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Proceedings Paper

High-throughput label-free detection of aggregate platelets with optofluidic time-stretch microscopy (Conference Presentation)

Paper Abstract

According to WHO, approximately 10 million new cases of thrombotic disorders are diagnosed worldwide every year. In the U.S. and Europe, their related diseases kill more people than those from AIDS, prostate cancer, breast cancer and motor vehicle accidents combined. Although thrombotic disorders, especially arterial ones, mainly result from enhanced platelet aggregability in the vascular system, visual detection of platelet aggregates in vivo is not employed in clinical settings. Here we present a high-throughput label-free platelet aggregate detection method, aiming at the diagnosis and monitoring of thrombotic disorders in clinical settings. With optofluidic time-stretch microscopy with a spatial resolution of 780 nm and an ultrahigh linear scanning rate of 75 MHz, it is capable of detecting aggregated platelets in lysed blood which flows through a hydrodynamic-focusing microfluidic device at a high throughput of 10,000 particles/s. With digital image processing and statistical analysis, we are able to distinguish them from single platelets and other blood cells via morphological features. The detection results are compared with results of fluorescence-based detection (which is slow and inaccurate, but established). Our results indicate that the method holds promise for real-time, low-cost, label-free, and minimally invasive detection of platelet aggregates, which is potentially applicable to detection of platelet aggregates in vivo and to the diagnosis and monitoring of thrombotic disorders in clinical settings. This technique, if introduced clinically, may provide important clinical information in addition to that obtained by conventional techniques for thrombotic disorder diagnosis, including ex vivo platelet aggregation tests.

Paper Details

Date Published: 24 April 2017
PDF: 1 pages
Proc. SPIE 10076, High-Speed Biomedical Imaging and Spectroscopy: Toward Big Data Instrumentation and Management II, 100760O (24 April 2017); doi: 10.1117/12.2250109
Show Author Affiliations
Yiyue Jiang, The Univ. of Tokyo (Japan)
Cheng Lei, The Univ. of Tokyo (Japan)
Tsinghua Univ. (China)
Atsushi Yasumoto, The Univ. of Tokyo (Japan)
Takuro Ito, The Univ. of Tokyo (Japan)
Japan Science and Technology Agency (Japan)
Baoshan Guo, The Univ. of Tokyo (Japan)
Hirofumi Kobayashi, The Univ. of Tokyo (Japan)
Yasuyuki Ozeki, The Univ. of Tokyo (Japan)
Yutaka Yatomi, The Univ. of Tokyo (Japan)
Keisuke Goda, The Univ. of Tokyo (Japan)
Japan Science and Technology Agency (Japan)
Univ. of Califonia, Los Angeles (United States)


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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