15 March 2018 High-throughput fluorescence imaging flow cytometry with light-sheet excitation and machine learning (Conference Presentation)
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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.
Conference Presentation
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Hideharu Mikami, Taichi Miura, Yasuyuki Ozeki, Keisuke Goda, "High-throughput fluorescence imaging flow cytometry with light-sheet excitation and machine learning (Conference Presentation)", 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; https://doi.org/10.1117/12.2289708
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