recent advances in understanding how local premotor networks in the VNC use convergent information to generate contextually appropriate activity, focusing on the example of posture control. We compare the role and advantages of distributed sensory processing over dedicated neuronal pathways, and the challenges of multimodal integration in distributed networks. We discuss how the gain of distributed networks may be tuned to enable the behavioral repertoire of these systems, and argue that insect premotor networks might compensate for their limited neuronal population size by, in comparison to vertebrate networks, relying more heavily on the specificity of their connections. At a time in which connectomics and physiological recording techniques enable anatomical and functional circuit dissection at an unprecedented resolution, insect motor systems offer unique opportunities to identify the mechanisms underlying multimodal integration for flexible motor control.
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Affiliation(s)
- Corinna Gebehart
- Champalimaud Foundation, Champalimaud Research, 1400-038 Lisbon, Portugal
| | - Ansgar Büschges
- Department of Animal Physiology, Institute of Zoology, Biocenter Cologne, University of Cologne, Zülpicher Strasse 47b, 50674 Cologne, Germany
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44
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Aksamit IC, Dorigão-Guimarães F, Gronenberg W, Godfrey RK. Brain size scaling through development in the whitelined sphinx moth (Hyles lineata) shows mass and cell number comparable to flies, bees, and wasps. ARTHROPOD STRUCTURE & DEVELOPMENT 2024; 78:101329. [PMID: 38171085 DOI: 10.1016/j.asd.2023.101329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 12/13/2023] [Accepted: 12/13/2023] [Indexed: 01/05/2024]
Abstract
Factors regulating larval growth and determinants of adult body size are described for several holometabolous insects, but less is known about brain size scaling through development. Here we use the isotropic fractionation ("brain soup") method to estimate the number of brain cells and cell density for the whitelined sphinx moth (Lepidoptera: Hyles lineata) from the first instar through the adult stage. We measure mass and brain cell number and find that, during the larval stages, body mass shows an exponential relationship with head width, while the total number of brain cells increases asymptotically. Larval brain cell number increases by a factor of ten from nearly 8000 in the first instar to over 80,000 in the fifth instar. Brain cell number increases by another factor of 10 during metamorphosis, with the adult brain containing more than 900,000 cells. This is similar to increases during development in the vinegar fly (Drosophila melanogaster) and the black soldier fly (Hermetia illucens). The adult brain falls slightly below the brain-to-body allometry for wasps and bees but is comparable in the number of cells per unit brain mass, indicating a general conservation of brain cell density across these divergent lineages.
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Affiliation(s)
- Isabel C Aksamit
- Department of Neuroscience, University of Arizona, Tucson, AZ, USA
| | - Felipe Dorigão-Guimarães
- Biodiversity Graduate Program, São Paulo State University (UNESP), Institute of Biosciences, Humanities and Exact Sciences (IBILCE), São José do Rio Preto, SP, Brazil
| | | | - R Keating Godfrey
- Entomology and Nematology Department, University of Florida, Gainesville, FL, USA.
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45
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Oliveira-Ferreira C, Gaspar M, Vasconcelos ML. Neuronal substrates of egg-laying behaviour at the abdominal ganglion of Drosophila melanogaster. Sci Rep 2023; 13:21941. [PMID: 38081887 PMCID: PMC10713638 DOI: 10.1038/s41598-023-48109-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Accepted: 11/22/2023] [Indexed: 12/18/2023] Open
Abstract
Egg-laying in Drosophila is the product of post-mating physiological and behavioural changes that culminate in a stereotyped sequence of actions. Egg-laying harbours a great potential as a paradigm to uncover how the appropriate motor circuits are organized and activated to generate behaviour. To study this programme, we first describe the different phases of the egg-laying programme and the specific actions associated with each phase. Using a combination of neuronal activation and silencing experiments, we identify neurons (OvAbg) in the abdominal ganglion as key players in egg-laying. To generate and functionally characterise subsets of OvAbg, we used an intersectional approach with neurotransmitter specific lines-VGlut, Cha and Gad1. We show that OvAbg/VGlut neurons promote initiation of egg deposition in a mating status dependent way. OvAbg/Cha neurons are required in exploration and egg deposition phases, though activation leads specifically to egg expulsion. Experiments with the OvAbg/Gad1 neurons show they participate in egg deposition. We further show a functional connection of OvAbg neurons with brain neurons. This study provides insight into the organization of neuronal circuits underlying complex motor behaviour.
