Micromachines (Feb 2019)

Size Distribution Analysis with On-Chip Multi-Imaging Cell Sorter for Unlabeled Identification of Circulating Tumor Cells in Blood

  • Masao Odaka,
  • Hyonchol Kim,
  • Yoshiyasu Nakamura,
  • Akihiro Hattori,
  • Kenji Matsuura,
  • Moe Iwamura,
  • Yohei Miyagi,
  • Kenji Yasuda

DOI
https://doi.org/10.3390/mi10020154
Journal volume & issue
Vol. 10, no. 2
p. 154

Abstract

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We report a change of the imaging biomarker distribution of circulating tumor cell (CTC) clusters in blood over time using an on-chip multi-imaging flow cytometry system, which can obtain morphometric parameters of cells and those clusters, such as cell number, perimeter, total cross-sectional area, aspect ratio, number of nuclei, and size of nuclei, as “imaging biomarkers„. Both bright-field (BF) and fluorescent (FL) images were acquired at 200 frames per second and analyzed within the intervals for real-time cell sorting. A green fluorescent protein-transfected prostate cancer cell line (MAT-LyLu-GFP) was implanted into Copenhagen rats, and the blood samples of these rats were collected 2 to 11 days later and measured using the system. The results showed that cells having BF area of 90 μm2 or larger increased in number seven days after the cancer cell implantation, which was specifically detected as a shift of the cell size distribution for blood samples of implanted rats, in comparison with that for control blood. All cells with BF area of 150 μm2 or larger were arranged in cell clusters composed of at least two cells, as confirmed by FL nucleus number and area measurements, and they constituted more than 1% of all white blood cells. These results indicate that the mapping of cell size distribution is useful for identifying an increase of irregular cells such as cell clusters in blood, and show that CTC clusters become more abundant in blood over time after malignant tumor formation. The results also reveal that a blood sample of only 50 μL is sufficient to acquire a stable size distribution map of all blood cells to predict the presence of CTC clusters.

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