1
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Eckstein N, Bates AS, Champion A, Du M, Yin Y, Schlegel P, Lu AKY, Rymer T, Finley-May S, Paterson T, Parekh R, Dorkenwald S, Matsliah A, Yu SC, McKellar C, Sterling A, Eichler K, Costa M, Seung S, Murthy M, Hartenstein V, Jefferis GSXE, Funke J. Neurotransmitter classification from electron microscopy images at synaptic sites in Drosophila melanogaster. Cell 2024; 187:2574-2594.e23. [PMID: 38729112 PMCID: PMC11106717 DOI: 10.1016/j.cell.2024.03.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Revised: 10/04/2023] [Accepted: 03/13/2024] [Indexed: 05/12/2024]
Abstract
High-resolution electron microscopy of nervous systems has enabled the reconstruction of synaptic connectomes. However, we do not know the synaptic sign for each connection (i.e., whether a connection is excitatory or inhibitory), which is implied by the released transmitter. We demonstrate that artificial neural networks can predict transmitter types for presynapses from electron micrographs: a network trained to predict six transmitters (acetylcholine, glutamate, GABA, serotonin, dopamine, octopamine) achieves an accuracy of 87% for individual synapses, 94% for neurons, and 91% for known cell types across a D. melanogaster whole brain. We visualize the ultrastructural features used for prediction, discovering subtle but significant differences between transmitter phenotypes. We also analyze transmitter distributions across the brain and find that neurons that develop together largely express only one fast-acting transmitter (acetylcholine, glutamate, or GABA). We hope that our publicly available predictions act as an accelerant for neuroscientific hypothesis generation for the fly.
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Affiliation(s)
- Nils Eckstein
- HHMI Janelia Research Campus, Ashburn, VA, USA; Institute of Neuroinformatics UZH/ETHZ, Zurich, Switzerland
| | - Alexander Shakeel Bates
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK; Centre for Neural Circuits and Behaviour, The University of Oxford, Tinsley Building, Mansfield Road, Oxford OX1 3SR, UK; Department of Neurobiology and Howard Hughes Medical Institute, Harvard Medical School, Boston, MA, USA
| | - Andrew Champion
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Michelle Du
- HHMI Janelia Research Campus, Ashburn, VA, USA
| | - Yijie Yin
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Philipp Schlegel
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK; Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | | | | | | | | | | | - Sven Dorkenwald
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Arie Matsliah
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Szi-Chieh Yu
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Claire McKellar
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Amy Sterling
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Katharina Eichler
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Marta Costa
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Sebastian Seung
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Mala Murthy
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Volker Hartenstein
- Department of Molecular Cell and Developmental Biology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Gregory S X E Jefferis
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK; Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK.
| | - Jan Funke
- HHMI Janelia Research Campus, Ashburn, VA, USA.
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2
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Schneider-Mizell CM, Bodor AL, Brittain D, Buchanan J, Bumbarger DJ, Elabbady L, Gamlin C, Kapner D, Kinn S, Mahalingam G, Seshamani S, Suckow S, Takeno M, Torres R, Yin W, Dorkenwald S, Bae JA, Castro MA, Halageri A, Jia Z, Jordan C, Kemnitz N, Lee K, Li K, Lu R, Macrina T, Mitchell E, Mondal SS, Mu S, Nehoran B, Popovych S, Silversmith W, Turner NL, Wong W, Wu J, Reimer J, Tolias AS, Seung HS, Reid RC, Collman F, Maçarico da Costa N. Cell-type-specific inhibitory circuitry from a connectomic census of mouse visual cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.01.23.525290. [PMID: 36747710 PMCID: PMC9900837 DOI: 10.1101/2023.01.23.525290] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Mammalian cortex features a vast diversity of neuronal cell types, each with characteristic anatomical, molecular and functional properties. Synaptic connectivity powerfully shapes how each cell type participates in the cortical circuit, but mapping connectivity rules at the resolution of distinct cell types remains difficult. Here, we used millimeter-scale volumetric electron microscopy1 to investigate the connectivity of all inhibitory neurons across a densely-segmented neuronal population of 1352 cells spanning all layers of mouse visual cortex, producing a wiring diagram of inhibitory connections with more than 70,000 synapses. Taking a data-driven approach inspired by classical neuroanatomy, we classified inhibitory neurons based on the relative targeting of dendritic compartments and other inhibitory cells and developed a novel classification of excitatory neurons based on the morphological and synaptic input properties. The synaptic connectivity between inhibitory cells revealed a novel class of disinhibitory specialist targeting basket cells, in addition to familiar subclasses. Analysis of the inhibitory connectivity onto excitatory neurons found widespread specificity, with many interneurons exhibiting differential targeting of certain subpopulations spatially intermingled with other potential targets. Inhibitory targeting was organized into "motif groups," diverse sets of cells that collectively target both perisomatic and dendritic compartments of the same excitatory targets. Collectively, our analysis identified new organizing principles for cortical inhibition and will serve as a foundation for linking modern multimodal neuronal atlases with the cortical wiring diagram.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Sam Kinn
- Allen Institute for Brain Science, Seattle, WA
| | | | | | | | - Marc Takeno
- Allen Institute for Brain Science, Seattle, WA
| | | | - Wenjing Yin
- Allen Institute for Brain Science, Seattle, WA
| | - Sven Dorkenwald
- Princeton Neuroscience Institute, Princeton University, NJ
- Computer Science Department, Princeton University
| | - J Alexander Bae
- Princeton Neuroscience Institute, Princeton University, NJ
- Electrical and Computer Engineering Department, Princeton University
| | | | | | - Zhen Jia
- Princeton Neuroscience Institute, Princeton University, NJ
- Computer Science Department, Princeton University
| | - Chris Jordan
- Princeton Neuroscience Institute, Princeton University, NJ
| | - Nico Kemnitz
- Princeton Neuroscience Institute, Princeton University, NJ
| | - Kisuk Lee
- Brain & Cognitive Sciences Department, Massachusetts Institute of Technology
| | - Kai Li
- Computer Science Department, Princeton University
| | - Ran Lu
- Princeton Neuroscience Institute, Princeton University, NJ
| | - Thomas Macrina
- Princeton Neuroscience Institute, Princeton University, NJ
- Computer Science Department, Princeton University
| | - Eric Mitchell
- Princeton Neuroscience Institute, Princeton University, NJ
| | - Shanka Subhra Mondal
- Princeton Neuroscience Institute, Princeton University, NJ
- Electrical and Computer Engineering Department, Princeton University
| | - Shang Mu
- Princeton Neuroscience Institute, Princeton University, NJ
| | - Barak Nehoran
- Princeton Neuroscience Institute, Princeton University, NJ
- Computer Science Department, Princeton University
| | - Sergiy Popovych
- Princeton Neuroscience Institute, Princeton University, NJ
- Computer Science Department, Princeton University
| | | | - Nicholas L Turner
- Princeton Neuroscience Institute, Princeton University, NJ
- Computer Science Department, Princeton University
| | - William Wong
- Princeton Neuroscience Institute, Princeton University, NJ
| | - Jingpeng Wu
- Princeton Neuroscience Institute, Princeton University, NJ
| | - Jacob Reimer
- Department of Neuroscience, Baylor College of Medicine, Houston, TX
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine
| | - Andreas S Tolias
- Department of Neuroscience, Baylor College of Medicine, Houston, TX
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine
- Department of Electrical and Computer Engineering, Rice University
| | - H Sebastian Seung
- Princeton Neuroscience Institute, Princeton University, NJ
- Computer Science Department, Princeton University
| | - R Clay Reid
- Allen Institute for Brain Science, Seattle, WA
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3
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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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4
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Ogawa Y, Nicholas S, Thyselius M, Leibbrandt R, Nowotny T, Knight JC, Nordström K. Descending neurons of the hoverfly respond to pursuits of artificial targets. Curr Biol 2023; 33:4392-4404.e5. [PMID: 37776861 DOI: 10.1016/j.cub.2023.08.091] [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: 05/19/2023] [Revised: 07/11/2023] [Accepted: 08/31/2023] [Indexed: 10/02/2023]
Abstract
Many animals use motion vision information to control dynamic behaviors. Predatory animals, for example, show an exquisite ability to detect rapidly moving prey, followed by pursuit and capture. Such target detection is not only used by predators but is also important in conspecific interactions, such as for male hoverflies defending their territories against conspecific intruders. Visual target detection is believed to be subserved by specialized target-tuned neurons found in a range of species, including vertebrates and arthropods. However, how these target-tuned neurons respond to actual pursuit trajectories is currently not well understood. To redress this, we recorded extracellularly from target-selective descending neurons (TSDNs) in male Eristalis tenax hoverflies. We show that they have dorso-frontal receptive fields with a preferred direction up and away from the visual midline. We reconstructed visual flow fields as experienced during pursuits of artificial targets (black beads). We recorded TSDN responses to six reconstructed pursuits and found that each neuron responded consistently at remarkably specific time points but that these time points differed between neurons. We found that the observed spike probability was correlated with the spike probability predicted from each neuron's receptive field and size tuning. Interestingly, however, the overall response rate was low, with individual neurons responding to only a small part of each reconstructed pursuit. In contrast, the TSDN population responded to substantially larger proportions of the pursuits but with lower probability. This large variation between neurons could be useful if different neurons control different parts of the behavioral output.
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Affiliation(s)
- Yuri Ogawa
- Flinders Health and Medical Research Institute, Flinders University, GPO Box 2100, Adelaide, SA 5001, Australia
| | - Sarah Nicholas
- Flinders Health and Medical Research Institute, Flinders University, GPO Box 2100, Adelaide, SA 5001, Australia
| | - Malin Thyselius
- Department of Medical Cell Biology, Uppsala University, 75123 Uppsala, Sweden; School of Medical Sciences, Faculty of Medicine and Health, Örebro University, Örebro 701 82, Sweden
| | - Richard Leibbrandt
- College of Science and Engineering, Flinders University, GPO Box 2100, Adelaide, SA 5001, Australia
| | - Thomas Nowotny
- School of Engineering and Informatics, University of Sussex, Brighton BN1 9QJ, UK
| | - James C Knight
- School of Engineering and Informatics, University of Sussex, Brighton BN1 9QJ, UK
| | - Karin Nordström
- Flinders Health and Medical Research Institute, Flinders University, GPO Box 2100, Adelaide, SA 5001, Australia; Department of Medical Cell Biology, Uppsala University, 75123 Uppsala, Sweden.
