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Ryu J, Nejatbakhsh A, Torkashvand M, Gangadharan S, Seyedolmohadesin M, Kim J, Paninski L, Venkatachalam V. Versatile multiple object tracking in sparse 2D/3D videos via deformable image registration. PLoS Comput Biol 2024; 20:e1012075. [PMID: 38768230 DOI: 10.1371/journal.pcbi.1012075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 04/14/2024] [Indexed: 05/22/2024] Open
Abstract
Tracking body parts in behaving animals, extracting fluorescence signals from cells embedded in deforming tissue, and analyzing cell migration patterns during development all require tracking objects with partially correlated motion. As dataset sizes increase, manual tracking of objects becomes prohibitively inefficient and slow, necessitating automated and semi-automated computational tools. Unfortunately, existing methods for multiple object tracking (MOT) are either developed for specific datasets and hence do not generalize well to other datasets, or require large amounts of training data that are not readily available. This is further exacerbated when tracking fluorescent sources in moving and deforming tissues, where the lack of unique features and sparsely populated images create a challenging environment, especially for modern deep learning techniques. By leveraging technology recently developed for spatial transformer networks, we propose ZephIR, an image registration framework for semi-supervised MOT in 2D and 3D videos. ZephIR can generalize to a wide range of biological systems by incorporating adjustable parameters that encode spatial (sparsity, texture, rigidity) and temporal priors of a given data class. We demonstrate the accuracy and versatility of our approach in a variety of applications, including tracking the body parts of a behaving mouse and neurons in the brain of a freely moving C. elegans. We provide an open-source package along with a web-based graphical user interface that allows users to provide small numbers of annotations to interactively improve tracking results.
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Affiliation(s)
- James Ryu
- Department of Physics, Northeastern University, Boston, Massachusetts, United States of America
| | - Amin Nejatbakhsh
- Department of Neuroscience, Columbia University, New York, New York, United States of America
| | - Mahdi Torkashvand
- Department of Physics, Northeastern University, Boston, Massachusetts, United States of America
| | - Sahana Gangadharan
- Department of Physics, Northeastern University, Boston, Massachusetts, United States of America
| | - Maedeh Seyedolmohadesin
- Department of Physics, Northeastern University, Boston, Massachusetts, United States of America
| | - Jinmahn Kim
- Department of Physics, Northeastern University, Boston, Massachusetts, United States of America
| | - Liam Paninski
- Department of Neuroscience, Columbia University, New York, New York, United States of America
| | - Vivek Venkatachalam
- Department of Physics, Northeastern University, Boston, Massachusetts, United States of America
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2
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Brezovec BE, Berger AB, Hao YA, Lin A, Ahmed OM, Pacheco DA, Thiberge SY, Murthy M, Clandinin TR. BIFROST: a method for registering diverse imaging datasets of the Drosophila brain. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.06.09.544408. [PMID: 37333105 PMCID: PMC10274908 DOI: 10.1101/2023.06.09.544408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
The heterogeneity of brain imaging methods in neuroscience provides rich data that cannot be captured by a single technique, and our interpretations benefit from approaches that enable easy comparison both within and across different data types. For example, comparing brain-wide neural dynamics across experiments and aligning such data to anatomical resources, such as gene expression patterns or connectomes, requires precise alignment to a common set of anatomical coordinates. However, this is challenging because registering in vivo functional imaging data to ex vivo reference atlases requires accommodating differences in imaging modality, microscope specification, and sample preparation. We overcome these challenges in Drosophila by building an in vivo reference atlas from multiphoton-imaged brains, called the Functional Drosophila Atlas (FDA). We then develop a two-step pipeline, BrIdge For Registering Over Statistical Templates (BIFROST), for transforming neural imaging data into this common space and for importing ex vivo resources such as connectomes. Using genetically labeled cell types as ground truth, we demonstrate registration with a precision of less than 10 microns. Overall, BIFROST provides a pipeline for registering functional imaging datasets in the fly, both within and across experiments. Significance Large-scale functional imaging experiments in Drosophila have given us new insights into neural activity in various sensory and behavioral contexts. However, precisely registering volumetric images from different studies has proven challenging, limiting quantitative comparisons of data across experiments. Here, we address this limitation by developing BIFROST, a registration pipeline robust to differences across experimental setups and datasets. We benchmark this pipeline by genetically labeling cell types in the fly brain and demonstrate sub-10 micron registration precision, both across specimens and across laboratories. We further demonstrate accurate registration between in-vivo brain volumes and ultrastructural connectomes, enabling direct structure-function comparisons in future experiments.
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3
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Emmons SW. FUNCTIONS OF C. ELEGANS NEURONS FROM SYNAPTIC CONNECTIVITY. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.08.584145. [PMID: 38562755 PMCID: PMC10983851 DOI: 10.1101/2024.03.08.584145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Despite decades of research on the C. elegans nervous system based on an anatomical description of synaptic connectivity, the circuits underlying behavior remain incompletely described and the functions of many neurons are still unknown. Updated and more complete chemical and gap junction connectomes of both adult sexes covering the entire animal including the muscle end organ have become available recently. Here these are analyzed to gain insight into the overall structure of the connectivity network and to suggest functions of individual neuron classes. Modularity analysis divides the connectome graph into ten communities that can be correlated with broad categories of behavior. A significant role of the body wall musculature end organ is emphasized as both a site of significant information convergence and as a source of sensory input in a feedback loop. Convergence of pathways for multisensory integration occurs throughout the network - most interneurons have similar indegrees and outdegrees and hence disperse information as much as they aggregate it. New insights include description of a set of high degree interneurons connected by many gap junctions running through the ventral cord that may represent a previously unrecognized locus of information processing. There is an apparent mechanosensory and proprioceptive field covering the entire body formed by connectivity of the many mechanosensory neurons of multiple types to two interneurons with output connections across the nervous system. Several additional significant, previously unrecognized circuits and pathways are uncovered, some involving unstudied neurons. The insights are valuable for guiding theoretical investigation of network properties as well as experimental studies of the functions of individual neurons, groups of neurons, and circuits.
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Affiliation(s)
- Scott W Emmons
- Department of Genetics and Dominic P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY 10461, USA
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4
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Kramer TS, Flavell SW. Building and integrating brain-wide maps of nervous system function in invertebrates. Curr Opin Neurobiol 2024; 86:102868. [PMID: 38569231 DOI: 10.1016/j.conb.2024.102868] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 02/13/2024] [Accepted: 03/07/2024] [Indexed: 04/05/2024]
Abstract
The selection and execution of context-appropriate behaviors is controlled by the integrated action of neural circuits throughout the brain. However, how activity is coordinated across brain regions, and how nervous system structure enables these functional interactions, remain open questions. Recent technical advances have made it feasible to build brain-wide maps of nervous system structure and function, such as brain activity maps, connectomes, and cell atlases. Here, we review recent progress in this area, focusing on C. elegans and D. melanogaster, as recent work has produced global maps of these nervous systems. We also describe neural circuit motifs elucidated in studies of specific networks, which highlight the complexities that must be captured to build accurate models of whole-brain function.
