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Chaudhary S, Lee SA, Li Y, Patel DS, Lu H. Graphical-model framework for automated annotation of cell identities in dense cellular images. eLife 2021; 10:e60321. [PMID: 33625357 PMCID: PMC8032398 DOI: 10.7554/elife.60321] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Accepted: 02/23/2021] [Indexed: 12/11/2022] Open
Abstract
Although identifying cell names in dense image stacks is critical in analyzing functional whole-brain data enabling comparison across experiments, unbiased identification is very difficult, and relies heavily on researchers' experiences. Here, we present a probabilistic-graphical-model framework, CRF_ID, based on Conditional Random Fields, for unbiased and automated cell identification. CRF_ID focuses on maximizing intrinsic similarity between shapes. Compared to existing methods, CRF_ID achieves higher accuracy on simulated and ground-truth experimental datasets, and better robustness against challenging noise conditions common in experimental data. CRF_ID can further boost accuracy by building atlases from annotated data in highly computationally efficient manner, and by easily adding new features (e.g. from new strains). We demonstrate cell annotation in Caenorhabditis elegans images across strains, animal orientations, and tasks including gene-expression localization, multi-cellular and whole-brain functional imaging experiments. Together, these successes demonstrate that unbiased cell annotation can facilitate biological discovery, and this approach may be valuable to annotation tasks for other systems.
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Affiliation(s)
- Shivesh Chaudhary
- School of Chemical & Biomolecular Engineering, Georgia Institute of TechnologyAtlantaUnited States
| | - Sol Ah Lee
- School of Chemical & Biomolecular Engineering, Georgia Institute of TechnologyAtlantaUnited States
| | - Yueyi Li
- School of Chemical & Biomolecular Engineering, Georgia Institute of TechnologyAtlantaUnited States
| | - Dhaval S Patel
- School of Chemical & Biomolecular Engineering, Georgia Institute of TechnologyAtlantaUnited States
| | - Hang Lu
- School of Chemical & Biomolecular Engineering, Georgia Institute of TechnologyAtlantaUnited States
- Petit Institute for Bioengineering and Bioscience, Georgia Institute of TechnologyAtlantaUnited States
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Toyoshima Y, Wu S, Kanamori M, Sato H, Jang MS, Oe S, Murakami Y, Teramoto T, Park C, Iwasaki Y, Ishihara T, Yoshida R, Iino Y. Neuron ID dataset facilitates neuronal annotation for whole-brain activity imaging of C. elegans. BMC Biol 2020; 18:30. [PMID: 32188430 PMCID: PMC7081613 DOI: 10.1186/s12915-020-0745-2] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2019] [Accepted: 01/29/2020] [Indexed: 11/10/2022] Open
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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Affiliation(s)
- Yu Toyoshima
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
| | - Stephen Wu
- The Institute of Statistical Mathematics, Research Organization of Information and Systems, Tachikawa, Tokyo, Japan
- The Graduate University for Advanced Studies, SOKENDAI, Mishima, 411-8540, Japan
| | - Manami Kanamori
- 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
| | - Moon Sun Jang
- 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, Higashi-ku, Fukuoka, Japan
| | - Yuko Murakami
- Department of Biology, Faculty of Sciences, Kyushu University, Higashi-ku, Fukuoka, Japan
| | - Takayuki Teramoto
- Department of Biology, Faculty of Sciences, Kyushu University, Higashi-ku, Fukuoka, Japan
| | - Chanhyun Park
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
| | - Yuishi Iwasaki
- Department of Mechanical Systems Engineering, Graduate School of Science and Engineering, Ibaraki University, Hitachi, Ibaraki, Japan
| | - Takeshi Ishihara
- Department of Biology, Faculty of Sciences, Kyushu University, Higashi-ku, Fukuoka, Japan
| | - Ryo Yoshida
- The Institute of Statistical Mathematics, Research Organization of Information and Systems, Tachikawa, Tokyo, Japan.
- The Graduate University for Advanced Studies, SOKENDAI, Mishima, 411-8540, Japan.
| | - Yuichi Iino
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo, Japan.
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Toyoshima Y, Tokunaga T, Hirose O, Kanamori M, Teramoto T, Jang MS, Kuge S, Ishihara T, Yoshida R, Iino Y. Accurate Automatic Detection of Densely Distributed Cell Nuclei in 3D Space. PLoS Comput Biol 2016; 12:e1004970. [PMID: 27271939 PMCID: PMC4894571 DOI: 10.1371/journal.pcbi.1004970] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2015] [Accepted: 05/03/2016] [Indexed: 11/18/2022] Open
Abstract
To measure the activity of neurons using whole-brain activity imaging, precise detection of each neuron or its nucleus is required. In the head region of the nematode C. elegans, the neuronal cell bodies are distributed densely in three-dimensional (3D) space. However, no existing computational methods of image analysis can separate them with sufficient accuracy. Here we propose a highly accurate segmentation method based on the curvatures of the iso-intensity surfaces. To obtain accurate positions of nuclei, we also developed a new procedure for least squares fitting with a Gaussian mixture model. Combining these methods enables accurate detection of densely distributed cell nuclei in a 3D space. The proposed method was implemented as a graphical user interface program that allows visualization and correction of the results of automatic detection. Additionally, the proposed method was applied to time-lapse 3D calcium imaging data, and most of the nuclei in the images were successfully tracked and measured.
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Affiliation(s)
- Yu Toyoshima
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
| | - Terumasa Tokunaga
- Department of Systems Design and Informatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka-shi, Fukuoka, Japan
- The Institute of Statistical Mathematics, Research Organization of Information and Systems, Tachikawa, Tokyo, Japan
| | - Osamu Hirose
- Faculty of Electrical and Computer Engineering, Institute of Science and Engineering, Kanazawa University, Kakuma, Kanazawa, Japan
| | - Manami Kanamori
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
| | - Takayuki Teramoto
- Department of Biology, Faculty of Sciences, Kyushu University, Higashi-ku, Fukuoka, Japan
| | - Moon Sun Jang
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
| | - Sayuri Kuge
- Department of Biology, Faculty of Sciences, Kyushu University, Higashi-ku, Fukuoka, Japan
| | - Takeshi Ishihara
- Department of Biology, Faculty of Sciences, Kyushu University, Higashi-ku, Fukuoka, Japan
| | - Ryo Yoshida
- The Institute of Statistical Mathematics, Research Organization of Information and Systems, Tachikawa, Tokyo, Japan
| | - Yuichi Iino
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
- CREST, Japan Science and Technology Corporation, Bunkyo-ku, Tokyo, Japan
- * E-mail:
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