Accurate classification of microalgal cells by frequency-division-multiplexed confocal imaging flow cytometry (Conference Presentation)

Abstract

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.