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Affiliation(s)
- Talya S Kramer
- Picower Institute for Learning and Memory, Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA; MIT Biology Graduate Program, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Steven W Flavell
- Picower Institute for Learning and Memory, Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA.
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5
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Toyoshima Y, Sato H, Nagata D, Kanamori M, Jang MS, Kuze K, Oe S, Teramoto T, Iwasaki Y, Yoshida R, Ishihara T, Iino Y. Ensemble dynamics and information flow deduction from whole-brain imaging data. PLoS Comput Biol 2024; 20:e1011848. [PMID: 38489379 PMCID: PMC10942262 DOI: 10.1371/journal.pcbi.1011848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 01/21/2024] [Indexed: 03/17/2024] Open
Abstract
The recent advancements in large-scale activity imaging of neuronal ensembles offer valuable opportunities to comprehend the process involved in generating brain activity patterns and understanding how information is transmitted between neurons or neuronal ensembles. However, existing methodologies for extracting the underlying properties that generate overall dynamics are still limited. In this study, we applied previously unexplored methodologies to analyze time-lapse 3D imaging (4D imaging) data of head neurons of the nematode Caenorhabditis elegans. By combining time-delay embedding with the independent component analysis, we successfully decomposed whole-brain activities into a small number of component dynamics. Through the integration of results from multiple samples, we extracted common dynamics from neuronal activities that exhibit apparent divergence across different animals. Notably, while several components show common cooperativity across samples, some component pairs exhibited distinct relationships between individual samples. We further developed time series prediction models of synaptic communications. By combining dimension reduction using the general framework, gradient kernel dimension reduction, and probabilistic modeling, the overall relationships of neural activities were incorporated. By this approach, the stochastic but coordinated dynamics were reproduced in the simulated whole-brain neural network. We found that noise in the nervous system is crucial for generating realistic whole-brain dynamics. Furthermore, by evaluating synaptic interaction properties in the models, strong interactions within the core neural circuit, variable sensory transmission and importance of gap junctions were inferred. Virtual optogenetics can be also performed using the model. These analyses provide a solid foundation for understanding information flow in real neural networks.
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Affiliation(s)
- Yu Toyoshima
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
| | - Hirofumi Sato
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
| | - Daiki Nagata
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
| | - Manami Kanamori
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
| | - Moon Sun Jang
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
| | - Koyo Kuze
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
| | - Suzu Oe
- Department of Biology, Faculty of Sciences, Kyushu University, Nishi-ku, Fukuoka, Japan
| | - Takayuki Teramoto
- Department of Biology, Faculty of Sciences, Kyushu University, Nishi-ku, Fukuoka, Japan
| | - Yuishi Iwasaki
- Department of Mechanical Systems Engineering, Graduate School of Science and Engineering, Ibaraki University, Hitachi, Ibaraki, Japan
| | - Ryo Yoshida
- The Institute of Statistical Mathematics, Research Organization of Information and Systems, Tachikawa, Tokyo, Japan
| | - Takeshi Ishihara
- Department of Biology, Faculty of Sciences, Kyushu University, Nishi-ku, Fukuoka, Japan
| | - Yuichi Iino
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
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6
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Dyballa L, Lang S, Haslund-Gourley A, Yemini E, Zucker SW. Learning dynamic representations of the functional connectome in neurobiological networks. ARXIV 2024:arXiv:2402.14102v2. [PMID: 38463505 PMCID: PMC10925416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
The static synaptic connectivity of neuronal circuits stands in direct contrast to the dynamics of their function. As in changing community interactions, different neurons can participate actively in various combinations to effect behaviors at different times. We introduce an unsupervised approach to learn the dynamic affinities between neurons in live, behaving animals, and to reveal which communities form among neurons at different times. The inference occurs in two major steps. First, pairwise non-linear affinities between neuronal traces from brain-wide calcium activity are organized by non-negative tensor factorization (NTF). Each factor specifies which groups of neurons are most likely interacting for an inferred interval in time, and for which animals. Finally, a generative model that allows for weighted community detection is applied to the functional motifs produced by NTF to reveal a dynamic functional connectome. Since time codes the different experimental variables (e.g., application of chemical stimuli), this provides an atlas of neural motifs active during separate stages of an experiment (e.g., stimulus application or spontaneous behaviors). Results from our analysis are experimentally validated, confirming that our method is able to robustly predict causal interactions between neurons to generate behavior.
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Affiliation(s)
| | - Samuel Lang
- Dept. Neurobiology, UMass Chan Medical School
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7
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Brezovec BE, Berger AB, Hao YA, Chen F, Druckmann S, Clandinin TR. Mapping the neural dynamics of locomotion across the Drosophila brain. Curr Biol 2024; 34:710-726.e4. [PMID: 38242122 DOI: 10.1016/j.cub.2023.12.063] [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: 08/16/2023] [Revised: 11/13/2023] [Accepted: 12/20/2023] [Indexed: 01/21/2024]
Abstract
Locomotion engages widely distributed networks of neurons. However, our understanding of the spatial architecture and temporal dynamics of the networks that underpin walking remains incomplete. We use volumetric two-photon imaging to map neural activity associated with walking across the entire brain of Drosophila. We define spatially clustered neural signals selectively associated with changes in either forward or angular velocity, demonstrating that neurons with similar behavioral selectivity are clustered. These signals reveal distinct topographic maps in diverse brain regions involved in navigation, memory, sensory processing, and motor control, as well as regions not previously linked to locomotion. We identify temporal trajectories of neural activity that sweep across these maps, including signals that anticipate future movement, representing the sequential engagement of clusters with different behavioral specificities. Finally, we register these maps to a connectome and identify neural networks that we propose underlie the observed signals, setting a foundation for subsequent circuit dissection. Overall, our work suggests a spatiotemporal framework for the emergence and execution of complex walking maneuvers and links this brain-wide neural activity to single neurons and local circuits.
