1
|
Vohra SK, Harth P, Isoe Y, Bahl A, Fotowat H, Engert F, Hege HC, Baum D. A Visual Interface for Exploring Hypotheses About Neural Circuits. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2024; 30:3945-3958. [PMID: 37022819 PMCID: PMC11252567 DOI: 10.1109/tvcg.2023.3243668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
One of the fundamental problems in neurobiological research is to understand how neural circuits generate behaviors in response to sensory stimuli. Elucidating such neural circuits requires anatomical and functional information about the neurons that are active during the processing of the sensory information and generation of the respective response, as well as an identification of the connections between these neurons. With modern imaging techniques, both morphological properties of individual neurons as well as functional information related to sensory processing, information integration and behavior can be obtained. Given the resulting information, neurobiologists are faced with the task of identifying the anatomical structures down to individual neurons that are linked to the studied behavior and the processing of the respective sensory stimuli. Here, we present a novel interactive tool that assists neurobiologists in the aforementioned tasks by allowing them to extract hypothetical neural circuits constrained by anatomical and functional data. Our approach is based on two types of structural data: brain regions that are anatomically or functionally defined, and morphologies of individual neurons. Both types of structural data are interlinked and augmented with additional information. The presented tool allows the expert user to identify neurons using Boolean queries. The interactive formulation of these queries is supported by linked views, using, among other things, two novel 2D abstractions of neural circuits. The approach was validated in two case studies investigating the neural basis of vision-based behavioral responses in zebrafish larvae. Despite this particular application, we believe that the presented tool will be of general interest for exploring hypotheses about neural circuits in other species, genera and taxa.
Collapse
|
2
|
Smolin N, Dombrovski M, Hina BW, Moreno-Sanchez A, Gossart R, Carmona CR, Rehan A, Hussein RH, Mirshahidi P, Ausborn J, Kurmangaliyev YZ, von Reyn CR. Neuronal identity control at the resolution of a single transcription factor isoform. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.14.598883. [PMID: 38915533 PMCID: PMC11195191 DOI: 10.1101/2024.06.14.598883] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Abstract
The brain exhibits remarkable neuronal diversity which is critical for its functional integrity. From the sheer number of cell types emerging from extensive transcriptional, morphological, and connectome datasets, the question arises of how the brain is capable of generating so many unique identities. 'Terminal selectors' are transcription factors hypothesized to determine the final identity characteristics in post-mitotic cells. Which transcription factors function as terminal selectors and the level of control they exert over different terminal characteristics are not well defined. Here, we establish a novel role for the transcription factor broad as a terminal selector in Drosophila melanogaster. We capitalize on existing large sequencing and connectomics datasets and employ a comprehensive characterization of terminal characteristics including Perturb-seq and whole-cell electrophysiology. We find a single isoform broad-z4 serves as the switch between the identity of two visual projection neurons LPLC1 and LPLC2. Broad-z4 is natively expressed in LPLC1, and is capable of transforming the transcriptome, morphology, and functional connectivity of LPLC2 cells into LPLC1 cells when perturbed. Our comprehensive work establishes a single isoform as the smallest unit underlying an identity switch, which may serve as a conserved strategy replicated across developmental programs.
Collapse
Affiliation(s)
- Natalie Smolin
- School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA
| | - Mark Dombrovski
- Howard Hughes Medical Institute, Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles, CA
| | - Bryce W. Hina
- School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA
| | - Anthony Moreno-Sanchez
- Department of Neurobiology and Anatomy, Drexel University College of Medicine, Philadelphia, PA
| | - Ryan Gossart
- Brandeis University, Department of Biology, Waltham, MA
| | | | - Aadil Rehan
- Howard Hughes Medical Institute, Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles, CA
| | - Roni H. Hussein
- Howard Hughes Medical Institute, Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles, CA
| | - Parmis Mirshahidi
- Howard Hughes Medical Institute, Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles, CA
| | - Jessica Ausborn
- Department of Neurobiology and Anatomy, Drexel University College of Medicine, Philadelphia, PA
| | | | - Catherine R. von Reyn
- School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA
- Department of Neurobiology and Anatomy, Drexel University College of Medicine, Philadelphia, PA
| |
Collapse
|
3
|
Rind FC. Recent advances in insect vision in a 3D world: looming stimuli and escape behaviour. CURRENT OPINION IN INSECT SCIENCE 2024; 63:101180. [PMID: 38432555 DOI: 10.1016/j.cois.2024.101180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 02/13/2024] [Accepted: 02/19/2024] [Indexed: 03/05/2024]
Abstract
Detecting looming motion directly towards the insect is vital to its survival. Looming detection in two insects, flies and locusts, is described and contrasted. Pathways using looming detectors to trigger action and their topographical layout in the brain is explored in relation to facilitating behavioural selection. Similar visual stimuli, such as looming motion, are processed by nearby glomeruli in the brain. Insect-inspired looming motion detectors are combined to detect and avoid collision in different scenarios by robots, vehicles and unmanned aerial vehicle (UAV)s.
Collapse
Affiliation(s)
- F Claire Rind
- Newcastle University Biosciences Institute (NUBI), UK.
| |
Collapse
|
4
|
Nern A, Loesche F, Takemura SY, Burnett LE, Dreher M, Gruntman E, Hoeller J, Huang GB, Januszewski M, Klapoetke NC, Koskela S, Longden KD, Lu Z, Preibisch S, Qiu W, Rogers EM, Seenivasan P, Zhao A, Bogovic J, Canino BS, Clements J, Cook M, Finley-May S, Flynn MA, Hameed I, Fragniere AMC, Hayworth KJ, Hopkins GP, Hubbard PM, Katz WT, Kovalyak J, Lauchie SA, Leonard M, Lohff A, Maldonado CA, Mooney C, Okeoma N, Olbris DJ, Ordish C, Paterson T, Phillips EM, Pietzsch T, Salinas JR, Rivlin PK, Schlegel P, Scott AL, Scuderi LA, Takemura S, Talebi I, Thomson A, Trautman ET, Umayam L, Walsh C, Walsh JJ, Xu CS, Yakal EA, Yang T, Zhao T, Funke J, George R, Hess HF, Jefferis GSXE, Knecht C, Korff W, Plaza SM, Romani S, Saalfeld S, Scheffer LK, Berg S, Rubin GM, Reiser MB. Connectome-driven neural inventory of a complete visual system. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.16.589741. [PMID: 38659887 PMCID: PMC11042306 DOI: 10.1101/2024.04.16.589741] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
Vision provides animals with detailed information about their surroundings, conveying diverse features such as color, form, and movement across the visual scene. Computing these parallel spatial features requires a large and diverse network of neurons, such that in animals as distant as flies and humans, visual regions comprise half the brain's volume. These visual brain regions often reveal remarkable structure-function relationships, with neurons organized along spatial maps with shapes that directly relate to their roles in visual processing. To unravel the stunning diversity of a complex visual system, a careful mapping of the neural architecture matched to tools for targeted exploration of that circuitry is essential. Here, we report a new connectome of the right optic lobe from a male Drosophila central nervous system FIB-SEM volume and a comprehensive inventory of the fly's visual neurons. We developed a computational framework to quantify the anatomy of visual neurons, establishing a basis for interpreting how their shapes relate to spatial vision. By integrating this analysis with connectivity information, neurotransmitter identity, and expert curation, we classified the ~53,000 neurons into 727 types, about half of which are systematically described and named for the first time. Finally, we share an extensive collection of split-GAL4 lines matched to our neuron type catalog. Together, this comprehensive set of tools and data unlock new possibilities for systematic investigations of vision in Drosophila, a foundation for a deeper understanding of sensory processing.
Collapse
Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Alexandra MC Fragniere
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Philipp Schlegel
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Gregory SXE Jefferis
- MRC Laboratory of Molecular Biology, Cambridge, UK and Department of Zoology, University of Cambridge, UK
| | | | | | | | | | | | | | | | | | | |
Collapse
|
5
|
Puri P, Wu ST, Su CY, Aljadeff J. Peripheral preprocessing in Drosophila facilitates odor classification. Proc Natl Acad Sci U S A 2024; 121:e2316799121. [PMID: 38753511 PMCID: PMC11126917 DOI: 10.1073/pnas.2316799121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Accepted: 04/16/2024] [Indexed: 05/18/2024] Open
Abstract
The mammalian brain implements sophisticated sensory processing algorithms along multilayered ("deep") neural networks. Strategies that insects use to meet similar computational demands, while relying on smaller nervous systems with shallow architectures, remain elusive. Using Drosophila as a model, we uncover the algorithmic role of odor preprocessing by a shallow network of compartmentalized olfactory receptor neurons. Each compartment operates as a ratiometric unit for specific odor-mixtures. This computation arises from a simple mechanism: electrical coupling between two differently sized neurons. We demonstrate that downstream synaptic connectivity is shaped to optimally leverage amplification of a hedonic value signal in the periphery. Furthermore, peripheral preprocessing is shown to markedly improve novel odor classification in a higher brain center. Together, our work highlights a far-reaching functional role of the sensory periphery for downstream processing. By elucidating the implementation of powerful computations by a shallow network, we provide insights into general principles of efficient sensory processing algorithms.
Collapse
Affiliation(s)
- Palka Puri
- Department of Physics, University of California, San Diego, La Jolla, CA92093
| | - Shiuan-Tze Wu
- Department of Neurobiology, University of California, San Diego, La Jolla, CA92093
| | - Chih-Ying Su
- Department of Neurobiology, University of California, San Diego, La Jolla, CA92093
| | - Johnatan Aljadeff
- Department of Neurobiology, University of California, San Diego, La Jolla, CA92093
| |
Collapse
|
6
|
Nguyen TH, Vicidomini R, Choudhury SD, Han TH, Maric D, Brody T, Serpe M. scRNA-seq data from the larval Drosophila ventral cord provides a resource for studying motor systems function and development. Dev Cell 2024; 59:1210-1230.e9. [PMID: 38569548 PMCID: PMC11078614 DOI: 10.1016/j.devcel.2024.03.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 12/05/2023] [Accepted: 03/06/2024] [Indexed: 04/05/2024]
Abstract
The Drosophila larval ventral nerve cord (VNC) shares many similarities with the spinal cord of vertebrates and has emerged as a major model for understanding the development and function of motor systems. Here, we use high-quality scRNA-seq, validated by anatomical identification, to create a comprehensive census of larval VNC cell types. We show that the neural lineages that comprise the adult VNC are already defined, but quiescent, at the larval stage. Using fluorescence-activated cell sorting (FACS)-enriched populations, we separate all motor neuron bundles and link individual neuron clusters to morphologically characterized known subtypes. We discovered a glutamate receptor subunit required for basal neurotransmission and homeostasis at the larval neuromuscular junction. We describe larval glia and endorse the general view that glia perform consistent activities throughout development. This census represents an extensive resource and a powerful platform for future discoveries of cellular and molecular mechanisms in repair, regeneration, plasticity, homeostasis, and behavioral coordination.
