1
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Christenson MP, Sanz Diez A, Heath SL, Saavedra-Weisenhaus M, Adachi A, Nern A, Abbott LF, Behnia R. Hue selectivity from recurrent circuitry in Drosophila. Nat Neurosci 2024; 27:1137-1147. [PMID: 38755272 DOI: 10.1038/s41593-024-01640-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Accepted: 04/04/2024] [Indexed: 05/18/2024]
Abstract
In the perception of color, wavelengths of light reflected off objects are transformed into the derived quantities of brightness, saturation and hue. Neurons responding selectively to hue have been reported in primate cortex, but it is unknown how their narrow tuning in color space is produced by upstream circuit mechanisms. We report the discovery of neurons in the Drosophila optic lobe with hue-selective properties, which enables circuit-level analysis of color processing. From our analysis of an electron microscopy volume of a whole Drosophila brain, we construct a connectomics-constrained circuit model that accounts for this hue selectivity. Our model predicts that recurrent connections in the circuit are critical for generating hue selectivity. Experiments using genetic manipulations to perturb recurrence in adult flies confirm this prediction. Our findings reveal a circuit basis for hue selectivity in color vision.
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Affiliation(s)
- Matthias P Christenson
- Zuckerman Institute, Columbia University, New York, NY, USA
- Center for Theoretical Neuroscience, Columbia University, New York, NY, USA
- Department of Neuroscience, Columbia University Medical Center, New York, NY, USA
| | - Alvaro Sanz Diez
- Zuckerman Institute, Columbia University, New York, NY, USA
- Department of Neuroscience, Columbia University Medical Center, New York, NY, USA
| | - Sarah L Heath
- Zuckerman Institute, Columbia University, New York, NY, USA
- Department of Neuroscience, Columbia University Medical Center, New York, NY, USA
| | - Maia Saavedra-Weisenhaus
- Zuckerman Institute, Columbia University, New York, NY, USA
- Department of Neuroscience, Columbia University Medical Center, New York, NY, USA
| | - Atsuko Adachi
- Zuckerman Institute, Columbia University, New York, NY, USA
- Department of Neuroscience, Columbia University Medical Center, New York, NY, USA
| | - Aljoscha Nern
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - L F Abbott
- Zuckerman Institute, Columbia University, New York, NY, USA
- Center for Theoretical Neuroscience, Columbia University, New York, NY, USA
- Department of Neuroscience, Columbia University Medical Center, New York, NY, USA
- Kavli Institute for Brain Science, Columbia University Medical Center, New York, NY, USA
| | - Rudy Behnia
- Zuckerman Institute, Columbia University, New York, NY, USA.
- Department of Neuroscience, Columbia University Medical Center, New York, NY, USA.
- Kavli Institute for Brain Science, Columbia University Medical Center, New York, NY, USA.
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2
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Feng R, Li S, Zhang Y. AI-powered microscopy image analysis for parasitology: integrating human expertise. Trends Parasitol 2024:S1471-4922(24)00134-X. [PMID: 38824067 DOI: 10.1016/j.pt.2024.05.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Revised: 05/06/2024] [Accepted: 05/07/2024] [Indexed: 06/03/2024]
Abstract
Microscopy image analysis plays a pivotal role in parasitology research. Deep learning (DL), a subset of artificial intelligence (AI), has garnered significant attention. However, traditional DL-based methods for general purposes are data-driven, often lacking explainability due to their black-box nature and sparse instructional resources. To address these challenges, this article presents a comprehensive review of recent advancements in knowledge-integrated DL models tailored for microscopy image analysis in parasitology. The massive amounts of human expert knowledge from parasitologists can enhance the accuracy and explainability of AI-driven decisions. It is expected that the adoption of knowledge-integrated DL models will open up a wide range of applications in the field of parasitology.
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Affiliation(s)
- Ruijun Feng
- College of Science, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, China; School of Computer Science and Engineering, University of New South Wales, Sydney, Australia
| | - Sen Li
- College of Science, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, China
| | - Yang Zhang
- College of Science, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, China.
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3
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Meschi E, Duquenoy L, Otto N, Dempsey G, Waddell S. Compensatory enhancement of input maintains aversive dopaminergic reinforcement in hungry Drosophila. Neuron 2024:S0896-6273(24)00325-8. [PMID: 38795709 DOI: 10.1016/j.neuron.2024.04.035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 03/12/2024] [Accepted: 04/30/2024] [Indexed: 05/28/2024]
Abstract
Hungry animals need compensatory mechanisms to maintain flexible brain function, while modulation reconfigures circuits to prioritize resource seeking. In Drosophila, hunger inhibits aversively reinforcing dopaminergic neurons (DANs) to permit the expression of food-seeking memories. Multitasking the reinforcement system for motivation potentially undermines aversive learning. We find that chronic hunger mildly enhances aversive learning and that satiated-baseline and hunger-enhanced learning require endocrine adipokinetic hormone (AKH) signaling. Circulating AKH influences aversive learning via its receptor in four neurons in the ventral brain, two of which are octopaminergic. Connectomics revealed AKH receptor-expressing neurons to be upstream of several classes of ascending neurons, many of which are presynaptic to aversively reinforcing DANs. Octopaminergic modulation of and output from at least one of these ascending pathways is required for shock- and bitter-taste-reinforced aversive learning. We propose that coordinated enhancement of input compensates for hunger-directed inhibition of aversive DANs to preserve reinforcement when required.
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Affiliation(s)
- Eleonora Meschi
- University of Oxford, Centre for Neural Circuits and Behaviour, Tinsley Building, Mansfield Road, Oxford OX1 3SR, UK
| | - Lucille Duquenoy
- University of Oxford, Centre for Neural Circuits and Behaviour, Tinsley Building, Mansfield Road, Oxford OX1 3SR, UK
| | - Nils Otto
- University of Oxford, Centre for Neural Circuits and Behaviour, Tinsley Building, Mansfield Road, Oxford OX1 3SR, UK
| | - Georgia Dempsey
- University of Oxford, Centre for Neural Circuits and Behaviour, Tinsley Building, Mansfield Road, Oxford OX1 3SR, UK
| | - Scott Waddell
- University of Oxford, Centre for Neural Circuits and Behaviour, Tinsley Building, Mansfield Road, Oxford OX1 3SR, UK.
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4
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Lee SYJ, Dallmann CJ, Cook AP, Tuthill JC, Agrawal S. Divergent neural circuits for proprioceptive and exteroceptive sensing of the Drosophila leg. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.23.590808. [PMID: 38712128 PMCID: PMC11071415 DOI: 10.1101/2024.04.23.590808] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Somatosensory neurons provide the nervous system with information about mechanical forces originating inside and outside the body. Here, we use connectomics to reconstruct and analyze neural circuits downstream of the largest somatosensory organ in the Drosophila leg, the femoral chordotonal organ (FeCO). The FeCO has been proposed to support both proprioceptive sensing of the fly's femur-tibia joint and exteroceptive sensing of substrate vibrations, but it remains unknown which sensory neurons and central circuits contribute to each of these functions. We found that different subtypes of FeCO sensory neurons feed into distinct proprioceptive and exteroceptive pathways. Position- and movement-encoding FeCO neurons connect to local leg motor control circuits in the ventral nerve cord (VNC), indicating a proprioceptive function. In contrast, signals from the vibration-encoding FeCO neurons are integrated across legs and transmitted to auditory regions in the brain, indicating an exteroceptive function. Overall, our analyses reveal the structure of specialized circuits for processing proprioceptive and exteroceptive signals from the fly leg. They also demonstrate how analyzing patterns of synaptic connectivity can distill organizing principles from complex sensorimotor circuits.
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5
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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] [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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6
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Hofbauer B, Zandawala M, Reinhard N, Rieger D, Werner C, Evers JF, Wegener C. The neuropeptide pigment-dispersing factor signals independently of Bruchpilot-labelled active zones in daily remodelled terminals of Drosophila clock neurons. Eur J Neurosci 2024; 59:2665-2685. [PMID: 38414155 DOI: 10.1111/ejn.16294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 02/06/2024] [Accepted: 02/08/2024] [Indexed: 02/29/2024]
Abstract
The small ventrolateral neurons (sLNvs) are key components of the central clock in the Drosophila brain. They signal via the neuropeptide pigment-dispersing factor (PDF) to align the molecular clockwork of different central clock neurons and to modulate downstream circuits. The dorsal terminals of the sLNvs undergo daily morphological changes that affect presynaptic sites organised by the active zone protein Bruchpilot (BRP), a homolog of mammalian ELKS proteins. However, the role of these presynaptic sites for PDF release is ill-defined. Here, we combined expansion microscopy with labelling of active zones by endogenously tagged BRP to examine the spatial correlation between PDF-containing dense-core vesicles and BRP-labelled active zones. We found that the number of BRP-labelled puncta in the sLNv terminals was similar while their density differed between Zeitgeber time (ZT) 2 and 14. The relative distance between BRP- and PDF-labelled puncta was increased in the morning, around the reported time of PDF release. Spontaneous dense-core vesicle release profiles of sLNvs in a publicly available ssTEM dataset (FAFB) consistently lacked spatial correlation to BRP-organised active zones. RNAi-mediated downregulation of brp and other active zone proteins expressed by the sLNvs did not affect PDF-dependent locomotor rhythmicity. In contrast, down-regulation of genes encoding proteins of the canonical vesicle release machinery, the dense-core vesicle-related protein CADPS, as well as PDF impaired locomotor rhythmicity. Taken together, our study suggests that PDF release from the sLNvs is independent of BRP-organised active zones, while BRP may be redistributed to active zones in a time-dependent manner.
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Affiliation(s)
- Benedikt Hofbauer
- Biocenter, Theodor-Boveri-Institute, Neurobiology and Genetics, Julius-Maximilians-Universität Würzburg, Würzburg, Germany
| | - Meet Zandawala
- Biocenter, Theodor-Boveri-Institute, Neurobiology and Genetics, Julius-Maximilians-Universität Würzburg, Würzburg, Germany
- Department of Biochemistry and Molecular Biology, University of Nevada Reno, Reno, NV, USA
| | - Nils Reinhard
- Biocenter, Theodor-Boveri-Institute, Neurobiology and Genetics, Julius-Maximilians-Universität Würzburg, Würzburg, Germany
| | - Dirk Rieger
- Biocenter, Theodor-Boveri-Institute, Neurobiology and Genetics, Julius-Maximilians-Universität Würzburg, Würzburg, Germany
| | - Christian Werner
- Biocenter, Theodor-Boveri-Institute, Department of Biotechnology and Biophysics, Julius-Maximilians-Universität Würzburg, Würzburg, Germany
| | - Jan Felix Evers
- Centre for organismal studies COS, Universität Heidelberg, Heidelberg, Germany
- Cairn GmbH, Heidelberg, Germany
| | - Christian Wegener
- Biocenter, Theodor-Boveri-Institute, Neurobiology and Genetics, Julius-Maximilians-Universität Würzburg, Würzburg, Germany
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7
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Müller A, Schmidt D, Albrecht JP, Rieckert L, Otto M, Galicia Garcia LE, Fabig G, Solimena M, Weigert M. Modular segmentation, spatial analysis and visualization of volume electron microscopy datasets. Nat Protoc 2024; 19:1436-1466. [PMID: 38424188 DOI: 10.1038/s41596-024-00957-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 11/24/2023] [Indexed: 03/02/2024]
Abstract
Volume electron microscopy is the method of choice for the in situ interrogation of cellular ultrastructure at the nanometer scale, and with the increase in large raw image datasets generated, improving computational strategies for image segmentation and spatial analysis is necessary. Here we describe a practical and annotation-efficient pipeline for organelle-specific segmentation, spatial analysis and visualization of large volume electron microscopy datasets using freely available, user-friendly software tools that can be run on a single standard workstation. The procedures are aimed at researchers in the life sciences with modest computational expertise, who use volume electron microscopy and need to generate three-dimensional (3D) segmentation labels for different types of cell organelles while minimizing manual annotation efforts, to analyze the spatial interactions between organelle instances and to visualize the 3D segmentation results. We provide detailed guidelines for choosing well-suited segmentation tools for specific cell organelles, and to bridge compatibility issues between freely available open-source tools, we distribute the critical steps as easily installable Album solutions for deep learning segmentation, spatial analysis and 3D rendering. Our detailed description can serve as a reference for similar projects requiring particular strategies for single- or multiple-organelle analysis, which can be achieved with computational resources commonly available to single-user setups.
