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Chan ICW, Chen N, Hernandez J, Meltzer H, Park A, Stahl A. Future avenues in Drosophila mushroom body research. Learn Mem 2024; 31:a053863. [PMID: 38862172 PMCID: PMC11199946 DOI: 10.1101/lm.053863.123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Accepted: 03/27/2024] [Indexed: 06/13/2024]
Abstract
How does the brain translate sensory information into complex behaviors? With relatively small neuronal numbers, readable behavioral outputs, and an unparalleled genetic toolkit, the Drosophila mushroom body (MB) offers an excellent model to address this question in the context of associative learning and memory. Recent technological breakthroughs, such as the freshly completed full-brain connectome, multiomics approaches, CRISPR-mediated gene editing, and machine learning techniques, led to major advancements in our understanding of the MB circuit at the molecular, structural, physiological, and functional levels. Despite significant progress in individual MB areas, the field still faces the fundamental challenge of resolving how these different levels combine and interact to ultimately control the behavior of an individual fly. In this review, we discuss various aspects of MB research, with a focus on the current knowledge gaps, and an outlook on the future methodological developments required to reach an overall view of the neurobiological basis of learning and memory.
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Affiliation(s)
- Ivy Chi Wai Chan
- Dynamics of Neuronal Circuits Group, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Developmental Biology, RWTH Aachen University, Aachen, Germany
| | - Nannan Chen
- School of Life Science and Technology, Key Laboratory of Developmental Genes and Human Disease, Southeast University, Nanjing 210096, China
| | - John Hernandez
- Neuroscience Department, Brown University, Providence, Rhode Island 02906, USA
| | - Hagar Meltzer
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 7610001, Israel
- Department of Molecular Neuroscience, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Annie Park
- Department of Physiology, Anatomy and Genetics, Centre for Neural Circuits and Behaviour, University of Oxford, Oxford, United Kingdom
| | - Aaron Stahl
- Neuroscience and Pharmacology, University of Iowa, Iowa City, Iowa 52242, USA
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Bai Y, Henry J, Cheng E, Perry S, Mawdsley D, Wong BBM, Kaslin J, Wlodkowic D. Toward Real-Time Animal Tracking with Integrated Stimulus Control for Automated Conditioning in Aquatic Eco-Neurotoxicology. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:19453-19462. [PMID: 37956114 DOI: 10.1021/acs.est.3c07013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Aquatic eco-neurotoxicology is an emerging field that requires new analytical systems to study the effects of pollutants on animal behaviors. This is especially true if we are to gain insights into one of the least studied aspects: the potential perturbations that neurotoxicants can have on cognitive behaviors. The paucity of experimental data is partly caused by a lack of low-cost technologies for the analysis of higher-level neurological functions (e.g., associative learning) in small aquatic organisms. Here, we present a proof-of-concept prototype that utilizes a new real-time animal tracking software for on-the-fly video analysis and closed-loop, external hardware communications to deliver stimuli based on specific behaviors in aquatic organisms, spanning three animal phyla: chordates (fish, frog), platyhelminthes (flatworm), and arthropods (crustacean). The system's open-source software features an intuitive graphical user interface and advanced adaptive threshold-based image segmentation for precise animal detection. We demonstrate the precision of animal tracking across multiple aquatic species with varying modes of locomotion. The presented technology interfaces easily with low-cost and open-source hardware such as the Arduino microcontroller family for closed-loop stimuli control. The new system has potential future applications in eco-neurotoxicology, where it could enable new opportunities for cognitive research in diverse small aquatic model organisms.
