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
  • Yoko Kawamura
    Thinkcyte Inc. 7‐3‐1 Hongo, Bunkyo‐ku Tokyo 113‐8654 Japan
  • Keiji Nakagawa
    Thinkcyte Inc. 7‐3‐1 Hongo, Bunkyo‐ku Tokyo 113‐8654 Japan
  • 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
  • Issei Sato
    Thinkcyte Inc. 7‐3‐1 Hongo, Bunkyo‐ku Tokyo 113‐8654 Japan
  • 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
  • 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
  • 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
  • Hiroyuki Noji
    Department of Applied Chemistry The University of Tokyo 7‐3‐1 Hongo, Bunkyo‐ku Tokyo 113‐8654 Japan
  • Sadao Ota
    Thinkcyte Inc. 7‐3‐1 Hongo, Bunkyo‐ku Tokyo 113‐8654 Japan

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<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>

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