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Affiliation(s)
- Andreas Müller
- Molecular Diabetology, University Hospital and Faculty of Medicine Carl Gustav Carus, TU Dresden, Dresden, Germany.
- Paul Langerhans Institute Dresden (PLID) of the Helmholtz Center Munich at the University Hospital Carl Gustav Carus and Faculty of Medicine of the TU Dresden, Dresden, Germany.
- German Center for Diabetes Research, Neuherberg, Germany.
| | - Deborah Schmidt
- HELMHOLTZ IMAGING, Max Delbrück Center for Molecular Medicine (MDC) in the Helmholtz Association, Berlin, Germany.
| | - Jan Philipp Albrecht
- HELMHOLTZ IMAGING, Max Delbrück Center for Molecular Medicine (MDC) in the Helmholtz Association, Berlin, Germany
- Humboldt-Universität zu Berlin, Faculty of Mathematics and Natural Sciences, Berlin, Germany
| | - Lucas Rieckert
- HELMHOLTZ IMAGING, Max Delbrück Center for Molecular Medicine (MDC) in the Helmholtz Association, Berlin, Germany
| | - Maximilian Otto
- HELMHOLTZ IMAGING, Max Delbrück Center for Molecular Medicine (MDC) in the Helmholtz Association, Berlin, Germany
| | - Leticia Elizabeth Galicia Garcia
- Molecular Diabetology, University Hospital and Faculty of Medicine Carl Gustav Carus, TU Dresden, Dresden, Germany
- Paul Langerhans Institute Dresden (PLID) of the Helmholtz Center Munich at the University Hospital Carl Gustav Carus and Faculty of Medicine of the TU Dresden, Dresden, Germany
- German Center for Diabetes Research, Neuherberg, Germany
- DFG Cluster of Excellence 'Physics of Life', TU Dresden, Dresden, Germany
| | - Gunar Fabig
- Experimental Center, Faculty of Medicine Carl Gustav Carus, Dresden, Dresden, Germany
| | - Michele Solimena
- Molecular Diabetology, University Hospital and Faculty of Medicine Carl Gustav Carus, TU Dresden, Dresden, Germany
- Paul Langerhans Institute Dresden (PLID) of the Helmholtz Center Munich at the University Hospital Carl Gustav Carus and Faculty of Medicine of the TU Dresden, Dresden, Germany
- German Center for Diabetes Research, Neuherberg, Germany
- DFG Cluster of Excellence 'Physics of Life', TU Dresden, Dresden, Germany
| | - Martin Weigert
- Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
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8
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Lackey EP, Moreira L, Norton A, Hemelt ME, Osorno T, Nguyen TM, Macosko EZ, Lee WCA, Hull CA, Regehr WG. Specialized connectivity of molecular layer interneuron subtypes leads to disinhibition and synchronous inhibition of cerebellar Purkinje cells. Neuron 2024:S0896-6273(24)00248-4. [PMID: 38692278 DOI: 10.1016/j.neuron.2024.04.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 01/12/2024] [Accepted: 04/08/2024] [Indexed: 05/03/2024]
Abstract
Molecular layer interneurons (MLIs) account for approximately 80% of the inhibitory interneurons in the cerebellar cortex and are vital to cerebellar processing. MLIs are thought to primarily inhibit Purkinje cells (PCs) and suppress the plasticity of synapses onto PCs. MLIs also inhibit, and are electrically coupled to, other MLIs, but the functional significance of these connections is not known. Here, we find that two recently recognized MLI subtypes, MLI1 and MLI2, have a highly specialized connectivity that allows them to serve distinct functional roles. MLI1s primarily inhibit PCs, are electrically coupled to each other, fire synchronously with other MLI1s on the millisecond timescale in vivo, and synchronously pause PC firing. MLI2s are not electrically coupled, primarily inhibit MLI1s and disinhibit PCs, and are well suited to gating cerebellar-dependent behavior and learning. The synchronous firing of electrically coupled MLI1s and disinhibition provided by MLI2s require a major re-evaluation of cerebellar processing.
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Affiliation(s)
| | - Luis Moreira
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Aliya Norton
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Marie E Hemelt
- Department of Neurobiology, Duke University Medical School, Durham, NC, USA
| | - Tomas Osorno
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Tri M Nguyen
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Evan Z Macosko
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA; Broad Institute of Harvard and MIT, Stanley Center for Psychiatric Research, Cambridge, MA, USA
| | - Wei-Chung Allen Lee
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA; Kirby Neurobiology Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Court A Hull
- Department of Neurobiology, Duke University Medical School, Durham, NC, USA
| | - Wade G Regehr
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA.
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9
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Eichler K, Hampel S, Alejandro-García A, Calle-Schuler SA, Santana-Cruz A, Kmecova L, Blagburn JM, Hoopfer ED, Seeds AM. Somatotopic organization among parallel sensory pathways that promote a grooming sequence in Drosophila. eLife 2024; 12:RP87602. [PMID: 38634460 PMCID: PMC11026096 DOI: 10.7554/elife.87602] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/19/2024] Open
Abstract
Mechanosensory neurons located across the body surface respond to tactile stimuli and elicit diverse behavioral responses, from relatively simple stimulus location-aimed movements to complex movement sequences. How mechanosensory neurons and their postsynaptic circuits influence such diverse behaviors remains unclear. We previously discovered that Drosophila perform a body location-prioritized grooming sequence when mechanosensory neurons at different locations on the head and body are simultaneously stimulated by dust (Hampel et al., 2017; Seeds et al., 2014). Here, we identify nearly all mechanosensory neurons on the Drosophila head that individually elicit aimed grooming of specific head locations, while collectively eliciting a whole head grooming sequence. Different tracing methods were used to reconstruct the projections of these neurons from different locations on the head to their distinct arborizations in the brain. This provides the first synaptic resolution somatotopic map of a head, and defines the parallel-projecting mechanosensory pathways that elicit head grooming.
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Affiliation(s)
- Katharina Eichler
- Institute of Neurobiology, University of Puerto Rico-Medical Sciences CampusSan JuanPuerto Rico
| | - Stefanie Hampel
- Institute of Neurobiology, University of Puerto Rico-Medical Sciences CampusSan JuanPuerto Rico
| | - Adrián Alejandro-García
- Institute of Neurobiology, University of Puerto Rico-Medical Sciences CampusSan JuanPuerto Rico
| | - Steven A Calle-Schuler
- Institute of Neurobiology, University of Puerto Rico-Medical Sciences CampusSan JuanPuerto Rico
| | - Alexis Santana-Cruz
- Institute of Neurobiology, University of Puerto Rico-Medical Sciences CampusSan JuanPuerto Rico
| | - Lucia Kmecova
- Institute of Neurobiology, University of Puerto Rico-Medical Sciences CampusSan JuanPuerto Rico
| | - Jonathan M Blagburn
- Institute of Neurobiology, University of Puerto Rico-Medical Sciences CampusSan JuanPuerto Rico
| | - Eric D Hoopfer
- Neuroscience Program, Carleton CollegeNorthfieldUnited States
| | - Andrew M Seeds
- Institute of Neurobiology, University of Puerto Rico-Medical Sciences CampusSan JuanPuerto Rico
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10
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Lin A, Yang R, Dorkenwald S, Matsliah A, Sterling AR, Schlegel P, Yu SC, McKellar CE, Costa M, Eichler K, Bates AS, Eckstein N, Funke J, Jefferis GSXE, Murthy M. Network Statistics of the Whole-Brain Connectome of Drosophila. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.07.29.551086. [PMID: 37547019 PMCID: PMC10402125 DOI: 10.1101/2023.07.29.551086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
Brains comprise complex networks of neurons and connections. Network analysis applied to the wiring diagrams of brains can offer insights into how brains support computations and regulate information flow. The completion of the first whole-brain connectome of an adult Drosophila, the largest connectome to date, containing 130,000 neurons and millions of connections, offers an unprecedented opportunity to analyze its network properties and topological features. To gain insights into local connectivity, we computed the prevalence of two- and three-node network motifs, examined their strengths and neurotransmitter compositions, and compared these topological metrics with wiring diagrams of other animals. We discovered that the network of the fly brain displays rich club organization, with a large population (30% percent of the connectome) of highly connected neurons. We identified subsets of rich club neurons that may serve as integrators or broadcasters of signals. Finally, we examined subnetworks based on 78 anatomically defined brain regions or neuropils. These data products are shared within the FlyWire Codex and will serve as a foundation for models and experiments exploring the relationship between neural activity and anatomical structure.
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Affiliation(s)
- Albert Lin
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Center for the Physics of Biological Function, Princeton University, Princeton, NJ, USA
| | - Runzhe Yang
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Computer Science Department, Princeton University, Princeton, NJ, USA
| | - Sven Dorkenwald
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Computer Science Department, Princeton University, Princeton, NJ, USA
| | - Arie Matsliah
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Amy R Sterling
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Philipp Schlegel
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Szi-Chieh Yu
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Claire E McKellar
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Marta Costa
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Katharina Eichler
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Alexander Shakeel Bates
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
- Centre for Neural Circuits and Behaviour, University of Oxford, Oxford, UK
| | - Nils Eckstein
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, USA
| | - Jan Funke
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, USA
| | - Gregory S X E Jefferis
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Mala Murthy
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
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11
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Tao L, Ayembem D, Barranca VJ, Bhandawat V. Neurons underlying aggressive actions that are shared by both males and females in Drosophila. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.26.582148. [PMID: 38464020 PMCID: PMC10925114 DOI: 10.1101/2024.02.26.582148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Aggression involves both sexually monomorphic and dimorphic actions. How the brain implements these two types of actions is poorly understood. We found that a set of neurons, which we call CL062, previously shown to mediate male aggression also mediate female aggression. These neurons elicit aggression acutely and without the presence of a target. Although the same set of actions is elicited in males and females, the overall behavior is sexually dimorphic. The CL062 neurons do not express fruitless , a gene required for sexual dimorphism in flies, and expressed by most other neurons important for controlling fly aggression. Connectomic analysis suggests that these neurons have limited connections with fruitless expressing neurons that have been shown to be important for aggression, and signal to different descending neurons. Thus, CL062 is part of a monomorphic circuit for aggression that functions parallel to the known dimorphic circuits.
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12
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Cornean J, Molina-Obando S, Gür B, Bast A, Ramos-Traslosheros G, Chojetzki J, Lörsch L, Ioannidou M, Taneja R, Schnaitmann C, Silies M. Heterogeneity of synaptic connectivity in the fly visual system. Nat Commun 2024; 15:1570. [PMID: 38383614 PMCID: PMC10882054 DOI: 10.1038/s41467-024-45971-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 02/08/2024] [Indexed: 02/23/2024] Open
Abstract
Visual systems are homogeneous structures, where repeating columnar units retinotopically cover the visual field. Each of these columns contain many of the same neuron types that are distinguished by anatomic, genetic and - generally - by functional properties. However, there are exceptions to this rule. In the 800 columns of the Drosophila eye, there is an anatomically and genetically identifiable cell type with variable functional properties, Tm9. Since anatomical connectivity shapes functional neuronal properties, we identified the presynaptic inputs of several hundred Tm9s across both optic lobes using the full adult female fly brain (FAFB) electron microscopic dataset and FlyWire connectome. Our work shows that Tm9 has three major and many sparsely distributed inputs. This differs from the presynaptic connectivity of other Tm neurons, which have only one major, and more stereotypic inputs than Tm9. Genetic synapse labeling showed that the heterogeneous wiring exists across individuals. Together, our data argue that the visual system uses heterogeneous, distributed circuit properties to achieve robust visual processing.
