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Proceedings Paper

High-speed stimulated Raman scattering microscopy for studying the metabolic diversity of motile Euglena gracilis
Author(s): Y. Suzuki; Y. Wakisaka; O. Iwata; A. Nakashima; T. Ito; M. Hirose; R. Domon; M. Sugawara; N. Tsumura; H. Watarai; T. Shimobaba; K. Suzuki; K. Goda; Y. Ozeki
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Paper Abstract

Microalgae have been receiving great attention for their ability to produce biomaterials that are applicable for food supplements, drugs, biodegradable plastics, and biofuels. Among such microalgae, Euglena gracilis has become a popular species by virtue of its capability of accumulating useful metabolites including paramylon and lipids. In order to maximize the production of desired metabolites, it is essential to find ideal culturing conditions and to develop efficient methods for genetic transformation. To achieve this, understanding and controlling cell-to-cell variations in response to external stress is essential, with chemically specific analysis of microalgal cells including E. gracilis. However, conventional analytical tools such as fluorescence microscopy and spontaneous Raman scattering are not suitable for evaluation of diverse populations of motile microalgae, being restricted either by the requirement for fluorescent labels or a limited imaging speed, respectively. Here we demonstrate video-rate label-free metabolite imaging of live E. gracilis using stimulated Raman scattering (SRS) – an optical spectroscopic method for probing the vibrational signatures of molecules with orders of magnitude higher sensitivity than spontaneous Raman scattering. Our SRS’s highspeed image acquisition (27 metabolite images per second) allows for population analysis of live E. gracilis cells cultured under nitrogen-deficiency - a technique for promoting the accumulation of paramylon and lipids within the cell body. Thus, our SRS system’s fast imaging capability enables quantification and analysis of previously unresolvable cell-to-cell variations in the metabolite accumulation of large motile E. gracilis cell populations.

Paper Details

Date Published: 22 February 2017
PDF: 6 pages
Proc. SPIE 10076, High-Speed Biomedical Imaging and Spectroscopy: Toward Big Data Instrumentation and Management II, 1007604 (22 February 2017); doi: 10.1117/12.2250455
Show Author Affiliations
Y. Suzuki, The Univ. of Tokyo (Japan)
Y. Wakisaka, The Univ. of Tokyo (Japan)
O. Iwata, euglena Co., Ltd. (Japan)
A. Nakashima, euglena Co., Ltd. (Japan)
T. Ito, Keio Univ. (Japan)
M. Hirose, Chiba Univ. (Japan)
R. Domon, Chiba Univ. (Japan)
M. Sugawara, Chiba Univ. (Japan)
N. Tsumura, Chiba Univ. (Japan)
H. Watarai, The Univ. of Tokyo (Japan)
T. Shimobaba, Chiba Univ. (Japan)
K. Suzuki, euglena Co., Ltd. (Japan)
K. Goda, The Univ. of Tokyo (Japan)
Univ. of California, Los Angeles (United States)
Japan Science and Technology Agency (Japan)
Y. Ozeki, The Univ. of Tokyo (Japan)


Published in SPIE Proceedings Vol. 10076:
High-Speed Biomedical Imaging and Spectroscopy: Toward Big Data Instrumentation and Management II
Kevin K. Tsia; Keisuke Goda, Editor(s)

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