Open AccessDevelopment of microfluidic flow cytometry capable of characterization of single-cell intrinsic structural and electrical parameters
Author(s) -
Hongyan Liang,
Yi Zhang,
Deyong Chen,
Yueying Li,
Yixiang Wang,
Junbo Wang,
Jian Chen
Publication year - 2022
Publication title -
journal of micromechanics and microengineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.494
H-Index - 132
eISSN - 1361-6439
pISSN - 0960-1317
DOI - 10.1088/1361-6439/ac5171
Subject(s) - jurkat cells , flow cytometry , microfluidics , k562 cells , cell , hela , chemistry , biophysics , analytical chemistry (journal) , cytometry , materials science , nanotechnology , microbiology and biotechnology , t cell , biology , chromatography , biochemistry , immunology , immune system
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.