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Affiliation(s)
- Jacqueline Cornean
- Institute of Developmental Biology and Neurobiology, Johannes-Gutenberg University, 55128, Mainz, Germany
| | - Sebastian Molina-Obando
- Institute of Developmental Biology and Neurobiology, Johannes-Gutenberg University, 55128, Mainz, Germany
| | - Burak Gür
- Institute of Developmental Biology and Neurobiology, Johannes-Gutenberg University, 55128, Mainz, Germany
| | - Annika Bast
- Institute of Developmental Biology and Neurobiology, Johannes-Gutenberg University, 55128, Mainz, Germany
| | - Giordano Ramos-Traslosheros
- Institute of Developmental Biology and Neurobiology, Johannes-Gutenberg University, 55128, Mainz, Germany
- Department of Neurobiology, Harvard Medical School, Boston, MA, 02115, USA
| | - Jonas Chojetzki
- Institute of Developmental Biology and Neurobiology, Johannes-Gutenberg University, 55128, Mainz, Germany
| | - Lena Lörsch
- Institute of Developmental Biology and Neurobiology, Johannes-Gutenberg University, 55128, Mainz, Germany
| | - Maria Ioannidou
- Institute of Developmental Biology and Neurobiology, Johannes-Gutenberg University, 55128, Mainz, Germany
| | - Rachita Taneja
- Institute of Developmental Biology and Neurobiology, Johannes-Gutenberg University, 55128, Mainz, Germany
| | - Christopher Schnaitmann
- Institute of Developmental Biology and Neurobiology, Johannes-Gutenberg University, 55128, Mainz, Germany
| | - Marion Silies
- Institute of Developmental Biology and Neurobiology, Johannes-Gutenberg University, 55128, Mainz, Germany.
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13
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Ros IG, Omoto JJ, Dickinson MH. Descending control and regulation of spontaneous flight turns in Drosophila. Curr Biol 2024; 34:531-540.e5. [PMID: 38228148 PMCID: PMC10872223 DOI: 10.1016/j.cub.2023.12.047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 12/12/2023] [Accepted: 12/14/2023] [Indexed: 01/18/2024]
Abstract
The clumped distribution of resources in the world has influenced the pattern of foraging behavior since the origins of locomotion, selecting for a common search motif in which straight movements through resource-poor regions alternate with zig-zag exploration in resource-rich domains. For example, during local search, flying flies spontaneously execute rapid flight turns, called body saccades, but suppress these maneuvers during long-distance dispersal or when surging upstream toward an attractive odor. Here, we describe the key cellular components of a neural network in flies that generate spontaneous turns as well as a specialized pair of neurons that inhibits the network and suppresses turning. Using 2-photon imaging, optogenetic activation, and genetic ablation, we show that only four descending neurons appear sufficient to generate the descending commands to execute flight saccades. The network is organized into two functional units-one for right turns and one for left-with each unit consisting of an excitatory (DNae014) and an inhibitory (DNb01) neuron that project to the flight motor neuropil within the ventral nerve cord. Using resources from recently published connectomes of the fly, we identified a pair of large, distinct interneurons (VES041) that form inhibitory connections to all four saccade command neurons and created specific genetic driver lines for this cell. As predicted by its connectivity, activation of VES041 strongly suppresses saccades, suggesting that it promotes straight flight to regulate the transition between local search and long-distance dispersal. These results thus identify the key elements of a network that may play a crucial role in foraging ecology.
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Affiliation(s)
- Ivo G Ros
- Division of Biology and Bioengineering, California Institute of Technology, 1200 E. California Blvd., Pasadena, CA 91125, USA
| | - Jaison J Omoto
- Division of Biology and Bioengineering, California Institute of Technology, 1200 E. California Blvd., Pasadena, CA 91125, USA
| | - Michael H Dickinson
- Division of Biology and Bioengineering, California Institute of Technology, 1200 E. California Blvd., Pasadena, CA 91125, USA.
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14
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Eichler K, Hampel S, Alejandro-García A, Calle-Schuler SA, Santana-Cruz A, Kmecova L, Blagburn JM, Hoopfer ED, Seeds AM. Somatotopic organization among parallel sensory pathways that promote a grooming sequence in Drosophila. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.11.528119. [PMID: 36798384 PMCID: PMC9934617 DOI: 10.1101/2023.02.11.528119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/14/2023]
Abstract
Mechanosensory neurons located across the body surface respond to tactile stimuli and elicit diverse behavioral responses, from relatively simple stimulus location-aimed movements to complex movement sequences. How mechanosensory neurons and their postsynaptic circuits influence such diverse behaviors remains unclear. We previously discovered that Drosophila perform a body location-prioritized grooming sequence when mechanosensory neurons at different locations on the head and body are simultaneously stimulated by dust (Hampel et al., 2017; Seeds et al., 2014). Here, we identify nearly all mechanosensory neurons on the Drosophila head that individually elicit aimed grooming of specific head locations, while collectively eliciting a whole head grooming sequence. Different tracing methods were used to reconstruct the projections of these neurons from different locations on the head to their distinct arborizations in the brain. This provides the first synaptic resolution somatotopic map of a head, and defines the parallel-projecting mechanosensory pathways that elicit head grooming.
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Affiliation(s)
- Katharina Eichler
- Institute of Neurobiology, University of Puerto Rico-Medical Sciences Campus, San Juan, Puerto Rico
- Contributed equally
| | - Stefanie Hampel
- Institute of Neurobiology, University of Puerto Rico-Medical Sciences Campus, San Juan, Puerto Rico
- Contributed equally
| | - Adrián Alejandro-García
- Institute of Neurobiology, University of Puerto Rico-Medical Sciences Campus, San Juan, Puerto Rico
- Contributed equally
| | - Steven A Calle-Schuler
- Institute of Neurobiology, University of Puerto Rico-Medical Sciences Campus, San Juan, Puerto Rico
| | - Alexis Santana-Cruz
- Institute of Neurobiology, University of Puerto Rico-Medical Sciences Campus, San Juan, Puerto Rico
| | - Lucia Kmecova
- Institute of Neurobiology, University of Puerto Rico-Medical Sciences Campus, San Juan, Puerto Rico
- Neuroscience Program, Carleton College, Northfield, Minnesota
- Contributed equally
| | - Jonathan M Blagburn
- Institute of Neurobiology, University of Puerto Rico-Medical Sciences Campus, San Juan, Puerto Rico
| | - Eric D Hoopfer
- Neuroscience Program, Carleton College, Northfield, Minnesota
| | - Andrew M Seeds
- Institute of Neurobiology, University of Puerto Rico-Medical Sciences Campus, San Juan, Puerto Rico
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15
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Dorkenwald S, Li PH, Januszewski M, Berger DR, Maitin-Shepard J, Bodor AL, Collman F, Schneider-Mizell CM, da Costa NM, Lichtman JW, Jain V. Multi-layered maps of neuropil with segmentation-guided contrastive learning. Nat Methods 2023; 20:2011-2020. [PMID: 37985712 PMCID: PMC10703674 DOI: 10.1038/s41592-023-02059-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Accepted: 10/02/2023] [Indexed: 11/22/2023]
Abstract
Maps of the nervous system that identify individual cells along with their type, subcellular components and connectivity have the potential to elucidate fundamental organizational principles of neural circuits. Nanometer-resolution imaging of brain tissue provides the necessary raw data, but inferring cellular and subcellular annotation layers is challenging. We present segmentation-guided contrastive learning of representations (SegCLR), a self-supervised machine learning technique that produces representations of cells directly from 3D imagery and segmentations. When applied to volumes of human and mouse cortex, SegCLR enables accurate classification of cellular subcompartments and achieves performance equivalent to a supervised approach while requiring 400-fold fewer labeled examples. SegCLR also enables inference of cell types from fragments as small as 10 μm, which enhances the utility of volumes in which many neurites are truncated at boundaries. Finally, SegCLR enables exploration of layer 5 pyramidal cell subtypes and automated large-scale analysis of synaptic partners in mouse visual cortex.
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Affiliation(s)
- Sven Dorkenwald
- Google Research, Mountain View, CA, USA
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Computer Science Department, Princeton University, Princeton, NJ, USA
| | | | | | - Daniel R Berger
- Department of Molecular and Cellular Biology, Center for Brain Science, Harvard, Cambridge, MA, USA
| | | | | | | | | | | | - Jeff W Lichtman
- Department of Molecular and Cellular Biology, Center for Brain Science, Harvard, Cambridge, MA, USA
| | - Viren Jain
- Google Research, Mountain View, CA, USA.
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16
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Garner D, Kind E, Nern A, Houghton L, Zhao A, Sancer G, Rubin GM, Wernet MF, Kim SS. Connectomic reconstruction predicts the functional organization of visual inputs to the navigation center of the Drosophila brain. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.29.569241. [PMID: 38076786 PMCID: PMC10705420 DOI: 10.1101/2023.11.29.569241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/22/2023]
Abstract
Many animals, including humans, navigate their surroundings by visual input, yet we understand little about how visual information is transformed and integrated by the navigation system. In Drosophila melanogaster, compass neurons in the donut-shaped ellipsoid body of the central complex generate a sense of direction by integrating visual input from ring neurons, a part of the anterior visual pathway (AVP). Here, we densely reconstruct all neurons in the AVP using FlyWire, an AI-assisted tool for analyzing electron-microscopy data. The AVP comprises four neuropils, sequentially linked by three major classes of neurons: MeTu neurons, which connect the medulla in the optic lobe to the small unit of anterior optic tubercle (AOTUsu) in the central brain; TuBu neurons, which connect the anterior optic tubercle to the bulb neuropil; and ring neurons, which connect the bulb to the ellipsoid body. Based on neuronal morphologies, connectivity between different neural classes, and the locations of synapses, we identified non-overlapping channels originating from four types of MeTu neurons, which we further divided into ten subtypes based on the presynaptic connections in medulla and postsynaptic connections in AOTUsu. To gain an objective measure of the natural variation within the pathway, we quantified the differences between anterior visual pathways from both hemispheres and between two electron-microscopy datasets. Furthermore, we infer potential visual features and the visual area from which any given ring neuron receives input by combining the connectivity of the entire AVP, the MeTu neurons' dendritic fields, and presynaptic connectivity in the optic lobes. These results provide a strong foundation for understanding how distinct visual features are extracted and transformed across multiple processing stages to provide critical information for computing the fly's sense of direction.
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Affiliation(s)
- Dustin Garner
- Molecular, Cellular, and Developmental Biology, University of California Santa Barbara, Santa Barbara, CA, USA
| | - Emil Kind
- Department of Biology, Freie Universität Berlin, Berlin, Germany
| | - Aljoscha Nern
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Lucy Houghton
- Molecular, Cellular, and Developmental Biology, University of California Santa Barbara, Santa Barbara, CA, USA
| | - Arthur Zhao
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Gizem Sancer
- Department of Biology, Freie Universität Berlin, Berlin, Germany
| | - Gerald M. Rubin
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | | | - Sung Soo Kim
- Molecular, Cellular, and Developmental Biology, University of California Santa Barbara, Santa Barbara, CA, USA
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17
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Han X, Li PH, Wang S, Sanchez M, Aggarwal S, Blakely T, Schalek R, Meirovitch Y, Lin Z, Berger D, Wu Y, Aly F, Bay S, Delatour B, LaFaye P, Pfister H, Wei D, Jain V, Ploegh H, Lichtman J. A large-scale volumetric correlated light and electron microscopy study localizes Alzheimer's disease-related molecules in the hippocampus. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.24.563674. [PMID: 37961104 PMCID: PMC10634883 DOI: 10.1101/2023.10.24.563674] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Connectomics is a nascent neuroscience field to map and analyze neuronal networks. It provides a new way to investigate abnormalities in brain tissue, including in models of Alzheimer's disease (AD). This age-related disease is associated with alterations in amyloid-β (Aβ) and phosphorylated tau (pTau). These alterations correlate with AD's clinical manifestations, but causal links remain unclear. Therefore, studying these molecular alterations within the context of the local neuronal and glial milieu may provide insight into disease mechanisms. Volume electron microscopy (vEM) is an ideal tool for performing connectomics studies at the ultrastructural level, but localizing specific biomolecules within large-volume vEM data has been challenging. Here we report a volumetric correlated light and electron microscopy (vCLEM) approach using fluorescent nanobodies as immuno-probes to localize Alzheimer's disease-related molecules in a large vEM volume. Three molecules (pTau, Aβ, and a marker for activated microglia (CD11b)) were labeled without the need for detergents by three nanobody probes in a sample of the hippocampus of the 3xTg Alzheimer's disease model mouse. Confocal microscopy followed by vEM imaging of the same sample allowed for registration of the location of the molecules within the volume. This dataset revealed several ultrastructural abnormalities regarding the localizations of Aβ and pTau in novel locations. For example, two pTau-positive post-synaptic spine-like protrusions innervated by axon terminals were found projecting from the axon initial segment of a pyramidal cell. Three pyramidal neurons with intracellular Aβ or pTau were 3D reconstructed. Automatic synapse detection, which is necessary for connectomics analysis, revealed the changes in density and volume of synapses at different distances from an Aβ plaque. This vCLEM approach is useful to uncover molecular alterations within large-scale volume electron microscopy data, opening a new connectomics pathway to study Alzheimer's disease and other types of dementia.
