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      Neuron ID dataset facilitates neuronal annotation for whole-brain activity imaging of C. elegans

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          Abstract

          Background

          Annotation of cell identity is an essential process in neuroscience that allows comparison of cells, including that of neural activities across different animals. In Caenorhabditis elegans, although unique identities have been assigned to all neurons, the number of annotatable neurons in an intact animal has been limited due to the lack of quantitative information on the location and identity of neurons.

          Results

          Here, we present a dataset that facilitates the annotation of neuronal identities, and demonstrate its application in a comprehensive analysis of whole-brain imaging. We systematically identified neurons in the head region of 311 adult worms using 35 cell-specific promoters and created a dataset of the expression patterns and the positions of the neurons. We found large positional variations that illustrated the difficulty of the annotation task. We investigated multiple combinations of cell-specific promoters driving distinct fluorescence and generated optimal strains for the annotation of most head neurons in an animal. We also developed an automatic annotation method with human interaction functionality that facilitates annotations needed for whole-brain imaging.

          Conclusion

          Our neuron ID dataset and optimal fluorescent strains enable the annotation of most neurons in the head region of adult C. elegans, both in full-automated fashion and a semi-automated version that includes human interaction functionalities. Our method can potentially be applied to model species used in research other than C. elegans, where the number of available cell-type-specific promoters and their variety will be an important consideration.

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          Most cited references31

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          Post-embryonic cell lineages of the nematode, Caenorhabditis elegans

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            Microfluidics for in vivo imaging of neuronal and behavioral activity in Caenorhabditis elegans.

            The nematode C. elegans is an excellent model organism for studying behavior at the neuronal level. Because of the organism's small size, it is challenging to deliver stimuli to C. elegans and monitor neuronal activity in a controlled environment. To address this problem, we developed two microfluidic chips, the 'behavior' chip and the 'olfactory' chip for imaging of neuronal and behavioral responses in C. elegans. We used the behavior chip to correlate the activity of AVA command interneurons with the worm locomotion pattern. We used the olfactory chip to record responses from ASH sensory neurons exposed to high-osmotic-strength stimulus. Observation of neuronal responses in these devices revealed previously unknown properties of AVA and ASH neurons. The use of these chips can be extended to correlate the activity of sensory neurons, interneurons and motor neurons with the worm's behavior.
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              Chemosensory neurons with overlapping functions direct chemotaxis to multiple chemicals in C. elegans.

              The functions of the 11 classes of exposed chemosensory neurons of C. elegans were tested by killing cells with a laser microbeam. One pair of neurons, the ASE neurons, is uniquely important for chemotaxis: killing the ASE neurons greatly reduced chemotaxis to cAMP, biotin, Cl-, and Na+. Additional chemosensory function is distributed among several other cell types. Thus, 3 pairs of chemosensory neurons (ADF, ASG, and ASI) contribute to a residual response to cAMP, biotin, Cl-, and Na+ after ASE is killed. Chemotaxis to lysine similarly depends on the partly redundant functions of 4 pairs of chemosensory neurons (ASE, ASG, ASI, and ASK). The combined activity of several neuron types that act in parallel might increase the fidelity of chemotaxis.
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                Author and article information

                Contributors
                yoshidar@ism.ac.jp
                iino@bs.s.u-tokyo.ac.jp
                Journal
                BMC Biol
                BMC Biol
                BMC Biology
                BioMed Central (London )
                1741-7007
                19 March 2020
                19 March 2020
                2020
                : 18
                : 30
                Affiliations
                [1 ]GRID grid.26999.3d, ISNI 0000 0001 2151 536X, Department of Biological Sciences, Graduate School of Science, , The University of Tokyo, ; Bunkyo-ku, Tokyo, Japan
                [2 ]GRID grid.418987.b, ISNI 0000 0004 1764 2181, The Institute of Statistical Mathematics, Research Organization of Information and Systems, ; Tachikawa, Tokyo Japan
                [3 ]GRID grid.275033.0, ISNI 0000 0004 1763 208X, The Graduate University for Advanced Studies, SOKENDAI, ; Mishima, 411-8540 Japan
                [4 ]GRID grid.177174.3, ISNI 0000 0001 2242 4849, Department of Biology, Faculty of Sciences, , Kyushu University, ; Higashi-ku, Fukuoka, Japan
                [5 ]GRID grid.410773.6, Department of Mechanical Systems Engineering, Graduate School of Science and Engineering, , Ibaraki University, ; Hitachi, Ibaraki Japan
                Author information
                http://orcid.org/0000-0002-0936-2660
                Article
                745
                10.1186/s12915-020-0745-2
                7081613
                32188430
                800e78a0-25f6-4f75-8b11-3128bf554f69
                © The Author(s). 2020

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 21 October 2019
                : 29 January 2020
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100003382, Core Research for Evolutional Science and Technology;
                Award ID: JPMJCR12W1
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100001691, Japan Society for the Promotion of Science;
                Award ID: JP17H06113
                Award ID: JP20115002
                Award ID: JP25115010
                Award ID: JP19H04980
                Award ID: JP26830006
                Award ID: JP18K14848
                Award ID: JP16H01418
                Award ID: JP18H04728
                Award ID: JP17H05970
                Award ID: JP19H04928
                Award ID: JP20115003
                Award ID: JP25115009
                Award ID: JP18H05135
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100004721, University of Tokyo;
                Award ID: CisHub
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100009023, Precursory Research for Embryonic Science and Technology;
                Award ID: JPMJPR1947
                Award Recipient :
                Categories
                Methodology Article
                Custom metadata
                © The Author(s) 2020

                Life sciences
                neuron identification,caenorhabditis elegans,cell-specific promoters,volumetric images,large dataset for cell positions,biological resources,computational method,whole-brain activity imaging

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