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5
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Schlegel P, Yin Y, Bates AS, Dorkenwald S, Eichler K, Brooks P, Han DS, Gkantia M, Dos Santos M, Munnelly EJ, Badalamente G, Capdevila LS, Sane VA, Pleijzier MW, Tamimi IFM, Dunne CR, Salgarella I, Javier A, Fang S, Perlman E, Kazimiers T, Jagannathan SR, Matsliah A, Sterling AR, Yu SC, McKellar CE, Costa M, Seung HS, Murthy M, Hartenstein V, Bock DD, Jefferis GSXE. Whole-brain annotation and multi-connectome cell typing quantifies circuit stereotypy in Drosophila. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.27.546055. [PMID: 37425808 PMCID: PMC10327018 DOI: 10.1101/2023.06.27.546055] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
The fruit fly Drosophila melanogaster combines surprisingly sophisticated behaviour with a highly tractable nervous system. A large part of the fly's success as a model organism in modern neuroscience stems from the concentration of collaboratively generated molecular genetic and digital resources. As presented in our FlyWire companion paper 1 , this now includes the first full brain connectome of an adult animal. Here we report the systematic and hierarchical annotation of this ~130,000-neuron connectome including neuronal classes, cell types and developmental units (hemilineages). This enables any researcher to navigate this huge dataset and find systems and neurons of interest, linked to the literature through the Virtual Fly Brain database 2 . Crucially, this resource includes 4,552 cell types. 3,094 are rigorous consensus validations of cell types previously proposed in the hemibrain connectome 3 . In addition, we propose 1,458 new cell types, arising mostly from the fact that the FlyWire connectome spans the whole brain, whereas the hemibrain derives from a subvolume. Comparison of FlyWire and the hemibrain showed that cell type counts and strong connections were largely stable, but connection weights were surprisingly variable within and across animals. Further analysis defined simple heuristics for connectome interpretation: connections stronger than 10 unitary synapses or providing >1% of the input to a target cell are highly conserved. Some cell types showed increased variability across connectomes: the most common cell type in the mushroom body, required for learning and memory, is almost twice as numerous in FlyWire as the hemibrain. We find evidence for functional homeostasis through adjustments of the absolute amount of excitatory input while maintaining the excitation-inhibition ratio. Finally, and surprisingly, about one third of the cell types proposed in the hemibrain connectome could not yet be reliably identified in the FlyWire connectome. We therefore suggest that cell types should be defined to be robust to inter-individual variation, namely as groups of cells that are quantitatively more similar to cells in a different brain than to any other cell in the same brain. Joint analysis of the FlyWire and hemibrain connectomes demonstrates the viability and utility of this new definition. Our work defines a consensus cell type atlas for the fly brain and provides both an intellectual framework and open source toolchain for brain-scale comparative connectomics.
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6
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Dorkenwald S, Matsliah A, Sterling AR, Schlegel P, Yu SC, McKellar CE, Lin A, Costa M, Eichler K, Yin Y, Silversmith W, Schneider-Mizell C, Jordan CS, Brittain D, Halageri A, Kuehner K, Ogedengbe O, Morey R, Gager J, Kruk K, Perlman E, Yang R, Deutsch D, Bland D, Sorek M, Lu R, Macrina T, Lee K, Bae JA, Mu S, Nehoran B, Mitchell E, Popovych S, Wu J, Jia Z, Castro M, Kemnitz N, Ih D, Bates AS, Eckstein N, Funke J, Collman F, Bock DD, Jefferis GS, Seung HS, Murthy M. Neuronal wiring diagram of an adult brain. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.27.546656. [PMID: 37425937 PMCID: PMC10327113 DOI: 10.1101/2023.06.27.546656] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
Connections between neurons can be mapped by acquiring and analyzing electron microscopic (EM) brain images. In recent years, this approach has been applied to chunks of brains to reconstruct local connectivity maps that are highly informative, yet inadequate for understanding brain function more globally. Here, we present the first neuronal wiring diagram of a whole adult brain, containing 5×107 chemical synapses between ~130,000 neurons reconstructed from a female Drosophila melanogaster. The resource also incorporates annotations of cell classes and types, nerves, hemilineages, and predictions of neurotransmitter identities. Data products are available by download, programmatic access, and interactive browsing and made interoperable with other fly data resources. We show how to derive a projectome, a map of projections between regions, from the connectome. We demonstrate the tracing of synaptic pathways and the analysis of information flow from inputs (sensory and ascending neurons) to outputs (motor, endocrine, and descending neurons), across both hemispheres, and between the central brain and the optic lobes. Tracing from a subset of photoreceptors all the way to descending motor pathways illustrates how structure can uncover putative circuit mechanisms underlying sensorimotor behaviors. The technologies and open ecosystem of the FlyWire Consortium set the stage for future large-scale connectome projects in other species.
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Affiliation(s)
- Sven Dorkenwald
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Computer Science Department, Princeton University, Princeton, USA
| | - Arie Matsliah
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Amy R Sterling
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Eyewire, Boston, USA
| | - Philipp Schlegel
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Szi-chieh Yu
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | | | - Albert Lin
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Center for the Physics of Biological Function, Princeton University, Princeton, USA
| | - Marta Costa
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Katharina Eichler
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Yijie Yin
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Will Silversmith
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | | | - Chris S. Jordan
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | | | - Akhilesh Halageri
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Kai Kuehner
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | | | - Ryan Morey
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Jay Gager
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | | | | | - Runzhe Yang
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Computer Science Department, Princeton University, Princeton, USA
| | - David Deutsch
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Department of Neurobiology, University of Haifa, Haifa, Israel
| | - Doug Bland
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Marissa Sorek
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Eyewire, Boston, USA
| | - Ran Lu
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Thomas Macrina
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Computer Science Department, Princeton University, Princeton, USA
| | - Kisuk Lee
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Brain & Cognitive Sciences Department, Massachusetts Institute of Technology, Cambridge, USA
| | - J. Alexander Bae
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Electrical and Computer Engineering Department, Princeton University, Princeton, USA
| | - Shang Mu
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Barak Nehoran
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Computer Science Department, Princeton University, Princeton, USA
| | - Eric Mitchell
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Sergiy Popovych
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Computer Science Department, Princeton University, Princeton, USA
| | - Jingpeng Wu
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Zhen Jia
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Manuel Castro
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Nico Kemnitz
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Dodam Ih
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Alexander Shakeel Bates
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
- Harvard Medical School, Boston, USA
- Centre for Neural Circuits and Behaviour, The University of Oxford, Oxford, UK
| | - Nils Eckstein
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, USA
| | - Jan Funke
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, USA
| | | | - Davi D. Bock
- Department of Neurological Sciences, Larner College of Medicine, University of Vermont, Burlington, USA
| | - Gregory S.X.E Jefferis
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - H. Sebastian Seung
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Computer Science Department, Princeton University, Princeton, USA
| | - Mala Murthy
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
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7
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Wilson RI. Neural Networks for Navigation: From Connections to Computations. Annu Rev Neurosci 2023; 46:403-423. [PMID: 37428603 DOI: 10.1146/annurev-neuro-110920-032645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/12/2023]
Abstract
Many animals can navigate toward a goal they cannot see based on an internal representation of that goal in the brain's spatial maps. These maps are organized around networks with stable fixed-point dynamics (attractors), anchored to landmarks, and reciprocally connected to motor control. This review summarizes recent progress in understanding these networks, focusing on studies in arthropods. One factor driving recent progress is the availability of the Drosophila connectome; however, it is increasingly clear that navigation depends on ongoing synaptic plasticity in these networks. Functional synapses appear to be continually reselected from the set of anatomical potential synapses based on the interaction of Hebbian learning rules, sensory feedback, attractor dynamics, and neuromodulation. This can explain how the brain's maps of space are rapidly updated; it may also explain how the brain can initialize goals as stable fixed points for navigation.
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Affiliation(s)
- Rachel I Wilson
- Department of Neurobiology, Harvard Medical School, Cambridge, Massachusetts, USA;
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8
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Barabási DL, Beynon T, Katona Á, Perez-Nieves N. Complex computation from developmental priors. Nat Commun 2023; 14:2226. [PMID: 37076523 PMCID: PMC10115783 DOI: 10.1038/s41467-023-37980-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 04/07/2023] [Indexed: 04/21/2023] Open
Abstract
Machine learning (ML) models have long overlooked innateness: how strong pressures for survival lead to the encoding of complex behaviors in the nascent wiring of a brain. Here, we derive a neurodevelopmental encoding of artificial neural networks that considers the weight matrix of a neural network to be emergent from well-studied rules of neuronal compatibility. Rather than updating the network's weights directly, we improve task fitness by updating the neurons' wiring rules, thereby mirroring evolutionary selection on brain development. We find that our model (1) provides sufficient representational power for high accuracy on ML benchmarks while also compressing parameter count, and (2) can act as a regularizer, selecting simple circuits that provide stable and adaptive performance on metalearning tasks. In summary, by introducing neurodevelopmental considerations into ML frameworks, we not only model the emergence of innate behaviors, but also define a discovery process for structures that promote complex computations.
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Affiliation(s)
| | | | - Ádám Katona
- Electrical and Electronic Engineering, Imperial College London, London, UK
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9
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Winding M, Pedigo BD, Barnes CL, Patsolic HG, Park Y, Kazimiers T, Fushiki A, Andrade IV, Khandelwal A, Valdes-Aleman J, Li F, Randel N, Barsotti E, Correia A, Fetter RD, Hartenstein V, Priebe CE, Vogelstein JT, Cardona A, Zlatic M. The connectome of an insect brain. Science 2023; 379:eadd9330. [PMID: 36893230 PMCID: PMC7614541 DOI: 10.1126/science.add9330] [Citation(s) in RCA: 76] [Impact Index Per Article: 76.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2022] [Accepted: 02/07/2023] [Indexed: 03/11/2023]
Abstract
Brains contain networks of interconnected neurons and so knowing the network architecture is essential for understanding brain function. We therefore mapped the synaptic-resolution connectome of an entire insect brain (Drosophila larva) with rich behavior, including learning, value computation, and action selection, comprising 3016 neurons and 548,000 synapses. We characterized neuron types, hubs, feedforward and feedback pathways, as well as cross-hemisphere and brain-nerve cord interactions. We found pervasive multisensory and interhemispheric integration, highly recurrent architecture, abundant feedback from descending neurons, and multiple novel circuit motifs. The brain's most recurrent circuits comprised the input and output neurons of the learning center. Some structural features, including multilayer shortcuts and nested recurrent loops, resembled state-of-the-art deep learning architectures. The identified brain architecture provides a basis for future experimental and theoretical studies of neural circuits.