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Affiliation(s)
- Bella E Brezovec
- Department of Neurobiology, Stanford University, Fairchild D200, 299 W. Campus Drive, Stanford, CA 94305, USA
| | - Andrew B Berger
- Department of Neurobiology, Stanford University, Fairchild D200, 299 W. Campus Drive, Stanford, CA 94305, USA
| | - Yukun A Hao
- Department of Neurobiology, Stanford University, Fairchild D200, 299 W. Campus Drive, Stanford, CA 94305, USA
| | - Feng Chen
- Department of Neurobiology, Stanford University, Fairchild D200, 299 W. Campus Drive, Stanford, CA 94305, USA
| | - Shaul Druckmann
- Department of Neurobiology, Stanford University, Fairchild D200, 299 W. Campus Drive, Stanford, CA 94305, USA
| | - Thomas R Clandinin
- Department of Neurobiology, Stanford University, Fairchild D200, 299 W. Campus Drive, Stanford, CA 94305, USA.
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8
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Zimnik AJ, Cora Ames K, An X, Driscoll L, Lara AH, Russo AA, Susoy V, Cunningham JP, Paninski L, Churchland MM, Glaser JI. Identifying Interpretable Latent Factors with Sparse Component Analysis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.05.578988. [PMID: 38370650 PMCID: PMC10871230 DOI: 10.1101/2024.02.05.578988] [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
In many neural populations, the computationally relevant signals are posited to be a set of 'latent factors' - signals shared across many individual neurons. Understanding the relationship between neural activity and behavior requires the identification of factors that reflect distinct computational roles. Methods for identifying such factors typically require supervision, which can be suboptimal if one is unsure how (or whether) factors can be grouped into distinct, meaningful sets. Here, we introduce Sparse Component Analysis (SCA), an unsupervised method that identifies interpretable latent factors. SCA seeks factors that are sparse in time and occupy orthogonal dimensions. With these simple constraints, SCA facilitates surprisingly clear parcellations of neural activity across a range of behaviors. We applied SCA to motor cortex activity from reaching and cycling monkeys, single-trial imaging data from C. elegans, and activity from a multitask artificial network. SCA consistently identified sets of factors that were useful in describing network computations.
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Affiliation(s)
- Andrew J Zimnik
- Department of Neuroscience, Columbia University Medical Center, New York, NY, USA
- Zuckerman Institute, Columbia University, New York, NY, USA
| | - K Cora Ames
- Department of Neuroscience, Columbia University Medical Center, New York, NY, USA
- Zuckerman Institute, Columbia University, New York, NY, USA
- Grossman Center for the Statistics of Mind, Columbia University, New York, NY, USA
- Center for Theoretical Neuroscience, Columbia University, New York, NY, USA
| | - Xinyue An
- Department of Neurology, Northwestern University, Chicago, IL, USA
- Interdepartmental Neuroscience Program, Northwestern University, Chicago, IL, USA
| | - Laura Driscoll
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
- Allen Institute for Neural Dynamics, Allen Institute, Seattle, CA, USA
| | - Antonio H Lara
- Department of Neuroscience, Columbia University Medical Center, New York, NY, USA
- Zuckerman Institute, Columbia University, New York, NY, USA
| | - Abigail A Russo
- Department of Neuroscience, Columbia University Medical Center, New York, NY, USA
- Zuckerman Institute, Columbia University, New York, NY, USA
| | - Vladislav Susoy
- Department of Physics, Harvard University, Cambridge, MA, USA
- Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - John P Cunningham
- Zuckerman Institute, Columbia University, New York, NY, USA
- Grossman Center for the Statistics of Mind, Columbia University, New York, NY, USA
- Center for Theoretical Neuroscience, Columbia University, New York, NY, USA
- Department of Statistics, Columbia University, New York, NY, USA
| | - Liam Paninski
- Zuckerman Institute, Columbia University, New York, NY, USA
- Grossman Center for the Statistics of Mind, Columbia University, New York, NY, USA
- Center for Theoretical Neuroscience, Columbia University, New York, NY, USA
- Department of Statistics, Columbia University, New York, NY, USA
| | - Mark M Churchland
- Department of Neuroscience, Columbia University Medical Center, New York, NY, USA
- Zuckerman Institute, Columbia University, New York, NY, USA
- Grossman Center for the Statistics of Mind, Columbia University, New York, NY, USA
- Kavli Institute for Brain Science, Columbia University Medical Center, New York, NY, USA
| | - Joshua I Glaser
- Department of Neurology, Northwestern University, Chicago, IL, USA
- Department of Computer Science, Northwestern University, Evanston, IL, USA
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9
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Wan Y, Macias LH, Garcia LR. Unraveling the hierarchical structure of posture and muscle activity changes during mating of Caenorhabditis elegans. PNAS NEXUS 2024; 3:pgae032. [PMID: 38312221 PMCID: PMC10837012 DOI: 10.1093/pnasnexus/pgae032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 01/16/2024] [Indexed: 02/06/2024]
Abstract
One goal of neurobiology is to explain how decision-making in neuromuscular circuits produces behaviors. However, two obstacles complicate such efforts: individual behavioral variability and the challenge of simultaneously assessing multiple neuronal activities during behavior. Here, we circumvent these obstacles by analyzing whole animal behavior from a library of Caenorhabditis elegans male mating recordings. The copulating males express the GCaMP calcium sensor in the muscles, allowing simultaneous recording of posture and muscle activities. Our library contains wild type and males with selective neuronal desensitization in serotonergic neurons, which include male-specific posterior cord motor/interneurons and sensory ray neurons that modulate mating behavior. Incorporating deep learning-enabled computer vision, we developed a software to automatically quantify posture and muscle activities. By modeling, the posture and muscle activity data are classified into stereotyped modules, with the behaviors represented by serial executions and transitions among the modules. Detailed analysis of the modules reveals previously unidentified subtypes of the male's copulatory spicule prodding behavior. We find that wild-type and serotonergic neurons-suppressed males had different usage preferences for those module subtypes, highlighting the requirement of serotonergic neurons in the coordinated function of some muscles. In the structure of the behavior, bi-module repeats coincide with most of the previously described copulation steps, suggesting a recursive "repeat until success/give up" program is used for each step during mating. On the other hand, the transition orders of the bi-module repeats reveal the sub-behavioral hierarchy males employ to locate and inseminate hermaphrodites.