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18
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Yang HH, Brezovec LE, Capdevila LS, Vanderbeck QX, Adachi A, Mann RS, Wilson RI. Fine-grained descending control of steering in walking Drosophila. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.15.562426. [PMID: 37904997 PMCID: PMC10614758 DOI: 10.1101/2023.10.15.562426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/02/2023]
Abstract
Locomotion involves rhythmic limb movement patterns that originate in circuits outside the brain. Purposeful locomotion requires descending commands from the brain, but we do not understand how these commands are structured. Here we investigate this issue, focusing on the control of steering in walking Drosophila. First, we describe different limb "gestures" associated with different steering maneuvers. Next, we identify a set of descending neurons whose activity predicts steering. Focusing on two descending cell types downstream from distinct brain networks, we show that they evoke specific limb gestures: one lengthens strides on the outside of a turn, while the other attenuates strides on the inside of a turn. Notably, a single descending neuron can have opposite effects during different locomotor rhythm phases, and we identify networks positioned to implement this phase-specific gating. Together, our results show how purposeful locomotion emerges from brain cells that drive specific, coordinated modulations of low-level patterns.
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Affiliation(s)
- Helen H. Yang
- Department of Neurobiology, Harvard Medical School, Boston, MA 02115 USA
| | - Luke E. Brezovec
- Department of Neurobiology, Stanford University, Stanford, CA 94305 USA
| | | | | | - Atsuko Adachi
- Department of Biochemistry and Molecular Biophysics, Department of Neuroscience, Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027 USA
| | - Richard S. Mann
- Department of Biochemistry and Molecular Biophysics, Department of Neuroscience, Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027 USA
| | - Rachel I. Wilson
- Department of Neurobiology, Harvard Medical School, Boston, MA 02115 USA
- Lead contact
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19
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Zhao A, Nern A, Koskela S, Dreher M, Erginkaya M, Laughland CW, Ludwigh H, Thomson A, Hoeller J, Parekh R, Romani S, Bock DD, Chiappe E, Reiser MB. A comprehensive neuroanatomical survey of the Drosophila Lobula Plate Tangential Neurons with predictions for their optic flow sensitivity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.16.562634. [PMID: 37904921 PMCID: PMC10614863 DOI: 10.1101/2023.10.16.562634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/01/2023]
Abstract
Flying insects exhibit remarkable navigational abilities controlled by their compact nervous systems. Optic flow, the pattern of changes in the visual scene induced by locomotion, is a crucial sensory cue for robust self-motion estimation, especially during rapid flight. Neurons that respond to specific, large-field optic flow patterns have been studied for decades, primarily in large flies, such as houseflies, blowflies, and hover flies. The best-known optic-flow sensitive neurons are the large tangential cells of the dipteran lobula plate, whose visual-motion responses, and to a lesser extent, their morphology, have been explored using single-neuron neurophysiology. Most of these studies have focused on the large, Horizontal and Vertical System neurons, yet the lobula plate houses a much larger set of 'optic-flow' sensitive neurons, many of which have been challenging to unambiguously identify or to reliably target for functional studies. Here we report the comprehensive reconstruction and identification of the Lobula Plate Tangential Neurons in an Electron Microscopy (EM) volume of a whole Drosophila brain. This catalog of 58 LPT neurons (per brain hemisphere) contains many neurons that are described here for the first time and provides a basis for systematic investigation of the circuitry linking self-motion to locomotion control. Leveraging computational anatomy methods, we estimated the visual motion receptive fields of these neurons and compared their tuning to the visual consequence of body rotations and translational movements. We also matched these neurons, in most cases on a one-for-one basis, to stochastically labeled cells in genetic driver lines, to the mirror-symmetric neurons in the same EM brain volume, and to neurons in an additional EM data set. Using cell matches across data sets, we analyzed the integration of optic flow patterns by neurons downstream of the LPTs and find that most central brain neurons establish sharper selectivity for global optic flow patterns than their input neurons. Furthermore, we found that self-motion information extracted from optic flow is processed in distinct regions of the central brain, pointing to diverse foci for the generation of visual behaviors.
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Affiliation(s)
- Arthur Zhao
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA USA
| | - Aljoscha Nern
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA USA
| | - Sanna Koskela
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA USA
| | - Marisa Dreher
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA USA
| | - Mert Erginkaya
- Champalimaud Neuroscience Programme, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Connor W Laughland
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA USA
| | - Henrique Ludwigh
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA USA
| | - Alex Thomson
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA USA
| | - Judith Hoeller
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA USA
| | - Ruchi Parekh
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA USA
| | - Sandro Romani
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA USA
| | - Davi D Bock
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA USA
- Department of Neurological Sciences, Larner College of Medicine, University of Vermont, USA
| | - Eugenia Chiappe
- Champalimaud Neuroscience Programme, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Michael B Reiser
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA USA
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20
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Ganguly I, Heckman EL, Litwin-Kumar A, Clowney EJ, Behnia R. Diversity of visual inputs to Kenyon cells of the Drosophila mushroom body. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.12.561793. [PMID: 37873086 PMCID: PMC10592809 DOI: 10.1101/2023.10.12.561793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
The arthropod mushroom body is well-studied as an expansion layer that represents olfactory stimuli and links them to contingent events. However, 8% of mushroom body Kenyon cells in Drosophila melanogaster receive predominantly visual input, and their tuning and function are poorly understood. Here, we use the FlyWire adult whole-brain connectome to identify inputs to visual Kenyon cells. The types of visual neurons we identify are similar across hemispheres and connectomes with certain inputs highly overrepresented. Many visual projection neurons presynaptic to Kenyon cells receive input from large swathes of visual space, while local visual interneurons, providing smaller fractions of input, receive more spatially restricted signals that may be tuned to specific features of the visual scene. Like olfactory Kenyon cells, visual Kenyon cells receive sparse inputs from different combinations of visual channels, including inputs from multiple optic lobe neuropils. The sets of inputs to individual visual Kenyon cells are consistent with random sampling of available inputs. These connectivity patterns suggest that visual coding in the mushroom body, like olfactory coding, is sparse, distributed, and combinatorial. However, the expansion coding properties appear different, with a specific repertoire of visual inputs projecting onto a relatively small number of visual Kenyon cells.
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Affiliation(s)
- Ishani Ganguly
- The Mortimer B. Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, NY 10027, USA
- Center for Theoretical Neuroscience, Columbia University, New York, NY 10027, USA
| | - Emily L Heckman
- Department of Molecular, Cellular, and Developmental Biology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Ashok Litwin-Kumar
- The Mortimer B. Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, NY 10027, USA
- Center for Theoretical Neuroscience, Columbia University, New York, NY 10027, USA
| | - E Josephine Clowney
- Department of Molecular, Cellular, and Developmental Biology, University of Michigan, Ann Arbor, MI 48109, USA
- Michigan Neuroscience Institute Affiliate
| | - Rudy Behnia
- The Mortimer B. Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, NY 10027, USA
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21
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Ros IG, Omoto JJ, Dickinson MH. Descending control and regulation of spontaneous flight turns in Drosophila. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.06.555791. [PMID: 37732262 PMCID: PMC10508747 DOI: 10.1101/2023.09.06.555791] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/22/2023]
Abstract
The clumped distribution of resources in the world has influenced the pattern of foraging behavior since the origins of life, selecting for a common locomotor search motif in which straight movements through resource-poor regions alternate with zig -zag exploration in resource-rich domains. For example, flies execute rapid changes in flight heading called body saccades during local search, but suppress these turns during long-distance dispersal or when surging upwind after encountering an attractive odor plume. Here, we describe the key cellular components of a neural network in flies that generates spontaneous turns as well as a specialized neuron that inhibits the network to promote straight flight. Using 2-photon imaging, optogenetic activation, and genetic ablation, we show that only four descending neurons appear sufficient to generate the descending commands to execute flight saccades. The network is organized into two functional couplets-one for right turns and one for left-with each couplet consisting of an excitatory (DNae014) and inhibitory (DNb01) neuron that project to the flight motor neuropil within the ventral nerve cord. Using resources from recently published connectomes of the fly brain, we identified a large, unique interneuron (VES041) that forms inhibitory connections to all four saccade command neurons and created specific genetic driver lines for this cell. As suggested by its connectivity, activation of VES041 strongly suppresses saccades, suggesting that it regulates the transition between local search and long-distance dispersal. These results thus identify the critical elements of a network that not only structures the locomotor behavior of flies, but may also play a crucial role in their natural foraging ecology.
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22
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Mabuchi Y, Cui X, Xie L, Kim H, Jiang T, Yapici N. Visual feedback neurons fine-tune Drosophila male courtship via GABA-mediated inhibition. Curr Biol 2023; 33:3896-3910.e7. [PMID: 37673068 PMCID: PMC10529139 DOI: 10.1016/j.cub.2023.08.034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 06/27/2023] [Accepted: 08/11/2023] [Indexed: 09/08/2023]
Abstract
Many species of animals use vision to regulate their social behaviors. However, the molecular and circuit mechanisms underlying visually guided social interactions remain largely unknown. Here, we show that the Drosophila ortholog of the human GABAA-receptor-associated protein (GABARAP) is required in a class of visual feedback neurons, lamina tangential (Lat) cells, to fine-tune male courtship. GABARAP is a ubiquitin-like protein that maintains cell-surface levels of GABAA receptors. We demonstrate that knocking down GABARAP or GABAAreceptors in Lat neurons or hyperactivating them induces male courtship toward other males. Inhibiting Lat neurons, on the other hand, delays copulation by impairing the ability of males to follow females. Remarkably, the fly GABARAP protein and its human ortholog share a strong sequence identity, and the fly GABARAP function in Lat neurons can be rescued by its human ortholog. Using in vivo two-photon imaging and optogenetics, we reveal that Lat neurons are functionally connected to neural circuits that mediate visually guided courtship pursuits in males. Our work identifies a novel physiological function for GABARAP in regulating visually guided courtship pursuits in Drosophila males. Reduced GABAA signaling has been linked to social deficits observed in the autism spectrum and bipolar disorders. The functional similarity between the human and the fly GABARAP raises the possibility of a conserved role for this gene in regulating social behaviors across insects and mammals.
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Affiliation(s)
- Yuta Mabuchi
- Department of Neurobiology and Behavior, Cornell University, Ithaca, NY 14853, USA
| | - Xinyue Cui
- Department of Neurobiology and Behavior, Cornell University, Ithaca, NY 14853, USA
| | - Lily Xie
- Department of Neurobiology and Behavior, Cornell University, Ithaca, NY 14853, USA
| | - Haein Kim
- Department of Neurobiology and Behavior, Cornell University, Ithaca, NY 14853, USA
| | - Tianxing Jiang
- Department of Neurobiology and Behavior, Cornell University, Ithaca, NY 14853, USA
| | - Nilay Yapici
- Department of Neurobiology and Behavior, Cornell University, Ithaca, NY 14853, USA.
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23
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González Segarra AJ, Pontes G, Jourjine N, Del Toro A, Scott K. Hunger- and thirst-sensing neurons modulate a neuroendocrine network to coordinate sugar and water ingestion. eLife 2023; 12:RP88143. [PMID: 37732734 PMCID: PMC10513480 DOI: 10.7554/elife.88143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/22/2023] Open
Abstract
Consumption of food and water is tightly regulated by the nervous system to maintain internal nutrient homeostasis. Although generally considered independently, interactions between hunger and thirst drives are important to coordinate competing needs. In Drosophila, four neurons called the interoceptive subesophageal zone neurons (ISNs) respond to intrinsic hunger and thirst signals to oppositely regulate sucrose and water ingestion. Here, we investigate the neural circuit downstream of the ISNs to examine how ingestion is regulated based on internal needs. Utilizing the recently available fly brain connectome, we find that the ISNs synapse with a novel cell-type bilateral T-shaped neuron (BiT) that projects to neuroendocrine centers. In vivo neural manipulations revealed that BiT oppositely regulates sugar and water ingestion. Neuroendocrine cells downstream of ISNs include several peptide-releasing and peptide-sensing neurons, including insulin producing cells (IPCs), crustacean cardioactive peptide (CCAP) neurons, and CCHamide-2 receptor isoform RA (CCHa2R-RA) neurons. These neurons contribute differentially to ingestion of sugar and water, with IPCs and CCAP neurons oppositely regulating sugar and water ingestion, and CCHa2R-RA neurons modulating only water ingestion. Thus, the decision to consume sugar or water occurs via regulation of a broad peptidergic network that integrates internal signals of nutritional state to generate nutrient-specific ingestion.