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Affiliation(s)
- Michael Winding
- University of Cambridge, Department of Zoology, Cambridge, UK
- MRC Laboratory of Molecular Biology, Neurobiology Division, Cambridge, UK
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Benjamin D. Pedigo
- Johns Hopkins University, Department of Biomedical Engineering, Baltimore, MD, USA
| | - Christopher L. Barnes
- MRC Laboratory of Molecular Biology, Neurobiology Division, Cambridge, UK
- University of Cambridge, Department of Physiology, Development, and Neuroscience, Cambridge, UK
| | - Heather G. Patsolic
- Johns Hopkins University, Department of Applied Mathematics and Statistics, Baltimore, MD, USA
- Accenture, Arlington, VA, USA
| | - Youngser Park
- Johns Hopkins University, Center for Imaging Science, Baltimore, MD, USA
| | - Tom Kazimiers
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
- kazmos GmbH, Dresden, Germany
| | - Akira Fushiki
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
| | - Ingrid V. Andrade
- University of California Los Angeles, Department of Molecular, Cell and Developmental Biology, Los Angeles, CA, USA
| | - Avinash Khandelwal
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Javier Valdes-Aleman
- University of Cambridge, Department of Zoology, Cambridge, UK
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Feng Li
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Nadine Randel
- University of Cambridge, Department of Zoology, Cambridge, UK
- MRC Laboratory of Molecular Biology, Neurobiology Division, Cambridge, UK
| | - Elizabeth Barsotti
- MRC Laboratory of Molecular Biology, Neurobiology Division, Cambridge, UK
- University of Cambridge, Department of Physiology, Development, and Neuroscience, Cambridge, UK
| | - Ana Correia
- MRC Laboratory of Molecular Biology, Neurobiology Division, Cambridge, UK
- University of Cambridge, Department of Physiology, Development, and Neuroscience, Cambridge, UK
| | - Richard D. Fetter
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
- Stanford University, Stanford, CA, USA
| | - Volker Hartenstein
- University of California Los Angeles, Department of Molecular, Cell and Developmental Biology, Los Angeles, CA, USA
| | - Carey E. Priebe
- Johns Hopkins University, Department of Applied Mathematics and Statistics, Baltimore, MD, USA
- Johns Hopkins University, Center for Imaging Science, Baltimore, MD, USA
| | - Joshua T. Vogelstein
- Johns Hopkins University, Department of Biomedical Engineering, Baltimore, MD, USA
- Johns Hopkins University, Center for Imaging Science, Baltimore, MD, USA
| | - Albert Cardona
- MRC Laboratory of Molecular Biology, Neurobiology Division, Cambridge, UK
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
- University of Cambridge, Department of Physiology, Development, and Neuroscience, Cambridge, UK
| | - Marta Zlatic
- University of Cambridge, Department of Zoology, Cambridge, UK
- MRC Laboratory of Molecular Biology, Neurobiology Division, Cambridge, UK
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
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10
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Meissner GW, Nern A, Dorman Z, DePasquale GM, Forster K, Gibney T, Hausenfluck JH, He Y, Iyer NA, Jeter J, Johnson L, Johnston RM, Lee K, Melton B, Yarbrough B, Zugates CT, Clements J, Goina C, Otsuna H, Rokicki K, Svirskas RR, Aso Y, Card GM, Dickson BJ, Ehrhardt E, Goldammer J, Ito M, Kainmueller D, Korff W, Mais L, Minegishi R, Namiki S, Rubin GM, Sterne GR, Wolff T, Malkesman O. A searchable image resource of Drosophila GAL4 driver expression patterns with single neuron resolution. eLife 2023; 12:e80660. [PMID: 36820523 PMCID: PMC10030108 DOI: 10.7554/elife.80660] [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: 05/30/2022] [Accepted: 02/21/2023] [Indexed: 02/24/2023] Open
Abstract
Precise, repeatable genetic access to specific neurons via GAL4/UAS and related methods is a key advantage of Drosophila neuroscience. Neuronal targeting is typically documented using light microscopy of full GAL4 expression patterns, which generally lack the single-cell resolution required for reliable cell type identification. Here, we use stochastic GAL4 labeling with the MultiColor FlpOut approach to generate cellular resolution confocal images at large scale. We are releasing aligned images of 74,000 such adult central nervous systems. An anticipated use of this resource is to bridge the gap between neurons identified by electron or light microscopy. Identifying individual neurons that make up each GAL4 expression pattern improves the prediction of split-GAL4 combinations targeting particular neurons. To this end, we have made the images searchable on the NeuronBridge website. We demonstrate the potential of NeuronBridge to rapidly and effectively identify neuron matches based on morphology across imaging modalities and datasets.
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Affiliation(s)
- Geoffrey W Meissner
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Aljoscha Nern
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Zachary Dorman
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Gina M DePasquale
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Kaitlyn Forster
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Theresa Gibney
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | | | - Yisheng He
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Nirmala A Iyer
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Jennifer Jeter
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Lauren Johnson
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Rebecca M Johnston
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Kelley Lee
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Brian Melton
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Brianna Yarbrough
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | | | - Jody Clements
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Cristian Goina
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Hideo Otsuna
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Konrad Rokicki
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Robert R Svirskas
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Yoshinori Aso
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Gwyneth M Card
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Barry J Dickson
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Erica Ehrhardt
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Jens Goldammer
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Masayoshi Ito
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Dagmar Kainmueller
- Max-Delbrueck-Center for Molecular Medicine in the Helmholtz Association (MDC)BerlinGermany
| | - Wyatt Korff
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Lisa Mais
- Max-Delbrueck-Center for Molecular Medicine in the Helmholtz Association (MDC)BerlinGermany
| | - Ryo Minegishi
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Shigehiro Namiki
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Gerald M Rubin
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Gabriella R Sterne
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Tanya Wolff
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Oz Malkesman
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
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11
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Schulze L, Zada D, Lovett-Barron M. A medley of gene expression adds new depth to zebrafish brain maps. SCIENCE ADVANCES 2023; 9:eadg8660. [PMID: 36812324 PMCID: PMC9946337 DOI: 10.1126/sciadv.adg8660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
The integration of large-scale gene expression mapping into a multifaceted larval zebrafish brain atlas accelerates the characterization of neurons in behaviorally relevant circuits.
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12
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Court R, Costa M, Pilgrim C, Millburn G, Holmes A, McLachlan A, Larkin A, Matentzoglu N, Kir H, Parkinson H, Brown NH, O’Kane CJ, Armstrong JD, Jefferis GSXE, Osumi-Sutherland D. Virtual Fly Brain-An interactive atlas of the Drosophila nervous system. Front Physiol 2023; 14:1076533. [PMID: 36776967 PMCID: PMC9908962 DOI: 10.3389/fphys.2023.1076533] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 01/02/2023] [Indexed: 01/27/2023] Open
Abstract
As a model organism, Drosophila is uniquely placed to contribute to our understanding of how brains control complex behavior. Not only does it have complex adaptive behaviors, but also a uniquely powerful genetic toolkit, increasingly complete dense connectomic maps of the central nervous system and a rapidly growing set of transcriptomic profiles of cell types. But this also poses a challenge: Given the massive amounts of available data, how are researchers to Find, Access, Integrate and Reuse (FAIR) relevant data in order to develop an integrated anatomical and molecular picture of circuits, inform hypothesis generation, and find reagents for experiments to test these hypotheses? The Virtual Fly Brain (virtualflybrain.org) web application & API provide a solution to this problem, using FAIR principles to integrate 3D images of neurons and brain regions, connectomics, transcriptomics and reagent expression data covering the whole CNS in both larva and adult. Users can search for neurons, neuroanatomy and reagents by name, location, or connectivity, via text search, clicking on 3D images, search-by-image, and queries by type (e.g., dopaminergic neuron) or properties (e.g., synaptic input in the antennal lobe). Returned results include cross-registered 3D images that can be explored in linked 2D and 3D browsers or downloaded under open licenses, and extensive descriptions of cell types and regions curated from the literature. These solutions are potentially extensible to cover similar atlasing and data integration challenges in vertebrates.
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Affiliation(s)
- Robert Court
- School of Informatics, University of Edinburgh, Edinburgh, United Kingtom
| | - Marta Costa
- Department of Zoology, University of Cambridge, Cambridge, United Kingtom
- Department of Genetics, University of Cambridge, Cambridge, United Kingtom
| | - Clare Pilgrim
- Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, United Kingtom
| | - Gillian Millburn
- Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, United Kingtom
| | - Alex Holmes
- Department of Genetics, University of Cambridge, Cambridge, United Kingtom
| | - Alex McLachlan
- Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, United Kingtom
| | - Aoife Larkin
- Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, United Kingtom
| | | | - Huseyin Kir
- European Bioinformatics Institute (EMBL-EBI), Hinxton, United Kingtom
| | - Helen Parkinson
- European Bioinformatics Institute (EMBL-EBI), Hinxton, United Kingtom
| | - Nicolas H. Brown
- Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, United Kingtom
| | - Cahir J. O’Kane
- Department of Genetics, University of Cambridge, Cambridge, United Kingtom
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13
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Brain Data Standards - A method for building data-driven cell-type ontologies. Sci Data 2023; 10:50. [PMID: 36693887 PMCID: PMC9873614 DOI: 10.1038/s41597-022-01886-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Accepted: 12/06/2022] [Indexed: 01/25/2023] Open
Abstract
Large-scale single-cell 'omics profiling is being used to define a complete catalogue of brain cell types, something that traditional methods struggle with due to the diversity and complexity of the brain. But this poses a problem: How do we organise such a catalogue - providing a standard way to refer to the cell types discovered, linking their classification and properties to supporting data? Cell ontologies provide a partial solution to these problems, but no existing ontology schemas support the definition of cell types by direct reference to supporting data, classification of cell types using classifications derived directly from data, or links from cell types to marker sets along with confidence scores. Here we describe a generally applicable schema that solves these problems and its application in a semi-automated pipeline to build a data-linked extension to the Cell Ontology representing cell types in the Primary Motor Cortex of humans, mice and marmosets. The methods and resulting ontology are designed to be scalable and applicable to similar whole-brain atlases currently in preparation.
