Development of microfluidic flow cytometry capable of characterization of single-cell intrinsic structural and electrical parameters. (1st March 2022)

• Record Type: Journal Article
• Authors: Liang, Hongyan; Zhang, Yi; Chen, Deyong; Li, Yueying; Wang, Yixiang; Wang, Junbo; Chen, Jian
• Is Part Of: Journal of micromechanics and microengineering. Volume 32:Number 3(2022)
• Publication Date: 2022-03-01
• Subjects: flow cytometry -- single-cell analysis -- microfluidics -- intrinsic structural and electrical parameters -- high throughput; Microelectromechanical systems -- Periodicals; Micromechanics -- Periodicals; 621.38105
• DOI: 10.1088/1361-6439/ac5171
• Languages: English
• ISSNs: 0960-1317

Abstract

Abstract: Although single-cell intrinsic structural and electrical parameters (e.g. D c of cell diameter, D n of nuclear diameter, σ cy of cytoplasmic conductivity and C sm of specific membrane capacitance) are promising for cell-type classification, they cannot be obtained simultaneously due to structural limitations of previously reported flow cytometry. This paper presented a microfluidic flow cytometry made of a double T-type constriction channel plus a predefined fluorescence detection domain, capable of high-throughput characterizing single-cell D c, D n, σ cy and C sm leveraging a home-developed impedance-fluorescence model. As a demonstration, the microfluidic platform quantified D c, D n, σ cy and C sm from ∼10 000 individual cells of three well-established tumor cell lines of A549, SW620 and HeLa where successful rates of cell-type classification were estimated as 54.5 ± 1.3% ( D c ), 68.9 ± 6.8% ( D c + D n ) and 84.8 ± 4.4% ( D c, D n, σ cy + C sm ) based on neural pattern recognition. Then D c, D n, σ cy and C sm derived from ∼10 000 single cells of K562 vs Jurkat of leukemia and SACC-LM vs CAL 27 of oral tumor were quantified and compared, where successful rates of cell-type classification were estimated as 87.3% (K562 vs Jurkat) and 79.5% (SACC-LM vs CAL 27), respectively. In summary, the microfluidic platform reported in this study could quantify single-cell intrinsic structural and electrical parameters simultaneously, leading to significant increases in successful rates of cell-type classification.