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Affiliation(s)
| | - Gina Pontes
- University of California, BerkeleyBerkeleyUnited States
| | | | | | - Kristin Scott
- University of California, BerkeleyBerkeleyUnited States
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24
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Christenson MP, Díez ÁS, Heath SL, Saavedra-Weisenhaus M, Adachi A, Abbott LF, Behnia R. Hue selectivity from recurrent circuitry in Drosophila. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.12.548573. [PMID: 37502934 PMCID: PMC10369983 DOI: 10.1101/2023.07.12.548573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
A universal principle of sensory perception is the progressive transformation of sensory information from broad non-specific signals to stimulus-selective signals that form the basis of perception. To perceive color, our brains must transform the wavelengths of light reflected off objects into the derived quantities of brightness, saturation and hue. Neurons responding selectively to hue have been reported in primate cortex, but it is unknown how their narrow tuning in color space is produced by upstream circuit mechanisms. To enable circuit level analysis of color perception, we here report the discovery of neurons in the Drosophila optic lobe with hue selective properties. Using the connectivity graph of the fly brain, we construct a connectomics-constrained circuit model that accounts for this hue selectivity. Unexpectedly, our model predicts that recurrent connections in the circuit are critical for hue selectivity. Experiments using genetic manipulations to perturb recurrence in adult flies confirms this prediction. Our findings reveal the circuit basis for hue selectivity in color vision.
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25
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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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26
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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, Consortium F, Costa M, Sebastian Seung H, Murthy M, Hartenstein V, Bock DD, Jefferis GSXE. A consensus cell type atlas from multiple connectomes reveals principles of circuit stereotypy and variation. 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: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [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 4,179 cell types of which 3,166 consensus cell types are robustly defined by comparison with a second dataset, the "hemibrain" connectome 3 . Comparative analysis 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 than in 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 recorded in the hemibrain connectome could not be robustly identified in the FlyWire connectome, cautioning against defining cell types based on single connectomes. We propose that a cell type should be robust to inter-individual variation, and therefore defined as a group of cells that are more similar to cells in a different brain than to any other cell in the same brain. We show that this new definition can be consistently applied to whole connectome datasets. 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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González-Segarra AJ, Pontes G, Jourjine N, Del Toro A, Scott K. Hunger- and thirst-sensing neurons modulate a neuroendocrine network to coordinate sugar and water ingestion. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.06.535891. [PMID: 37066363 PMCID: PMC10104137 DOI: 10.1101/2023.04.06.535891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
Consumption of food and water is tightly regulated by the nervous system to maintain internal nutrient homeostasis. Although generally considered independently, interactions between hunger and thirst drives are important to coordinate competing needs. In Drosophila , four neurons called the Interoceptive Subesophageal zone Neurons (ISNs) respond to intrinsic hunger and thirst signals to oppositely regulate sucrose and water ingestion. Here, we investigate the neural circuit downstream of the ISNs to examine how ingestion is regulated based on internal needs. Utilizing the recently available fly brain connectome, we find that the ISNs synapse with a novel cell type Bilateral T-shaped neuron (BiT) that projects to neuroendocrine centers. In vivo neural manipulations revealed that BiT oppositely regulates sugar and water ingestion. Neuroendocrine cells downstream of ISNs include several peptide-releasing and peptide-sensing neurons, including insulin producing cells (IPC), crustacean cardioactive peptide (CCAP) neurons, and CCHamide-2 receptor isoform RA (CCHa2R-RA) neurons. These neurons contribute differentially to ingestion of sugar and water, with IPCs and CCAP neurons oppositely regulating sugar and water ingestion, and CCHa2R-RA neurons modulating only water ingestion. Thus, the decision to consume sugar or water occurs via regulation of a broad peptidergic network that integrates internal signals of nutritional state to generate nutrient-specific ingestion.
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Affiliation(s)
| | - Gina Pontes
- University of California, Berkeley, United States
- present address: IBBEA, CONICET-UBA, Buenos Aires, Argentina
| | - Nicholas Jourjine
- University of California, Berkeley, United States
- present address: Harvard University, Cambridge, United States
| | - Alexander Del Toro
- University of California, Berkeley, United States
- present address: Brown University, Rhode Island, United States
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28
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Braun A, Borst A, Meier M. Disynaptic inhibition shapes tuning of OFF-motion detectors in Drosophila. Curr Biol 2023:S0960-9822(23)00601-2. [PMID: 37236181 DOI: 10.1016/j.cub.2023.05.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 04/02/2023] [Accepted: 05/03/2023] [Indexed: 05/28/2023]
Abstract
The circuitry underlying the detection of visual motion in Drosophila melanogaster is one of the best studied networks in neuroscience. Lately, electron microscopy reconstructions, algorithmic models, and functional studies have proposed a common motif for the cellular circuitry of an elementary motion detector based on both supralinear enhancement for preferred direction and sublinear suppression for null-direction motion. In T5 cells, however, all columnar input neurons (Tm1, Tm2, Tm4, and Tm9) are excitatory. So, how is null-direction suppression realized there? Using two-photon calcium imaging in combination with thermogenetics, optogenetics, apoptotics, and pharmacology, we discovered that it is via CT1, the GABAergic large-field amacrine cell, where the different processes have previously been shown to act in an electrically isolated way. Within each column, CT1 receives excitatory input from Tm9 and Tm1 and provides the sign-inverted, now inhibitory input signal onto T5. Ablating CT1 or knocking down GABA-receptor subunit Rdl significantly broadened the directional tuning of T5 cells. It thus appears that the signal of Tm1 and Tm9 is used both as an excitatory input for preferred direction enhancement and, through a sign inversion within the Tm1/Tm9-CT1 microcircuit, as an inhibitory input for null-direction suppression.
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Affiliation(s)
- Amalia Braun
- Max Planck Institute for Biological Intelligence, Department of Circuits - Computation - Models, Am Klopferspitz 18, 82152 Martinsried, Germany.
| | - Alexander Borst
- Max Planck Institute for Biological Intelligence, Department of Circuits - Computation - Models, Am Klopferspitz 18, 82152 Martinsried, Germany
| | - Matthias Meier
- Max Planck Institute for Biological Intelligence, Department of Circuits - Computation - Models, Am Klopferspitz 18, 82152 Martinsried, Germany.
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29
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Laturney M, Sterne GR, Scott K. Mating activates neuroendocrine pathways signaling hunger in Drosophila females. eLife 2023; 12:e85117. [PMID: 37184218 PMCID: PMC10229122 DOI: 10.7554/elife.85117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 05/13/2023] [Indexed: 05/16/2023] Open
Abstract
Mated females reallocate resources to offspring production, causing changes to nutritional requirements and challenges to energy homeostasis. Although observed across species, the neural and endocrine mechanisms that regulate the nutritional needs of mated females are not well understood. Here, we find that mated Drosophila melanogaster females increase sugar intake, which is regulated by the activity of sexually dimorphic insulin receptor (Lgr3) neurons. In virgins, Lgr3+ cells have reduced activity as they receive inhibitory input from active, female-specific pCd-2 cells, restricting sugar intake. During copulation, males deposit sex peptide into the female reproductive tract, which silences a three-tier mating status circuit and initiates the female postmating response. We show that pCd-2 neurons also become silenced after mating due to the direct synaptic input from the mating status circuit. Thus, in mated females pCd-2 inhibition is attenuated, activating downstream Lgr3+ neurons and promoting sugar intake. Together, this circuit transforms the mated signal into a long-term hunger signal. Our results demonstrate that the mating circuit alters nutrient sensing centers to increase feeding in mated females, providing a mechanism to increase intake in anticipation of the energetic costs associated with reproduction.
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Affiliation(s)
| | | | - Kristin Scott
- University of California, BerkeleyBerkeleyUnited States
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30
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Shiu PK, Sterne GR, Spiller N, Franconville R, Sandoval A, Zhou J, Simha N, Kang CH, Yu S, Kim JS, Dorkenwald S, Matsliah A, Schlegel P, Szi-chieh Y, McKellar CE, Sterling A, Costa M, Eichler K, Jefferis GS, Murthy M, Bates AS, Eckstein N, Funke J, Bidaye SS, Hampel S, Seeds AM, Scott K. A leaky integrate-and-fire computational model based on the connectome of the entire adult Drosophila brain reveals insights into sensorimotor processing. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.02.539144. [PMID: 37205514 PMCID: PMC10187186 DOI: 10.1101/2023.05.02.539144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
The forthcoming assembly of the adult Drosophila melanogaster central brain connectome, containing over 125,000 neurons and 50 million synaptic connections, provides a template for examining sensory processing throughout the brain. Here, we create a leaky integrate-and-fire computational model of the entire Drosophila brain, based on neural connectivity and neurotransmitter identity, to study circuit properties of feeding and grooming behaviors. We show that activation of sugar-sensing or water-sensing gustatory neurons in the computational model accurately predicts neurons that respond to tastes and are required for feeding initiation. Computational activation of neurons in the feeding region of the Drosophila brain predicts those that elicit motor neuron firing, a testable hypothesis that we validate by optogenetic activation and behavioral studies. Moreover, computational activation of different classes of gustatory neurons makes accurate predictions of how multiple taste modalities interact, providing circuit-level insight into aversive and appetitive taste processing. Our computational model predicts that the sugar and water pathways form a partially shared appetitive feeding initiation pathway, which our calcium imaging and behavioral experiments confirm. Additionally, we applied this model to mechanosensory circuits and found that computational activation of mechanosensory neurons predicts activation of a small set of neurons comprising the antennal grooming circuit that do not overlap with gustatory circuits, and accurately describes the circuit response upon activation of different mechanosensory subtypes. Our results demonstrate that modeling brain circuits purely from connectivity and predicted neurotransmitter identity generates experimentally testable hypotheses and can accurately describe complete sensorimotor transformations.
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Affiliation(s)
- Philip K. Shiu
- Department of Molecular and Cell Biology and Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA
| | - Gabriella R. Sterne
- Department of Molecular and Cell Biology and Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA
- University of Rochester Medical Center, Department of Biomedical Genetics
| | - Nico Spiller
- Max Planck Florida Institute for Neuroscience, Jupiter, FL, USA
| | | | - Andrea Sandoval
- Department of Molecular and Cell Biology and Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA
| | - Joie Zhou
- Department of Molecular and Cell Biology and Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA
| | - Neha Simha
- Department of Molecular and Cell Biology and Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA
| | - Chan Hyuk Kang
- Department of Biological Sciences, Sungkyunkwan University, Suwon, 16419, South Korea
| | - Seongbong Yu
- Department of Biological Sciences, Sungkyunkwan University, Suwon, 16419, South Korea
| | - Jinseop S. Kim
- Department of Biological Sciences, Sungkyunkwan University, Suwon, 16419, South Korea
| | - Sven Dorkenwald
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Computer Science Department, Princeton University, Princeton, NJ, USA
| | - Arie Matsliah
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Philipp Schlegel
- Department of Zoology, University of Cambridge
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge
| | - Yu Szi-chieh
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Claire E. McKellar
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Amy Sterling
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Marta Costa
- Department of Zoology, University of Cambridge
| | | | - Gregory S.X.E. Jefferis
- Department of Zoology, University of Cambridge
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge
| | - Mala Murthy
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Alexander Shakeel Bates
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge
- Centre for Neural Circuits and Behaviour, The University of Oxford
- Department of Neurobiology and Howard Hughes Medical Institute, Harvard Medical School, Boston, MA, USA
| | | | - Jan Funke
- HHMI Janelia Research Campus, Ashburn, USA
| | - Salil S. Bidaye
- Max Planck Florida Institute for Neuroscience, Jupiter, FL, USA
| | - Stefanie Hampel
- Institute of Neurobiology, University of Puerto Rico-Medical Sciences Campus, San Juan, Puerto Rico
| | - Andrew M. Seeds
- Institute of Neurobiology, University of Puerto Rico-Medical Sciences Campus, San Juan, Puerto Rico
| | - Kristin Scott
- Department of Molecular and Cell Biology and Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA
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31
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Gallusser B, Maltese G, Di Caprio G, Vadakkan TJ, Sanyal A, Somerville E, Sahasrabudhe M, O’Connor J, Weigert M, Kirchhausen T. Deep neural network automated segmentation of cellular structures in volume electron microscopy. J Cell Biol 2023; 222:e202208005. [PMID: 36469001 PMCID: PMC9728137 DOI: 10.1083/jcb.202208005] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 10/03/2022] [Accepted: 11/14/2022] [Indexed: 12/12/2022] Open
Abstract
Volume electron microscopy is an important imaging modality in contemporary cell biology. Identification of intracellular structures is a laborious process limiting the effective use of this potentially powerful tool. We resolved this bottleneck with automated segmentation of intracellular substructures in electron microscopy (ASEM), a new pipeline to train a convolutional neural network to detect structures of a wide range in size and complexity. We obtained dedicated models for each structure based on a small number of sparsely annotated ground truth images from only one or two cells. Model generalization was improved with a rapid, computationally effective strategy to refine a trained model by including a few additional annotations. We identified mitochondria, Golgi apparatus, endoplasmic reticulum, nuclear pore complexes, caveolae, clathrin-coated pits, and vesicles imaged by focused ion beam scanning electron microscopy. We uncovered a wide range of membrane-nuclear pore diameters within a single cell and derived morphological metrics from clathrin-coated pits and vesicles, consistent with the classical constant-growth assembly model.