Collapse
Affiliation(s)
| | | | | | | | - Dragan Maric
- Flow and Imaging Cytometry Core, NINDS, NIH, Bethesda, MD 20892, USA
| | | | | |
Collapse
|
7
|
Schmidt M, Motta A, Sievers M, Helmstaedter M. RoboEM: automated 3D flight tracing for synaptic-resolution connectomics. Nat Methods 2024; 21:908-913. [PMID: 38514779 PMCID: PMC11093750 DOI: 10.1038/s41592-024-02226-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 02/26/2024] [Indexed: 03/23/2024]
Abstract
Mapping neuronal networks from three-dimensional electron microscopy (3D-EM) data still poses substantial reconstruction challenges, in particular for thin axons. Currently available automated image segmentation methods require manual proofreading for many types of connectomic analysis. Here we introduce RoboEM, an artificial intelligence-based self-steering 3D 'flight' system trained to navigate along neurites using only 3D-EM data as input. Applied to 3D-EM data from mouse and human cortex, RoboEM substantially improves automated state-of-the-art segmentations and can replace manual proofreading for more complex connectomic analysis problems, yielding computational annotation cost for cortical connectomes about 400-fold lower than the cost of manual error correction.
Collapse
Affiliation(s)
- Martin Schmidt
- Department of Connectomics, Max Planck Institute for Brain Research, Frankfurt, Germany.
| | - Alessandro Motta
- Department of Connectomics, Max Planck Institute for Brain Research, Frankfurt, Germany
| | - Meike Sievers
- Department of Connectomics, Max Planck Institute for Brain Research, Frankfurt, Germany
- Faculty of Science, Radboud University, Nijmegen, the Netherlands
| | - Moritz Helmstaedter
- Department of Connectomics, Max Planck Institute for Brain Research, Frankfurt, Germany.
| |
Collapse
|
8
|
Pang MM, Chen F, Xie M, Druckmann S, Clandinin TR, Yang HH. A recurrent neural circuit in Drosophila deblurs visual inputs. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.19.590352. [PMID: 38712245 PMCID: PMC11071408 DOI: 10.1101/2024.04.19.590352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
A critical goal of vision is to detect changes in light intensity, even when these changes are blurred by the spatial resolution of the eye and the motion of the animal. Here we describe a recurrent neural circuit in Drosophila that compensates for blur and thereby selectively enhances the perceived contrast of moving edges. Using in vivo , two-photon voltage imaging, we measured the temporal response properties of L1 and L2, two cell types that receive direct synaptic input from photoreceptors. These neurons have biphasic responses to brief flashes of light, a hallmark of cells that encode changes in stimulus intensity. However, the second phase was often much larger than the first, creating an unusual temporal filter. Genetic dissection revealed that recurrent neural circuitry strongly shapes the second phase of the response, informing the structure of a dynamical model. By applying this model to moving natural images, we demonstrate that rather than veridically representing stimulus changes, this temporal processing strategy systematically enhances them, amplifying and sharpening responses. Comparing the measured responses of L2 to model predictions across both artificial and natural stimuli revealed that L2 tunes its properties as the model predicts in order to deblur images. Since this strategy is tunable to behavioral context, generalizable to any time-varying sensory input, and implementable with a common circuit motif, we propose that it could be broadly used to selectively enhance sharp and salient changes.
Collapse
|
9
|
Matsliah A, Yu SC, Kruk K, Bland D, Burke A, Gager J, Hebditch J, Silverman B, Willie K, Willie RW, Sorek M, Sterling AR, Kind E, Garner D, Sancer G, Wernet MF, Kim SS, Murthy M, Seung HS. Neuronal "parts list" and wiring diagram for a visual system. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.10.12.562119. [PMID: 37873160 PMCID: PMC10592826 DOI: 10.1101/2023.10.12.562119] [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
A catalog of neuronal cell types has often been called a "parts list" of the brain, and regarded as a prerequisite for understanding brain function. In the optic lobe of Drosophila, rules of connectivity between cell types have already proven essential for understanding fly vision. Here we analyze the fly connectome to complete the list of cell types intrinsic to the optic lobe, as well as the rules governing their connectivity. We more than double the list of known types. Most new cell types contain between 10 and 100 cells, and integrate information over medium distances in the visual field. Some existing type families (Tm, Li, and LPi) at least double in number of types. We introduce a new Sm interneuron family, which contains more types than any other, and three new families of cross-neuropil types. Self-consistency of cell types is demonstrated through automatic assignment of cells to types by distance in high-dimensional feature space, and further validation is provided by algorithms that select small subsets of discriminative features. Cell types with similar connectivity patterns divide into clusters that are interpretable in terms of motion, object, and color vision. Our work showcases the advantages of connectomic cell typing: complete and unbiased sampling, a rich array of features based on connectivity, and reduction of the connectome to a drastically simpler wiring diagram of cell types, with immediate relevance for brain function and development.
Collapse
Affiliation(s)
| | - Szi-Chieh Yu
- Neuroscience Institute, Princeton University, USA
| | | | - Doug Bland
- Neuroscience Institute, Princeton University, USA
| | - Austin Burke
- Neuroscience Institute, Princeton University, USA
| | - Jay Gager
- Neuroscience Institute, Princeton University, USA
| | | | | | - Kyle Willie
- Neuroscience Institute, Princeton University, USA
| | | | | | | | - Emil Kind
- Institut für Biologie - Neurobiologie, Freie Universität B erlin, Germany
| | - Dustin Garner
- Molecular, Cellular, and Developmental Biology, Univ. C alifornia Santa Barbara, USA
| | - Gizem Sancer
- Institut für Biologie - Neurobiologie, Freie Universität B erlin, Germany
| | - Mathias F Wernet
- Institut für Biologie - Neurobiologie, Freie Universität B erlin, Germany
| | - Sung Soo Kim
- Molecular, Cellular, and Developmental Biology, Univ. C alifornia Santa Barbara, USA
| | - Mala Murthy
- Neuroscience Institute, Princeton University, USA
| | - H Sebastian Seung
- Neuroscience Institute, Princeton University, USA
- Computer Science Department, Princeton University, U SA
| |
Collapse
|
10
|
Gowda SBM, Banu A, Hussain S, Mohammad F. Neuronal mechanisms regulating locomotion in adult Drosophila. J Neurosci Res 2024; 102:e25332. [PMID: 38646942 DOI: 10.1002/jnr.25332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Revised: 04/01/2024] [Accepted: 04/03/2024] [Indexed: 04/25/2024]
Abstract
The coordinated action of multiple leg joints and muscles is required even for the simplest movements. Understanding the neuronal circuits and mechanisms that generate precise movements is essential for comprehending the neuronal basis of the locomotion and to infer the neuronal mechanisms underlying several locomotor-related diseases. Drosophila melanogaster provides an excellent model system for investigating the neuronal circuits underlying motor behaviors due to its simple nervous system and genetic accessibility. This review discusses current genetic methods for studying locomotor circuits and their function in adult Drosophila. We highlight recently identified neuronal pathways that modulate distinct forward and backward locomotion and describe the underlying neuronal control of leg swing and stance phases in freely moving flies. We also report various automated leg tracking methods to measure leg motion parameters and define inter-leg coordination, gait and locomotor speed of freely moving adult flies. Finally, we emphasize the role of leg proprioceptive signals to central motor circuits in leg coordination. Together, this review highlights the utility of adult Drosophila as a model to uncover underlying motor circuitry and the functional organization of the leg motor system that governs correct movement.
Collapse
Affiliation(s)
- Swetha B M Gowda
- Division of Biological and Biomedical Sciences (BBS), College of Health and Life Sciences (CHLS), Hamad Bin Khalifa University (HBKU), Doha, Qatar
| | - Ayesha Banu
- Division of Biological and Biomedical Sciences (BBS), College of Health and Life Sciences (CHLS), Hamad Bin Khalifa University (HBKU), Doha, Qatar
| | - Sadam Hussain
- Division of Biological and Biomedical Sciences (BBS), College of Health and Life Sciences (CHLS), Hamad Bin Khalifa University (HBKU), Doha, Qatar
| | - Farhan Mohammad
- Division of Biological and Biomedical Sciences (BBS), College of Health and Life Sciences (CHLS), Hamad Bin Khalifa University (HBKU), Doha, Qatar
| |
Collapse
|
11
|
Sanfilippo P, Kim AJ, Bhukel A, Yoo J, Mirshahidi PS, Pandey V, Bevir H, Yuen A, Mirshahidi PS, Guo P, Li HS, Wohlschlegel JA, Aso Y, Zipursky SL. Mapping of multiple neurotransmitter receptor subtypes and distinct protein complexes to the connectome. Neuron 2024; 112:942-958.e13. [PMID: 38262414 PMCID: PMC10957333 DOI: 10.1016/j.neuron.2023.12.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 12/03/2023] [Accepted: 12/20/2023] [Indexed: 01/25/2024]
Abstract
Neurons express various combinations of neurotransmitter receptor (NR) subunits and receive inputs from multiple neuron types expressing different neurotransmitters. Localizing NR subunits to specific synaptic inputs has been challenging. Here, we use epitope-tagged endogenous NR subunits, expansion light-sheet microscopy, and electron microscopy (EM) connectomics to molecularly characterize synapses in Drosophila. We show that in directionally selective motion-sensitive neurons, different multiple NRs elaborated a highly stereotyped molecular topography with NR localized to specific domains receiving cell-type-specific inputs. Developmental studies suggested that NRs or complexes of them with other membrane proteins determine patterns of synaptic inputs. In support of this model, we identify a transmembrane protein selectively associated with a subset of spatially restricted synapses and demonstrate its requirement for synapse formation through genetic analysis. We propose that mechanisms that regulate the precise spatial distribution of NRs provide a molecular cartography specifying the patterns of synaptic connections onto dendrites.
Collapse
Affiliation(s)
- Piero Sanfilippo
- Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Howard Hughes Medical Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Alexander J Kim
- Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Anuradha Bhukel
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Juyoun Yoo
- Neuroscience Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Pegah S Mirshahidi
- Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Vijaya Pandey
- Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Harry Bevir
- Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Ashley Yuen
- Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Parmis S Mirshahidi
- Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Peiyi Guo
- Department of Neurobiology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA
| | - Hong-Sheng Li
- Department of Neurobiology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA
| | - James A Wohlschlegel
- Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Yoshinori Aso
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - S Lawrence Zipursky
- Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Howard Hughes Medical Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA.
| |
Collapse
|
12
|
Clements J, Goina C, Hubbard PM, Kawase T, Olbris DJ, Otsuna H, Svirskas R, Rokicki K. NeuronBridge: an intuitive web application for neuronal morphology search across large data sets. BMC Bioinformatics 2024; 25:114. [PMID: 38491365 PMCID: PMC10943809 DOI: 10.1186/s12859-024-05732-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 03/06/2024] [Indexed: 03/18/2024] Open
Abstract
BACKGROUND Neuroscience research in Drosophila is benefiting from large-scale connectomics efforts using electron microscopy (EM) to reveal all the neurons in a brain and their connections. To exploit this knowledge base, researchers relate a connectome's structure to neuronal function, often by studying individual neuron cell types. Vast libraries of fly driver lines expressing fluorescent reporter genes in sets of neurons have been created and imaged using confocal light microscopy (LM), enabling the targeting of neurons for experimentation. However, creating a fly line for driving gene expression within a single neuron found in an EM connectome remains a challenge, as it typically requires identifying a pair of driver lines where only the neuron of interest is expressed in both. This task and other emerging scientific workflows require finding similar neurons across large data sets imaged using different modalities. RESULTS Here, we present NeuronBridge, a web application for easily and rapidly finding putative morphological matches between large data sets of neurons imaged using different modalities. We describe the functionality and construction of the NeuronBridge service, including its user-friendly graphical user interface (GUI), extensible data model, serverless cloud architecture, and massively parallel image search engine. CONCLUSIONS NeuronBridge fills a critical gap in the Drosophila research workflow and is used by hundreds of neuroscience researchers around the world. We offer our software code, open APIs, and processed data sets for integration and reuse, and provide the application as a service at http://neuronbridge.janelia.org .