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Affiliation(s)
- Yufeng Wan
- Department of Biology, Texas A&M University, 3258 TAMU, College Station, TX 77843, USA
| | - Luca Henze Macias
- Department of Biology, Texas A&M University, 3258 TAMU, College Station, TX 77843, USA
| | - Luis Rene Garcia
- Department of Biology, Texas A&M University, 3258 TAMU, College Station, TX 77843, USA
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10
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Park CF, Barzegar-Keshteli M, Korchagina K, Delrocq A, Susoy V, Jones CL, Samuel ADT, Rahi SJ. Automated neuron tracking inside moving and deforming C. elegans using deep learning and targeted augmentation. Nat Methods 2024; 21:142-149. [PMID: 38052988 DOI: 10.1038/s41592-023-02096-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Accepted: 10/20/2023] [Indexed: 12/07/2023]
Abstract
Reading out neuronal activity from three-dimensional (3D) functional imaging requires segmenting and tracking individual neurons. This is challenging in behaving animals if the brain moves and deforms. The traditional approach is to train a convolutional neural network with ground-truth (GT) annotations of images representing different brain postures. For 3D images, this is very labor intensive. We introduce 'targeted augmentation', a method to automatically synthesize artificial annotations from a few manual annotations. Our method ('Targettrack') learns the internal deformations of the brain to synthesize annotations for new postures by deforming GT annotations. This reduces the need for manual annotation and proofreading. A graphical user interface allows the application of the method end-to-end. We demonstrate Targettrack on recordings where neurons are labeled as key points or 3D volumes. Analyzing freely moving animals exposed to odor pulses, we uncover rich patterns in interneuron dynamics, including switching neuronal entrainment on and off.
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Affiliation(s)
- Core Francisco Park
- Department of Physics and Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - Mahsa Barzegar-Keshteli
- Laboratory of the Physics of Biological Systems, Institute of Physics, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Kseniia Korchagina
- Laboratory of the Physics of Biological Systems, Institute of Physics, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Ariane Delrocq
- Laboratory of the Physics of Biological Systems, Institute of Physics, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Vladislav Susoy
- Department of Physics and Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - Corinne L Jones
- Swiss Data Science Center, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Aravinthan D T Samuel
- Department of Physics and Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - Sahand Jamal Rahi
- Laboratory of the Physics of Biological Systems, Institute of Physics, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
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11
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Prakash SJ, Van Auken KM, Hill DP, Sternberg PW. Semantic representation of neural circuit knowledge in Caenorhabditis elegans. Brain Inform 2023; 10:30. [PMID: 37947958 PMCID: PMC10638142 DOI: 10.1186/s40708-023-00208-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 09/22/2023] [Indexed: 11/12/2023] Open
Abstract
In modern biology, new knowledge is generated quickly, making it challenging for researchers to efficiently acquire and synthesise new information from the large volume of primary publications. To address this problem, computational approaches that generate machine-readable representations of scientific findings in the form of knowledge graphs have been developed. These representations can integrate different types of experimental data from multiple papers and biological knowledge bases in a unifying data model, providing a complementary method to manual review for interacting with published knowledge. The Gene Ontology Consortium (GOC) has created a semantic modelling framework that extends individual functional gene annotations to structured descriptions of causal networks representing biological processes (Gene Ontology-Causal Activity Modelling, or GO-CAM). In this study, we explored whether the GO-CAM framework could represent knowledge of the causal relationships between environmental inputs, neural circuits and behavior in the model nematode C. elegans [C. elegans Neural-Circuit Causal Activity Modelling (CeN-CAM)]. We found that, given extensions to several relevant ontologies, a wide variety of author statements from the literature about the neural circuit basis of egg-laying and carbon dioxide (CO2) avoidance behaviors could be faithfully represented with CeN-CAM. Through this process, we were able to generate generic data models for several categories of experimental results. We also discuss how semantic modelling may be used to functionally annotate the C. elegans connectome. Thus, Gene Ontology-based semantic modelling has the potential to support various machine-readable representations of neurobiological knowledge.
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Affiliation(s)
- Sharan J Prakash
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, 91125, USA
| | - Kimberly M Van Auken
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, 91125, USA
| | - David P Hill
- The Jackson Laboratory, Bar Harbor, ME, 04609, USA
| | - Paul W Sternberg
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, 91125, USA.
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12
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Susoy V, Samuel ADT. Evolutionarily conserved behavioral plasticity enables context-dependent mating in C. elegans. Curr Biol 2023; 33:4532-4537.e3. [PMID: 37769659 PMCID: PMC10615801 DOI: 10.1016/j.cub.2023.09.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 07/20/2023] [Accepted: 09/01/2023] [Indexed: 10/03/2023]
Abstract
Behavioral plasticity helps humans and animals to achieve their goals by adapting their behaviors to different environments.1,2 Although behavioral plasticity is ubiquitous, many innate species-specific behaviors, such as mating, are often assumed to be stereotyped and unaffected by plasticity or learning, especially in invertebrates. Here, we describe a novel case of behavioral plasticity in the nematode C. elegans. Under standard lab conditions (agar plates with bacterial food), the male performs parallel mating,3,4,5 a largely two-dimensional behavioral strategy where his body and tail remain flat on the surface and slide alongside the partner's body from initial contact to copulation. But when placed in liquid media, the male performs spiral mating, a distinctly three-dimensional behavioral strategy where he winds around the partner's body in a helical embrace. The performance of spiral mating does not require a long-term change in growing conditions, but it does improve with experience. This experience-dependent improvement appears to involve a critical period-a time window around the L4 larval stage to the early adult stage-which coincides with the development of most male-specific neurons. We tested several wild isolates of C. elegans and other Caenorhabditis species and found that most were capable of parallel mating on surfaces and spiral mating in liquids. We suggest that two- and three-dimensional mating strategies in Caenorhabditis are plastic, conditionally expressed phenotypes conserved across the genus, which can be genetically "fixed" in some species.
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Affiliation(s)
- Vladislav Susoy
- Department of Physics and Center for Brain Science, Harvard University, Cambridge, MA 02138, USA.
| | - Aravinthan D T Samuel
- Department of Physics and Center for Brain Science, Harvard University, Cambridge, MA 02138, USA.
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13
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Ji H, Fouad AD, Li Z, Ruba A, Fang-Yen C. A proprioceptive feedback circuit drives Caenorhabditis elegans locomotor adaptation through dopamine signaling. Proc Natl Acad Sci U S A 2023; 120:e2219341120. [PMID: 37155851 PMCID: PMC10193984 DOI: 10.1073/pnas.2219341120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Accepted: 04/14/2023] [Indexed: 05/10/2023] Open
Abstract
An animal adapts its motor behavior to navigate the external environment. This adaptation depends on proprioception, which provides feedback on an animal's body postures. How proprioception mechanisms interact with motor circuits and contribute to locomotor adaptation remains unclear. Here, we describe and characterize proprioception-mediated homeostatic control of undulatory movement in the roundworm Caenorhabditis elegans. We found that the worm responds to optogenetically or mechanically induced decreases in midbody bending amplitude by increasing its anterior amplitude. Conversely, it responds to increased midbody amplitude by decreasing the anterior amplitude. Using genetics, microfluidic and optogenetic perturbation response analyses, and optical neurophysiology, we elucidated the neural circuit underlying this compensatory postural response. The dopaminergic PDE neurons proprioceptively sense midbody bending and signal to AVK interneurons via the D2-like dopamine receptor DOP-3. The FMRFamide-like neuropeptide FLP-1, released by AVK, regulates SMB head motor neurons to modulate anterior bending. We propose that this homeostatic behavioral control optimizes locomotor efficiency. Our findings demonstrate a mechanism in which proprioception works with dopamine and neuropeptide signaling to mediate motor control, a motif that may be conserved in other animals.