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Affiliation(s)
| | - Miguel Gaspar
- Neuroscience Programme, Champalimaud Foundation, Lisbon, Portugal
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46
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Molotkov D, Ferrarese L, Boissonnet T, Asari H. Topographic axonal projection at single-cell precision supports local retinotopy in the mouse superior colliculus. Nat Commun 2023; 14:7418. [PMID: 37973798 PMCID: PMC10654506 DOI: 10.1038/s41467-023-43218-x] [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: 06/08/2022] [Accepted: 11/03/2023] [Indexed: 11/19/2023] Open
Abstract
Retinotopy, like all long-range projections, can arise from the axons themselves or their targets. The underlying connectivity pattern, however, remains elusive at the fine scale in the mammalian brain. To address this question, we functionally mapped the spatial organization of the input axons and target neurons in the female mouse retinocollicular pathway at single-cell resolution using in vivo two-photon calcium imaging. We found a near-perfect retinotopic tiling of retinal ganglion cell axon terminals, with an average error below 30 μm or 2° of visual angle. The precision of retinotopy was relatively lower for local neurons in the superior colliculus. Subsequent data-driven modeling ascribed it to a low input convergence, on average 5.5 retinal ganglion cell inputs per postsynaptic cell in the superior colliculus. These results indicate that retinotopy arises largely from topographically precise input from presynaptic cells, rather than elaborating local circuitry to reconstruct the topography by postsynaptic cells.
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Affiliation(s)
- Dmitry Molotkov
- Epigenetics and Neurobiology Unit, EMBL Rome, European Molecular Biology Laboratory, Monterotondo, 00015, Italy
| | - Leiron Ferrarese
- Epigenetics and Neurobiology Unit, EMBL Rome, European Molecular Biology Laboratory, Monterotondo, 00015, Italy
| | - Tom Boissonnet
- Epigenetics and Neurobiology Unit, EMBL Rome, European Molecular Biology Laboratory, Monterotondo, 00015, Italy
- Collaboration for joint PhD degree between EMBL and Université Grenoble Alpes, Grenoble Institut des Neurosciences, La Tronche, 38700, France
- Center for Advanced Imaging, Heinrich-Heine-Universität Düsseldorf, Düsseldorf, 40225, Germany
| | - Hiroki Asari
- Epigenetics and Neurobiology Unit, EMBL Rome, European Molecular Biology Laboratory, Monterotondo, 00015, Italy.
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47
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Ripoll-Sánchez L, Watteyne J, Sun H, Fernandez R, Taylor SR, Weinreb A, Bentley BL, Hammarlund M, Miller DM, Hobert O, Beets I, Vértes PE, Schafer WR. The neuropeptidergic connectome of C. elegans. Neuron 2023; 111:3570-3589.e5. [PMID: 37935195 PMCID: PMC7615469 DOI: 10.1016/j.neuron.2023.09.043] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 08/02/2023] [Accepted: 09/29/2023] [Indexed: 11/09/2023]
Abstract
Efforts are ongoing to map synaptic wiring diagrams, or connectomes, to understand the neural basis of brain function. However, chemical synapses represent only one type of functionally important neuronal connection; in particular, extrasynaptic, "wireless" signaling by neuropeptides is widespread and plays essential roles in all nervous systems. By integrating single-cell anatomical and gene-expression datasets with biochemical analysis of receptor-ligand interactions, we have generated a draft connectome of neuropeptide signaling in the C. elegans nervous system. This network is characterized by high connection density, extended signaling cascades, autocrine foci, and a decentralized topology, with a large, highly interconnected core containing three constituent communities sharing similar patterns of input connectivity. Intriguingly, several key network hubs are little-studied neurons that appear specialized for peptidergic neuromodulation. We anticipate that the C. elegans neuropeptidergic connectome will serve as a prototype to understand how networks of neuromodulatory signaling are organized.
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Affiliation(s)
- Lidia Ripoll-Sánchez
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK; Department of Psychiatry, Cambridge University, Cambridge, UK
| | - Jan Watteyne
- Department of Biology, KU Leuven, Leuven, Belgium
| | - HaoSheng Sun
- Department of Biological Sciences/HHMI, Columbia University, New York, NY, USA; Department of Cell, Developmental, and Integrative Biology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Robert Fernandez
- Department of Biological Sciences/HHMI, Columbia University, New York, NY, USA
| | - Seth R Taylor
- Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Alexis Weinreb
- Departments of Genetics and Neuroscience, Yale University School of Medicine, New Haven, CT, USA
| | - Barry L Bentley
- Cardiff School of Technologies, Cardiff Metropolitan University, Cardiff, UK
| | - Marc Hammarlund
- Departments of Genetics and Neuroscience, Yale University School of Medicine, New Haven, CT, USA
| | - David M Miller
- Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Oliver Hobert
- Department of Biological Sciences/HHMI, Columbia University, New York, NY, USA
| | - Isabel Beets
- Department of Biology, KU Leuven, Leuven, Belgium
| | - Petra E Vértes
- Department of Psychiatry, Cambridge University, Cambridge, UK
| | - William R Schafer
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK; Department of Biology, KU Leuven, Leuven, Belgium.