Collapse
Affiliation(s)
- Jody Clements
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, USA
| | - Cristian Goina
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, USA
| | - Philip M Hubbard
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, USA
| | - Takashi Kawase
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, USA
| | - Donald J Olbris
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, USA
| | - Hideo Otsuna
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, USA
| | - Robert Svirskas
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, USA
| | - Konrad Rokicki
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, USA.
| |
Collapse
|
13
|
Cardona A. Wiring up for controlled flight. eLife 2024; 13:e95989. [PMID: 38436656 PMCID: PMC10911749 DOI: 10.7554/elife.95989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/05/2024] Open
Abstract
A map showing how neurons that process motion are wired together in the visual system of fruit flies provides new insights into how animals navigate and remain stable when flying.
Collapse
Affiliation(s)
- Albert Cardona
- MRC Laboratory of Molecular Biology, University of CambridgeCambridgeUnited Kingdom
- Department of Physiology, Development and Neuroscience, University of CambridgeCambridgeUnited Kingdom
| |
Collapse
|
14
|
Zhang Y, Li X. Development of the Drosophila Optic Lobe. Cold Spring Harb Protoc 2024; 2024:108156. [PMID: 37758285 DOI: 10.1101/pdb.top108156] [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: 10/03/2023]
Abstract
The Drosophila visual system has been a great model to study fundamental questions in neurobiology, such as neural fate specification, axon guidance, circuit formation, and information processing. The Drosophila visual system is composed of the compound eye and the optic lobe. The optic lobe is divided into four neuropils-namely, the lamina, medulla, lobula, and lobula plate. There are around 200 types of optic lobe neurons, which wire together to form a complex neural structure to processes visual information. These neurons are derived from two neuroepithelial structures-namely, the outer proliferation center (OPC) and the inner proliferation center (IPC), in the larval brain. Recent work on the Drosophila optic lobe has revealed basic principles underlying the development of this complex neural structure, and immunostaining has been a key tool in these studies. Here, we provide a brief overview of the Drosophila optic lobe structure and development, as revealed by immunostaining. First, we introduce the structure of the adult optic lobe. Then, we summarize recent advances in the study of neural fate specification during development of different parts of the optic lobe. Last, we briefly summarize general aspects of axon guidance and neuropil assembly in the optic lobe. With this review, we aim to familiarize readers with this complex neural structure and highlight the power of this great model to study neural development to facilitate further developmental and functional studies using this system.
Collapse
Affiliation(s)
- Yu Zhang
- Department of Cell and Developmental Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
| | - Xin Li
- Department of Cell and Developmental Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
| |
Collapse
|
15
|
Castaneda AN, Huda A, Whitaker IBM, Reilly JE, Shelby GS, Bai H, Ni L. Functional labeling of individualized postsynaptic neurons using optogenetics and trans-Tango in Drosophila (FLIPSOT). PLoS Genet 2024; 20:e1011190. [PMID: 38483970 PMCID: PMC10965055 DOI: 10.1371/journal.pgen.1011190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 03/26/2024] [Accepted: 02/20/2024] [Indexed: 03/27/2024] Open
Abstract
A population of neurons interconnected by synapses constitutes a neural circuit, which performs specific functions upon activation. It is essential to identify both anatomical and functional entities of neural circuits to comprehend the components and processes necessary for healthy brain function and the changes that characterize brain disorders. To date, few methods are available to study these two aspects of a neural circuit simultaneously. In this study, we developed FLIPSOT, or functional labeling of individualized postsynaptic neurons using optogenetics and trans-Tango. FLIPSOT uses (1) trans-Tango to access postsynaptic neurons genetically, (2) optogenetic approaches to activate (FLIPSOTa) or inhibit (FLIPSOTi) postsynaptic neurons in a random and sparse manner, and (3) fluorescence markers tagged with optogenetic genes to visualize these neurons. Therefore, FLIPSOT allows using a presynaptic driver to identify the behavioral function of individual postsynaptic neurons. It is readily applied to identify functions of individual postsynaptic neurons and has the potential to be adapted for use in mammalian circuits.
Collapse
Affiliation(s)
- Allison N. Castaneda
- School of Neuroscience, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, United States of America
| | - Ainul Huda
- School of Neuroscience, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, United States of America
| | - Iona B. M. Whitaker
- School of Neuroscience, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, United States of America
| | - Julianne E. Reilly
- School of Neuroscience, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, United States of America
| | - Grace S. Shelby
- School of Neuroscience, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, United States of America
| | - Hua Bai
- School of Neuroscience, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, United States of America
| | - Lina Ni
- School of Neuroscience, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, United States of America
| |
Collapse
|
16
|
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.
Collapse
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
| |
Collapse
|
17
|
Osaka J, Ishii A, Wang X, Iwanaga R, Kawamura H, Akino S, Sugie A, Hakeda-Suzuki S, Suzuki T. Complex formation of immunoglobulin superfamily molecules Side-IV and Beat-IIb regulates synaptic specificity. Cell Rep 2024; 43:113798. [PMID: 38381608 DOI: 10.1016/j.celrep.2024.113798] [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: 06/29/2023] [Revised: 08/03/2023] [Accepted: 01/31/2024] [Indexed: 02/23/2024] Open
Abstract
Neurons establish specific synapses based on the adhesive properties of cell-surface proteins while also retaining the ability to form synapses in a relatively non-selective manner. However, comprehensive understanding of the underlying mechanism reconciling these opposing characteristics remains incomplete. Here, we have identified Side-IV/Beat-IIb, members of the Drosophila immunoglobulin superfamily, as a combination of cell-surface recognition molecules inducing synapse formation. The Side-IV/Beat-IIb combination transduces bifurcated signaling with Side-IV's co-receptor, Kirre, and a synaptic scaffold protein, Dsyd-1. Genetic experiments and subcellular protein localization analyses showed the Side-IV/Beat-IIb/Kirre/Dsyd-1 complex to have two essential functions. First, it narrows neuronal binding specificity through Side-IV/Beat-IIb extracellular interactions. Second, it recruits synapse formation factors, Kirre and Dsyd-1, to restrict synaptic loci and inhibit miswiring. This dual function explains how the combinations of cell-surface molecules enable the ranking of preferred interactions among neuronal pairs to achieve synaptic specificity in complex circuits in vivo.
Collapse
Affiliation(s)
- Jiro Osaka
- School of Life Science and Technology, Tokyo Institute of Technology, Yokohama 226-8501, Japan; Brain Research Institute, Niigata University, Niigata 951-8585, Japan
| | - Arisa Ishii
- School of Life Science and Technology, Tokyo Institute of Technology, Yokohama 226-8501, Japan
| | - Xu Wang
- School of Life Science and Technology, Tokyo Institute of Technology, Yokohama 226-8501, Japan
| | - Riku Iwanaga
- School of Life Science and Technology, Tokyo Institute of Technology, Yokohama 226-8501, Japan
| | - Hinata Kawamura
- School of Life Science and Technology, Tokyo Institute of Technology, Yokohama 226-8501, Japan
| | - Shogo Akino
- School of Life Science and Technology, Tokyo Institute of Technology, Yokohama 226-8501, Japan
| | - Atsushi Sugie
- Brain Research Institute, Niigata University, Niigata 951-8585, Japan
| | - Satoko Hakeda-Suzuki
- School of Life Science and Technology, Tokyo Institute of Technology, Yokohama 226-8501, Japan; Research Initiatives and Promotion Organization, Yokohama National University, Yokohama 240-8501, Japan
| | - Takashi Suzuki
- School of Life Science and Technology, Tokyo Institute of Technology, Yokohama 226-8501, Japan.
| |
Collapse
|
18
|
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.
Collapse
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.
| |
Collapse
|
19
|
Tegethoff L, Briggman KL. Quantitative evaluation of embedding resins for volume electron microscopy. Front Neurosci 2024; 18:1286991. [PMID: 38406585 PMCID: PMC10884229 DOI: 10.3389/fnins.2024.1286991] [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: 09/01/2023] [Accepted: 01/29/2024] [Indexed: 02/27/2024] Open
Abstract
Optimal epoxy resin embedding is crucial for obtaining consistent serial sections from large tissue samples, especially for block faces spanning >1 mm2. We report a method to quantify non-uniformity in resin curing using block hardness measurements from block faces. We identify conditions that lead to non-uniform curing as well as a procedure to monitor the hardness of blocks for a wide range of common epoxy resins used for volume electron microscopy. We also assess cutting repeatability and uniformity by quantifying the transverse and sectional cutting forces during ultrathin sectioning using a sample-mounted force sensor. Our findings indicate that screening and optimizing resin formulations is required to achieve the best repeatability in terms of section thickness. Finally, we explore the encapsulation of irregularly shaped tissue samples in a gelatin matrix prior to epoxy resin embedding to yield more uniform sections.
Collapse
Affiliation(s)
| | - Kevin L. Briggman
- Max Planck Institute for Neurobiology of Behavior - caesar, Bonn, Germany
| |
Collapse
|
20
|
Simon F, Holguera I, Chen YC, Malin J, Valentino P, Erclik T, Desplan C. High-throughput identification of the spatial origins of Drosophila optic lobe neurons using single-cell mRNA-sequencing. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.05.578975. [PMID: 38370610 PMCID: PMC10871188 DOI: 10.1101/2024.02.05.578975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
The medulla is the largest neuropil of the Drosophila optic lobe. It contains about 100 neuronal types that have been comprehensively characterized morphologically and molecularly. These neuronal types are specified from a larval neuroepithelium called the Outer Proliferation Center (OPC) via the integration of temporal, spatial, and Notch-driven mechanisms. Although we recently characterized the temporal windows of origin of all medulla neurons, as well as their Notch status, their spatial origins remained unknown. Here, we isolated cells from different OPC spatial domains and performed single-cell mRNA-sequencing to identify the neuronal types produced in these domains. This allowed us to characterize in a high-throughput manner the spatial origins of all medulla neurons and to identify two new spatial subdivisions of the OPC. Moreover, our work shows that the most abundant neuronal types are produced from epithelial domains of different sizes despite being present in a similar number of copies. Combined with our previously published scRNA-seq developmental atlas of the optic lobe, our work opens the door for further studies on how specification factor expression in progenitors impacts gene expression in developing and adult neurons.