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Affiliation(s)
- Benjamin Gallusser
- Program in Cellular and Molecular Medicine, Boston Children’s Hospital, Boston, MA
- Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Giorgio Maltese
- Program in Cellular and Molecular Medicine, Boston Children’s Hospital, Boston, MA
| | - Giuseppe Di Caprio
- Program in Cellular and Molecular Medicine, Boston Children’s Hospital, Boston, MA
- Department of Pediatrics, Harvard Medical School, Boston, MA
| | - Tegy John Vadakkan
- Program in Cellular and Molecular Medicine, Boston Children’s Hospital, Boston, MA
| | - Anwesha Sanyal
- Program in Cellular and Molecular Medicine, Boston Children’s Hospital, Boston, MA
- Department of Cell Biology, Harvard Medical School, Boston, MA
| | - Elliott Somerville
- Program in Cellular and Molecular Medicine, Boston Children’s Hospital, Boston, MA
| | - Mihir Sahasrabudhe
- Program in Cellular and Molecular Medicine, Boston Children’s Hospital, Boston, MA
- Université Paris-Saclay, CentraleSupélec, Mathématiques et Informatique pour la Complexité et les Systèmes, Gif-sur-Yvette, France
| | - Justin O’Connor
- Department of Biological Chemistry & Molecular Pharmacology, Harvard Medical School, Boston, MA
| | - Martin Weigert
- Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Tom Kirchhausen
- Program in Cellular and Molecular Medicine, Boston Children’s Hospital, Boston, MA
- Department of Pediatrics, Harvard Medical School, Boston, MA
- Department of Cell Biology, Harvard Medical School, Boston, MA
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32
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Mabuchi Y, Cui X, Xie L, Kim H, Jiang T, Yapici N. GABA-mediated inhibition in visual feedback neurons fine-tunes Drosophila male courtship. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.25.525544. [PMID: 36747836 PMCID: PMC9900824 DOI: 10.1101/2023.01.25.525544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Vision is critical for the regulation of mating behaviors in many species. Here, we discovered that the Drosophila ortholog of human GABA A -receptor-associated protein (GABARAP) is required to fine-tune male courtship by modulating the activity of visual feedback neurons, lamina tangential cells (Lat). GABARAP is a ubiquitin-like protein that regulates cell-surface levels of GABA A receptors. Knocking down GABARAP or GABA A receptors in Lat neurons or hyperactivating them induces male courtship toward other males. Inhibiting Lat neurons, on the other hand, delays copulation by impairing the ability of males to follow females. Remarkably, the human ortholog of Drosophila GABARAP restores function in Lat neurons. Using in vivo two-photon imaging and optogenetics, we show that Lat neurons are functionally connected to neural circuits that mediate visually-guided courtship pursuits in males. Our work reveals a novel physiological role for GABARAP in fine-tuning the activity of a visual circuit that tracks a mating partner during courtship.
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Affiliation(s)
- Yuta Mabuchi
- Department of Neurobiology and Behavior, Cornell University, 14853, Ithaca, NY, USA
| | - Xinyue Cui
- Department of Neurobiology and Behavior, Cornell University, 14853, Ithaca, NY, USA
| | - Lily Xie
- Department of Neurobiology and Behavior, Cornell University, 14853, Ithaca, NY, USA
| | - Haein Kim
- Department of Neurobiology and Behavior, Cornell University, 14853, Ithaca, NY, USA
| | - Tianxing Jiang
- Department of Neurobiology and Behavior, Cornell University, 14853, Ithaca, NY, USA
| | - Nilay Yapici
- Department of Neurobiology and Behavior, Cornell University, 14853, Ithaca, NY, USA
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33
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Glancy B. MitoNet: A generalizable model for segmentation of individual mitochondria within electron microscopy datasets. Cell Syst 2023; 14:7-8. [PMID: 36657392 DOI: 10.1016/j.cels.2022.12.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Volume electron microscopy provides a powerful approach to investigating physical connectivity within biological systems. In an article in this issue of Cell Systems, Conrad and Narayan overcome a major hurdle in volume electron microscopy by developing "MitoNet," a broadly applicable model for labeling individual mitochondria across volume electron microscopy datasets.
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Affiliation(s)
- Brian Glancy
- National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD 20892, USA; National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, Bethesda, MD 20892, USA.
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Conrad R, Narayan K. Instance segmentation of mitochondria in electron microscopy images with a generalist deep learning model trained on a diverse dataset. Cell Syst 2023; 14:58-71.e5. [PMID: 36657391 PMCID: PMC9883049 DOI: 10.1016/j.cels.2022.12.006] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 10/10/2022] [Accepted: 12/14/2022] [Indexed: 01/19/2023]
Abstract
Mitochondria are extremely pleomorphic organelles. Automatically annotating each one accurately and precisely in any 2D or volume electron microscopy (EM) image is an unsolved computational challenge. Current deep learning-based approaches train models on images that provide limited cellular contexts, precluding generality. To address this, we amassed a highly heterogeneous ∼1.5 × 106 image 2D unlabeled cellular EM dataset and segmented ∼135,000 mitochondrial instances therein. MitoNet, a model trained on these resources, performs well on challenging benchmarks and on previously unseen volume EM datasets containing tens of thousands of mitochondria. We release a Python package and napari plugin, empanada, to rapidly run inference, visualize, and proofread instance segmentations. A record of this paper's transparent peer review process is included in the supplemental information.
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Affiliation(s)
- Ryan Conrad
- Center for Molecular Microscopy, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda 20892, Maryland, USA.,Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick 21702, Maryland, USA
| | - Kedar Narayan
- Center for Molecular Microscopy, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda 20892, Maryland, USA.,Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick 21702, Maryland, USA
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35
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Sheridan A, Nguyen TM, Deb D, Lee WCA, Saalfeld S, Turaga SC, Manor U, Funke J. Local shape descriptors for neuron segmentation. Nat Methods 2023; 20:295-303. [PMID: 36585455 PMCID: PMC9911350 DOI: 10.1038/s41592-022-01711-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Accepted: 11/01/2022] [Indexed: 12/31/2022]
Abstract
We present an auxiliary learning task for the problem of neuron segmentation in electron microscopy volumes. The auxiliary task consists of the prediction of local shape descriptors (LSDs), which we combine with conventional voxel-wise direct neighbor affinities for neuron boundary detection. The shape descriptors capture local statistics about the neuron to be segmented, such as diameter, elongation, and direction. On a study comparing several existing methods across various specimen, imaging techniques, and resolutions, auxiliary learning of LSDs consistently increases segmentation accuracy of affinity-based methods over a range of metrics. Furthermore, the addition of LSDs promotes affinity-based segmentation methods to be on par with the current state of the art for neuron segmentation (flood-filling networks), while being two orders of magnitudes more efficient-a critical requirement for the processing of future petabyte-sized datasets.
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Affiliation(s)
- Arlo Sheridan
- grid.443970.dHHMI Janelia, Ashburn, VA USA ,grid.250671.70000 0001 0662 7144Waitt Advanced Biophotonics Center, Salk Institute for Biological Studies, La Jolla, CA USA
| | - Tri M. Nguyen
- grid.38142.3c000000041936754XDepartment of Neurobiology, Harvard Medical School, Boston, MA USA
| | | | - Wei-Chung Allen Lee
- grid.38142.3c000000041936754XF.M. Kirby Neurobiology Center, Boston Children’s Hospital, Harvard Medical School, Boston, MA USA
| | | | | | - Uri Manor
- grid.250671.70000 0001 0662 7144Waitt Advanced Biophotonics Center, Salk Institute for Biological Studies, La Jolla, CA USA
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36
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Structured cerebellar connectivity supports resilient pattern separation. Nature 2023; 613:543-549. [PMID: 36418404 DOI: 10.1038/s41586-022-05471-w] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Accepted: 10/20/2022] [Indexed: 11/25/2022]
Abstract
The cerebellum is thought to help detect and correct errors between intended and executed commands1,2 and is critical for social behaviours, cognition and emotion3-6. Computations for motor control must be performed quickly to correct errors in real time and should be sensitive to small differences between patterns for fine error correction while being resilient to noise7. Influential theories of cerebellar information processing have largely assumed random network connectivity, which increases the encoding capacity of the network's first layer8-13. However, maximizing encoding capacity reduces the resilience to noise7. To understand how neuronal circuits address this fundamental trade-off, we mapped the feedforward connectivity in the mouse cerebellar cortex using automated large-scale transmission electron microscopy and convolutional neural network-based image segmentation. We found that both the input and output layers of the circuit exhibit redundant and selective connectivity motifs, which contrast with prevailing models. Numerical simulations suggest that these redundant, non-random connectivity motifs increase the resilience to noise at a negligible cost to the overall encoding capacity. This work reveals how neuronal network structure can support a trade-off between encoding capacity and redundancy, unveiling principles of biological network architecture with implications for the design of artificial neural networks.
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37
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Galili DS, Jefferis GS, Costa M. Connectomics and the neural basis of behaviour. CURRENT OPINION IN INSECT SCIENCE 2022; 54:100968. [PMID: 36113710 PMCID: PMC7614087 DOI: 10.1016/j.cois.2022.100968] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 08/30/2022] [Accepted: 09/06/2022] [Indexed: 06/15/2023]
Abstract
Methods to acquire and process synaptic-resolution electron-microscopy datasets have progressed very rapidly, allowing production and annotation of larger, more complete connectomes. More accurate neuronal matching techniques are enriching cell type data with gene expression, neuron activity, behaviour and developmental information, providing ways to test hypotheses of circuit function. In a variety of behaviours such as learned and innate olfaction, navigation and sexual behaviour, connectomics has already revealed interconnected modules with a hierarchical structure, recurrence and integration of sensory streams. Comparing individual connectomes to determine which circuit features are robust and which are variable is one key research area; new work in comparative connectomics across development, experience, sex and species will establish strong links between neuronal connectivity and brain function.
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Affiliation(s)
- Dana S Galili
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge CB2 0QH, UK
| | - Gregory Sxe Jefferis
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge CB2 0QH, UK; Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Downing Street, Cambridge CB2 3EJ, UK
| | - Marta Costa
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Downing Street, Cambridge CB2 3EJ, UK.
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38
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SyConn2: dense synaptic connectivity inference for volume electron microscopy. Nat Methods 2022; 19:1367-1370. [PMID: 36280715 PMCID: PMC9636020 DOI: 10.1038/s41592-022-01624-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 08/24/2022] [Indexed: 12/28/2022]
Abstract
The ability to acquire ever larger datasets of brain tissue using volume electron microscopy leads to an increasing demand for the automated extraction of connectomic information. We introduce SyConn2, an open-source connectome analysis toolkit, which works with both on-site high-performance compute environments and rentable cloud computing clusters. SyConn2 was tested on connectomic datasets with more than 10 million synapses, provides a web-based visualization interface and makes these data amenable to complex anatomical and neuronal connectivity queries. SyConn2 is a machine learning-based framework for inferring and analyzing the connectomes contained in a volume electron microscopy dataset of brain tissue, for example from the zebra finch.