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14
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Galili DS, Jefferis GS, Costa M. Connectomics and the neural basis of behaviour. CURRENT OPINION IN INSECT SCIENCE 2022; 54:100968. [PMID: 36113710 PMCID: PMC7614087 DOI: 10.1016/j.cois.2022.100968] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 08/30/2022] [Accepted: 09/06/2022] [Indexed: 06/15/2023]
Abstract
Methods to acquire and process synaptic-resolution electron-microscopy datasets have progressed very rapidly, allowing production and annotation of larger, more complete connectomes. More accurate neuronal matching techniques are enriching cell type data with gene expression, neuron activity, behaviour and developmental information, providing ways to test hypotheses of circuit function. In a variety of behaviours such as learned and innate olfaction, navigation and sexual behaviour, connectomics has already revealed interconnected modules with a hierarchical structure, recurrence and integration of sensory streams. Comparing individual connectomes to determine which circuit features are robust and which are variable is one key research area; new work in comparative connectomics across development, experience, sex and species will establish strong links between neuronal connectivity and brain function.
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Affiliation(s)
- Dana S Galili
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge CB2 0QH, UK
| | - Gregory Sxe Jefferis
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge CB2 0QH, UK; Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Downing Street, Cambridge CB2 3EJ, UK
| | - Marta Costa
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Downing Street, Cambridge CB2 3EJ, UK.
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15
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Dillon N, Cocanougher B, Sood C, Yuan X, Kohn AB, Moroz LL, Siegrist SE, Zlatic M, Doe CQ. Single cell RNA-seq analysis reveals temporally-regulated and quiescence-regulated gene expression in Drosophila larval neuroblasts. Neural Dev 2022; 17:7. [PMID: 36002894 PMCID: PMC9404614 DOI: 10.1186/s13064-022-00163-7] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 05/19/2022] [Indexed: 12/12/2022] Open
Abstract
The mechanisms that generate neural diversity during development remains largely unknown. Here, we use scRNA-seq methodology to discover new features of the Drosophila larval CNS across several key developmental timepoints. We identify multiple progenitor subtypes - both stem cell-like neuroblasts and intermediate progenitors - that change gene expression across larval development, and report on new candidate markers for each class of progenitors. We identify a pool of quiescent neuroblasts in newly hatched larvae and show that they are transcriptionally primed to respond to the insulin signaling pathway to exit from quiescence, including relevant pathway components in the adjacent glial signaling cell type. We identify candidate "temporal transcription factors" (TTFs) that are expressed at different times in progenitor lineages. Our work identifies many cell type specific genes that are candidates for functional roles, and generates new insight into the differentiation trajectory of larval neurons.
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Affiliation(s)
- Noah Dillon
- Institute of Neuroscience, Howard Hughes Medical Institute, University of Oregon, OR, 97403, Eugene, USA
| | - Ben Cocanougher
- Department of Zoology, University of Cambridge, Cambridge, UK
| | - Chhavi Sood
- Department of Biology, University of Virginia, VA, 22904, Charlottesville, USA
| | - Xin Yuan
- Department of Biology, University of Virginia, VA, 22904, Charlottesville, USA
| | - Andrea B Kohn
- Whitney Laboratory for Marine Biosciences, University of Florida, FL, 32080, St. Augustine, USA
| | - Leonid L Moroz
- Whitney Laboratory for Marine Biosciences, University of Florida, FL, 32080, St. Augustine, USA
| | - Sarah E Siegrist
- Department of Biology, University of Virginia, VA, 22904, Charlottesville, USA
| | - Marta Zlatic
- MRC Laboratory of Molecular Biology, Dept of Zoology, University of Cambridge, Cambridge, UK.,Janelia Research Campus, VA, Ashburn, USA
| | - Chris Q Doe
- Institute of Neuroscience, Howard Hughes Medical Institute, University of Oregon, OR, 97403, Eugene, USA.
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16
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Zeng H. What is a cell type and how to define it? Cell 2022; 185:2739-2755. [PMID: 35868277 DOI: 10.1016/j.cell.2022.06.031] [Citation(s) in RCA: 125] [Impact Index Per Article: 62.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Revised: 06/14/2022] [Accepted: 06/14/2022] [Indexed: 12/20/2022]
Abstract
Cell types are the basic functional units of an organism. Cell types exhibit diverse phenotypic properties at multiple levels, making them challenging to define, categorize, and understand. This review provides an overview of the basic principles of cell types rooted in evolution and development and discusses approaches to characterize and classify cell types and investigate how they contribute to the organism's function, using the mammalian brain as a primary example. I propose a roadmap toward a conceptual framework and knowledge base of cell types that will enable a deeper understanding of the dynamic changes of cellular function under healthy and diseased conditions.
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Affiliation(s)
- Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA 98109, USA.
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17
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Inagaki HK, Chen S, Daie K, Finkelstein A, Fontolan L, Romani S, Svoboda K. Neural Algorithms and Circuits for Motor Planning. Annu Rev Neurosci 2022; 45:249-271. [PMID: 35316610 DOI: 10.1146/annurev-neuro-092021-121730] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The brain plans and executes volitional movements. The underlying patterns of neural population activity have been explored in the context of movements of the eyes, limbs, tongue, and head in nonhuman primates and rodents. How do networks of neurons produce the slow neural dynamics that prepare specific movements and the fast dynamics that ultimately initiate these movements? Recent work exploits rapid and calibrated perturbations of neural activity to test specific dynamical systems models that are capable of producing the observed neural activity. These joint experimental and computational studies show that cortical dynamics during motor planning reflect fixed points of neural activity (attractors). Subcortical control signals reshape and move attractors over multiple timescales, causing commitment to specific actions and rapid transitions to movement execution. Experiments in rodents are beginning to reveal how these algorithms are implemented at the level of brain-wide neural circuits.
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Affiliation(s)
| | - Susu Chen
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, USA
| | - Kayvon Daie
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, USA.,Allen Institute for Neural Dynamics, Seattle, Washington, USA;
| | - Arseny Finkelstein
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, USA.,Department of Physiology and Pharmacology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv-Yafo, Israel
| | - Lorenzo Fontolan
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, USA
| | - Sandro Romani
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, USA
| | - Karel Svoboda
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, USA.,Allen Institute for Neural Dynamics, Seattle, Washington, USA;
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18
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Jiao W, Spreemann G, Ruchti E, Banerjee S, Vernon S, Shi Y, Stowers RS, Hess K, McCabe BD. Intact Drosophila central nervous system cellular quantitation reveals sexual dimorphism. eLife 2022; 11:74968. [PMID: 35801638 PMCID: PMC9270032 DOI: 10.7554/elife.74968] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 06/09/2022] [Indexed: 12/15/2022] Open
Abstract
Establishing with precision the quantity and identity of the cell types of the brain is a prerequisite for a detailed compendium of gene and protein expression in the central nervous system (CNS). Currently, however, strict quantitation of cell numbers has been achieved only for the nervous system of Caenorhabditis elegans. Here, we describe the development of a synergistic pipeline of molecular genetic, imaging, and computational technologies designed to allow high-throughput, precise quantitation with cellular resolution of reporters of gene expression in intact whole tissues with complex cellular constitutions such as the brain. We have deployed the approach to determine with exactitude the number of functional neurons and glia in the entire intact larval Drosophila CNS, revealing fewer neurons and more glial cells than previously predicted. We also discover an unexpected divergence between the sexes at this juvenile developmental stage, with the female CNS having significantly more neurons than that of males. Topological analysis of our data establishes that this sexual dimorphism extends to deeper features of CNS organisation. We additionally extended our analysis to quantitate the expression of voltage-gated potassium channel family genes throughout the CNS and uncover substantial differences in abundance. Our methodology enables robust and accurate quantification of the number and positioning of cells within intact organs, facilitating sophisticated analysis of cellular identity, diversity, and gene expression characteristics.
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Affiliation(s)
- Wei Jiao
- Brain Mind Institute, EPFL - Swiss Federal Institute of Technology
| | - Gard Spreemann
- Brain Mind Institute, EPFL - Swiss Federal Institute of Technology
| | - Evelyne Ruchti
- Brain Mind Institute, EPFL - Swiss Federal Institute of Technology
| | - Soumya Banerjee
- Brain Mind Institute, EPFL - Swiss Federal Institute of Technology
| | - Samuel Vernon
- Brain Mind Institute, EPFL - Swiss Federal Institute of Technology
| | - Ying Shi
- Brain Mind Institute, EPFL - Swiss Federal Institute of Technology
| | - R Steven Stowers
- Department of Microbiology and Cell Biology, Montana State University
| | - Kathryn Hess
- Brain Mind Institute, EPFL - Swiss Federal Institute of Technology
| | - Brian D McCabe
- Brain Mind Institute, EPFL - Swiss Federal Institute of Technology
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19
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Phan MS, Matho K, Beaurepaire E, Livet J, Chessel A. nAdder: A scale-space approach for the 3D analysis of neuronal traces. PLoS Comput Biol 2022; 18:e1010211. [PMID: 35789212 PMCID: PMC9286273 DOI: 10.1371/journal.pcbi.1010211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 07/15/2022] [Accepted: 05/16/2022] [Indexed: 11/19/2022] Open
Abstract
Tridimensional microscopy and algorithms for automated segmentation and tracing are revolutionizing neuroscience through the generation of growing libraries of neuron reconstructions. Innovative computational methods are needed to analyze these neuronal traces. In particular, means to characterize the geometric properties of traced neurites along their trajectory have been lacking. Here, we propose a local tridimensional (3D) scale metric derived from differential geometry, measuring for each point of a curve the characteristic length where it is fully 3D as opposed to being embedded in a 2D plane or 1D line. The larger this metric is and the more complex the local 3D loops and turns of the curve are. Available through the GeNePy3D open-source Python quantitative geometry library (https://genepy3d.gitlab.io), this approach termed nAdder offers new means of describing and comparing axonal and dendritic arbors. We validate this metric on simulated and real traces. By reanalysing a published zebrafish larva whole brain dataset, we show its ability to characterize different population of commissural axons, distinguish afferent connections to a target region and differentiate portions of axons and dendrites according to their behavior, shedding new light on the stereotypical nature of neurites' local geometry.