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Affiliation(s)
- Hongfei Ji
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA19104
- Department of Biomedical Engineering, College of Engineering, The Ohio State University, Columbus, OH43210
| | - Anthony D. Fouad
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA19104
| | - Zihao Li
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA19104
| | - Andrew Ruba
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA19104
| | - Christopher Fang-Yen
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA19104
- Department of Biomedical Engineering, College of Engineering, The Ohio State University, Columbus, OH43210
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
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14
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Thomson EE, Harfouche M, Kim K, Konda PC, Seitz CW, Cooke C, Xu S, Jacobs WS, Blazing R, Chen Y, Sharma S, Dunn TW, Park J, Horstmeyer RW, Naumann EA. Gigapixel imaging with a novel multi-camera array microscope. eLife 2022; 11:e74988. [PMID: 36515989 PMCID: PMC9917455 DOI: 10.7554/elife.74988] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 11/23/2022] [Indexed: 12/15/2022] Open
Abstract
The dynamics of living organisms are organized across many spatial scales. However, current cost-effective imaging systems can measure only a subset of these scales at once. We have created a scalable multi-camera array microscope (MCAM) that enables comprehensive high-resolution recording from multiple spatial scales simultaneously, ranging from structures that approach the cellular scale to large-group behavioral dynamics. By collecting data from up to 96 cameras, we computationally generate gigapixel-scale images and movies with a field of view over hundreds of square centimeters at an optical resolution of 18 µm. This allows us to observe the behavior and fine anatomical features of numerous freely moving model organisms on multiple spatial scales, including larval zebrafish, fruit flies, nematodes, carpenter ants, and slime mold. Further, the MCAM architecture allows stereoscopic tracking of the z-position of organisms using the overlapping field of view from adjacent cameras. Overall, by removing the bottlenecks imposed by single-camera image acquisition systems, the MCAM provides a powerful platform for investigating detailed biological features and behavioral processes of small model organisms across a wide range of spatial scales.
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Affiliation(s)
- Eric E Thomson
- Department of Neurobiology, Duke School of MedicineDurhamUnited States
| | | | - Kanghyun Kim
- Biomedical Engineering, Duke UniversityDurhamUnited States
| | - Pavan C Konda
- Biomedical Engineering, Duke UniversityDurhamUnited States
| | - Catherine W Seitz
- Department of Neurobiology, Duke School of MedicineDurhamUnited States
| | - Colin Cooke
- Biomedical Engineering, Duke UniversityDurhamUnited States
| | - Shiqi Xu
- Biomedical Engineering, Duke UniversityDurhamUnited States
| | - Whitney S Jacobs
- Department of Neurobiology, Duke School of MedicineDurhamUnited States
| | - Robin Blazing
- Department of Neurobiology, Duke School of MedicineDurhamUnited States
| | - Yang Chen
- Department of Neurobiology, Duke School of MedicineDurhamUnited States
| | | | - Timothy W Dunn
- Biomedical Engineering, Duke UniversityDurhamUnited States
| | | | - Roarke W Horstmeyer
- Ramona Optics IncDurhamUnited States
- Biomedical Engineering, Duke UniversityDurhamUnited States
| | - Eva A Naumann
- Department of Neurobiology, Duke School of MedicineDurhamUnited States
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15
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Wu Y, Wu S, Wang X, Lang C, Zhang Q, Wen Q, Xu T. Rapid detection and recognition of whole brain activity in a freely behaving Caenorhabditis elegans. PLoS Comput Biol 2022; 18:e1010594. [PMID: 36215325 PMCID: PMC9584436 DOI: 10.1371/journal.pcbi.1010594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 10/20/2022] [Accepted: 09/22/2022] [Indexed: 11/07/2022] Open
Abstract
Advanced volumetric imaging methods and genetically encoded activity indicators have permitted a comprehensive characterization of whole brain activity at single neuron resolution in Caenorhabditis elegans. The constant motion and deformation of the nematode nervous system, however, impose a great challenge for consistent identification of densely packed neurons in a behaving animal. Here, we propose a cascade solution for long-term and rapid recognition of head ganglion neurons in a freely moving C. elegans. First, potential neuronal regions from a stack of fluorescence images are detected by a deep learning algorithm. Second, 2-dimensional neuronal regions are fused into 3-dimensional neuron entities. Third, by exploiting the neuronal density distribution surrounding a neuron and relative positional information between neurons, a multi-class artificial neural network transforms engineered neuronal feature vectors into digital neuronal identities. With a small number of training samples, our bottom-up approach is able to process each volume—1024 × 1024 × 18 in voxels—in less than 1 second and achieves an accuracy of 91% in neuronal detection and above 80% in neuronal tracking over a long video recording. Our work represents a step towards rapid and fully automated algorithms for decoding whole brain activity underlying naturalistic behaviors. An important question in neuroscience is to understand the relationship between brain dynamics and naturalistic behaviors when an animal is freely exploring its environment. In the last decade, it has become possible to genetically engineer animals whose neurons produce fluorescence reporters that change their brightness in response to brain activity. In small animals such as the nematode C. elegans, we can now record the fluorescence changes in and thereby infer neural activity from most neurons in the head of a worm, when the animal is freely moving. These neurons are densely packed in a small volume. Since the brain and body are moving and its shape undergoes significant deformation, a human expert, even after long hours of inspection, may still have difficulty to tell where the neurons are and who they are. We sought to develop an automatic method for rapidly detecting and tracking most of these neurons in a moving animal. To do this, we asked a human expert to annotate all head neurons—their locations and digital identities—across a small number of volumes. Then, we trained a computer to learn the locations and digital identities of these neurons across different imaging volumes. Our machine inference method is fast and accurate. While it takes a human expert several hours to complete a sequence of volumes, a machine can finish the task in a few minutes. We hope our method provides a better and more efficient engine for extracting knowledge from whole brain imaging datasets and animal behaviors.
