Virtual-freezing fluorescence imaging flow cytometry

Hideharu Mikami (), Makoto Kawaguchi, Chun-Jung Huang, Hiroki Matsumura, Takeaki Sugimura, Kangrui Huang, Cheng Lei, Shunnosuke Ueno, Taichi Miura, Takuro Ito, Kazumichi Nagasawa, Takanori Maeno, Hiroshi Watarai, Mai Yamagishi, Sotaro Uemura, Shinsuke Ohnuki, Yoshikazu Ohya, Hiromi Kurokawa, Satoshi Matsusaka, Chia-Wei Sun, Yasuyuki Ozeki () and Keisuke Goda ()
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Hideharu Mikami: The University of Tokyo
Makoto Kawaguchi: The University of Tokyo
Chun-Jung Huang: The University of Tokyo
Hiroki Matsumura: The University of Tokyo
Takeaki Sugimura: The University of Tokyo
Kangrui Huang: The University of Tokyo
Cheng Lei: The University of Tokyo
Shunnosuke Ueno: The University of Tokyo
Taichi Miura: The University of Tokyo
Takuro Ito: The University of Tokyo
Kazumichi Nagasawa: The University of Tokyo
Takanori Maeno: The University of Tokyo
Hiroshi Watarai: The University of Tokyo
Mai Yamagishi: The University of Tokyo
Sotaro Uemura: The University of Tokyo
Shinsuke Ohnuki: The University of Tokyo
Yoshikazu Ohya: The University of Tokyo
Hiromi Kurokawa: University of Tsukuba
Satoshi Matsusaka: University of Tsukuba
Chia-Wei Sun: National Chiao Tung University
Yasuyuki Ozeki: The University of Tokyo
Keisuke Goda: The University of Tokyo

Nature Communications, 2020, vol. 11, issue 1, 1-11

Abstract: Abstract By virtue of the combined merits of flow cytometry and fluorescence microscopy, imaging flow cytometry (IFC) has become an established tool for cell analysis in diverse biomedical fields such as cancer biology, microbiology, immunology, hematology, and stem cell biology. However, the performance and utility of IFC are severely limited by the fundamental trade-off between throughput, sensitivity, and spatial resolution. Here we present an optomechanical imaging method that overcomes the trade-off by virtually freezing the motion of flowing cells on the image sensor to effectively achieve 1000 times longer exposure time for microscopy-grade fluorescence image acquisition. Consequently, it enables high-throughput IFC of single cells at >10,000 cells s−1 without sacrificing sensitivity and spatial resolution. The availability of numerous information-rich fluorescence cell images allows high-dimensional statistical analysis and accurate classification with deep learning, as evidenced by our demonstration of unique applications in hematology and microbiology.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-14929-2

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DOI: 10.1038/s41467-020-14929-2

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