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Affiliation(s)
- Minh Son Phan
- Laboratory for Optics and Biosciences, CNRS, INSERM, Ecole Polytechnique, IP Paris, Palaiseau, France
- Institut Pasteur, Université de Paris Cité, Image Analysis Hub,Paris, France
| | - Katherine Matho
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, Paris, France
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America
| | - Emmanuel Beaurepaire
- Laboratory for Optics and Biosciences, CNRS, INSERM, Ecole Polytechnique, IP Paris, Palaiseau, France
| | - Jean Livet
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, Paris, France
| | - Anatole Chessel
- Laboratory for Optics and Biosciences, CNRS, INSERM, Ecole Polytechnique, IP Paris, Palaiseau, France
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20
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Agrawal S, Tuthill JC. The two-body problem: Proprioception and motor control across the metamorphic divide. Curr Opin Neurobiol 2022; 74:102546. [PMID: 35512562 DOI: 10.1016/j.conb.2022.102546] [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: 12/14/2021] [Revised: 03/11/2022] [Accepted: 03/27/2022] [Indexed: 11/17/2022]
Abstract
Like a rocket being propelled into space, evolution has engineered flies to launch into adulthood via multiple stages. Flies develop and deploy two distinct bodies, linked by the transformative process of metamorphosis. The fly larva is a soft hydraulic tube that can crawl to find food and avoid predators. The adult fly has a stiff exoskeleton with articulated limbs that enable long-distance navigation and rich social interactions. Because the larval and adult forms are so distinct in structure, they require distinct strategies for sensing and moving the body. The metamorphic divide thus presents an opportunity for comparative analysis of neural circuits. Here, we review recent progress toward understanding the neural mechanisms of proprioception and motor control in larval and adult Drosophila. We highlight commonalities that point toward general principles of sensorimotor control and differences that may reflect unique constraints imposed by biomechanics. Finally, we discuss emerging opportunities for comparative analysis of neural circuit architecture in the fly and other animal species.
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Affiliation(s)
- Sweta Agrawal
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA.
| | - John C Tuthill
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
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21
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Nagai H, Miura M, Nakajima YI. Cellular mechanisms underlying adult tissue plasticity in Drosophila. Fly (Austin) 2022; 16:190-206. [PMID: 35470772 PMCID: PMC9045823 DOI: 10.1080/19336934.2022.2066952] [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] [Indexed: 11/26/2022] Open
Abstract
Adult tissues in Metazoa dynamically remodel their structures in response to environmental challenges including sudden injury, pathogen infection, and nutritional fluctuation, while maintaining quiescence under homoeostatic conditions. This characteristic, hereafter referred to as adult tissue plasticity, can prevent tissue dysfunction and improve the fitness of organisms in continuous and/or severe change of environments. With its relatively simple tissue structures and genetic tools, studies using the fruit fly Drosophila melanogaster have provided insights into molecular mechanisms that control cellular responses, particularly during regeneration and nutrient adaptation. In this review, we present the current understanding of cellular mechanisms, stem cell proliferation, polyploidization, and cell fate plasticity, all of which enable adult tissue plasticity in various Drosophila adult organs including the midgut, the brain, and the gonad, and discuss the organismal strategy in response to environmental changes and future directions of the research.
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Affiliation(s)
- Hiroki Nagai
- Graduate School of Pharmaceutical Sciences, the University of Tokyo, Tokyo, Japan
| | - Masayuki Miura
- Graduate School of Pharmaceutical Sciences, the University of Tokyo, Tokyo, Japan
| | - Yu-Ichiro Nakajima
- Graduate School of Pharmaceutical Sciences, the University of Tokyo, Tokyo, Japan
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22
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Zhu H, Zhao SD, Ray A, Zhang Y, Li X. A comprehensive temporal patterning gene network in Drosophila medulla neuroblasts revealed by single-cell RNA sequencing. Nat Commun 2022; 13:1247. [PMID: 35273186 PMCID: PMC8913700 DOI: 10.1038/s41467-022-28915-3] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 02/12/2022] [Indexed: 12/24/2022] Open
Abstract
During development, neural progenitors are temporally patterned to sequentially generate a variety of neural types. In Drosophila neural progenitors called neuroblasts, temporal patterning is regulated by cascades of Temporal Transcription Factors (TTFs). However, known TTFs were mostly identified through candidate approaches and may not be complete. In addition, many fundamental questions remain concerning the TTF cascade initiation, progression, and termination. In this work, we use single-cell RNA sequencing of Drosophila medulla neuroblasts of all ages to identify a list of previously unknown TTFs, and experimentally characterize their roles in temporal patterning and neuronal specification. Our study reveals a comprehensive temporal gene network that patterns medulla neuroblasts from start to end. Furthermore, the speed of the cascade progression is regulated by Lola transcription factors expressed in all medulla neuroblasts. Our comprehensive study of the medulla neuroblast temporal cascade illustrates mechanisms that may be conserved in the temporal patterning of neural progenitors. During development, neural progenitors generate a variety of neural types sequentially. Here the authors examine gene expression patterns in Drosophila neural progenitors at single-cell level, and identify a gene regulatory network controlling the sequential generation of different neural types.
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Affiliation(s)
- Hailun Zhu
- Department of Cell and Developmental Biology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
| | - Sihai Dave Zhao
- Department of Statistics, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
| | - Alokananda Ray
- Department of Cell and Developmental Biology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
| | - Yu Zhang
- Department of Cell and Developmental Biology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
| | - Xin Li
- Department of Cell and Developmental Biology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA.
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23
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Rapti G. Open Frontiers in Neural Cell Type Investigations; Lessons From Caenorhabditis elegans and Beyond, Toward a Multimodal Integration. Front Neurosci 2022; 15:787753. [PMID: 35321480 PMCID: PMC8934944 DOI: 10.3389/fnins.2021.787753] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 12/30/2021] [Indexed: 11/13/2022] Open
Abstract
Nervous system cells, the building blocks of circuits, have been studied with ever-progressing resolution, yet neural circuits appear still resistant to schemes of reductionist classification. Due to their sheer numbers, complexity and diversity, their systematic study requires concrete classifications that can serve reduced dimensionality, reproducibility, and information integration. Conventional hierarchical schemes transformed through the history of neuroscience by prioritizing criteria of morphology, (electro)physiological activity, molecular content, and circuit function, influenced by prevailing methodologies of the time. Since the molecular biology revolution and the recent advents in transcriptomics, molecular profiling gains ground toward the classification of neurons and glial cell types. Yet, transcriptomics entails technical challenges and more importantly uncovers unforeseen spatiotemporal heterogeneity, in complex and simpler nervous systems. Cells change states dynamically in space and time, in response to stimuli or throughout their developmental trajectory. Mapping cell type and state heterogeneity uncovers uncharted terrains in neurons and especially in glial cell biology, that remains understudied in many aspects. Examining neurons and glial cells from the perspectives of molecular neuroscience, physiology, development and evolution highlights the advantage of multifaceted classification schemes. Among the amalgam of models contributing to neuroscience research, Caenorhabditis elegans combines nervous system anatomy, lineage, connectivity and molecular content, all mapped at single-cell resolution, and can provide valuable insights for the workflow and challenges of the multimodal integration of cell type features. This review reflects on concepts and practices of neuron and glial cells classification and how research, in C. elegans and beyond, guides nervous system experimentation through integrated multidimensional schemes. It highlights underlying principles, emerging themes, and open frontiers in the study of nervous system development, regulatory logic and evolution. It proposes unified platforms to allow integrated annotation of large-scale datasets, gene-function studies, published or unpublished findings and community feedback. Neuroscience is moving fast toward interdisciplinary, high-throughput approaches for combined mapping of the morphology, physiology, connectivity, molecular function, and the integration of information in multifaceted schemes. A closer look in mapped neural circuits and understudied terrains offers insights for the best implementation of these approaches.
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24
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Arendt D, Urzainqui IQ, Vergara HM. The conserved core of the nereid brain: Circular CNS, apical nervous system and lhx6-arx-dlx neurons. Curr Opin Neurobiol 2021; 71:178-187. [PMID: 34861534 DOI: 10.1016/j.conb.2021.11.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2021] [Revised: 11/03/2021] [Accepted: 11/09/2021] [Indexed: 11/28/2022]
Abstract
When bilaterian animals first emerged, an enhanced perception of the Precambrian environment was key to their stunning success. This occurred through the acquisition of an anterior brain, as found in most extant bilaterians. What were the core circuits of the first brain, and how do they relate to today's diversity? With two landmark resources - the full connectome and a multimodal cellular atlas combining gene expression and ultrastructure - the young worm of the marine annelid Platynereis dumerilii takes center stage in comparative bilaterian neuroanatomy. The new data suggest a composite structure of the ancestral bilaterian brain, with the anterior end of a circular CNS fused to a sensory-neurosecretory apical system, and with lhx6-arx-dlx chemosensory circuits giving rise to associative centers in the descending bilaterian lineages.
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Affiliation(s)
- Detlev Arendt
- European Molecular Biology Laboratory, Developmental Biology Unit, Meyerhofstrasse 1, 69012, Heidelberg, Germany.
| | - Idoia Quintana Urzainqui
- European Molecular Biology Laboratory, Developmental Biology Unit, Meyerhofstrasse 1, 69012, Heidelberg, Germany
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25
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Chen C, Agrawal S, Mark B, Mamiya A, Sustar A, Phelps JS, Lee WCA, Dickson BJ, Card GM, Tuthill JC. Functional architecture of neural circuits for leg proprioception in Drosophila. Curr Biol 2021; 31:5163-5175.e7. [PMID: 34637749 PMCID: PMC8665017 DOI: 10.1016/j.cub.2021.09.035] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 08/30/2021] [Accepted: 09/15/2021] [Indexed: 11/30/2022]
Abstract
To effectively control their bodies, animals rely on feedback from proprioceptive mechanosensory neurons. In the Drosophila leg, different proprioceptor subtypes monitor joint position, movement direction, and vibration. Here, we investigate how these diverse sensory signals are integrated by central proprioceptive circuits. We find that signals for leg joint position and directional movement converge in second-order neurons, revealing pathways for local feedback control of leg posture. Distinct populations of second-order neurons integrate tibia vibration signals across pairs of legs, suggesting a role in detecting external substrate vibration. In each pathway, the flow of sensory information is dynamically gated and sculpted by inhibition. Overall, our results reveal parallel pathways for processing of internal and external mechanosensory signals, which we propose mediate feedback control of leg movement and vibration sensing, respectively. The existence of a functional connectivity map also provides a resource for interpreting connectomic reconstruction of neural circuits for leg proprioception. To understand how diverse proprioceptive signals from the Drosophila leg are integrated by downstream circuits, Chen et al. use optogenetics and calcium imaging to map functional connectivity between sensory and central neurons. This work identifies parallel neural pathways for processing leg vibration vs. joint position and movement.
