Use of Ghost Cytometry to Differentiate Cells with Similar Gross Morphologic Characteristics
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- Hiroaki Adachi
- Thinkcyte Inc. 7‐3‐1 Hongo, Bunkyo‐ku Tokyo 113‐8654 Japan
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- Yoko Kawamura
- Thinkcyte Inc. 7‐3‐1 Hongo, Bunkyo‐ku Tokyo 113‐8654 Japan
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- Keiji Nakagawa
- Thinkcyte Inc. 7‐3‐1 Hongo, Bunkyo‐ku Tokyo 113‐8654 Japan
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- Ryoichi Horisaki
- Department of Information and Physical Sciences, Graduate School of Information Science and Technology Osaka University 1‐5 Yamadaoka, Suita Osaka 565‐0871 Japan
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- Issei Sato
- Thinkcyte Inc. 7‐3‐1 Hongo, Bunkyo‐ku Tokyo 113‐8654 Japan
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- Satoko Yamaguchi
- Department of Ubiquitous Health Informatics, Graduate School of Medicine The University of Tokyo 7‐3‐1 Hongo, Bunkyo‐ku Tokyo 113‐8655 Japan
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- Katsuhito Fujiu
- Department of Ubiquitous Health Informatics, Graduate School of Medicine The University of Tokyo 7‐3‐1 Hongo, Bunkyo‐ku Tokyo 113‐8655 Japan
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- Kayo Waki
- Department of Ubiquitous Health Informatics, Graduate School of Medicine The University of Tokyo 7‐3‐1 Hongo, Bunkyo‐ku Tokyo 113‐8655 Japan
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- Hiroyuki Noji
- Department of Applied Chemistry The University of Tokyo 7‐3‐1 Hongo, Bunkyo‐ku Tokyo 113‐8654 Japan
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- Sadao Ota
- Thinkcyte Inc. 7‐3‐1 Hongo, Bunkyo‐ku Tokyo 113‐8654 Japan
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説明
<jats:title>Abstract</jats:title><jats:p>Imaging flow cytometry shows significant potential for increasing our understanding of heterogeneous and complex life systems and is useful for biomedical applications. Ghost cytometry is a recently proposed approach for directly analyzing compressively measured signals of cells, thereby relieving a computational bottleneck for real‐time data analysis in high‐throughput imaging cytometry. In our previous work, we demonstrated that this image‐free approach could distinguish cells from two cell lines prepared with the same fluorescence staining method. However, the demonstration using different cell lines could not exclude the possibility that classification was based on non‐morphological factors such as the speed of cells in flow, which could be encoded in the compressed signals. In this study, we show that GC can classify cells from the same cell line but with different fluorescence distributions in space, supporting the strength of our image‐free approach for accurate morphological cell analysis. © 2020 International Society for Advancement of Cytometry</jats:p>
収録刊行物
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- Cytometry Part A
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Cytometry Part A 97 (4), 415-422, 2020-03
Wiley
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キーワード
- FOS: Computer and information sciences
- Computer Science - Machine Learning
- Staining and Labeling
- Image and Video Processing (eess.IV)
- Electrical Engineering and Systems Science - Image and Video Processing
- Flow Cytometry
- Quantitative Biology - Quantitative Methods
- Machine Learning (cs.LG)
- FOS: Biological sciences
- FOS: Electrical engineering, electronic engineering, information engineering
- Quantitative Methods (q-bio.QM)
- Image Cytometry
詳細情報 詳細情報について
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- CRID
- 1360584343750413440
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- ISSN
- 15524930
- 15524922
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- PubMed
- 32115874
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- データソース種別
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- Crossref
- OpenAIRE