Share Email Print
cover

Proceedings Paper

High-throughput fluorescence imaging flow cytometry with light-sheet excitation and machine learning (Conference Presentation)
Author(s): Hideharu Mikami; Taichi Miura; Yasuyuki Ozeki; Keisuke Goda
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Fluorescence imaging flow cytometry offers highly accurate analysis of a large number of cells compared with conventional flow cytometry by virtue of its imaging capability. Unfortunately, the throughput of conventional fluorescence imaging flow cytometers is limited to ~1,000 cells/sec, which is one order of magnitude lower than that of conventional non-imaging flow cytometers. This is due to the low data transfer rate of a CCD image sensor with a time-delay integration technique employed to achieve sufficient sensitivity for fluorescence imaging of fast flowing cells. Replacing the CCD image sensor with a CMOS image sensor can potentially overcome the throughput limitation by virtue of its high data transfer rate, but critically sacrifice the imaging sensitivity because the time-delay integration cannot be employed to current CMOS image sensors. Here we present a fluorescence imaging flow cytometer that achieves comparable throughput and sensitivity with non-imaging flow cytometers. It is enabled by high-energy-density light-sheet excitation of flowing cells on a mirror-embedded PDMS-based microfluidic chip and by fluorescence image acquisition with a CMOS image sensor. The light-sheet excitation allows us obtain fluorescence images of flowing cells at a speed of >1 m/s, corresponding to a high throughput of >10,000 cells/sec. To show its biomedical utility, we use it combined with machine learning to demonstrate accurate screening of white blood cells and real-time identification of cancer cells in blood.

Paper Details

Date Published: 15 March 2018
PDF
Proc. SPIE 10505, High-Speed Biomedical Imaging and Spectroscopy III: Toward Big Data Instrumentation and Management, 105050E (15 March 2018); doi: 10.1117/12.2289708
Show Author Affiliations
Hideharu Mikami, The Univ. of Tokyo (Japan)
Taichi Miura, The Univ. of Tokyo (Japan)
Yasuyuki Ozeki, The Univ. of Tokyo (Japan)
Keisuke Goda, The Univ. of Tokyo (Japan)
Univ. of California, Los Angeles (United States)
Japan Science and Technology Agency (Japan)


Published in SPIE Proceedings Vol. 10505:
High-Speed Biomedical Imaging and Spectroscopy III: Toward Big Data Instrumentation and Management
Kevin K. Tsia; Keisuke Goda, Editor(s)

© SPIE. Terms of Use
Back to Top