Collapse
Affiliation(s)
- Félix Simon
- Department of Biology, New York University, New York, NY 10003, USA
| | - Isabel Holguera
- Department of Biology, New York University, New York, NY 10003, USA
| | - Yen-Chung Chen
- Department of Biology, New York University, New York, NY 10003, USA
| | - Jennifer Malin
- Department of Biology, New York University, New York, NY 10003, USA
| | - Priscilla Valentino
- Department of Biology, University of Toronto Mississauga, Mississauga, ON L5L 1C6, Canada
- Department of Cell and Systems Biology, University of Toronto, Toronto, ON M5S 1A1, Canada
| | - Ted Erclik
- Department of Biology, University of Toronto Mississauga, Mississauga, ON L5L 1C6, Canada
- Department of Cell and Systems Biology, University of Toronto, Toronto, ON M5S 1A1, Canada
| | - Claude Desplan
- Department of Biology, New York University, New York, NY 10003, USA
| |
Collapse
|
21
|
Wakita D, Shibasaki H, Kinoshita M, Arikawa K. Morphology and spectral sensitivity of long visual fibers and lamina monopolar cells in the butterfly Papilio xuthus. J Comp Neurol 2024; 532:e25579. [PMID: 38204156 DOI: 10.1002/cne.25579] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 10/16/2023] [Accepted: 12/17/2023] [Indexed: 01/12/2024]
Abstract
Extensive analysis of the flower-visiting behavior of a butterfly, Papilio xuthus, has indicated complex interaction between chromatic, achromatic, and motion cues. Their eyes are spectrally rich with six classes of photoreceptors, respectively sensitive in the ultraviolet, violet, blue, green, red, and broad-band wavelength regions. Here, we studied the anatomy and physiology of photoreceptors and second-order neurons of P. xuthus, focusing on their spectral sensitivities and projection terminals to address where the early visual integration takes place. We thus found the ultraviolet, violet, and blue photoreceptors and all second-order neurons terminate in the distal region of the second optic ganglion, the medulla. We identified five types of second-order neurons based on the arborization in the first optic ganglion, the lamina, and the shape of the medulla terminals. Their spectral sensitivity is independent of the morphological types but reflects the combination of pre-synaptic photoreceptors. The results indicate that the distal medulla is the most plausible region for early visual integration.
Collapse
Affiliation(s)
- Daiki Wakita
- Laboratory of Neuroethology, Research Center for Integrative Evolutionary Science, SOKENDAI, Hayama, Japan
| | - Hiromichi Shibasaki
- Laboratory of Neuroethology, Research Center for Integrative Evolutionary Science, SOKENDAI, Hayama, Japan
| | - Michiyo Kinoshita
- Laboratory of Neuroethology, Research Center for Integrative Evolutionary Science, SOKENDAI, Hayama, Japan
| | - Kentaro Arikawa
- Laboratory of Neuroethology, Research Center for Integrative Evolutionary Science, SOKENDAI, Hayama, Japan
| |
Collapse
|
22
|
Patel D, Shetty S, Acha C, Pantoja IEM, Zhao A, George D, Gracias DH. Microinstrumentation for Brain Organoids. Adv Healthc Mater 2024:e2302456. [PMID: 38217546 DOI: 10.1002/adhm.202302456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Revised: 12/10/2023] [Indexed: 01/15/2024]
Abstract
Brain organoids are three-dimensional aggregates of self-organized differentiated stem cells that mimic the structure and function of human brain regions. Organoids bridge the gaps between conventional drug screening models such as planar mammalian cell culture, animal studies, and clinical trials. They can revolutionize the fields of developmental biology, neuroscience, toxicology, and computer engineering. Conventional microinstrumentation for conventional cellular engineering, such as planar microfluidic chips; microelectrode arrays (MEAs); and optical, magnetic, and acoustic techniques, has limitations when applied to three-dimensional (3D) organoids, primarily due to their limits with inherently two-dimensional geometry and interfacing. Hence, there is an urgent need to develop new instrumentation compatible with live cell culture techniques and with scalable 3D formats relevant to organoids. This review discusses conventional planar approaches and emerging 3D microinstrumentation necessary for advanced organoid-machine interfaces. Specifically, this article surveys recently developed microinstrumentation, including 3D printed and curved microfluidics, 3D and fast-scan optical techniques, buckling and self-folding MEAs, 3D interfaces for electrochemical measurements, and 3D spatially controllable magnetic and acoustic technologies relevant to two-way information transfer with brain organoids. This article highlights key challenges that must be addressed for robust organoid culture and reliable 3D spatiotemporal information transfer.
Collapse
Affiliation(s)
- Devan Patel
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Saniya Shetty
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Chris Acha
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Itzy E Morales Pantoja
- Center for Alternatives to Animal Testing (CAAT), Department of Environmental Health and Engineering, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, 21205, USA
| | - Alice Zhao
- Department of Biology, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Derosh George
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - David H Gracias
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
- Department of Chemistry, Johns Hopkins University, Baltimore, MD, 21218, USA
- Department of Materials Science and Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
- Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA
- Laboratory for Computational Sensing and Robotics (LCSR), Johns Hopkins University, Baltimore, MD, 21218, USA
- Sidney Kimmel Comprehensive Cancer Center (SKCCC), Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
- Center for MicroPhysiological Systems (MPS), Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
| |
Collapse
|
23
|
Randel N, Jékely G. Comparative connectomics: Wiring diagram of a miniature insect compound eye. Curr Biol 2023; 33:R1226-R1228. [PMID: 38052170 DOI: 10.1016/j.cub.2023.10.031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/07/2023]
Abstract
A new connectomics study of the compound eye of the miniature wasp Megaphragma viggianii highlights how the study of small animals in a comparative framework can give fresh insights into circuit evolution and function.
Collapse
Affiliation(s)
- Nadine Randel
- Department of Zoology, University of Cambridge, Cambridge CB2 3EJ, UK.
| | - Gáspár Jékely
- Centre for Organismal Studies (COS), Heidelberg University, 69120 Heidelberg, Germany.
| |
Collapse
|
24
|
Lazar AA, Zhou Y. Divisive normalization processors in the early visual system of the Drosophila brain. BIOLOGICAL CYBERNETICS 2023; 117:411-431. [PMID: 37702831 PMCID: PMC10752861 DOI: 10.1007/s00422-023-00972-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Accepted: 08/06/2023] [Indexed: 09/14/2023]
Abstract
Divisive normalization is a model of canonical computation of brain circuits. We demonstrate that two cascaded divisive normalization processors (DNPs), carrying out intensity/contrast gain control and elementary motion detection, respectively, can model the robust motion detection realized by the early visual system of the fruit fly. We first introduce a model of elementary motion detection and rewrite its underlying phase-based motion detection algorithm as a feedforward divisive normalization processor. We then cascade the DNP modeling the photoreceptor/amacrine cell layer with the motion detection DNP. We extensively evaluate the DNP for motion detection in dynamic environments where light intensity varies by orders of magnitude. The results are compared to other bio-inspired motion detectors as well as state-of-the-art optic flow algorithms under natural conditions. Our results demonstrate the potential of DNPs as canonical building blocks modeling the analog processing of early visual systems. The model highlights analog processing for accurately detecting visual motion, in both vertebrates and invertebrates. The results presented here shed new light on employing DNP-based algorithms in computer vision.
Collapse
Affiliation(s)
- Aurel A Lazar
- Department of Electrical Engineering, Columbia University, New York, NY, 10027, USA.
| | - Yiyin Zhou
- Department of Electrical Engineering, Columbia University, New York, NY, 10027, USA
- Department of Computer and Information Science, Fordham University, New York, NY, 10023, USA
| |
Collapse
|
25
|
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.
Collapse
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
| |
Collapse
|
26
|
Longden KD, Rogers EM, Nern A, Dionne H, Reiser MB. Different spectral sensitivities of ON- and OFF-motion pathways enhance the detection of approaching color objects in Drosophila. Nat Commun 2023; 14:7693. [PMID: 38001097 PMCID: PMC10673857 DOI: 10.1038/s41467-023-43566-8] [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: 11/03/2021] [Accepted: 11/14/2023] [Indexed: 11/26/2023] Open
Abstract
Color and motion are used by many species to identify salient objects. They are processed largely independently, but color contributes to motion processing in humans, for example, enabling moving colored objects to be detected when their luminance matches the background. Here, we demonstrate an unexpected, additional contribution of color to motion vision in Drosophila. We show that behavioral ON-motion responses are more sensitive to UV than for OFF-motion, and we identify cellular pathways connecting UV-sensitive R7 photoreceptors to ON and OFF-motion-sensitive T4 and T5 cells, using neurogenetics and calcium imaging. Remarkably, this contribution of color circuitry to motion vision enhances the detection of approaching UV discs, but not green discs with the same chromatic contrast, and we show how this could generalize for systems with ON- and OFF-motion pathways. Our results provide a computational and circuit basis for how color enhances motion vision to favor the detection of saliently colored objects.
Collapse
Affiliation(s)
- Kit D Longden
- HHMI Janelia Research Campus, 19700 Helix Drive, Ashburn, VA, 20147, USA.
| | - Edward M Rogers
- HHMI Janelia Research Campus, 19700 Helix Drive, Ashburn, VA, 20147, USA
| | - Aljoscha Nern
- HHMI Janelia Research Campus, 19700 Helix Drive, Ashburn, VA, 20147, USA
| | - Heather Dionne
- HHMI Janelia Research Campus, 19700 Helix Drive, Ashburn, VA, 20147, USA
| | - Michael B Reiser
- HHMI Janelia Research Campus, 19700 Helix Drive, Ashburn, VA, 20147, USA.
| |
Collapse
|
27
|
Tanaka R, Zhou B, Agrochao M, Badwan BA, Au B, Matos NCB, Clark DA. Neural mechanisms to incorporate visual counterevidence in self-movement estimation. Curr Biol 2023; 33:4960-4979.e7. [PMID: 37918398 PMCID: PMC10848174 DOI: 10.1016/j.cub.2023.10.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2023] [Revised: 10/07/2023] [Accepted: 10/09/2023] [Indexed: 11/04/2023]
Abstract
In selecting appropriate behaviors, animals should weigh sensory evidence both for and against specific beliefs about the world. For instance, animals measure optic flow to estimate and control their own rotation. However, existing models of flow detection can be spuriously triggered by visual motion created by objects moving in the world. Here, we show that stationary patterns on the retina, which constitute evidence against observer rotation, suppress inappropriate stabilizing rotational behavior in the fruit fly Drosophila. In silico experiments show that artificial neural networks (ANNs) that are optimized to distinguish observer movement from external object motion similarly detect stationarity and incorporate negative evidence. Employing neural measurements and genetic manipulations, we identified components of the circuitry for stationary pattern detection, which runs parallel to the fly's local motion and optic-flow detectors. Our results show how the fly brain incorporates negative evidence to improve heading stability, exemplifying how a compact brain exploits geometrical constraints of the visual world.