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39
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Park A, Croset V, Otto N, Agarwal D, Treiber CD, Meschi E, Sims D, Waddell S. Gliotransmission of D-serine promotes thirst-directed behaviors in Drosophila. Curr Biol 2022; 32:3952-3970.e8. [PMID: 35963239 PMCID: PMC9616736 DOI: 10.1016/j.cub.2022.07.038] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 07/04/2022] [Accepted: 07/15/2022] [Indexed: 12/13/2022]
Abstract
Thirst emerges from a range of cellular changes that ultimately motivate an animal to consume water. Although thirst-responsive neuronal signals have been reported, the full complement of brain responses is unclear. Here, we identify molecular and cellular adaptations in the brain using single-cell sequencing of water-deprived Drosophila. Water deficiency primarily altered the glial transcriptome. Screening the regulated genes revealed astrocytic expression of the astray-encoded phosphoserine phosphatase to bi-directionally regulate water consumption. Astray synthesizes the gliotransmitter D-serine, and vesicular release from astrocytes is required for drinking. Moreover, dietary D-serine rescues aay-dependent drinking deficits while facilitating water consumption and expression of water-seeking memory. D-serine action requires binding to neuronal NMDA-type glutamate receptors. Fly astrocytes contribute processes to tripartite synapses, and the proportion of astrocytes that are themselves activated by glutamate increases with water deprivation. We propose that thirst elevates astrocytic D-serine release, which awakens quiescent glutamatergic circuits to enhance water procurement.
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Affiliation(s)
- Annie Park
- Centre for Neural Circuits & Behaviour, University of Oxford, Oxford OX1 3TA, UK
| | - Vincent Croset
- Centre for Neural Circuits & Behaviour, University of Oxford, Oxford OX1 3TA, UK; Department of Biosciences, Durham University, Durham DH1 3LE, UK.
| | - Nils Otto
- Centre for Neural Circuits & Behaviour, University of Oxford, Oxford OX1 3TA, UK
| | - Devika Agarwal
- Centre for Neural Circuits & Behaviour, University of Oxford, Oxford OX1 3TA, UK; MRC Computational Genomics Analysis and Training Programme (CGAT), MRC Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, John Radcliffe Hospital, Headington, Oxford OX3 9DS, UK
| | - Christoph D Treiber
- Centre for Neural Circuits & Behaviour, University of Oxford, Oxford OX1 3TA, UK
| | - Eleonora Meschi
- Centre for Neural Circuits & Behaviour, University of Oxford, Oxford OX1 3TA, UK
| | - David Sims
- MRC Computational Genomics Analysis and Training Programme (CGAT), MRC Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, John Radcliffe Hospital, Headington, Oxford OX3 9DS, UK
| | - Scott Waddell
- Centre for Neural Circuits & Behaviour, University of Oxford, Oxford OX1 3TA, UK.
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40
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Bates AS, Jefferis G. Systems neuroscience: Auditory processing at synaptic resolution. Curr Biol 2022; 32:R830-R833. [PMID: 35944481 DOI: 10.1016/j.cub.2022.07.001] [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] [Indexed: 11/29/2022]
Abstract
Deeper layers of the auditory system have not been fully mapped in any model system. A new study links comprehensive connectomics of Drosophila song perception circuits to physiological response profiles.
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Affiliation(s)
- Alexander Shakeel Bates
- Department of Neurobiology and Howard Hughes Medical Institute, Harvard Medical School, Boston, MA, USA
| | - Greg 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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41
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Baker CA, McKellar C, Pang R, Nern A, Dorkenwald S, Pacheco DA, Eckstein N, Funke J, Dickson BJ, Murthy M. Neural network organization for courtship-song feature detection in Drosophila. Curr Biol 2022; 32:3317-3333.e7. [PMID: 35793679 PMCID: PMC9378594 DOI: 10.1016/j.cub.2022.06.019] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Revised: 05/18/2022] [Accepted: 06/08/2022] [Indexed: 10/17/2022]
Abstract
Animals communicate using sounds in a wide range of contexts, and auditory systems must encode behaviorally relevant acoustic features to drive appropriate reactions. How feature detection emerges along auditory pathways has been difficult to solve due to challenges in mapping the underlying circuits and characterizing responses to behaviorally relevant features. Here, we study auditory activity in the Drosophila melanogaster brain and investigate feature selectivity for the two main modes of fly courtship song, sinusoids and pulse trains. We identify 24 new cell types of the intermediate layers of the auditory pathway, and using a new connectomic resource, FlyWire, we map all synaptic connections between these cell types, in addition to connections to known early and higher-order auditory neurons-this represents the first circuit-level map of the auditory pathway. We additionally determine the sign (excitatory or inhibitory) of most synapses in this auditory connectome. We find that auditory neurons display a continuum of preferences for courtship song modes and that neurons with different song-mode preferences and response timescales are highly interconnected in a network that lacks hierarchical structure. Nonetheless, we find that the response properties of individual cell types within the connectome are predictable from their inputs. Our study thus provides new insights into the organization of auditory coding within the Drosophila brain.
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Affiliation(s)
- Christa A Baker
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Claire McKellar
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA; Janelia Research Campus, HHMI, Ashburn, VA, USA
| | - Rich Pang
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | | | - Sven Dorkenwald
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA; Computer Science, Princeton University, Princeton, NJ, USA
| | - Diego A Pacheco
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Nils Eckstein
- Janelia Research Campus, HHMI, Ashburn, VA, USA; Institute of Neuroinformatics UZH/ETHZ, Zurich, Switzerland
| | - Jan Funke
- Janelia Research Campus, HHMI, Ashburn, VA, USA
| | | | - Mala Murthy
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.
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42
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Zhang A, Shailja S, Borba C, Miao Y, Goebel M, Ruschel R, Ryan K, Smith W, Manjunath BS. Automatic classification and neurotransmitter prediction of synapses in electron microscopy. BIOLOGICAL IMAGING 2022; 2:e6. [PMID: 38486830 PMCID: PMC10936391 DOI: 10.1017/s2633903x2200006x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Revised: 06/24/2022] [Accepted: 07/07/2022] [Indexed: 03/17/2024]
Abstract
This paper presents a deep-learning-based workflow to detect synapses and predict their neurotransmitter type in the primitive chordate Ciona intestinalis (Ciona) electron microscopic (EM) images. Identifying synapses from EM images to build a full map of connections between neurons is a labor-intensive process and requires significant domain expertise. Automation of synapse classification would hasten the generation and analysis of connectomes. Furthermore, inferences concerning neuron type and function from synapse features are in many cases difficult to make. Finding the connection between synapse structure and function is an important step in fully understanding a connectome. Class Activation Maps derived from the convolutional neural network provide insights on important features of synapses based on cell type and function. The main contribution of this work is in the differentiation of synapses by neurotransmitter type through the structural information in their EM images. This enables the prediction of neurotransmitter types for neurons in Ciona, which were previously unknown. The prediction model with code is available on GitHub.
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Affiliation(s)
- Angela Zhang
- Vision Research Laboratory, University of California, Santa Barbara, Santa Barbara, California, USA
| | - S. Shailja
- Vision Research Laboratory, University of California, Santa Barbara, Santa Barbara, California, USA
| | - Cezar Borba
- Neuroscience Research Institute, University of California, Santa Barbara, Santa Barbara, California, USA
- Department of Molecular, Cellular, and Developmental Biology, University of California, Santa Barbara, Santa Barbara, California, USA
| | - Yishen Miao
- Neuroscience Research Institute, University of California, Santa Barbara, Santa Barbara, California, USA
- Department of Molecular, Cellular, and Developmental Biology, University of California, Santa Barbara, Santa Barbara, California, USA
| | - Michael Goebel
- Vision Research Laboratory, University of California, Santa Barbara, Santa Barbara, California, USA
| | - Raphael Ruschel
- Vision Research Laboratory, University of California, Santa Barbara, Santa Barbara, California, USA
| | - Kerrianne Ryan
- Meinertzhagen Laboratory of Invertebrate Neurobiology, Dalhousie University, Halifax, Nova Scotia, Canada
| | - William Smith
- Neuroscience Research Institute, University of California, Santa Barbara, Santa Barbara, California, USA
- Department of Molecular, Cellular, and Developmental Biology, University of California, Santa Barbara, Santa Barbara, California, USA
| | - B. S. Manjunath
- Vision Research Laboratory, University of California, Santa Barbara, Santa Barbara, California, USA
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43
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Kievits AJ, Lane R, Carroll EC, Hoogenboom JP. How innovations in methodology offer new prospects for volume electron microscopy. J Microsc 2022; 287:114-137. [PMID: 35810393 PMCID: PMC9546337 DOI: 10.1111/jmi.13134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 06/29/2022] [Accepted: 07/06/2022] [Indexed: 11/29/2022]
Abstract
Detailed knowledge of biological structure has been key in understanding biology at several levels of organisation, from organs to cells and proteins. Volume electron microscopy (volume EM) provides high resolution 3D structural information about tissues on the nanometre scale. However, the throughput rate of conventional electron microscopes has limited the volume size and number of samples that can be imaged. Recent improvements in methodology are currently driving a revolution in volume EM, making possible the structural imaging of whole organs and small organisms. In turn, these recent developments in image acquisition have created or stressed bottlenecks in other parts of the pipeline, like sample preparation, image analysis and data management. While the progress in image analysis is stunning due to the advent of automatic segmentation and server‐based annotation tools, several challenges remain. Here we discuss recent trends in volume EM, emerging methods for increasing throughput and implications for sample preparation, image analysis and data management.
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Affiliation(s)
- Arent J. Kievits
- Imaging Physics Delft University of Technology Delft 2624CJ The Netherlands
| | - Ryan Lane
- Imaging Physics Delft University of Technology Delft 2624CJ The Netherlands
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44
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Peddie CJ, Genoud C, Kreshuk A, Meechan K, Micheva KD, Narayan K, Pape C, Parton RG, Schieber NL, Schwab Y, Titze B, Verkade P, Aubrey A, Collinson LM. Volume electron microscopy. NATURE REVIEWS. METHODS PRIMERS 2022; 2:51. [PMID: 37409324 PMCID: PMC7614724 DOI: 10.1038/s43586-022-00131-9] [Citation(s) in RCA: 35] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 05/10/2022] [Indexed: 07/07/2023]
Abstract
Life exists in three dimensions, but until the turn of the century most electron microscopy methods provided only 2D image data. Recently, electron microscopy techniques capable of delving deep into the structure of cells and tissues have emerged, collectively called volume electron microscopy (vEM). Developments in vEM have been dubbed a quiet revolution as the field evolved from established transmission and scanning electron microscopy techniques, so early publications largely focused on the bioscience applications rather than the underlying technological breakthroughs. However, with an explosion in the uptake of vEM across the biosciences and fast-paced advances in volume, resolution, throughput and ease of use, it is timely to introduce the field to new audiences. In this Primer, we introduce the different vEM imaging modalities, the specialized sample processing and image analysis pipelines that accompany each modality and the types of information revealed in the data. We showcase key applications in the biosciences where vEM has helped make breakthrough discoveries and consider limitations and future directions. We aim to show new users how vEM can support discovery science in their own research fields and inspire broader uptake of the technology, finally allowing its full adoption into mainstream biological imaging.
