Research Article: Label-free classification of cells based on supervised machine learning of subcellular structures

Date Published: January 29, 2019

Publisher: Public Library of Science

Author(s): Yusuke Ozaki, Hidenao Yamada, Hirotoshi Kikuchi, Amane Hirotsu, Tomohiro Murakami, Tomohiro Matsumoto, Toshiki Kawabata, Yoshihiro Hiramatsu, Kinji Kamiya, Toyohiko Yamauchi, Kentaro Goto, Yukio Ueda, Shigetoshi Okazaki, Masatoshi Kitagawa, Hiroya Takeuchi, Hiroyuki Konno, Wajid Mumtaz.


It is demonstrated that cells can be classified by pattern recognition of the subcellular structure of non-stained live cells, and the pattern recognition was performed by machine learning. Human white blood cells and five types of cancer cell lines were imaged by quantitative phase microscopy, which provides morphological information without staining quantitatively in terms of optical thickness of cells. Subcellular features were then extracted from the obtained images as training data sets for the machine learning. The built classifier successfully classified WBCs from cell lines (area under ROC curve = 0.996). This label-free, non-cytotoxic cell classification based on the subcellular structure of QPM images has the potential to serve as an automated diagnosis of single cells.

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Morphological classification of cells and tissue on a subcellular scale under a microscope has a long history in pathology, including cytology and histology. The subcellular organelles cause subcellular features such as increased nuclear-to-cytoplasmic ratio, granular cytoplasm, and a large round nucleus with prominent nucleolus [1]. In identifying and classifying diseases, physicians recognize and analyze the pattern (features) in the microscopic image and interpret its meaning from their past training (experience). In cell biology, cytology, and pathology, the features of cells or tissue to be recognized and analyzed can be enhanced in two ways: one is staining with dyes or labeling the molecules to be observed with fluorescence light; the other is optical filtering by dark- or bright-field microscopy, including label-free optical imaging such as phase-contrast and differential-interference-contrast imaging. The former describes subcellular features as a distribution map of specific proteins or molecules. The later describes the features as a refractive index map of various proteins or molecules. In this paper, we refer to the refractive index map inside a cell as a subcellular structure.

A method for label-free cell classification by computer-vision technologies for pattern recognition based on subcellular structures of QPM images of cells was demonstrated. This cell-recognition method differs from conventional methods in several respects. First, because QPM does not require a contrast agent nor staining to observe live cells, image acquisition by QPM is much less harmful to cells. This aspect is a great advantage for cell sorting based on features in QPM images. For example, the sorted cells can be cultivated to perform biological investigations. Second, cell classification is based on the heterogeneity of subcellular structures rather than the cellular outline used in conventional methods. The difference between weight vectors for WBCs and cancer cell lines in Fig 11 reflects the heterogeneity of subcellular components because compared with benign cells, malignant cells express a heterogeneous distribution of subcellular components [52]. As far as cancer CLs are concerned, the proposed classifier could find the difference between them.




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