Development and Evaluation of Biochips Specialized for Cell Counting

Masato Sawatari; Shunko A.  Inada

doi:10.35882/jeeemi.v6i1.339

Masato Sawatari Department of Computer Science and Electrical Engineering, Graduate School of Science and Technology, Kumamoto University, Kumamoto, Japan
Shunko A. Inada Division of Biomedical Engineering, Faculty of Advanced Science and Technology, Kumamoto University, Kumamoto, Japan

DOI: https://doi.org/10.35882/jeeemi.v6i1.339

Keywords: Biochip, Cell observation, Image Processing

Abstract

Cell counters, which are dedicated cell analyzers, can be used to analyze cellular status. Cell counters are smaller and less expensive (about $13,000) than other cell analysis devices such as flow cytometers (FACS), real-time PCR, and sequencers, and can discriminate between life and death of fluorescently stained cells. Cell death can be roughly divided into two types: apoptosis and necrosis, but Cell counters cannot distinguish between apoptosis and necrosis in cells. This study developed a biochip system for inexpensive, simple, and capable of distinguishing between live, apoptotic, and necrotic cells. This biochip system (70 x 150 x 80 mm) comprises a slide into which fluorescently stained cells are injected, an LED light source, and a camera system. When cells stained with a fluorescent reagent are irradiated at the excitation wavelength, they fluoresce. By changing the combination of fluorescent reagent and excitation wavelength, live, apoptotic, and necrotic cells can be photographed. Then they are processed by a cell counting program using existing methods to determine numbers of live, dead, and necrotic cells. To demonstrate the effectiveness of this system, we conducted live cell, apoptosis, and necrosis detection experiments using colon cancer cells. Results of each experiment using the biochip system were compared with visual cell counts made by an operator. The novel biochip system successfully distinguishes between live, apoptotic and necrotic cells. Detection time was <1 s, and the detection error was 9%, compared to visual inspection.

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