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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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Gattuso H, Nuñez K, de la Rea B, Ermentrout B, Victor J, Nagel K. Inhibitory control of locomotor statistics in walking Drosophila. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.15.589655. [PMID: 38659800 PMCID: PMC11042290 DOI: 10.1101/2024.04.15.589655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
In order to forage for food, many animals regulate not only specific limb movements but the statistics of locomotor behavior over time, for example switching between long-range dispersal behaviors and more localized search depending on the availability of resources. How pre-motor circuits regulate such locomotor statistics is not clear. Here we took advantage of the robust changes in locomotor statistics evoked by attractive odors in walking Drosophila to investigate their neural control. We began by analyzing the statistics of ground speed and angular velocity during three well-defined motor regimes: baseline walking, upwind running during odor, and search behavior following odor offset. We find that during search behavior, flies adopt higher angular velocities and slower ground speeds, and tend to turn for longer periods of time in one direction. We further find that flies spontaneously adopt periods of different mean ground speed, and that these changes in state influence the length of odor-evoked runs. We next developed a simple physiologically-inspired computational model of locomotor control that can recapitulate these statistical features of fly locomotion. Our model suggests that contralateral inhibition plays a key role both in regulating the difference between baseline and search behavior, and in modulating the response to odor with ground speed. As the fly connectome predicts decussating inhibitory neurons in the lateral accessory lobe (LAL), a pre-motor structure, we generated genetic tools to target these neurons and test their role in behavior. Consistent with our model, we found that activation of neurons labeled in one line increased curvature. In a second line labeling distinct neurons, activation and inactivation strongly and reciprocally regulated ground speed and altered the length of the odor-evoked run. Additional targeted light activation experiments argue that these effects arise from the brain rather than from neurons in the ventral nerve cord, while sparse activation experiments argue that speed control in the second line arises from both LAL neurons and a population of neurons in the dorsal superior medial protocerebrum (SMP). Together, our work develops a biologically plausible computational architecture that captures the statistical features of fly locomotion across behavioral states and identifies potential neural substrates of these computations.
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Affiliation(s)
- Hannah Gattuso
- Department of Neuroscience, NYU School of Medicine, 435 E 30 St. New York, NY 10016, USA
| | - Kavin Nuñez
- Department of Neuroscience, NYU School of Medicine, 435 E 30 St. New York, NY 10016, USA
| | - Beatriz de la Rea
- Department of Neuroscience, NYU School of Medicine, 435 E 30 St. New York, NY 10016, USA
| | - Bard Ermentrout
- Department of Mathematics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jonathan Victor
- Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
| | - Katherine Nagel
- Department of Neuroscience, NYU School of Medicine, 435 E 30 St. New York, NY 10016, USA
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Gautham AK, Miner LE, Franco MN, Thornquist SC, Crickmore MA. Molecular control of temporal integration matches decision-making to motivational state. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.01.582988. [PMID: 38496671 PMCID: PMC10942309 DOI: 10.1101/2024.03.01.582988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Motivations bias our responses to stimuli, producing behavioral outcomes that match our needs and goals. We describe a mechanism behind this phenomenon: adjusting the time over which stimulus-derived information is permitted to accumulate toward a decision. As a Drosophila copulation progresses, the male becomes less likely to continue mating through challenges. We show that a set of Copulation Decision Neurons (CDNs) flexibly integrates information about competing drives to mediate this decision. Early in mating, dopamine signaling restricts CDN integration time by potentiating CaMKII activation in response to stimulatory inputs, imposing a high threshold for changing behaviors. Later into mating, the timescale over which the CDNs integrate termination-promoting information expands, increasing the likelihood of switching behaviors. We suggest scalable windows of temporal integration at dedicated circuit nodes as a key but underappreciated variable in state-based decision-making.
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Affiliation(s)
- Aditya K. Gautham
- FM Kirby Neurobiology Center, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115
| | - Lauren E. Miner
- FM Kirby Neurobiology Center, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115
| | - Marco N. Franco
- FM Kirby Neurobiology Center, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115
| | | | - Michael A. Crickmore
- FM Kirby Neurobiology Center, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115
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Westeinde EA, Kellogg E, Dawson PM, Lu J, Hamburg L, Midler B, Druckmann S, Wilson RI. Transforming a head direction signal into a goal-oriented steering command. Nature 2024; 626:819-826. [PMID: 38326621 PMCID: PMC10881397 DOI: 10.1038/s41586-024-07039-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Accepted: 01/05/2024] [Indexed: 02/09/2024]
Abstract
To navigate, we must continuously estimate the direction we are headed in, and we must correct deviations from our goal1. Direction estimation is accomplished by ring attractor networks in the head direction system2,3. However, we do not fully understand how the sense of direction is used to guide action. Drosophila connectome analyses4,5 reveal three cell populations (PFL3R, PFL3L and PFL2) that connect the head direction system to the locomotor system. Here we use imaging, electrophysiology and chemogenetic stimulation during navigation to show how these populations function. Each population receives a shifted copy of the head direction vector, such that their three reference frames are shifted approximately 120° relative to each other. Each cell type then compares its own head direction vector with a common goal vector; specifically, it evaluates the congruence of these vectors via a nonlinear transformation. The output of all three cell populations is then combined to generate locomotor commands. PFL3R cells are recruited when the fly is oriented to the left of its goal, and their activity drives rightward turning; the reverse is true for PFL3L. Meanwhile, PFL2 cells increase steering speed, and are recruited when the fly is oriented far from its goal. PFL2 cells adaptively increase the strength of steering as directional error increases, effectively managing the tradeoff between speed and accuracy. Together, our results show how a map of space in the brain can be combined with an internal goal to generate action commands, via a transformation from world-centric coordinates to body-centric coordinates.
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Affiliation(s)
| | - Emily Kellogg
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Paul M Dawson
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Jenny Lu
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Lydia Hamburg
- Department of Neurobiology, Stanford University School of Medicine, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
| | - Benjamin Midler
- Department of Neurobiology, Stanford University School of Medicine, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
| | - Shaul Druckmann
- Department of Neurobiology, Stanford University School of Medicine, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
| | - Rachel I Wilson
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA.
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