Collapse
Affiliation(s)
- Ryosuke Tanaka
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT 06511, USA
| | - Baohua Zhou
- Department of Molecular Cellular and Developmental Biology, Yale University, New Haven, CT 06511, USA; Department of Statistics and Data Science, Yale University, New Haven, CT 06511, USA
| | - Margarida Agrochao
- Department of Molecular Cellular and Developmental Biology, Yale University, New Haven, CT 06511, USA
| | - Bara A Badwan
- School of Engineering and Applied Science, Yale University, New Haven, CT 06511, USA
| | - Braedyn Au
- Department of Physics, Yale University, New Haven, CT 06511, USA
| | - Natalia C B Matos
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT 06511, USA
| | - Damon A Clark
- Department of Molecular Cellular and Developmental Biology, Yale University, New Haven, CT 06511, USA; Department of Physics, Yale University, New Haven, CT 06511, USA; Department of Neuroscience, Yale University, New Haven, CT 06511, USA; Wu Tsai Institute, Yale University, New Haven, CT 06511, USA; Quantitative Biology Institute, Yale University, New Haven, CT 06511, USA.
| |
Collapse
|
28
|
Aimino MA, Humenik J, Parisi MJ, Duhart JC, Mosca TJ. SynLight: a bicistronic strategy for simultaneous active zone and cell labeling in the Drosophila nervous system. G3 (BETHESDA, MD.) 2023; 13:jkad221. [PMID: 37757863 PMCID: PMC10627267 DOI: 10.1093/g3journal/jkad221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 07/14/2023] [Accepted: 09/17/2023] [Indexed: 09/29/2023]
Abstract
At synapses, chemical neurotransmission mediates the exchange of information between neurons, leading to complex movement, behaviors, and stimulus processing. The immense number and variety of neurons within the nervous system make discerning individual neuron populations difficult, necessitating the development of advanced neuronal labeling techniques. In Drosophila, Bruchpilot-Short and mCD8-GFP, which label presynaptic active zones and neuronal membranes, respectively, have been widely used to study synapse development and organization. This labeling is often achieved via the expression of 2 independent constructs by a single binary expression system, but expression can weaken when multiple transgenes are expressed by a single driver. Recent work has sought to circumvent these drawbacks by developing methods that encode multiple proteins from a single transcript. Self-cleaving peptides, specifically 2A peptides, have emerged as effective sequences for accomplishing this task. We leveraged 2A ribosomal skipping peptides to engineer a construct that produces both Bruchpilot-Short-mStraw and mCD8-GFP from the same mRNA, which we named SynLight. Using SynLight, we visualized the putative synaptic active zones and membranes of multiple classes of olfactory, visual, and motor neurons and observed the correct separation of signal, confirming that both proteins are being generated separately. Furthermore, we demonstrate proof of principle by quantifying synaptic puncta number and neurite volume in olfactory neurons and finding no difference between the synapse densities of neurons expressing SynLight or neurons expressing both transgenes separately. At the neuromuscular junction, we determined that the synaptic puncta number labeled by SynLight was comparable to the endogenous puncta labeled by antibody staining. Overall, SynLight is a versatile tool for examining synapse density in any nervous system region of interest and allows new questions to be answered about synaptic development and organization.
Collapse
Affiliation(s)
- Michael A Aimino
- Department of Neuroscience, Vickie and Jack Farber Institute of Neuroscience, Thomas Jefferson University, Bluemle Life Sciences Building, Philadelphia, PA 19107, USA
| | - Jesse Humenik
- Department of Neuroscience, Vickie and Jack Farber Institute of Neuroscience, Thomas Jefferson University, Bluemle Life Sciences Building, Philadelphia, PA 19107, USA
| | - Michael J Parisi
- Department of Neuroscience, Vickie and Jack Farber Institute of Neuroscience, Thomas Jefferson University, Bluemle Life Sciences Building, Philadelphia, PA 19107, USA
| | - Juan Carlos Duhart
- Department of Neuroscience, Vickie and Jack Farber Institute of Neuroscience, Thomas Jefferson University, Bluemle Life Sciences Building, Philadelphia, PA 19107, USA
| | - Timothy J Mosca
- Department of Neuroscience, Vickie and Jack Farber Institute of Neuroscience, Thomas Jefferson University, Bluemle Life Sciences Building, Philadelphia, PA 19107, USA
| |
Collapse
|
29
|
|
30
|
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.
Collapse
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
| |
Collapse
|
31
|
Sanfilippo P, Kim AJ, Bhukel A, Yoo J, Mirshahidi PS, Pandey V, Bevir H, Yuen A, Mirshahidi PS, Guo P, Li HS, Wohlschlegel JA, Aso Y, Zipursky SL. Mapping of multiple neurotransmitter receptor subtypes and distinct protein complexes to the connectome. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.02.560011. [PMID: 37873314 PMCID: PMC10592863 DOI: 10.1101/2023.10.02.560011] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Neurons express different combinations of neurotransmitter receptor (NR) subunits and receive inputs from multiple neuron types expressing different neurotransmitters. Localizing NR subunits to specific synaptic inputs has been challenging. Here we use epitope tagged endogenous NR subunits, expansion light-sheet microscopy, and EM connectomics to molecularly characterize synapses in Drosophila. We show that in directionally selective motion sensitive neurons, different multiple NRs elaborated a highly stereotyped molecular topography with NR localized to specific domains receiving cell-type specific inputs. Developmental studies suggested that NRs or complexes of them with other membrane proteins determines patterns of synaptic inputs. In support of this model, we identify a transmembrane protein associated selectively with a subset of spatially restricted synapses and demonstrate through genetic analysis its requirement for synapse formation. We propose that mechanisms which regulate the precise spatial distribution of NRs provide a molecular cartography specifying the patterns of synaptic connections onto dendrites.
Collapse
Affiliation(s)
- Piero Sanfilippo
- Department of Biological Chemistry, University of California Los Angeles, Los Angeles, CA, USA
- Howard Hughes Medical Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Alexander J Kim
- Department of Biological Chemistry, University of California Los Angeles, Los Angeles, CA, USA
| | - Anuradha Bhukel
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Juyoun Yoo
- Neuroscience Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA
| | - Pegah S Mirshahidi
- Department of Biological Chemistry, University of California Los Angeles, Los Angeles, CA, USA
| | - Vijaya Pandey
- Department of Biological Chemistry, University of California Los Angeles, Los Angeles, CA, USA
| | - Harry Bevir
- Department of Biological Chemistry, University of California Los Angeles, Los Angeles, CA, USA
| | - Ashley Yuen
- Department of Biological Chemistry, University of California Los Angeles, Los Angeles, CA, USA
| | - Parmis S Mirshahidi
- Department of Biological Chemistry, University of California Los Angeles, Los Angeles, CA, USA
| | - Peiyi Guo
- Department of Neurobiology, University of Massachusetts Medical School, Worcester, MA, USA
| | - Hong-Sheng Li
- Department of Neurobiology, University of Massachusetts Medical School, Worcester, MA, USA
| | - James A Wohlschlegel
- Department of Biological Chemistry, University of California Los Angeles, Los Angeles, CA, USA
| | - Yoshinori Aso
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - S Lawrence Zipursky
- Department of Biological Chemistry, University of California Los Angeles, Los Angeles, CA, USA
- Howard Hughes Medical Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- Lead Contact
| |
Collapse
|
32
|
Salzberg Y, Haque R, Oren-Suissa M. The synaptic basis for sexual dimorphism in the invertebrate nervous system. Curr Opin Neurobiol 2023; 82:102757. [PMID: 37572555 PMCID: PMC10506627 DOI: 10.1016/j.conb.2023.102757] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 07/10/2023] [Accepted: 07/17/2023] [Indexed: 08/14/2023]
Abstract
Many animal behaviors are manifested differently in the two sexes of a given species, but how such sexual dimorphism is imprinted in the nervous system is not always clear. One mechanism involved is synaptic dimorphism, by which the same neurons exist in the two sexes, but form synapses that differ in features such as anatomy, molecular content or fate. While some evidence for synaptic dimorphism exists in humans and mammals, identifying these mechanisms in invertebrates has proven simpler, due to their smaller nervous systems and absence of external regulation by sex hormones. This review aims to present the current status of the field in invertebrates, the available toolkit for the study of synaptic dimorphism, and the standing questions that still remain incompletely answered.
Collapse
Affiliation(s)
- Yehuda Salzberg
- Department of Brain Science, Weizmann Institute of Science, Rehovot, 7610001, Israel
| | - Rizwanul Haque
- Department of Brain Science, Weizmann Institute of Science, Rehovot, 7610001, Israel
| | - Meital Oren-Suissa
- Department of Brain Science, Weizmann Institute of Science, Rehovot, 7610001, Israel.
| |
Collapse
|
33
|
Wang H, Zhao J, Wang H, Hu C, Peng J, Yue S. Attention and Prediction-Guided Motion Detection for Low-Contrast Small Moving Targets. IEEE TRANSACTIONS ON CYBERNETICS 2023; 53:6340-6352. [PMID: 35533156 DOI: 10.1109/tcyb.2022.3170699] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Small target motion detection within complex natural environments is an extremely challenging task for autonomous robots. Surprisingly, the visual systems of insects have evolved to be highly efficient in detecting mates and tracking prey, even though targets occupy as small as a few degrees of their visual fields. The excellent sensitivity to small target motion relies on a class of specialized neurons, called small target motion detectors (STMDs). However, existing STMD-based models are heavily dependent on visual contrast and perform poorly in complex natural environments, where small targets generally exhibit extremely low contrast against neighboring backgrounds. In this article, we develop an attention-and-prediction-guided visual system to overcome this limitation. The developed visual system comprises three main subsystems, namely: 1) an attention module; 2) an STMD-based neural network; and 3) a prediction module. The attention module searches for potential small targets in the predicted areas of the input image and enhances their contrast against a complex background. The STMD-based neural network receives the contrast-enhanced image and discriminates small moving targets from background false positives. The prediction module foresees future positions of the detected targets and generates a prediction map for the attention module. The three subsystems are connected in a recurrent architecture, allowing information to be processed sequentially to activate specific areas for small target detection. Extensive experiments on synthetic and real-world datasets demonstrate the effectiveness and superiority of the proposed visual system for detecting small, low-contrast moving targets against complex natural environments.
Collapse
|
34
|
Mano O, Choi M, Tanaka R, Creamer MS, Matos NCB, Shomar JW, Badwan BA, Clandinin TR, Clark DA. Long-timescale anti-directional rotation in Drosophila optomotor behavior. eLife 2023; 12:e86076. [PMID: 37751469 PMCID: PMC10522332 DOI: 10.7554/elife.86076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 09/12/2023] [Indexed: 09/28/2023] Open
Abstract
Locomotor movements cause visual images to be displaced across the eye, a retinal slip that is counteracted by stabilizing reflexes in many animals. In insects, optomotor turning causes the animal to turn in the direction of rotating visual stimuli, thereby reducing retinal slip and stabilizing trajectories through the world. This behavior has formed the basis for extensive dissections of motion vision. Here, we report that under certain stimulus conditions, two Drosophila species, including the widely studied Drosophila melanogaster, can suppress and even reverse the optomotor turning response over several seconds. Such 'anti-directional turning' is most strongly evoked by long-lasting, high-contrast, slow-moving visual stimuli that are distinct from those that promote syn-directional optomotor turning. Anti-directional turning, like the syn-directional optomotor response, requires the local motion detecting neurons T4 and T5. A subset of lobula plate tangential cells, CH cells, show involvement in these responses. Imaging from a variety of direction-selective cells in the lobula plate shows no evidence of dynamics that match the behavior, suggesting that the observed inversion in turning direction emerges downstream of the lobula plate. Further, anti-directional turning declines with age and exposure to light. These results show that Drosophila optomotor turning behaviors contain rich, stimulus-dependent dynamics that are inconsistent with simple reflexive stabilization responses.