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Affiliation(s)
- Yuxiang Wu
- Chinese Academy of Sciences Key Laboratory of Brain Function and Diseases, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
- Hefei National Laboratory for Physical Sciences at the Microscale, Center for Integrative Imaging, University of Science and Technology of China, Hefei, China
| | - Shang Wu
- Chinese Academy of Sciences Key Laboratory of Brain Function and Diseases, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
- Hefei National Laboratory for Physical Sciences at the Microscale, Center for Integrative Imaging, University of Science and Technology of China, Hefei, China
| | - Xin Wang
- John Hopcroft Center for Computer Science, School of electronic information and electrical engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Chengtian Lang
- Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, China
| | - Quanshi Zhang
- John Hopcroft Center for Computer Science, School of electronic information and electrical engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Quan Wen
- Chinese Academy of Sciences Key Laboratory of Brain Function and Diseases, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
- Hefei National Laboratory for Physical Sciences at the Microscale, Center for Integrative Imaging, University of Science and Technology of China, Hefei, China
- * E-mail: (QW); (TX)
| | - Tianqi Xu
- Chinese Academy of Sciences Key Laboratory of Brain Function and Diseases, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
- Hefei National Laboratory for Physical Sciences at the Microscale, Center for Integrative Imaging, University of Science and Technology of China, Hefei, China
- * E-mail: (QW); (TX)
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16
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Yu YV, Xue W, Chen Y. Multisensory Integration in Caenorhabditis elegans in Comparison to Mammals. Brain Sci 2022; 12:brainsci12101368. [PMID: 36291302 PMCID: PMC9599712 DOI: 10.3390/brainsci12101368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 09/28/2022] [Accepted: 10/03/2022] [Indexed: 11/30/2022] Open
Abstract
Multisensory integration refers to sensory inputs from different sensory modalities being processed simultaneously to produce a unitary output. Surrounded by stimuli from multiple modalities, animals utilize multisensory integration to form a coherent and robust representation of the complex environment. Even though multisensory integration is fundamentally essential for animal life, our understanding of the underlying mechanisms, especially at the molecular, synaptic and circuit levels, remains poorly understood. The study of sensory perception in Caenorhabditis elegans has begun to fill this gap. We have gained a considerable amount of insight into the general principles of sensory neurobiology owing to C. elegans’ highly sensitive perceptions, relatively simple nervous system, ample genetic tools and completely mapped neural connectome. Many interesting paradigms of multisensory integration have been characterized in C. elegans, for which input convergence occurs at the sensory neuron or the interneuron level. In this narrative review, we describe some representative cases of multisensory integration in C. elegans, summarize the underlying mechanisms and compare them with those in mammalian systems. Despite the differences, we believe C. elegans is able to provide unique insights into how processing and integrating multisensory inputs can generate flexible and adaptive behaviors. With the emergence of whole brain imaging, the ability of C. elegans to monitor nearly the entire nervous system may be crucial for understanding the function of the brain as a whole.
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Affiliation(s)
- Yanxun V. Yu
- Department of Neurology, Medical Research Institute, Zhongnan Hospital of Wuhan University, Wuhan University, Wuhan 430070, China
- Frontier Science Center for Immunology and Metabolism, Wuhan University, Wuhan 430070, China
- Correspondence: or
| | - Weikang Xue
- Department of Neurology, Medical Research Institute, Zhongnan Hospital of Wuhan University, Wuhan University, Wuhan 430070, China
| | - Yuanhua Chen
- Department of Neurology, Medical Research Institute, Zhongnan Hospital of Wuhan University, Wuhan University, Wuhan 430070, China
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17
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Gadenne MJ, Hardege I, Yemini E, Suleski D, Jaggers P, Beets I, Schafer WR, Chew YL. Neuropeptide signalling shapes feeding and reproductive behaviours in male Caenorhabditis elegans. Life Sci Alliance 2022; 5:5/10/e202201420. [PMID: 35738805 PMCID: PMC9233197 DOI: 10.26508/lsa.202201420] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 06/03/2022] [Accepted: 06/03/2022] [Indexed: 11/24/2022] Open
Abstract
LURY-1 peptides are expressed in distinct cells in different sexes and have sex-specific effects on feeding and mating, providing further evidence for the role of neuromodulators in sexual dimorphism. Sexual dimorphism occurs where different sexes of the same species display differences in characteristics not limited to reproduction. For the nematode Caenorhabditis elegans, in which the complete neuroanatomy has been solved for both hermaphrodites and males, sexually dimorphic features have been observed both in terms of the number of neurons and in synaptic connectivity. In addition, male behaviours, such as food-leaving to prioritise searching for mates, have been attributed to neuropeptides released from sex-shared or sex-specific neurons. In this study, we show that the lury-1 neuropeptide gene shows a sexually dimorphic expression pattern; being expressed in pharyngeal neurons in both sexes but displaying additional expression in tail neurons only in the male. We also show that lury-1 mutant animals show sex differences in feeding behaviours, with pharyngeal pumping elevated in hermaphrodites but reduced in males. LURY-1 also modulates male mating efficiency, influencing motor events during contact with a hermaphrodite. Our findings indicate sex-specific roles of this peptide in feeding and reproduction in C. elegans, providing further insight into neuromodulatory control of sexually dimorphic behaviours.
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Affiliation(s)
- Matthew J Gadenne
- Molecular Horizons, University of Wollongong and Illawarra Health and Medical Research Institute, Wollongong, Australia
| | - Iris Hardege
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK
| | - Eviatar Yemini
- Department of Neurobiology, UMass Chan Medical School, Worcester, MA, USA
| | - Djordji Suleski
- Molecular Horizons, University of Wollongong and Illawarra Health and Medical Research Institute, Wollongong, Australia
| | - Paris Jaggers
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK
| | - Isabel Beets
- Department of Biology, KU Leuven, Leuven, Belgium
| | - William R Schafer
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK.,Department of Biology, KU Leuven, Leuven, Belgium
| | - Yee Lian Chew
- Flinders Health and Medical Research Institute and College of Medicine and Public Health, Flinders University, Adelaide, Australia
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18
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Brugman KI, Susoy V, Whittaker AJ, Palma W, Nava S, Samuel ADT, Sternberg PW. PEZO-1 and TRP-4 mechanosensors are involved in mating behavior in Caenorhabditis elegans. PNAS NEXUS 2022; 1:pgac213. [PMID: 36712331 PMCID: PMC9802279 DOI: 10.1093/pnasnexus/pgac213] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Accepted: 09/22/2022] [Indexed: 02/01/2023]
Abstract
Male mating in Caenorhabditis elegans is a complex behavior with a strong mechanosensory component. C. elegans has several characterized mechanotransducer proteins, but few have been shown to contribute to mating. Here, we investigated the roles of PEZO-1, a piezo channel, and TRP-4, a mechanotransducing TRPN channel, in male mating behavior. We show that pezo-1 is expressed in several male-specific neurons with known roles in mating. We show that, among other neurons, trp-4 is expressed in the Post-Cloacal sensilla neuron type A (PCA) sensory neuron, which monitors relative sliding between the male and the hermaphrodite and inhibits neurons involved in vulva detection. Mutations in both genes compromise many steps of mating, including initial response to the hermaphrodite, scanning, turning, and vulva detection. We performed pan-neuronal imaging during mating between freely moving mutant males and hermaphrodites. Both pezo-1 and trp-4 mutants showed spurious activation of the sensory neurons involved in vulva detection. In trp-4 mutants, this spurious activation might be caused by PCA failure to inhibit vulva-detecting neurons during scanning. Indeed, we show that without functional TRP-4, PCA fails to detect the relative sliding between the male and hermaphrodite. Cell-specific TRP-4 expression restores PCA's mechanosensory function. Our results demonstrate new roles for both PEZO-1 and TRP-4 mechanotransducers in C. elegans mating behavior.
