Use of Ghost Cytometry to Differentiate Cells with Similar Gross Morphologic Characteristics. Issue 4 (1st March 2020)

• Record Type: Journal Article
• Authors: Adachi, Hiroaki; Kawamura, Yoko; Nakagawa, Keiji; Horisaki, Ryoichi; Sato, Issei; Yamaguchi, Satoko; Fujiu, Katsuhito; Waki, Kayo; Noji, Hiroyuki; Ota, Sadao
• Is Part Of: Cytometry. Volume 97:Issue 4(2020)
• Page Start: 415
• Page End: 422
• Publication Date: 2020-03-01
• Subjects: machine learning -- flow cytometry -- imaging flow cytometry -- single‐cell analysis -- optofludics; Flow cytometry -- Periodicals; Imaging systems in biology -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnostic imaging -- Periodicals; 571.605
• DOI: 10.1002/cyto.a.23989
• Languages: English
• ISSNs: 1552-4922

Abstract

Abstract: 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