Collapse
Affiliation(s)
- Omer Mano
- Department of Molecular, Cellular, and Developmental Biology, Yale UniversityNew HavenUnited States
| | - Minseung Choi
- Department of Neurobiology, Stanford UniversityStanfordUnited States
| | - Ryosuke Tanaka
- Interdepartmental Neuroscience Program, Yale UniversityNew HavenUnited States
| | - Matthew S Creamer
- Interdepartmental Neuroscience Program, Yale UniversityNew HavenUnited States
| | - Natalia CB Matos
- Interdepartmental Neuroscience Program, Yale UniversityNew HavenUnited States
| | - Joseph W Shomar
- Department of Physics, Yale UniversityNew HavenUnited States
| | - Bara A Badwan
- Department of Chemical Engineering, Yale UniversityNew HavenUnited States
| | | | - Damon A Clark
- Department of Molecular, Cellular, and Developmental Biology, Yale UniversityNew HavenUnited States
- Interdepartmental Neuroscience Program, Yale UniversityNew HavenUnited States
- Department of Physics, Yale UniversityNew HavenUnited States
- Department of Neuroscience, Yale UniversityNew HavenUnited States
| |
Collapse
|
35
|
Kunin AB, Guo J, Bassler KE, Pitkow X, Josić K. Hierarchical Modular Structure of the Drosophila Connectome. J Neurosci 2023; 43:6384-6400. [PMID: 37591738 PMCID: PMC10501013 DOI: 10.1523/jneurosci.0134-23.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2023] [Revised: 07/20/2023] [Accepted: 07/24/2023] [Indexed: 08/19/2023] Open
Abstract
The structure of neural circuitry plays a crucial role in brain function. Previous studies of brain organization generally had to trade off between coarse descriptions at a large scale and fine descriptions on a small scale. Researchers have now reconstructed tens to hundreds of thousands of neurons at synaptic resolution, enabling investigations into the interplay between global, modular organization, and cell type-specific wiring. Analyzing data of this scale, however, presents unique challenges. To address this problem, we applied novel community detection methods to analyze the synapse-level reconstruction of an adult female Drosophila melanogaster brain containing >20,000 neurons and 10 million synapses. Using a machine-learning algorithm, we find the most densely connected communities of neurons by maximizing a generalized modularity density measure. We resolve the community structure at a range of scales, from large (on the order of thousands of neurons) to small (on the order of tens of neurons). We find that the network is organized hierarchically, and larger-scale communities are composed of smaller-scale structures. Our methods identify well-known features of the fly brain, including its sensory pathways. Moreover, focusing on specific brain regions, we are able to identify subnetworks with distinct connectivity types. For example, manual efforts have identified layered structures in the fan-shaped body. Our methods not only automatically recover this layered structure, but also resolve finer connectivity patterns to downstream and upstream areas. We also find a novel modular organization of the superior neuropil, with distinct clusters of upstream and downstream brain regions dividing the neuropil into several pathways. These methods show that the fine-scale, local network reconstruction made possible by modern experimental methods are sufficiently detailed to identify the organization of the brain across scales, and enable novel predictions about the structure and function of its parts.Significance Statement The Hemibrain is a partial connectome of an adult female Drosophila melanogaster brain containing >20,000 neurons and 10 million synapses. Analyzing the structure of a network of this size requires novel and efficient computational tools. We applied a new community detection method to automatically uncover the modular structure in the Hemibrain dataset by maximizing a generalized modularity measure. This allowed us to resolve the community structure of the fly hemibrain at a range of spatial scales revealing a hierarchical organization of the network, where larger-scale modules are composed of smaller-scale structures. The method also allowed us to identify subnetworks with distinct cell and connectivity structures, such as the layered structures in the fan-shaped body, and the modular organization of the superior neuropil. Thus, network analysis methods can be adopted to the connectomes being reconstructed using modern experimental methods to reveal the organization of the brain across scales. This supports the view that such connectomes will allow us to uncover the organizational structure of the brain, which can ultimately lead to a better understanding of its function.
Collapse
Affiliation(s)
- Alexander B Kunin
- Department of Mathematics, Creighton University, Omaha, Nebraska 68178
- Department of Neuroscience, Baylor College of Medicine, Houston, Texas 77030
| | - Jiahao Guo
- Department of Physics, University of Houston, Houston, Texas 77204
- Texas Center for Superconductivity, University of Houston, Houston, Texas 77204
| | - Kevin E Bassler
- Department of Physics, University of Houston, Houston, Texas 77204
- Texas Center for Superconductivity, University of Houston, Houston, Texas 77204
- Department of Mathematics, University of Houston, Houston, Texas 77204
| | - Xaq Pitkow
- Department of Neuroscience, Baylor College of Medicine, Houston, Texas 77030
- Department of Electrical and Computer Engineering, Rice University, Houston, Texas 77005
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, Texas
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213
- Department of Machine Learning, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213
| | - Krešimir Josić
- Department of Mathematics, University of Houston, Houston, Texas 77204
- Department of Biology and Biochemistry, University of Houston, Houston, Texas 77204
| |
Collapse
|
36
|
Zhao H, Shao C, Shi Z, He S, Gong Z. The Intrinsic Similarity of Topological Structure in Biological Neural Networks. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:3292-3305. [PMID: 37224366 DOI: 10.1109/tcbb.2023.3279443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
Most previous studies mainly have focused on the analysis of structural properties of individual neuronal networks from C. elegans. In recent years, an increasing number of synapse-level neural maps, also known as biological neural networks, have been reconstructed. However, it is not clear whether there are intrinsic similarities of structural properties of biological neural networks from different brain compartments or species. To explore this issue, we collected nine connectomes at synaptic resolution including C. elegans, and analyzed their structural properties. We found that these biological neural networks possess small-world properties and modules. Excluding the Drosophila larval visual system, these networks have rich clubs. The distributions of synaptic connection strength for these networks can be fitted by the truncated pow-law distributions. Additionally, compared with the power-law model, a log-normal distribution is a better model to fit the complementary cumulative distribution function (CCDF) of degree for these neuronal networks. Moreover, we also observed that these neural networks belong to the same superfamily based on the significance profile (SP) of small subgraphs in the network. Taken together, these findings suggest that biological neural networks share intrinsic similarities in their topological structure, revealing some principles underlying the formation of biological neural networks within and across species.
Collapse
|
37
|
Chiappe ME. Circuits for self-motion estimation and walking control in Drosophila. Curr Opin Neurobiol 2023; 81:102748. [PMID: 37453230 DOI: 10.1016/j.conb.2023.102748] [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: 04/19/2023] [Revised: 06/11/2023] [Accepted: 06/13/2023] [Indexed: 07/18/2023]
Abstract
The brain's evolution and operation are inextricably linked to animal movement, and critical functions, such as motor control, spatial perception, and navigation, rely on precise knowledge of body movement. Such internal estimates of self-motion emerge from the integration of mechanosensory and visual feedback with motor-related signals. Thus, this internal representation likely depends on the activity of circuits distributed across the central nervous system. However, the circuits responsible for self-motion estimation, and the exact mechanisms by which motor-sensory coordination occurs within these circuits remain poorly understood. Recent technological advances have positioned Drosophila melanogaster as an advantageous model for investigating the emergence, maintenance, and utilization of self-motion representations during naturalistic walking behaviors. In this review, I will illustrate how the adult fly is providing insights into the fundamental problems of self-motion computations and walking control, which have relevance for all animals.
Collapse
Affiliation(s)
- M Eugenia Chiappe
- Champalimaud Neuroscience Programme, Champalimaud Centre for the Unknown, Lisbon, Portugal.
| |
Collapse
|
38
|
Puri P, Wu ST, Su CY, Aljadeff J. Shallow networks run deep: Peripheral preprocessing facilitates odor classification. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.23.550211. [PMID: 37546820 PMCID: PMC10401955 DOI: 10.1101/2023.07.23.550211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
The mammalian brain implements sophisticated sensory processing algorithms along multilayered ('deep') neural-networks. Strategies that insects use to meet similar computational demands, while relying on smaller nervous systems with shallow architectures, remain elusive. Using Drosophila as a model, we uncover the algorithmic role of odor preprocessing by a shallow network of compartmentalized olfactory receptor neurons. Each compartment operates as a ratiometric unit for specific odor-mixtures. This computation arises from a simple mechanism: electrical coupling between two differently-sized neurons. We demonstrate that downstream synaptic connectivity is shaped to optimally leverage amplification of a hedonic value signal in the periphery. Furthermore, peripheral preprocessing is shown to markedly improve novel odor classification in a higher brain center. Together, our work highlights a far-reaching functional role of the sensory periphery for downstream processing. By elucidating the implementation of powerful computations by a shallow network, we provide insights into general principles of efficient sensory processing algorithms.
Collapse
Affiliation(s)
- Palka Puri
- Department of Physics, University of California San Diego, La Jolla, CA, 92093, USA
| | - Shiuan-Tze Wu
- Department of Neurobiology, University of California San Diego, La Jolla, CA, 92093, USA
| | - Chih-Ying Su
- Department of Neurobiology, University of California San Diego, La Jolla, CA, 92093, USA
| | - Johnatan Aljadeff
- Department of Neurobiology, University of California San Diego, La Jolla, CA, 92093, USA
| |
Collapse
|
39
|
Chapochnikov NM, Pehlevan C, Chklovskii DB. Normative and mechanistic model of an adaptive circuit for efficient encoding and feature extraction. Proc Natl Acad Sci U S A 2023; 120:e2117484120. [PMID: 37428907 PMCID: PMC10629579 DOI: 10.1073/pnas.2117484120] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Accepted: 05/08/2023] [Indexed: 07/12/2023] Open
Abstract
One major question in neuroscience is how to relate connectomes to neural activity, circuit function, and learning. We offer an answer in the peripheral olfactory circuit of the Drosophila larva, composed of olfactory receptor neurons (ORNs) connected through feedback loops with interconnected inhibitory local neurons (LNs). We combine structural and activity data and, using a holistic normative framework based on similarity-matching, we formulate biologically plausible mechanistic models of the circuit. In particular, we consider a linear circuit model, for which we derive an exact theoretical solution, and a nonnegative circuit model, which we examine through simulations. The latter largely predicts the ORN [Formula: see text] LN synaptic weights found in the connectome and demonstrates that they reflect correlations in ORN activity patterns. Furthermore, this model accounts for the relationship between ORN [Formula: see text] LN and LN-LN synaptic counts and the emergence of different LN types. Functionally, we propose that LNs encode soft cluster memberships of ORN activity, and partially whiten and normalize the stimulus representations in ORNs through inhibitory feedback. Such a synaptic organization could, in principle, autonomously arise through Hebbian plasticity and would allow the circuit to adapt to different environments in an unsupervised manner. We thus uncover a general and potent circuit motif that can learn and extract significant input features and render stimulus representations more efficient. Finally, our study provides a unified framework for relating structure, activity, function, and learning in neural circuits and supports the conjecture that similarity-matching shapes the transformation of neural representations.