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Affiliation(s)
| | | | - Allyson J Whittaker
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Wilber Palma
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Stephanie Nava
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Aravinthan D T Samuel
- Department of Physics and Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
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19
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Fast, efficient, and accurate neuro-imaging denoising via supervised deep learning. Nat Commun 2022; 13:5165. [PMID: 36056020 PMCID: PMC9440141 DOI: 10.1038/s41467-022-32886-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 08/18/2022] [Indexed: 11/08/2022] Open
Abstract
Volumetric functional imaging is widely used for recording neuron activities in vivo, but there exist tradeoffs between the quality of the extracted calcium traces, imaging speed, and laser power. While deep-learning methods have recently been applied to denoise images, their applications to downstream analyses, such as recovering high-SNR calcium traces, have been limited. Further, these methods require temporally-sequential pre-registered data acquired at ultrafast rates. Here, we demonstrate a supervised deep-denoising method to circumvent these tradeoffs for several applications, including whole-brain imaging, large-field-of-view imaging in freely moving animals, and recovering complex neurite structures in C. elegans. Our framework has 30× smaller memory footprint, and is fast in training and inference (50–70 ms); it is highly accurate and generalizable, and further, trained with only small, non-temporally-sequential, independently-acquired training datasets (∼500 pairs of images). We envision that the framework will enable faster and long-term imaging experiments necessary to study neuronal mechanisms of many behaviors. Volumetric functional imaging is widely used for recording neuron activities in vivo for many experimental organisms. Here the authors report supervised deep-denoising methods for improved whole-brain imaging, large field-of-view imaging in freely moving animals, and recovering complex neurite structures in C. elegans.
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20
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A set of hub neurons and non-local connectivity features support global brain dynamics in C. elegans. Curr Biol 2022; 32:3443-3459.e8. [PMID: 35809568 DOI: 10.1016/j.cub.2022.06.039] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 05/17/2022] [Accepted: 06/13/2022] [Indexed: 11/20/2022]
Abstract
The wiring architecture of neuronal networks is assumed to be a strong determinant of their dynamical computations. An ongoing effort in neuroscience is therefore to generate comprehensive synapse-resolution connectomes alongside brain-wide activity maps. However, the structure-function relationship, i.e., how the anatomical connectome and neuronal dynamics relate to each other on a global scale, remains unsolved. Systematically, comparing graph features in the C. elegans connectome with correlations in nervous system-wide neuronal dynamics, we found that few local connectivity motifs and mostly other non-local features such as triplet motifs and input similarities can predict functional relationships between neurons. Surprisingly, quantities such as connection strength and amount of common inputs do not improve these predictions, suggesting that the network's topology is sufficient. We demonstrate that hub neurons in the connectome are key to these relevant graph features. Consistently, inhibition of multiple hub neurons specifically disrupts brain-wide correlations. Thus, we propose that a set of hub neurons and non-local connectivity features provide an anatomical substrate for global brain dynamics.
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21
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Lin A, Witvliet D, Hernandez-Nunez L, Linderman SW, Samuel ADT, Venkatachalam V. Imaging whole-brain activity to understand behavior. NATURE REVIEWS. PHYSICS 2022; 4:292-305. [PMID: 37409001 PMCID: PMC10320740 DOI: 10.1038/s42254-022-00430-w] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 01/25/2022] [Indexed: 07/07/2023]
Abstract
The brain evolved to produce behaviors that help an animal inhabit the natural world. During natural behaviors, the brain is engaged in many levels of activity from the detection of sensory inputs to decision-making to motor planning and execution. To date, most brain studies have focused on small numbers of neurons that interact in limited circuits. This allows analyzing individual computations or steps of neural processing. During behavior, however, brain activity must integrate multiple circuits in different brain regions. The activities of different brain regions are not isolated, but may be contingent on one another. Coordinated and concurrent activity within and across brain areas is organized by (1) sensory information from the environment, (2) the animal's internal behavioral state, and (3) recurrent networks of synaptic and non-synaptic connectivity. Whole-brain recording with cellular resolution provides a new opportunity to dissect the neural basis of behavior, but whole-brain activity is also mutually contingent on behavior itself. This is especially true for natural behaviors like navigation, mating, or hunting, which require dynamic interaction between the animal, its environment, and other animals. In such behaviors, the sensory experience of an unrestrained animal is actively shaped by its movements and decisions. Many of the signaling and feedback pathways that an animal uses to guide behavior only occur in freely moving animals. Recent technological advances have enabled whole-brain recording in small behaving animals including nematodes, flies, and zebrafish. These whole-brain experiments capture neural activity with cellular resolution spanning sensory, decision-making, and motor circuits, and thereby demand new theoretical approaches that integrate brain dynamics with behavioral dynamics. Here, we review the experimental and theoretical methods that are being employed to understand animal behavior and whole-brain activity, and the opportunities for physics to contribute to this emerging field of systems neuroscience.
