Fluorescence imaging flow cytometry is an emerging technique for analyzing a large number of cells with high accuracy over conventional flow cytometry by virtue of its imaging capability. Unfortunately, the cell throughput of conventional fluorescence imaging flow cytometers (~1,000 cells/sec) is much lower than that of standard non-imaging flow cytometers due to the use of a CCD image sensor having a limited data transfer rate, making it difficult to analyze a large population of cells. Here we report our experimental demonstration of highly accurate classification of microalgae with a frequency-division-multiplexed confocal imaging flow cytometer (IFC) that enables imaging of every single microalgal cell with an unprecedentedly high throughput of 20,000 cells/sec. The high-speed imaging performance of the IFC is enabled by employing frequency-division-multiplexed confocal microscopy, which uses a sensitive single-pixel photodetector such as an avalanche photodetector or a photomultiplier tube to obtain images of flowing cells. We stained three species of microalgae (Chlamydomonas reinhardtii, Haematococcus lacustris, and Euglena gracilis) with SYTO16 and obtained three-color images of the cells (bright-field, fluorescence staining of nuclei, and autofluorescence of chlorophyll). We extracted 243-dimensional features from each three-color image and employed a support vector machine to classify the cells with the obtained multi-dimensional data. As a result, the cells were successfully classified with an accuracy of 99.7%. Due to the IFC’s multi-color imaging capability with an unprecedentedly high throughput, our technique has a wide variety of potential applications other than microalga classification, such as accurate blood screening and liquid biopsy.
Hideharu Mikami, Jeffrey Harmon, Yasuyuki Ozeki, and Keisuke Goda, "Accurate classification of microalgal cells by frequency-division-multiplexed confocal imaging flow cytometry (Conference Presentation)," Proc. SPIE 10505, High-Speed Biomedical Imaging and Spectroscopy III: Toward Big Data Instrumentation and Management, 105050I (Presented at SPIE BiOS: January 30, 2018; Published: 15 March 2018); https://doi.org/10.1117/12.2288017.5752194222001.
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