Collapse
Affiliation(s)
- Nikolai M. Chapochnikov
- Center for Computation Neuroscience, Flatiron Institute, New York, NY10010
- Department of Neurology, New York University School of Medicine, New York, NY10016
| | - Cengiz Pehlevan
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA02138
- Center for Brain Science, Harvard University, Cambridge, MA02138
- Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University, Cambridge, MA02138
| | - Dmitri B. Chklovskii
- Center for Computation Neuroscience, Flatiron Institute, New York, NY10010
- Neuroscience Institute, New York University School of Medicine, New York, NY10016
| |
Collapse
|
40
|
Aimino MA, Humenik J, Parisi MJ, Duhart JC, Mosca TJ. SynLight: a dicistronic strategy for simultaneous active zone and cell labeling in the Drosophila nervous system. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.17.549367. [PMID: 37502901 PMCID: PMC10370149 DOI: 10.1101/2023.07.17.549367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
At synapses, chemical neurotransmission mediates the exchange of information between neurons, leading to complex movement behaviors and stimulus processing. The immense number and variety of neurons within the nervous system makes discerning individual neuron populations difficult, necessitating the development of advanced neuronal labeling techniques. In Drosophila , Bruchpilot-Short and mCD8-GFP, which label presynaptic active zones and neuronal membranes, respectively, have been widely used to study synapse development and organization. This labeling is often achieved via expression of two independent constructs by a single binary expression system, but expression can weaken when multiple transgenes are expressed by a single driver. Ensuring adequate expression of each transgene is essential to enable more complex experiments; as such, work has sought to circumvent these drawbacks by developing methods that encode multiple proteins from a single transcript. Self-cleaving peptides, specifically 2A peptides, have emerged as effective sequences for accomplishing this task. We leveraged 2A ribosomal skipping peptides to engineer a construct that produces both Bruchpilot-Short and mCD8-GFP from the same mRNA, which we named SynLight. Using SynLight, we visualized the putative synaptic active zones and membranes of multiple classes of olfactory, visual, and motor neurons and observed correct separation of signal, confirming that both proteins are being generated separately. Furthermore, we demonstrate proof-of-principle by quantifying synaptic puncta number and neurite volume in olfactory neurons and finding no difference between the synapse densities of neurons expressing SynLight or neurons expressing both transgenes separately. At the neuromuscular junction, we determined that synaptic puncta number labeled by SynLight was comparable to endogenous puncta labeled by antibody staining. Overall, SynLight is a versatile tool for examining synapse density in any nervous system region of interest and allows new questions to be answered about synaptic development and organization.
Collapse
Affiliation(s)
- Michael A. Aimino
- Department of Neuroscience, Vickie and Jack Farber Institute of Neuroscience, Thomas Jefferson University, Bluemle Life Sciences Building, Philadelphia, PA 19107
| | - Jesse Humenik
- Department of Neuroscience, Vickie and Jack Farber Institute of Neuroscience, Thomas Jefferson University, Bluemle Life Sciences Building, Philadelphia, PA 19107
| | - Michael J. Parisi
- Department of Neuroscience, Vickie and Jack Farber Institute of Neuroscience, Thomas Jefferson University, Bluemle Life Sciences Building, Philadelphia, PA 19107
| | - Juan Carlos Duhart
- Department of Neuroscience, Vickie and Jack Farber Institute of Neuroscience, Thomas Jefferson University, Bluemle Life Sciences Building, Philadelphia, PA 19107
| | - Timothy J. Mosca
- Department of Neuroscience, Vickie and Jack Farber Institute of Neuroscience, Thomas Jefferson University, Bluemle Life Sciences Building, Philadelphia, PA 19107
| |
Collapse
|
41
|
Tanaka R, Zhou B, Agrochao M, Badwan BA, Au B, Matos NCB, Clark DA. Neural mechanisms to incorporate visual counterevidence in self motion estimation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.04.522814. [PMID: 36711843 PMCID: PMC9881891 DOI: 10.1101/2023.01.04.522814] [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: 01/08/2023]
Abstract
In selecting appropriate behaviors, animals should weigh sensory evidence both for and against specific beliefs about the world. For instance, animals measure optic flow to estimate and control their own rotation. However, existing models of flow detection can confuse the movement of external objects with genuine self motion. Here, we show that stationary patterns on the retina, which constitute negative evidence against self rotation, are used by the fruit fly Drosophila to suppress inappropriate stabilizing rotational behavior. In silico experiments show that artificial neural networks optimized to distinguish self and world motion similarly detect stationarity and incorporate negative evidence. Employing neural measurements and genetic manipulations, we identified components of the circuitry for stationary pattern detection, which runs parallel to the fly's motion- and optic flow-detectors. Our results exemplify how the compact brain of the fly incorporates negative evidence to improve heading stability, exploiting geometrical constraints of the visual world.
Collapse
Affiliation(s)
- Ryosuke Tanaka
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT 06511, USA
- Present Address: Institute of Neuroscience, Technical University of Munich, Munich 80802, Germany
| | - Baohua Zhou
- Department of Molecular Cellular and Developmental Biology, Yale University, New Haven, CT 06511, USA
- Department of Statistics and Data Science, Yale University, New Haven, CT 06511, USA
| | - Margarida Agrochao
- Department of Molecular Cellular and Developmental Biology, Yale University, New Haven, CT 06511, USA
| | - Bara A. Badwan
- School of Engineering and Applied Science, Yale University, New Haven, CT 06511, USA
| | - Braedyn Au
- Department of Physics, Yale University, New Haven, CT 06511, USA
| | - Natalia C. B. Matos
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT 06511, USA
| | - Damon A. Clark
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT 06511, USA
- Department of Molecular Cellular and Developmental Biology, Yale University, New Haven, CT 06511, USA
- Department of Physics, Yale University, New Haven, CT 06511, USA
- Department of Neuroscience, Yale University, New Haven, CT 06511, USA
- Wu Tsai Institute, Yale University, New Haven, CT 06511, USA
| |
Collapse
|
42
|
Dorkenwald S, Matsliah A, Sterling AR, Schlegel P, Yu SC, McKellar CE, Lin A, Costa M, Eichler K, Yin Y, Silversmith W, Schneider-Mizell C, Jordan CS, Brittain D, Halageri A, Kuehner K, Ogedengbe O, Morey R, Gager J, Kruk K, Perlman E, Yang R, Deutsch D, Bland D, Sorek M, Lu R, Macrina T, Lee K, Bae JA, Mu S, Nehoran B, Mitchell E, Popovych S, Wu J, Jia Z, Castro M, Kemnitz N, Ih D, Bates AS, Eckstein N, Funke J, Collman F, Bock DD, Jefferis GS, Seung HS, Murthy M. Neuronal wiring diagram of an adult brain. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.27.546656. [PMID: 37425937 PMCID: PMC10327113 DOI: 10.1101/2023.06.27.546656] [Citation(s) in RCA: 33] [Impact Index Per Article: 33.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
Connections between neurons can be mapped by acquiring and analyzing electron microscopic (EM) brain images. In recent years, this approach has been applied to chunks of brains to reconstruct local connectivity maps that are highly informative, yet inadequate for understanding brain function more globally. Here, we present the first neuronal wiring diagram of a whole adult brain, containing 5×107 chemical synapses between ~130,000 neurons reconstructed from a female Drosophila melanogaster. The resource also incorporates annotations of cell classes and types, nerves, hemilineages, and predictions of neurotransmitter identities. Data products are available by download, programmatic access, and interactive browsing and made interoperable with other fly data resources. We show how to derive a projectome, a map of projections between regions, from the connectome. We demonstrate the tracing of synaptic pathways and the analysis of information flow from inputs (sensory and ascending neurons) to outputs (motor, endocrine, and descending neurons), across both hemispheres, and between the central brain and the optic lobes. Tracing from a subset of photoreceptors all the way to descending motor pathways illustrates how structure can uncover putative circuit mechanisms underlying sensorimotor behaviors. The technologies and open ecosystem of the FlyWire Consortium set the stage for future large-scale connectome projects in other species.
Collapse
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
| | | |
Collapse
|
43
|
Dipietro L, Gonzalez-Mego P, Ramos-Estebanez C, Zukowski LH, Mikkilineni R, Rushmore RJ, Wagner T. The evolution of Big Data in neuroscience and neurology. JOURNAL OF BIG DATA 2023; 10:116. [PMID: 37441339 PMCID: PMC10333390 DOI: 10.1186/s40537-023-00751-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Accepted: 05/08/2023] [Indexed: 07/15/2023]
Abstract
Neurological diseases are on the rise worldwide, leading to increased healthcare costs and diminished quality of life in patients. In recent years, Big Data has started to transform the fields of Neuroscience and Neurology. Scientists and clinicians are collaborating in global alliances, combining diverse datasets on a massive scale, and solving complex computational problems that demand the utilization of increasingly powerful computational resources. This Big Data revolution is opening new avenues for developing innovative treatments for neurological diseases. Our paper surveys Big Data's impact on neurological patient care, as exemplified through work done in a comprehensive selection of areas, including Connectomics, Alzheimer's Disease, Stroke, Depression, Parkinson's Disease, Pain, and Addiction (e.g., Opioid Use Disorder). We present an overview of research and the methodologies utilizing Big Data in each area, as well as their current limitations and technical challenges. Despite the potential benefits, the full potential of Big Data in these fields currently remains unrealized. We close with recommendations for future research aimed at optimizing the use of Big Data in Neuroscience and Neurology for improved patient outcomes. Supplementary Information The online version contains supplementary material available at 10.1186/s40537-023-00751-2.
Collapse
Affiliation(s)
| | - Paola Gonzalez-Mego
- Spaulding Rehabilitation/Neuromodulation Lab, Harvard Medical School, Cambridge, MA USA
| | | | | | | | | | - Timothy Wagner
- Highland Instruments, Cambridge, MA USA
- Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA USA
| |
Collapse
|
44
|
Abstract
How neurons detect the direction of motion is a prime example of neural computation: Motion vision is found in the visual systems of virtually all sighted animals, it is important for survival, and it requires interesting computations with well-defined linear and nonlinear processing steps-yet the whole process is of moderate complexity. The genetic methods available in the fruit fly Drosophila and the charting of a connectome of its visual system have led to rapid progress and unprecedented detail in our understanding of how neurons compute the direction of motion in this organism. The picture that emerged incorporates not only the identity, morphology, and synaptic connectivity of each neuron involved but also its neurotransmitters, its receptors, and their subcellular localization. Together with the neurons' membrane potential responses to visual stimulation, this information provides the basis for a biophysically realistic model of the circuit that computes the direction of visual motion.