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Affiliation(s)
- Albert Lin
- Department of Physics, Harvard University, Cambridge, MA, USA
- Center for Brain Science, Harvard University, Cambridge, MA, USA
- Center for the Physics of Biological Function, Princeton University, Princeton, NJ, USA
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Daniel Witvliet
- Department of Physics, Harvard University, Cambridge, MA, USA
- Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - Luis Hernandez-Nunez
- Center for Brain Science, Harvard University, Cambridge, MA, USA
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA
| | - Scott W Linderman
- Department of Statistics, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
| | - Aravinthan D T Samuel
- Department of Physics, Harvard University, Cambridge, MA, USA
- Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - Vivek Venkatachalam
- Center for Brain Science, Harvard University, Cambridge, MA, USA
- Department of Physics, Northeastern University, Boston, MA, USA
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22
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Flavell SW, Gordus A. Dynamic functional connectivity in the static connectome of Caenorhabditis elegans. Curr Opin Neurobiol 2022; 73:102515. [PMID: 35183877 PMCID: PMC9621599 DOI: 10.1016/j.conb.2021.12.002] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 12/16/2021] [Accepted: 12/22/2021] [Indexed: 01/01/2023]
Abstract
A hallmark of adaptive behavior is the ability to flexibly respond to sensory cues. To understand how neural circuits implement this flexibility, it is critical to resolve how a static anatomical connectome can be modulated such that functional connectivity in the network can be dynamically regulated. Here, we review recent work in the roundworm Caenorhabditis elegans on this topic. EM studies have mapped anatomical connectomes of many C. elegans animals, highlighting the level of stereotypy in the anatomical network. Brain-wide calcium imaging and studies of specified neural circuits have uncovered striking flexibility in the functional coupling of neurons. The coupling between neurons is controlled by neuromodulators that act over long timescales. This gives rise to persistent behavioral states that animals switch between, allowing them to generate adaptive behavioral responses across environmental conditions. Thus, the dynamic coupling of neurons enables multiple behavioral states to be encoded in a physically stereotyped connectome.
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Affiliation(s)
- Steven W Flavell
- Picower Institute for Learning and Memory, Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA.
| | - Andrew Gordus
- Department of Biology, Solomon H. Snyder Department of Neuroscience, Johns Hopkins University, Baltimore, MD, USA.
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23
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Connecting the dots in ethology: applying network theory to understand neural and animal collectives. Curr Opin Neurobiol 2022; 73:102532. [DOI: 10.1016/j.conb.2022.102532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2021] [Revised: 02/04/2022] [Accepted: 03/02/2022] [Indexed: 11/24/2022]
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24
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Toker IA, Lev I, Mor Y, Gurevich Y, Fisher D, Houri-Zeevi L, Antonova O, Doron H, Anava S, Gingold H, Hadany L, Shaham S, Rechavi O. Transgenerational inheritance of sexual attractiveness via small RNAs enhances evolvability in C. elegans. Dev Cell 2022; 57:298-309.e9. [PMID: 35134343 PMCID: PMC8826646 DOI: 10.1016/j.devcel.2022.01.005] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Revised: 09/12/2021] [Accepted: 01/05/2022] [Indexed: 11/16/2022]
Abstract
It is unknown whether transient transgenerational epigenetic responses to environmental challenges affect the process of evolution, which typically unfolds over many generations. Here, we show that in C. elegans, inherited small RNAs control genetic variation by regulating the crucial decision of whether to self-fertilize or outcross. We found that under stressful temperatures, younger hermaphrodites secrete a male-attracting pheromone. Attractiveness transmits transgenerationally to unstressed progeny via heritable small RNAs and the Argonaute Heritable RNAi Deficient-1 (HRDE-1). We identified an endogenous small interfering RNA pathway, enriched in endo-siRNAs that target sperm genes, that transgenerationally regulates sexual attraction, male prevalence, and outcrossing rates. Multigenerational mating competition experiments and mathematical simulations revealed that over generations, animals that inherit attractiveness mate more and their alleles spread in the population. We propose that the sperm serves as a "stress-sensor" that, via small RNA inheritance, promotes outcrossing in challenging environments when increasing genetic variation is advantageous.
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Affiliation(s)
- Itai Antoine Toker
- Department of Neurobiology, Faculty of Life Sciences & Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel.
| | - Itamar Lev
- Department of Neurobiology, Faculty of Life Sciences & Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel.
| | - Yael Mor
- Department of Neurobiology, Faculty of Life Sciences & Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel.
| | - Yael Gurevich
- School of Plant Sciences and Food Security, Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel
| | - Doron Fisher
- Department of Neurobiology, Faculty of Life Sciences & Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Leah Houri-Zeevi
- Department of Neurobiology, Faculty of Life Sciences & Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Olga Antonova
- Department of Neurobiology, Faculty of Life Sciences & Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Hila Doron
- Department of Neurobiology, Faculty of Life Sciences & Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Sarit Anava
- Department of Neurobiology, Faculty of Life Sciences & Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Hila Gingold
- Department of Neurobiology, Faculty of Life Sciences & Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Lilach Hadany
- School of Plant Sciences and Food Security, Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel
| | - Shai Shaham
- Laboratory of Developmental Genetics, The Rockefeller University, New York, NY, USA
| | - Oded Rechavi
- Department of Neurobiology, Faculty of Life Sciences & Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel.
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Batta I, Yao Q, Sabrin KM, Dovrolis C. A weighted network analysis framework for the hourglass effect-And its application in the C. elegans connectome. PLoS One 2021; 16:e0249846. [PMID: 34705821 PMCID: PMC8550382 DOI: 10.1371/journal.pone.0249846] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Accepted: 09/09/2021] [Indexed: 11/18/2022] Open
Abstract
Understanding hierarchy and modularity in natural as well as technological networks is of utmost importance. A major aspect of such analysis involves identifying the nodes that are crucial to the overall processing structure of the network. More recently, the approach of hourglass analysis has been developed for the purpose of quantitatively analyzing whether only a few intermediate nodes mediate the information processing between a large number of inputs and outputs of a network. We develop a new framework for hourglass analysis that takes network weights into account while identifying the core nodes and the extent of hourglass effect in a given weighted network. We use this framework to study the structural connectome of the C. elegans and identify intermediate neurons that form the core of sensori-motor pathways in the organism. Our results show that the neurons forming the core of the connectome show significant differences across the male and hermaphrodite sexes, with most core nodes in the male concentrated in sex-organs while they are located in the head for the hermaphrodite. Our work demonstrates that taking weights into account for network analysis framework leads to emergence of different network patterns in terms of identification of core nodes and hourglass structure in the network, which otherwise would be missed by unweighted approaches.
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Affiliation(s)
- Ishaan Batta
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States of America
| | - Qihang Yao
- School of Computer Science, Georgia Institute of Technology, Atlanta, Georgia, United States of America
| | - Kaeser M. Sabrin
- School of Computer Science, Georgia Institute of Technology, Atlanta, Georgia, United States of America
| | - Constantine Dovrolis
- School of Computer Science, Georgia Institute of Technology, Atlanta, Georgia, United States of America
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