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Affiliation(s)
- Yutao Bai
- The Neurotoxicology Laboratory, School of Science, RMIT University, Melbourne, VIC 3083, Australia
| | - Jason Henry
- The Neurotoxicology Laboratory, School of Science, RMIT University, Melbourne, VIC 3083, Australia
| | - Eva Cheng
- Faculty of Engineering and IT, School of Electrical and Data Engineering, University of Technology Sydney, Sydney, NSW 2007, Australia
| | - Stuart Perry
- Faculty of Engineering and IT, School of Electrical and Data Engineering, University of Technology Sydney, Sydney, NSW 2007, Australia
| | - David Mawdsley
- Defence Science and Technology Group, Melbourne, VIC 3207, Australia
| | - Bob B M Wong
- School of Biological Sciences, Monash University, Melbourne, VIC 3800, Australia
| | - Jan Kaslin
- Australian Regenerative Medicine Institute, Monash University, Melbourne, VIC 3800, Australia
| | - Donald Wlodkowic
- The Neurotoxicology Laboratory, School of Science, RMIT University, Melbourne, VIC 3083, Australia
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Thane M, Paisios E, Stöter T, Krüger AR, Gläß S, Dahse AK, Scholz N, Gerber B, Lehmann DJ, Schleyer M. High-resolution analysis of individual Drosophila melanogaster larvae uncovers individual variability in locomotion and its neurogenetic modulation. Open Biol 2023; 13:220308. [PMID: 37072034 PMCID: PMC10113034 DOI: 10.1098/rsob.220308] [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: 10/12/2022] [Accepted: 03/05/2023] [Indexed: 04/20/2023] Open
Abstract
Neuronally orchestrated muscular movement and locomotion are defining faculties of multicellular animals. Due to its simple brain and genetic accessibility, the larva of the fruit fly Drosophila melanogaster allows one to study these processes at tractable levels of complexity. However, although the faculty of locomotion clearly pertains to the individual, most studies of locomotion in larvae use measurements aggregated across animals, or animals tested one by one, an extravagance for larger-scale analyses. This prevents grasping the inter- and intra-individual variability in locomotion and its neurogenetic determinants. Here, we present the IMBA (individual maggot behaviour analyser) for analysing the behaviour of individual larvae within groups, reliably resolving individual identity across collisions. We use the IMBA to systematically describe the inter- and intra-individual variability in locomotion of wild-type animals, and how the variability is reduced by associative learning. We then report a novel locomotion phenotype of an adhesion GPCR mutant. We further investigated the modulation of locomotion across repeated activations of dopamine neurons in individual animals, and the transient backward locomotion induced by brief optogenetic activation of the brain-descending 'mooncrawler' neurons. In summary, the IMBA is an easy-to-use toolbox allowing an unprecedentedly rich view of the behaviour and its variability of individual larvae, with utility in multiple biomedical research contexts.
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Affiliation(s)
- Michael Thane
- Department Genetics of Learning and Memory, Leibniz Institute for Neurobiology, Magdeburg, Germany
- Department of Simulation and Graphics, Otto von Guerike University, Magdeburg, Germany
| | - Emmanouil Paisios
- Department Genetics of Learning and Memory, Leibniz Institute for Neurobiology, Magdeburg, Germany
| | - Torsten Stöter
- Combinatorial NeuroImaging Core Facility, Leibniz Institute for Neurobiology, Magdeburg, Germany
| | - Anna-Rosa Krüger
- Department Genetics of Learning and Memory, Leibniz Institute for Neurobiology, Magdeburg, Germany
- Institute of Biology, Free University of Berlin, Berlin, Germany
| | - Sebastian Gläß
- Department Genetics of Learning and Memory, Leibniz Institute for Neurobiology, Magdeburg, Germany
| | - Anne-Kristin Dahse
- Division of General Biochemistry, Rudolf Schönheimer Institute of Biochemistry, Medical Faculty, Leipzig University, Leipzig, Germany
| | - Nicole Scholz
- Division of General Biochemistry, Rudolf Schönheimer Institute of Biochemistry, Medical Faculty, Leipzig University, Leipzig, Germany
| | - Bertram Gerber
- Department Genetics of Learning and Memory, Leibniz Institute for Neurobiology, Magdeburg, Germany
- Institute of Biology, Otto von Guericke University Magdeburg, Magdeburg, Germany
- Center for Behavioral Brain Sciences, Magdeburg, Germany
| | - Dirk J. Lehmann
- Department of Simulation and Graphics, Otto von Guerike University, Magdeburg, Germany
- Department for Information Engineering, Faculty of Computer Science, Ostfalia University of Applied Science, Brunswick-Wolfenbuettel, Germany
| | - Michael Schleyer
- Department Genetics of Learning and Memory, Leibniz Institute for Neurobiology, Magdeburg, Germany
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