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Affiliation(s)
- Christopher J. Peddie
- Electron Microscopy Science Technology Platform, The Francis Crick Institute, London, UK
| | - Christel Genoud
- Electron Microscopy Facility, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Anna Kreshuk
- Cell Biology and Biophysics Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Kimberly Meechan
- Cell Biology and Biophysics Unit, European Molecular Biology Laboratory, Heidelberg, Germany
- Present address: Faculty of Biosciences, Heidelberg University, Heidelberg, Germany
| | - Kristina D. Micheva
- Department of Molecular and Cellular Physiology, Stanford University, Palo Alto, CA, USA
| | - Kedar Narayan
- Center for Molecular Microscopy, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
- Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Constantin Pape
- Cell Biology and Biophysics Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Robert G. Parton
- The Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
- Centre for Microscopy and Microanalysis, The University of Queensland, Brisbane, Queensland, Australia
| | - Nicole L. Schieber
- Centre for Microscopy and Microanalysis, The University of Queensland, Brisbane, Queensland, Australia
| | - Yannick Schwab
- Cell Biology and Biophysics Unit/ Electron Microscopy Core Facility, European Molecular Biology Laboratory, Heidelberg, Germany
| | | | - Paul Verkade
- School of Biochemistry, University of Bristol, Bristol, UK
| | - Aubrey Aubrey
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Lucy M. Collinson
- Electron Microscopy Science Technology Platform, The Francis Crick Institute, London, UK
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45
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Shiu PK, Sterne GR, Engert S, Dickson BJ, Scott K. Taste quality and hunger interactions in a feeding sensorimotor circuit. eLife 2022; 11:e79887. [PMID: 35791902 PMCID: PMC9292995 DOI: 10.7554/elife.79887] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 06/27/2022] [Indexed: 11/30/2022] Open
Abstract
Taste detection and hunger state dynamically regulate the decision to initiate feeding. To study how context-appropriate feeding decisions are generated, we combined synaptic resolution circuit reconstruction with targeted genetic access to specific neurons to elucidate a gustatory sensorimotor circuit for feeding initiation in adult Drosophila melanogaster. This circuit connects gustatory sensory neurons to proboscis motor neurons through three intermediate layers. Most neurons in this pathway are necessary and sufficient for proboscis extension, a feeding initiation behavior, and respond selectively to sugar taste detection. Pathway activity is amplified by hunger signals that act at select second-order neurons to promote feeding initiation in food-deprived animals. In contrast, the feeding initiation circuit is inhibited by a bitter taste pathway that impinges on premotor neurons, illuminating a local motif that weighs sugar and bitter taste detection to adjust the behavioral outcomes. Together, these studies reveal central mechanisms for the integration of external taste detection and internal nutritive state to flexibly execute a critical feeding decision.
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Affiliation(s)
- Philip K Shiu
- University of California, BerkeleyBerkeleyUnited States
| | - Gabriella R Sterne
- University of California, BerkeleyBerkeleyUnited States
- Janelia Research Campus, Howard Hughes Medical InstituteChevy ChaseUnited States
| | | | - Barry J Dickson
- Janelia Research Campus, Howard Hughes Medical InstituteChevy ChaseUnited States
- Queensland Brain Institute, University of QueenslandBrisbaneAustralia
| | - Kristin Scott
- University of California, BerkeleyBerkeleyUnited States
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46
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Engert S, Sterne GR, Bock DD, Scott K. Drosophila gustatory projections are segregated by taste modality and connectivity. eLife 2022; 11:78110. [PMID: 35611959 PMCID: PMC9170244 DOI: 10.7554/elife.78110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 05/24/2022] [Indexed: 11/13/2022] Open
Abstract
Gustatory sensory neurons detect caloric and harmful compounds in potential food and convey this information to the brain to inform feeding decisions. To examine the signals that gustatory neurons transmit and receive, we reconstructed gustatory axons and their synaptic sites in the adult Drosophila melanogaster brain, utilizing a whole-brain electron microscopy volume. We reconstructed 87 gustatory projections from the proboscis labellum in the right hemisphere and 57 from the left, representing the majority of labellar gustatory axons. Gustatory neurons contain a nearly equal number of interspersed pre-and post-synaptic sites, with extensive synaptic connectivity among gustatory axons. Morphology- and connectivity-based clustering revealed six distinct groups, likely representing neurons recognizing different taste modalities. The vast majority of synaptic connections are between neurons of the same group. This study resolves the anatomy of labellar gustatory projections, reveals that gustatory projections are segregated based on taste modality, and uncovers synaptic connections that may alter the transmission of gustatory signals.
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Affiliation(s)
- Stefanie Engert
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, United States
| | - Gabriella R Sterne
- Department of Molecular and Cell Biology, University of California Berkeley, Berkeley, United States
| | - Davi D Bock
- Department of Neurological Sciences, University of Vermont, Burlington, United States
| | - Kristin Scott
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, United States
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47
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Yuan Y, Liu J, Zhao P, Wang W, Gu X, Rong Y, Lai T, Chen Y, Xin K, Niu X, Xiang F, Huo H, Li Z, Fang T. A Graph Network Model for Neural Connection Prediction and Connection Strength Estimation. J Neural Eng 2022; 19. [DOI: 10.1088/1741-2552/ac69bd] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Accepted: 04/23/2022] [Indexed: 11/11/2022]
Abstract
Abstract
Objective. Reconstruction of connectomes at the cellular scale is a prerequisite for understanding the principles of neural circuits. However, due to methodological limits, scientists have reconstructed the connectomes of only a few organisms such as C. elegans, and estimated synaptic strength indirectly according to their size and number. Approach. Here, we propose a graph network model to predict synaptic connections and estimate synaptic strength by using the calcium activity data from C. elegans. Main results. The results show that this model can reliably predict synaptic connections in the neural circuits of C. elegans, and estimate their synaptic strength, which is an intricate and comprehensive reflection of multiple factors such as synaptic type and size, neurotransmitter and receptor type, and even activity dependence. In addition, the excitability or inhibition of synapses can be identified by this model. We also found that chemical synaptic strength is almost linearly positively correlated to electrical synaptic strength, and the influence of one neuron on another is non-linearly correlated with the number between them. This reflects the intrinsic interaction between electrical and chemical synapses. Significance. Our model is expected to provide a more accessible quantitative and data-driven approach for the reconstruction of connectomes in more complex nervous systems, as well as a promising method for accurately estimating synaptic strength.
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Task D, Lin CC, Vulpe A, Afify A, Ballou S, Brbic M, Schlegel P, Raji J, Jefferis GSXE, Li H, Menuz K, Potter CJ. Chemoreceptor co-expression in Drosophila melanogaster olfactory neurons. eLife 2022; 11:e72599. [PMID: 35442190 PMCID: PMC9020824 DOI: 10.7554/elife.72599] [Citation(s) in RCA: 41] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 03/07/2022] [Indexed: 12/20/2022] Open
Abstract
Drosophila melanogaster olfactory neurons have long been thought to express only one chemosensory receptor gene family. There are two main olfactory receptor gene families in Drosophila, the odorant receptors (ORs) and the ionotropic receptors (IRs). The dozens of odorant-binding receptors in each family require at least one co-receptor gene in order to function: Orco for ORs, and Ir25a, Ir8a, and Ir76b for IRs. Using a new genetic knock-in strategy, we targeted the four co-receptors representing the main chemosensory families in D. melanogaster (Orco, Ir8a, Ir76b, Ir25a). Co-receptor knock-in expression patterns were verified as accurate representations of endogenous expression. We find extensive overlap in expression among the different co-receptors. As defined by innervation into antennal lobe glomeruli, Ir25a is broadly expressed in 88% of all olfactory sensory neuron classes and is co-expressed in 82% of Orco+ neuron classes, including all neuron classes in the maxillary palp. Orco, Ir8a, and Ir76b expression patterns are also more expansive than previously assumed. Single sensillum recordings from Orco-expressing Ir25a mutant antennal and palpal neurons identify changes in olfactory responses. We also find co-expression of Orco and Ir25a in Drosophila sechellia and Anopheles coluzzii olfactory neurons. These results suggest that co-expression of chemosensory receptors is common in insect olfactory neurons. Together, our data present the first comprehensive map of chemosensory co-receptor expression and reveal their unexpected widespread co-expression in the fly olfactory system.
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Affiliation(s)
- Darya Task
- The Solomon H. Snyder Department of Neuroscience, Center for Sensory Biology, Johns Hopkins University School of MedicineBaltimoreUnited States
| | - Chun-Chieh Lin
- The Solomon H. Snyder Department of Neuroscience, Center for Sensory Biology, Johns Hopkins University School of MedicineBaltimoreUnited States
- Mortimer B. Zuckermann Mind Brain Behavior Institute, Columbia UniversityNew YorkUnited States
| | - Alina Vulpe
- Physiology & Neurobiology Department, University of ConnecticutMansfieldUnited States
| | - Ali Afify
- The Solomon H. Snyder Department of Neuroscience, Center for Sensory Biology, Johns Hopkins University School of MedicineBaltimoreUnited States
| | - Sydney Ballou
- Physiology & Neurobiology Department, University of ConnecticutMansfieldUnited States
| | - Maria Brbic
- Department of Computer Science, Stanford UniversityStanfordUnited States
| | - Philipp Schlegel
- Drosophila Connectomics Group, Department of Zoology, University of CambridgeCambridgeUnited Kingdom
| | - Joshua Raji
- The Solomon H. Snyder Department of Neuroscience, Center for Sensory Biology, Johns Hopkins University School of MedicineBaltimoreUnited States
| | - Gregory SXE Jefferis
- Drosophila Connectomics Group, Department of Zoology, University of CambridgeCambridgeUnited Kingdom
- Neurobiology Division, MRC Laboratory of Molecular BiologyCambridgeUnited Kingdom
| | - Hongjie Li
- Department of Biology, Howard Hughes Medical Institute, Stanford UniversityStanfordUnited States
| | - Karen Menuz
- Physiology & Neurobiology Department, University of ConnecticutMansfieldUnited States
| | - Christopher J Potter
- The Solomon H. Snyder Department of Neuroscience, Center for Sensory Biology, Johns Hopkins University School of MedicineBaltimoreUnited States
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Connecting the dots in ethology: applying network theory to understand neural and animal collectives. Curr Opin Neurobiol 2022; 73:102532. [DOI: 10.1016/j.conb.2022.102532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2021] [Revised: 02/04/2022] [Accepted: 03/02/2022] [Indexed: 11/24/2022]
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Park C, Gim J, Lee S, Lee KJ, Kim JS. Automated Synapse Detection Method for Cerebellar Connectomics. Front Neuroanat 2022; 16:760279. [PMID: 35360651 PMCID: PMC8963724 DOI: 10.3389/fnana.2022.760279] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Accepted: 02/14/2022] [Indexed: 11/17/2022] Open
Abstract
The connectomic analyses of large-scale volumetric electron microscope (EM) images enable the discovery of hidden neural connectivity. While the technologies for neuronal reconstruction of EM images are under rapid progress, the technologies for synapse detection are lagging behind. Here, we propose a method that automatically detects the synapses in the 3D EM images, specifically for the mouse cerebellar molecular layer (CML). The method aims to accurately detect the synapses between the reconstructed neuronal fragments whose types can be identified. It extracts the contacts between the reconstructed neuronal fragments and classifies them as synaptic or non-synaptic with the help of type information and two deep learning artificial intelligences (AIs). The method can also assign the pre- and postsynaptic sides of a synapse and determine excitatory and inhibitory synapse types. The accuracy of this method is estimated to be 0.955 in F1-score for a test volume of CML containing 508 synapses. To demonstrate the usability, we measured the size and number of the synapses in the volume and investigated the subcellular connectivity between the CML neuronal fragments. The basic idea of the method to exploit tissue-specific properties can be extended to other brain regions.
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Affiliation(s)
- Changjoo Park
- Department of Biological Sciences, Sungkyunkwan University, Suwon-si, South Korea
- Laboratory of Computational Neuroscience, Korea Brain Research Institute, Daegu, South Korea
- Department of Brain and Cognitive Sciences, Daegu Gyeongbuk Institute of Science and Technology, Daegu, South Korea
| | - Jawon Gim
- Laboratory of Computational Neuroscience, Korea Brain Research Institute, Daegu, South Korea
- Laboratory of Synaptic Circuit Plasticity in Neural Circuits Research Group, Korea Brain Research Institute, Daegu, South Korea
| | - Sungjin Lee
- Department of Electrical Engineering and Computer Science, Daegu Gyeongbuk Institute of Science and Technology, Daegu, South Korea
| | - Kea Joo Lee
- Laboratory of Synaptic Circuit Plasticity in Neural Circuits Research Group, Korea Brain Research Institute, Daegu, South Korea
| | - Jinseop S. Kim
- Department of Biological Sciences, Sungkyunkwan University, Suwon-si, South Korea
- Laboratory of Computational Neuroscience, Korea Brain Research Institute, Daegu, South Korea
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