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Affiliation(s)
- Chenghao Chen
- Department of Physiology and Biophysics, University of Washington, 1705 N.E. Pacific Street, Seattle, WA 98195, USA; Janelia Research Campus, Howard Hughes Medical Institute, 19700 Helix Drive, Ashburn, VA 20147, USA
| | - Sweta Agrawal
- Department of Physiology and Biophysics, University of Washington, 1705 N.E. Pacific Street, Seattle, WA 98195, USA
| | - Brandon Mark
- Department of Physiology and Biophysics, University of Washington, 1705 N.E. Pacific Street, Seattle, WA 98195, USA
| | - Akira Mamiya
- Department of Physiology and Biophysics, University of Washington, 1705 N.E. Pacific Street, Seattle, WA 98195, USA
| | - Anne Sustar
- Department of Physiology and Biophysics, University of Washington, 1705 N.E. Pacific Street, Seattle, WA 98195, USA
| | - Jasper S Phelps
- Department of Neurobiology, Harvard Medical School, 220 Longwood Avenue, Boston, MA 02115, USA
| | - Wei-Chung Allen Lee
- Department of Neurobiology, Harvard Medical School, 220 Longwood Avenue, Boston, MA 02115, USA
| | - Barry J Dickson
- Janelia Research Campus, Howard Hughes Medical Institute, 19700 Helix Drive, Ashburn, VA 20147, USA
| | - Gwyneth M Card
- Janelia Research Campus, Howard Hughes Medical Institute, 19700 Helix Drive, Ashburn, VA 20147, USA
| | - John C Tuthill
- Department of Physiology and Biophysics, University of Washington, 1705 N.E. Pacific Street, Seattle, WA 98195, USA.
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26
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Whole-body integration of gene expression and single-cell morphology. Cell 2021; 184:4819-4837.e22. [PMID: 34380046 PMCID: PMC8445025 DOI: 10.1016/j.cell.2021.07.017] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Revised: 05/05/2021] [Accepted: 07/14/2021] [Indexed: 01/10/2023]
Abstract
Animal bodies are composed of cell types with unique expression programs that implement their distinct locations, shapes, structures, and functions. Based on these properties, cell types assemble into specific tissues and organs. To systematically explore the link between cell-type-specific gene expression and morphology, we registered an expression atlas to a whole-body electron microscopy volume of the nereid Platynereis dumerilii. Automated segmentation of cells and nuclei identifies major cell classes and establishes a link between gene activation, chromatin topography, and nuclear size. Clustering of segmented cells according to gene expression reveals spatially coherent tissues. In the brain, genetically defined groups of neurons match ganglionic nuclei with coherent projections. Besides interneurons, we uncover sensory-neurosecretory cells in the nereid mushroom bodies, which thus qualify as sensory organs. They furthermore resemble the vertebrate telencephalon by molecular anatomy. We provide an integrated browser as a Fiji plugin for remote exploration of all available multimodal datasets. A cellular atlas integrates gene expression and ultrastructure for an entire annelid Morphometry of all segmented cells, nuclei, and chromatin categorizes cell classes Molecular anatomy and projectome of head ganglionic nuclei and mushroom bodies An open-source browser for multimodal big image data exploration and analysis
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27
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Schlegel P, Bates AS, Stürner T, Jagannathan SR, Drummond N, Hsu J, Serratosa Capdevila L, Javier A, Marin EC, Barth-Maron A, Tamimi IFM, Li F, Rubin GM, Plaza SM, Costa M, Jefferis GSXE. Information flow, cell types and stereotypy in a full olfactory connectome. eLife 2021; 10:e66018. [PMID: 34032214 PMCID: PMC8298098 DOI: 10.7554/elife.66018] [Citation(s) in RCA: 60] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Accepted: 05/24/2021] [Indexed: 12/19/2022] Open
Abstract
The hemibrain connectome provides large-scale connectivity and morphology information for the majority of the central brain of Drosophila melanogaster. Using this data set, we provide a complete description of the Drosophila olfactory system, covering all first, second and lateral horn-associated third-order neurons. We develop a generally applicable strategy to extract information flow and layered organisation from connectome graphs, mapping olfactory input to descending interneurons. This identifies a range of motifs including highly lateralised circuits in the antennal lobe and patterns of convergence downstream of the mushroom body and lateral horn. Leveraging a second data set we provide a first quantitative assessment of inter- versus intra-individual stereotypy. Comparing neurons across two brains (three hemispheres) reveals striking similarity in neuronal morphology across brains. Connectivity correlates with morphology and neurons of the same morphological type show similar connection variability within the same brain as across two brains.
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Affiliation(s)
- Philipp Schlegel
- Neurobiology Division, MRC Laboratory of Molecular BiologyCambridgeUnited Kingdom
- Department of Zoology, University of CambridgeCambridgeUnited Kingdom
| | | | - Tomke Stürner
- Department of Zoology, University of CambridgeCambridgeUnited Kingdom
| | | | - Nikolas Drummond
- Department of Zoology, University of CambridgeCambridgeUnited Kingdom
| | - Joseph Hsu
- Department of Zoology, University of CambridgeCambridgeUnited Kingdom
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | | | - Alexandre Javier
- Department of Zoology, University of CambridgeCambridgeUnited Kingdom
| | - Elizabeth C Marin
- Department of Zoology, University of CambridgeCambridgeUnited Kingdom
| | - Asa Barth-Maron
- Department of Neurobiology, Harvard Medical SchoolBostonUnited States
| | - Imaan FM Tamimi
- Department of Zoology, University of CambridgeCambridgeUnited Kingdom
| | - Feng Li
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Gerald M Rubin
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Stephen M Plaza
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Marta Costa
- Department of Zoology, University of CambridgeCambridgeUnited Kingdom
| | - Gregory S X E Jefferis
- Neurobiology Division, MRC Laboratory of Molecular BiologyCambridgeUnited Kingdom
- Department of Zoology, University of CambridgeCambridgeUnited Kingdom
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28
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Neurodevelopment: Comparative connectomics and the study of circuit assembly. Curr Biol 2021; 31:R452-R454. [PMID: 33974875 DOI: 10.1016/j.cub.2021.03.053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
A new study reveals the effects of specific single cell type perturbations on the assembly and function of a mechanosensory circuit in the Drosophila melanogaster larva with synaptic resolution. This work paves the way for the use of comparative connectomics to understand general principles governing the development of wiring specificity.
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29
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Transcription factor encoding of neuron subtype: Strategies that specify arbor pattern. Curr Opin Neurobiol 2021; 69:149-158. [PMID: 33895620 DOI: 10.1016/j.conb.2021.03.013] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 03/21/2021] [Accepted: 03/23/2021] [Indexed: 01/01/2023]
Abstract
Dendrite and axon arbors form scaffolds that connect a neuron to its partners; they are patterned to support the specific connectivity and computational requirements of each neuron subtype. Transcription factor networks control the specification of neuron subtypes, and the consequent diversification of their stereotyped arbor patterns during differentiation. We outline how the differentiation trajectories of stereotyped arbors are shaped by hierarchical deployment of precursor cell and postmitotic transcription factors. These transcription factors exert modular control over the dendrite and axon features of a single neuron, create spatial and functional compartmentalization of an arbor, instruct implementation of developmental patterning rules, and exert operational control over the cell biological processes that construct an arbor.
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30
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Agrawal S, Dickinson ES, Sustar A, Gurung P, Shepherd D, Truman JW, Tuthill JC. Central processing of leg proprioception in Drosophila. eLife 2020; 9:e60299. [PMID: 33263281 PMCID: PMC7752136 DOI: 10.7554/elife.60299] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Accepted: 12/01/2020] [Indexed: 12/28/2022] Open
Abstract
Proprioception, the sense of self-movement and position, is mediated by mechanosensory neurons that detect diverse features of body kinematics. Although proprioceptive feedback is crucial for accurate motor control, little is known about how downstream circuits transform limb sensory information to guide motor output. Here we investigate neural circuits in Drosophila that process proprioceptive information from the fly leg. We identify three cell types from distinct developmental lineages that are positioned to receive input from proprioceptor subtypes encoding tibia position, movement, and vibration. 13Bα neurons encode femur-tibia joint angle and mediate postural changes in tibia position. 9Aα neurons also drive changes in leg posture, but encode a combination of directional movement, high frequency vibration, and joint angle. Activating 10Bα neurons, which encode tibia vibration at specific joint angles, elicits pausing in walking flies. Altogether, our results reveal that central circuits integrate information across proprioceptor subtypes to construct complex sensorimotor representations that mediate diverse behaviors, including reflexive control of limb posture and detection of leg vibration.
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Affiliation(s)
- Sweta Agrawal
- Department of Physiology and Biophysics, University of WashingtonSeattleUnited States
| | - Evyn S Dickinson
- Department of Physiology and Biophysics, University of WashingtonSeattleUnited States
| | - Anne Sustar
- Department of Physiology and Biophysics, University of WashingtonSeattleUnited States
| | - Pralaksha Gurung
- Department of Physiology and Biophysics, University of WashingtonSeattleUnited States
| | - David Shepherd
- School of Natural Sciences, Bangor UniversityBangorUnited Kingdom
| | - James W Truman
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
- Friday Harbor Laboratories, University of WashingtonFriday HarborUnited States
| | - John C Tuthill
- Department of Physiology and Biophysics, University of WashingtonSeattleUnited States
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31
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Cachero S, Gkantia M, Bates AS, Frechter S, Blackie L, McCarthy A, Sutcliffe B, Strano A, Aso Y, Jefferis GSXE. BAcTrace, a tool for retrograde tracing of neuronal circuits in Drosophila. Nat Methods 2020; 17:1254-1261. [PMID: 33139893 PMCID: PMC7610425 DOI: 10.1038/s41592-020-00989-1] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Accepted: 10/05/2020] [Indexed: 11/26/2022]
Abstract
Animal behavior is encoded in neuronal circuits in the brain. To elucidate the function of these circuits, it is necessary to identify, record from and manipulate networks of connected neurons. Here we present BAcTrace (Botulinum Activated Tracer), a genetically encoded, retro-grade, transsynaptic labelling system. BAcTrace is based on C. botulinum neurotoxin A, Botox, which we have engineered to travel retrogradely between neurons to activate an otherwise silent transcription factor. We validate BAcTrace at three neuronal connections in the Drosophila olfactory system. We show that BAcTrace-mediated labeling allows electrophysiological recordings of connected neurons. Finally, in a challenging circuit with highly divergent connections, BAcTrace correctly identifies 12 out of 16 connections, which were previously observed by electron microscopy.