Collapse
Affiliation(s)
- Alexander Borst
- Max Planck Institute for Biological Intelligence, Martinsried, Germany; ,
| | - Lukas N Groschner
- Max Planck Institute for Biological Intelligence, Martinsried, Germany; ,
| |
Collapse
|
45
|
Ren X, Bok I, Vareberg A, Hai A. Stimulation-mediated reverse engineering of silent neural networks. J Neurophysiol 2023; 129:1505-1514. [PMID: 37222450 PMCID: PMC10311990 DOI: 10.1152/jn.00100.2023] [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: 03/10/2023] [Revised: 05/16/2023] [Accepted: 05/23/2023] [Indexed: 05/25/2023] Open
Abstract
Reconstructing connectivity of neuronal networks from single-cell activity is essential to understanding brain function, but the challenge of deciphering connections from populations of silent neurons has been largely unmet. We demonstrate a protocol for deriving connectivity of simulated silent neuronal networks using stimulation combined with a supervised learning algorithm, which enables inferring connection weights with high fidelity and predicting spike trains at the single-spike and single-cell levels with high accuracy. We apply our method on rat cortical recordings fed through a circuit of heterogeneously connected leaky integrate-and-fire neurons firing at typical lognormal distributions and demonstrate improved performance during stimulation for multiple subpopulations. These testable predictions about the number and protocol of the required stimulations are expected to enhance future efforts for deriving neuronal connectivity and drive new experiments to better understand brain function.NEW & NOTEWORTHY We introduce a new concept for reverse engineering silent neuronal networks using a supervised learning algorithm combined with stimulation. We quantify the performance of the algorithm and the precision of deriving synaptic weights in inhibitory and excitatory subpopulations. We then show that stimulation enables deciphering connectivity of heterogeneous circuits fed with real electrode array recordings, which could extend in the future to deciphering connectivity in broad biological and artificial neural networks.
Collapse
Affiliation(s)
- Xiaoxuan Ren
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, Wisconsin, United States
- Department of Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, Wisconsin, United States
| | - Ilhan Bok
- Department of Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, Wisconsin, United States
| | - Adam Vareberg
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, Wisconsin, United States
| | - Aviad Hai
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, Wisconsin, United States
- Department of Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, Wisconsin, United States
- Wisconsin Institute for Translational Neuroengineering (WITNe), Madison, Wisconsin, United States
| |
Collapse
|
46
|
Ketkar MD, Shao S, Gjorgjieva J, Silies M. Multifaceted luminance gain control beyond photoreceptors in Drosophila. Curr Biol 2023:S0960-9822(23)00619-X. [PMID: 37285845 DOI: 10.1016/j.cub.2023.05.024] [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: 04/03/2023] [Revised: 05/10/2023] [Accepted: 05/11/2023] [Indexed: 06/09/2023]
Abstract
Animals navigating in natural environments must handle vast changes in their sensory input. Visual systems, for example, handle changes in luminance at many timescales, from slow changes across the day to rapid changes during active behavior. To maintain luminance-invariant perception, visual systems must adapt their sensitivity to changing luminance at different timescales. We demonstrate that luminance gain control in photoreceptors alone is insufficient to explain luminance invariance at both fast and slow timescales and reveal the algorithms that adjust gain past photoreceptors in the fly eye. We combined imaging and behavioral experiments with computational modeling to show that downstream of photoreceptors, circuitry taking input from the single luminance-sensitive neuron type L3 implements gain control at fast and slow timescales. This computation is bidirectional in that it prevents the underestimation of contrasts in low luminance and overestimation in high luminance. An algorithmic model disentangles these multifaceted contributions and shows that the bidirectional gain control occurs at both timescales. The model implements a nonlinear interaction of luminance and contrast to achieve gain correction at fast timescales and a dark-sensitive channel to improve the detection of dim stimuli at slow timescales. Together, our work demonstrates how a single neuronal channel performs diverse computations to implement gain control at multiple timescales that are together important for navigation in natural environments.
Collapse
Affiliation(s)
- Madhura D Ketkar
- Institute of Developmental and Neurobiology, Johannes-Gutenberg University Mainz, Hanns-Dieter-Hüsch-Weg 15, 55128 Mainz, Germany
| | - Shuai Shao
- Max Planck Institute for Brain Research, Max-von-Laue-Straße 4, 60438 Frankfurt am Main, Germany; Department of Neurophysiology, Radboud University, Heyendaalseweg 135, 6525 EN Nijmegen, the Netherlands
| | - Julijana Gjorgjieva
- Max Planck Institute for Brain Research, Max-von-Laue-Straße 4, 60438 Frankfurt am Main, Germany; School of Life Sciences, Technical University Munich, Maximus-von-Imhof-Forum 3, 85354 Freising, Germany.
| | - Marion Silies
- Institute of Developmental and Neurobiology, Johannes-Gutenberg University Mainz, Hanns-Dieter-Hüsch-Weg 15, 55128 Mainz, Germany.
| |
Collapse
|
47
|
Zhu J, Boivin JC, Pang S, Xu CS, Lu Z, Saalfeld S, Hess HF, Ohyama T. Comparative connectomics and escape behavior in larvae of closely related Drosophila species. Curr Biol 2023:S0960-9822(23)00675-9. [PMID: 37285846 DOI: 10.1016/j.cub.2023.05.043] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2022] [Revised: 05/02/2023] [Accepted: 05/17/2023] [Indexed: 06/09/2023]
Abstract
Evolution has generated an enormous variety of morphological, physiological, and behavioral traits in animals. How do behaviors evolve in different directions in species equipped with similar neurons and molecular components? Here we adopted a comparative approach to investigate the similarities and differences of escape behaviors in response to noxious stimuli and their underlying neural circuits between closely related drosophilid species. Drosophilids show a wide range of escape behaviors in response to noxious cues, including escape crawling, stopping, head casting, and rolling. Here we find that D. santomea, compared with its close relative D. melanogaster, shows a higher probability of rolling in response to noxious stimulation. To assess whether this behavioral difference could be attributed to differences in neural circuitry, we generated focused ion beam-scanning electron microscope volumes of the ventral nerve cord of D. santomea to reconstruct the downstream partners of mdIV, a nociceptive sensory neuron in D. melanogaster. Along with partner interneurons of mdVI (including Basin-2, a multisensory integration neuron necessary for rolling) previously identified in D. melanogaster, we identified two additional partners of mdVI in D. santomea. Finally, we showed that joint activation of one of the partners (Basin-1) and a common partner (Basin-2) in D. melanogaster increased rolling probability, suggesting that the high rolling probability in D. santomea is mediated by the additional activation of Basin-1 by mdIV. These results provide a plausible mechanistic explanation for how closely related species exhibit quantitative differences in the likelihood of expressing the same behavior.
Collapse
Affiliation(s)
- Jiayi Zhu
- Department of Biology, McGill University, Docteur Penfield, Montreal, QC H3A 1B1, Canada; Integrated Program of Neuroscience, McGill University, Pine Avenue W., Montreal, QC H3A 1A1, Canada
| | - Jean-Christophe Boivin
- Department of Biology, McGill University, Docteur Penfield, Montreal, QC H3A 1B1, Canada; Integrated Program of Neuroscience, McGill University, Pine Avenue W., Montreal, QC H3A 1A1, Canada
| | - Song Pang
- Janelia Research Campus, Howard Hughes Medical Institute, Helix Drive, Ashburn, VA 20147, USA
| | - C Shan Xu
- Janelia Research Campus, Howard Hughes Medical Institute, Helix Drive, Ashburn, VA 20147, USA
| | - Zhiyuan Lu
- Janelia Research Campus, Howard Hughes Medical Institute, Helix Drive, Ashburn, VA 20147, USA
| | - Stephan Saalfeld
- Janelia Research Campus, Howard Hughes Medical Institute, Helix Drive, Ashburn, VA 20147, USA
| | - Harald F Hess
- Janelia Research Campus, Howard Hughes Medical Institute, Helix Drive, Ashburn, VA 20147, USA
| | - Tomoko Ohyama
- Department of Biology, McGill University, Docteur Penfield, Montreal, QC H3A 1B1, Canada; Alan Edwards Center for Research on Pain, McGill University, University Street, Montreal, QC H3A 2B4, Canada.
| |
Collapse
|
48
|
Currier TA, Pang MM, Clandinin TR. Visual processing in the fly, from photoreceptors to behavior. Genetics 2023; 224:iyad064. [PMID: 37128740 PMCID: PMC10213501 DOI: 10.1093/genetics/iyad064] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Accepted: 03/22/2023] [Indexed: 05/03/2023] Open
Abstract
Originally a genetic model organism, the experimental use of Drosophila melanogaster has grown to include quantitative behavioral analyses, sophisticated perturbations of neuronal function, and detailed sensory physiology. A highlight of these developments can be seen in the context of vision, where pioneering studies have uncovered fundamental and generalizable principles of sensory processing. Here we begin with an overview of vision-guided behaviors and common methods for probing visual circuits. We then outline the anatomy and physiology of brain regions involved in visual processing, beginning at the sensory periphery and ending with descending motor control. Areas of focus include contrast and motion detection in the optic lobe, circuits for visual feature selectivity, computations in support of spatial navigation, and contextual associative learning. Finally, we look to the future of fly visual neuroscience and discuss promising topics for further study.
Collapse
Affiliation(s)
- Timothy A Currier
- Department of Neurobiology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Michelle M Pang
- Department of Neurobiology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Thomas R Clandinin
- Department of Neurobiology, Stanford University School of Medicine, Stanford, CA 94305, USA
| |
Collapse
|
49
|
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.
Collapse
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.
| |
Collapse
|
50
|
Parisi MJ, Aimino MA, Mosca TJ. A conditional strategy for cell-type-specific labeling of endogenous excitatory synapses in Drosophila. CELL REPORTS METHODS 2023; 3:100477. [PMID: 37323572 PMCID: PMC10261928 DOI: 10.1016/j.crmeth.2023.100477] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 02/28/2023] [Accepted: 04/19/2023] [Indexed: 06/17/2023]
Abstract
Chemical neurotransmission occurs at specialized contacts where neurotransmitter release machinery apposes neurotransmitter receptors to underlie circuit function. A series of complex events underlies pre- and postsynaptic protein recruitment to neuronal connections. To better study synaptic development in individual neurons, we need cell-type-specific strategies to visualize endogenous synaptic proteins. Although presynaptic strategies exist, postsynaptic proteins remain less studied because of a paucity of cell-type-specific reagents. To study excitatory postsynapses with cell-type specificity, we engineered dlg1[4K], a conditionally labeled marker of Drosophila excitatory postsynaptic densities. With binary expression systems, dlg1[4K] labels central and peripheral postsynapses in larvae and adults. Using dlg1[4K], we find that distinct rules govern postsynaptic organization in adult neurons, multiple binary expression systems can concurrently label pre- and postsynapse in a cell-type-specific manner, and neuronal DLG1 can sometimes localize presynaptically. These results validate our strategy for conditional postsynaptic labeling and demonstrate principles of synaptic organization.
Collapse
Affiliation(s)
- Michael J. Parisi
- Department of Neuroscience, Vickie and Jack Farber Institute of Neuroscience, Thomas Jefferson University, Bluemle Life Sciences Building, Philadelphia, PA 19107, USA
| | - Michael A. Aimino
- Department of Neuroscience, Vickie and Jack Farber Institute of Neuroscience, Thomas Jefferson University, Bluemle Life Sciences Building, Philadelphia, PA 19107, USA
| | - Timothy J. Mosca
- Department of Neuroscience, Vickie and Jack Farber Institute of Neuroscience, Thomas Jefferson University, Bluemle Life Sciences Building, Philadelphia, PA 19107, USA
| |
Collapse
|