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48
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Chua NJ, Makarova AA, Gunn P, Villani S, Cohen B, Thasin M, Wu J, Shefter D, Pang S, Xu CS, Hess HF, Polilov AA, Chklovskii DB. A complete reconstruction of the early visual system of an adult insect. Curr Biol 2023; 33:4611-4623.e4. [PMID: 37774707 DOI: 10.1016/j.cub.2023.09.021] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 09/07/2023] [Accepted: 09/08/2023] [Indexed: 10/01/2023]
Abstract
For most model organisms in neuroscience, research into visual processing in the brain is difficult because of a lack of high-resolution maps that capture complex neuronal circuitry. The microinsect Megaphragma viggianii, because of its small size and non-trivial behavior, provides a unique opportunity for tractable whole-organism connectomics. We image its whole head using serial electron microscopy. We reconstruct its compound eye and analyze the optical properties of the ommatidia as well as the connectome of the first visual neuropil-the lamina. Compared with the fruit fly and the honeybee, Megaphragma visual system is highly simplified: it has 29 ommatidia per eye and 6 lamina neuron types. We report features that are both stereotypical among most ommatidia and specialized to some. By identifying the "barebones" circuits critical for flying insects, our results will facilitate constructing computational models of visual processing in insects.
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Affiliation(s)
- Nicholas J Chua
- Center for Computational Neuroscience, Flatiron Institute, New York, NY 10010, USA; Department of Biological Sciences, Columbia University, New York, NY 10027, USA
| | | | - Pat Gunn
- Center for Computational Neuroscience, Flatiron Institute, New York, NY 10010, USA
| | - Sonia Villani
- Center for Computational Neuroscience, Flatiron Institute, New York, NY 10010, USA
| | - Ben Cohen
- Center for Computational Neuroscience, Flatiron Institute, New York, NY 10010, USA
| | - Myisha Thasin
- Center for Computational Neuroscience, Flatiron Institute, New York, NY 10010, USA
| | - Jingpeng Wu
- Center for Computational Neuroscience, Flatiron Institute, New York, NY 10010, USA
| | - Deena Shefter
- Center for Computational Neuroscience, Flatiron Institute, New York, NY 10010, USA
| | - Song Pang
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - C Shan Xu
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Harald F Hess
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Alexey A Polilov
- Faculty of Biology, Lomonosov Moscow State University, Moscow 119991, Russia
| | - Dmitri B Chklovskii
- Center for Computational Neuroscience, Flatiron Institute, New York, NY 10010, USA; Neuroscience Institute, New York University Langone Medical Center, New York, NY 10016, USA.
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49
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Ammer G, Serbe-Kamp E, Mauss AS, Richter FG, Fendl S, Borst A. Multilevel visual motion opponency in Drosophila. Nat Neurosci 2023; 26:1894-1905. [PMID: 37783895 PMCID: PMC10620086 DOI: 10.1038/s41593-023-01443-z] [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: 02/03/2023] [Accepted: 08/30/2023] [Indexed: 10/04/2023]
Abstract
Inhibitory interactions between opponent neuronal pathways constitute a common circuit motif across brain areas and species. However, in most cases, synaptic wiring and biophysical, cellular and network mechanisms generating opponency are unknown. Here, we combine optogenetics, voltage and calcium imaging, connectomics, electrophysiology and modeling to reveal multilevel opponent inhibition in the fly visual system. We uncover a circuit architecture in which a single cell type implements direction-selective, motion-opponent inhibition at all three network levels. This inhibition, mediated by GluClα receptors, is balanced with excitation in strength, despite tenfold fewer synapses. The different opponent network levels constitute a nested, hierarchical structure operating at increasing spatiotemporal scales. Electrophysiology and modeling suggest that distributing this computation over consecutive network levels counteracts a reduction in gain, which would result from integrating large opposing conductances at a single instance. We propose that this neural architecture provides resilience to noise while enabling high selectivity for relevant sensory information.
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Affiliation(s)
- Georg Ammer
- Max Planck Institute for Biological Intelligence, Martinsried, Germany.
| | - Etienne Serbe-Kamp
- Max Planck Institute for Biological Intelligence, Martinsried, Germany
- Ludwig Maximilian University of Munich, Munich, Germany
| | - Alex S Mauss
- Max Planck Institute for Biological Intelligence, Martinsried, Germany
| | - Florian G Richter
- Max Planck Institute for Biological Intelligence, Martinsried, Germany
| | - Sandra Fendl
- Max Planck Institute for Biological Intelligence, Martinsried, Germany
| | - Alexander Borst
- Max Planck Institute for Biological Intelligence, Martinsried, Germany
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50
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