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Label-free chemical imaging flow cytometry by high-speed multicolor stimulated Raman scattering. 2019
Yuta Suzuki, and
Koya Kobayashi, and
Yoshifumi Wakisaka, and
Dinghuan Deng, and
Shunji Tanaka, and
Chun-Jung Huang, and
Cheng Lei, and
Chia-Wei Sun, and
Hanqin Liu, and
Yasuhiro Fujiwaki, and
Sangwook Lee, and
Akihiro Isozaki, and
Yusuke Kasai, and
Takeshi Hayakawa, and
Shinya Sakuma, and
Fumihito Arai, and
Kenichi Koizumi, and
Hiroshi Tezuka, and
Mary Inaba, and
Kei Hiraki, and
Takuro Ito, and
Misa Hase, and
Satoshi Matsusaka, and
Kiyotaka Shiba, and
Kanako Suga, and
Masako Nishikawa, and
Masahiro Jona, and
Yutaka Yatomi, and
Yaxiaer Yalikun, and
Yo Tanaka, and
Takeaki Sugimura, and
Nao Nitta, and
Keisuke Goda, and
Yasuyuki Ozeki
Department of Electrical Engineering and Information Systems, The University of Tokyo, 113-8656 Tokyo, Japan.
Combining the strength of flow cytometry with fluorescence imaging and digital image analysis, imaging flow cytometry is a powerful tool in diverse fields including cancer biology, immunology, drug discovery, microbiology, and metabolic engineering. It enables measurements and statistical analyses of chemical, structural, and morphological phenotypes of numerous living cells to provide systematic insights into biological processes. However, its utility is constrained by its requirement of fluorescent labeling for phenotyping. Here we present label-free chemical imaging flow cytometry to overcome the issue. It builds on a pulse pair-resolved wavelength-switchable Stokes laser for the fastest-to-date multicolor stimulated Raman scattering (SRS) microscopy of fast-flowing cells on a 3D acoustic focusing microfluidic chip, enabling an unprecedented throughput of up to ∼140 cells/s. To show its broad utility, we use the SRS imaging flow cytometry with the aid of deep learning to study the metabolic heterogeneity of microalgal cells and perform marker-free cancer detection in blood.
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