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Affiliation(s)
- Sebastian Cachero
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK.
| | - Marina Gkantia
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK
| | - Alexander S Bates
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK
| | - Shahar Frechter
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK
| | - Laura Blackie
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK.,MRC London Institute of Medical Sciences, Imperial College London, London, UK
| | - Amy McCarthy
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK.,Cancer Research UK Manchester Institute, The University of Manchester, Manchester, UK
| | - Ben Sutcliffe
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK
| | - Alessio Strano
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK.,Department of Cancer and Developmental Biology & Zayed Centre for Research into Rare Disease in Children, University College London, London, UK
| | - Yoshinori Aso
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
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32
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Nandakumar S, Grushko O, Buttitta LA. Polyploidy in the adult Drosophila brain. eLife 2020; 9:e54385. [PMID: 32840209 PMCID: PMC7447450 DOI: 10.7554/elife.54385] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Accepted: 08/07/2020] [Indexed: 12/12/2022] Open
Abstract
Long-lived cells such as terminally differentiated postmitotic neurons and glia must cope with the accumulation of damage over the course of an animal's lifespan. How long-lived cells deal with ageing-related damage is poorly understood. Here we show that polyploid cells accumulate in the adult fly brain and that polyploidy protects against DNA damage-induced cell death. Multiple types of neurons and glia that are diploid at eclosion, become polyploid in the adult Drosophila brain. The optic lobes exhibit the highest levels of polyploidy, associated with an elevated DNA damage response in this brain region. Inducing oxidative stress or exogenous DNA damage leads to an earlier onset of polyploidy, and polyploid cells in the adult brain are more resistant to DNA damage-induced cell death than diploid cells. Our results suggest polyploidy may serve a protective role for neurons and glia in adult Drosophila melanogaster brains.
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Affiliation(s)
- Shyama Nandakumar
- Molecular, Cellular, and Developmental Biology, University of MichiganAnn ArborUnited States
| | - Olga Grushko
- Molecular, Cellular, and Developmental Biology, University of MichiganAnn ArborUnited States
| | - Laura A Buttitta
- Molecular, Cellular, and Developmental Biology, University of MichiganAnn ArborUnited States
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33
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Bates AS, Schlegel P, Roberts RJV, Drummond N, Tamimi IFM, Turnbull R, Zhao X, Marin EC, Popovici PD, Dhawan S, Jamasb A, Javier A, Serratosa Capdevila L, Li F, Rubin GM, Waddell S, Bock DD, Costa M, Jefferis GSXE. Complete Connectomic Reconstruction of Olfactory Projection Neurons in the Fly Brain. Curr Biol 2020; 30:3183-3199.e6. [PMID: 32619485 PMCID: PMC7443706 DOI: 10.1016/j.cub.2020.06.042] [Citation(s) in RCA: 79] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Revised: 05/07/2020] [Accepted: 06/12/2020] [Indexed: 12/21/2022]
Abstract
Nervous systems contain sensory neurons, local neurons, projection neurons, and motor neurons. To understand how these building blocks form whole circuits, we must distil these broad classes into neuronal cell types and describe their network connectivity. Using an electron micrograph dataset for an entire Drosophila melanogaster brain, we reconstruct the first complete inventory of olfactory projections connecting the antennal lobe, the insect analog of the mammalian olfactory bulb, to higher-order brain regions in an adult animal brain. We then connect this inventory to extant data in the literature, providing synaptic-resolution "holotypes" both for heavily investigated and previously unknown cell types. Projection neurons are approximately twice as numerous as reported by light level studies; cell types are stereotyped, but not identical, in cell and synapse numbers between brain hemispheres. The lateral horn, the insect analog of the mammalian cortical amygdala, is the main target for this olfactory information and has been shown to guide innate behavior. Here, we find new connectivity motifs, including axo-axonic connectivity between projection neurons, feedback, and lateral inhibition of these axons by a large population of neurons, and the convergence of different inputs, including non-olfactory inputs and memory-related feedback onto third-order olfactory neurons. These features are less prominent in the mushroom body calyx, the insect analog of the mammalian piriform cortex and a center for associative memory. Our work provides a complete neuroanatomical platform for future studies of the adult Drosophila olfactory system.
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Affiliation(s)
- Alexander S Bates
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge CB2 0QH, UK
| | - Philipp Schlegel
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge CB2 0QH, UK; Department of Zoology, University of Cambridge, Cambridge CB2 3EJ, UK
| | | | - Nikolas Drummond
- Department of Zoology, University of Cambridge, Cambridge CB2 3EJ, UK
| | - Imaan F M Tamimi
- Department of Zoology, University of Cambridge, Cambridge CB2 3EJ, UK
| | - Robert Turnbull
- Department of Zoology, University of Cambridge, Cambridge CB2 3EJ, UK
| | - Xincheng Zhao
- Department of Zoology, University of Cambridge, Cambridge CB2 3EJ, UK; Department of Entomology, College of Plant Protection, Henan Agricultural University, Zhengzhou 450002, China
| | - Elizabeth C Marin
- Department of Zoology, University of Cambridge, Cambridge CB2 3EJ, UK
| | - Patricia D Popovici
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge CB2 0QH, UK
| | - Serene Dhawan
- Department of Zoology, University of Cambridge, Cambridge CB2 3EJ, UK
| | - Arian Jamasb
- Department of Zoology, University of Cambridge, Cambridge CB2 3EJ, UK
| | - Alexandre Javier
- Department of Zoology, University of Cambridge, Cambridge CB2 3EJ, UK
| | | | - Feng Li
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Gerald M Rubin
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Scott Waddell
- Centre for Neural Circuits and Behaviour, The University of Oxford, Oxford OX1 3SR, UK
| | - Davi D Bock
- Department of Neurological Sciences, Larner College of Medicine, University of Vermont, VT 05405, USA
| | - Marta Costa
- Department of Zoology, University of Cambridge, Cambridge CB2 3EJ, UK
| | - Gregory S X E Jefferis
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge CB2 0QH, UK; Department of Zoology, University of Cambridge, Cambridge CB2 3EJ, UK.
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34
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Coates KE, Calle-Schuler SA, Helmick LM, Knotts VL, Martik BN, Salman F, Warner LT, Valla SV, Bock DD, Dacks AM. The Wiring Logic of an Identified Serotonergic Neuron That Spans Sensory Networks. J Neurosci 2020; 40:6309-6327. [PMID: 32641403 PMCID: PMC7424878 DOI: 10.1523/jneurosci.0552-20.2020] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2020] [Revised: 06/16/2020] [Accepted: 06/25/2020] [Indexed: 12/21/2022] Open
Abstract
Serotonergic neurons project widely throughout the brain to modulate diverse physiological and behavioral processes. However, a single-cell resolution understanding of the connectivity of serotonergic neurons is currently lacking. Using a whole-brain EM dataset of a female Drosophila, we comprehensively determine the wiring logic of a broadly projecting serotonergic neuron (the CSDn) that spans several olfactory regions. Within the antennal lobe, the CSDn differentially innervates each glomerulus, yet surprisingly, this variability reflects a diverse set of presynaptic partners, rather than glomerulus-specific differences in synaptic output, which is predominately to local interneurons. Moreover, the CSDn has distinct connectivity relationships with specific local interneuron subtypes, suggesting that the CSDn influences distinct aspects of local network processing. Across olfactory regions, the CSDn has different patterns of connectivity, even having different connectivity with individual projection neurons that also span these regions. Whereas the CSDn targets inhibitory local neurons in the antennal lobe, the CSDn has more distributed connectivity in the LH, preferentially synapsing with principal neuron types based on transmitter content. Last, we identify individual novel synaptic partners associated with other sensory domains that provide strong, top-down input to the CSDn. Together, our study reveals the complex connectivity of serotonergic neurons, which combine the integration of local and extrinsic synaptic input in a nuanced, region-specific manner.SIGNIFICANCE STATEMENT All sensory systems receive serotonergic modulatory input. However, a comprehensive understanding of the synaptic connectivity of individual serotonergic neurons is lacking. In this study, we use a whole-brain EM microscopy dataset to comprehensively determine the wiring logic of a broadly projecting serotonergic neuron in the olfactory system of Drosophila Collectively, our study demonstrates, at a single-cell level, the complex connectivity of serotonergic neurons within their target networks, identifies specific cell classes heavily targeted for serotonergic modulation in the olfactory system, and reveals novel extrinsic neurons that provide strong input to this serotonergic system outside of the context of olfaction. Elucidating the connectivity logic of individual modulatory neurons provides a ground plan for the seemingly heterogeneous effects of modulatory systems.
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Affiliation(s)
- Kaylynn E Coates
- Department of Biology, West Virginia University, Morgantown, West Virginia 26506
| | | | - Levi M Helmick
- Department of Biology, West Virginia University, Morgantown, West Virginia 26506
| | - Victoria L Knotts
- Department of Biology, West Virginia University, Morgantown, West Virginia 26506
| | - Brennah N Martik
- Department of Biology, West Virginia University, Morgantown, West Virginia 26506
| | - Farzaan Salman
- Department of Biology, West Virginia University, Morgantown, West Virginia 26506
| | - Lauren T Warner
- Department of Biology, West Virginia University, Morgantown, West Virginia 26506
| | - Sophia V Valla
- Department of Biology, West Virginia University, Morgantown, West Virginia 26506
| | - Davi D Bock
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia 20147
- Department of Neurological Sciences, Larner College of Medicine, University of Vermont, Burlington, Vermont 05405
| | - Andrew M Dacks
- Department of Biology, West Virginia University, Morgantown, West Virginia 26506
- Department of Neuroscience, West Virginia University, Morgantown, West Virginia 26506
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35
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Smith SJ, Hawrylycz M, Rossier J, Sümbül U. New light on cortical neuropeptides and synaptic network plasticity. Curr Opin Neurobiol 2020; 63:176-188. [PMID: 32679509 DOI: 10.1016/j.conb.2020.04.002] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2020] [Revised: 04/11/2020] [Accepted: 04/13/2020] [Indexed: 01/14/2023]
Abstract
Neuropeptides, members of a large and evolutionarily ancient family of proteinaceous cell-cell signaling molecules, are widely recognized as extremely potent regulators of brain function and behavior. At the cellular level, neuropeptides are known to act mainly via modulation of ion channel and synapse function, but functional impacts emerging at the level of complex cortical synaptic networks have resisted mechanistic analysis. New findings from single-cell RNA-seq transcriptomics now illuminate intricate patterns of cortical neuropeptide signaling gene expression and new tools now offer powerful molecular access to cortical neuropeptide signaling. Here we highlight some of these new findings and tools, focusing especially on prospects for experimental and theoretical exploration of peptidergic and synaptic networks interactions underlying cortical function and plasticity.
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Affiliation(s)
- Stephen J Smith
- Allen Institute for Brain Science, 615 Westlake Ave N, Seattle WA, USA.
| | - Michael Hawrylycz
- Allen Institute for Brain Science, 615 Westlake Ave N, Seattle WA, USA
| | - Jean Rossier
- Neuroscience Paris Seine, Sorbonne Université, Paris, France
| | - Uygar Sümbül
- Allen Institute for Brain Science, 615 Westlake Ave N, Seattle WA, USA
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36
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Sizemore TR, Hurley LM, Dacks AM. Serotonergic modulation across sensory modalities. J Neurophysiol 2020; 123:2406-2425. [PMID: 32401124 PMCID: PMC7311732 DOI: 10.1152/jn.00034.2020] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2020] [Revised: 05/04/2020] [Accepted: 05/12/2020] [Indexed: 12/24/2022] Open
Abstract
The serotonergic system has been widely studied across animal taxa and different functional networks. This modulatory system is therefore well positioned to compare the consequences of neuromodulation for sensory processing across species and modalities at multiple levels of sensory organization. Serotonergic neurons that innervate sensory networks often bidirectionally exchange information with these networks but also receive input representative of motor events or motivational state. This convergence of information supports serotonin's capacity for contextualizing sensory information according to the animal's physiological state and external events. At the level of sensory circuitry, serotonin can have variable effects due to differential projections across specific sensory subregions, as well as differential serotonin receptor type expression within those subregions. Functionally, this infrastructure may gate or filter sensory inputs to emphasize specific stimulus features or select among different streams of information. The near-ubiquitous presence of serotonin and other neuromodulators within sensory regions, coupled with their strong effects on stimulus representation, suggests that these signaling pathways should be considered integral components of sensory systems.
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Affiliation(s)
- Tyler R Sizemore
- Department of Biology, West Virginia University, Morgantown, West Virginia
| | - Laura M Hurley
- Department of Biology, Indiana University, Bloomington, Indiana
| | - Andrew M Dacks
- Department of Biology, West Virginia University, Morgantown, West Virginia
- Department of Neuroscience, West Virginia University, Morgantown, West Virginia
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37
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Allen AM, Neville MC, Birtles S, Croset V, Treiber CD, Waddell S, Goodwin SF. A single-cell transcriptomic atlas of the adult Drosophila ventral nerve cord. eLife 2020; 9:e54074. [PMID: 32314735 PMCID: PMC7173974 DOI: 10.7554/elife.54074] [Citation(s) in RCA: 72] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2019] [Accepted: 04/03/2020] [Indexed: 02/07/2023] Open
Abstract
The Drosophila ventral nerve cord (VNC) receives and processes descending signals from the brain to produce a variety of coordinated locomotor outputs. It also integrates sensory information from the periphery and sends ascending signals to the brain. We used single-cell transcriptomics to generate an unbiased classification of cellular diversity in the VNC of five-day old adult flies. We produced an atlas of 26,000 high-quality cells, representing more than 100 transcriptionally distinct cell types. The predominant gene signatures defining neuronal cell types reflect shared developmental histories based on the neuroblast from which cells were derived, as well as their birth order. The relative position of cells along the anterior-posterior axis could also be assigned using adult Hox gene expression. This single-cell transcriptional atlas of the adult fly VNC will be a valuable resource for future studies of neurodevelopment and behavior.
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Affiliation(s)
- Aaron M Allen
- Centre for Neural Circuits and Behaviour, University of OxfordOxfordUnited Kingdom
| | - Megan C Neville
- Centre for Neural Circuits and Behaviour, University of OxfordOxfordUnited Kingdom
| | - Sebastian Birtles
- Centre for Neural Circuits and Behaviour, University of OxfordOxfordUnited Kingdom
| | - Vincent Croset
- Centre for Neural Circuits and Behaviour, University of OxfordOxfordUnited Kingdom
| | | | - Scott Waddell
- Centre for Neural Circuits and Behaviour, University of OxfordOxfordUnited Kingdom
| | - Stephen F Goodwin
- Centre for Neural Circuits and Behaviour, University of OxfordOxfordUnited Kingdom
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38
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Bates AS, Manton JD, Jagannathan SR, Costa M, Schlegel P, Rohlfing T, Jefferis GSXE. The natverse, a versatile toolbox for combining and analysing neuroanatomical data. eLife 2020; 9:e53350. [PMID: 32286229 PMCID: PMC7242028 DOI: 10.7554/elife.53350] [Citation(s) in RCA: 92] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Accepted: 04/11/2020] [Indexed: 11/18/2022] Open
Abstract
To analyse neuron data at scale, neuroscientists expend substantial effort reading documentation, installing dependencies and moving between analysis and visualisation environments. To facilitate this, we have developed a suite of interoperable open-source R packages called the natverse. The natverse allows users to read local and remote data, perform popular analyses including visualisation and clustering and graph-theoretic analysis of neuronal branching. Unlike most tools, the natverse enables comparison across many neurons of morphology and connectivity after imaging or co-registration within a common template space. The natverse also enables transformations between different template spaces and imaging modalities. We demonstrate tools that integrate the vast majority of Drosophila neuroanatomical light microscopy and electron microscopy connectomic datasets. The natverse is an easy-to-use environment for neuroscientists to solve complex, large-scale analysis challenges as well as an open platform to create new code and packages to share with the community.
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Affiliation(s)
| | - James D Manton
- Neurobiology Division, MRC Laboratory of Molecular BiologyCambridgeUnited Kingdom
| | - Sridhar R Jagannathan
- Drosophila Connectomics Group, Department of Zoology, University of CambridgeCambridgeUnited Kingdom
| | - Marta Costa
- Neurobiology Division, MRC Laboratory of Molecular BiologyCambridgeUnited Kingdom
- Drosophila Connectomics Group, Department of Zoology, University of CambridgeCambridgeUnited Kingdom
| | - Philipp Schlegel
- Neurobiology Division, MRC Laboratory of Molecular BiologyCambridgeUnited Kingdom
- Drosophila Connectomics Group, Department of Zoology, University of CambridgeCambridgeUnited Kingdom
| | - Torsten Rohlfing
- SRI International, Neuroscience Program, Center for Health SciencesMenlo ParkUnited States
| | - Gregory SXE Jefferis
- Neurobiology Division, MRC Laboratory of Molecular BiologyCambridgeUnited Kingdom
- Drosophila Connectomics Group, Department of Zoology, University of CambridgeCambridgeUnited Kingdom
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39
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Simpson JH. Watching gene expression in color. eLife 2019; 8:49414. [PMID: 31348001 PMCID: PMC6660214 DOI: 10.7554/elife.49414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2019] [Accepted: 07/24/2019] [Indexed: 11/13/2022] Open
Abstract
A combination of two fluorescent proteins with different half-lives allows gene expression to be followed with improved time resolution.
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Affiliation(s)
- Julie H Simpson
- Department of Molecular, Cellular, and Developmental Biology, University of California, Santa Barbara, Santa Barbara, United States.,Neuroscience Research Institute, University of California, Santa Barbara, Santa Barbara, United States
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40
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Frechter S, Bates AS, Tootoonian S, Dolan MJ, Manton J, Jamasb AR, Kohl J, Bock D, Jefferis G. Functional and anatomical specificity in a higher olfactory centre. eLife 2019; 8:44590. [PMID: 31112127 PMCID: PMC6550879 DOI: 10.7554/elife.44590] [Citation(s) in RCA: 59] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Accepted: 04/12/2019] [Indexed: 12/16/2022] Open
Abstract
Most sensory systems are organized into parallel neuronal pathways that process distinct aspects of incoming stimuli. In the insect olfactory system, second order projection neurons target both the mushroom body, required for learning, and the lateral horn (LH), proposed to mediate innate olfactory behavior. Mushroom body neurons form a sparse olfactory population code, which is not stereotyped across animals. In contrast, odor coding in the LH remains poorly understood. We combine genetic driver lines, anatomical and functional criteria to show that the Drosophila LH has ~1400 neurons and >165 cell types. Genetically labeled LHNs have stereotyped odor responses across animals and on average respond to three times more odors than single projection neurons. LHNs are better odor categorizers than projection neurons, likely due to stereotyped pooling of related inputs. Our results reveal some of the principles by which a higher processing area can extract innate behavioral significance from sensory stimuli.
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Affiliation(s)
- Shahar Frechter
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, United Kingdom
| | | | - Sina Tootoonian
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, United Kingdom.,Department of Neuroscience, Physiology and Pharmacology, University College London, London, United Kingdom.,Neurophysiology of Behaviour Laboratory, The Francis Crick Institute, London, United Kingdom
| | - Michael-John Dolan
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, United Kingdom.,Janelia Research Campus, Howard Hughes Medical Institute, Chevy Chase, United States
| | - James Manton
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, United Kingdom
| | | | - Johannes Kohl
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, United Kingdom
| | - Davi Bock
- Janelia Research Campus, Howard Hughes Medical Institute, Chevy Chase, United States
| | - Gregory Jefferis
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, United Kingdom.,Department of Zoology, University of Cambridge, Cambridge, United Kingdom
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41
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Abstract
A new study investigates the distinct male courtship songs of two related Drosophila species and the neurons controlling this behavior, localizing a site of evolutionary divergence to the motor system, downstream of the central brain.
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Affiliation(s)
- Dana S Galili
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge CB2 0QH, UK
| | - Gregory S X E Jefferis
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge CB2 0QH, UK; Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Downing Street, Cambridge CB2 3EJ, UK.
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