A microfluidic system for cell type classification based on cellular size-independent electrical properties

This paper presents a microfluidic system enabling cell type classification based on continuous characterization of size-independent electrical properties (e.g., specific membrane capacitance (C_{specific membrane}) and cytoplasm conductivity (σ_cytoplasm)). In this study, cells were aspirated continuously through a constriction channel, while cell elongation and impedance profiles at two frequencies (1 kHz and 100 kHz) were measured simultaneously. Based on a proposed distributed equivalent circuit model, 1 kHz impedance data were used to evaluate cellular sealing properties with constriction channel walls and 100 kHz impedance data were translated to C _{specific membrane} and σ_cytoplasm. Two lung cancer cell lines of CRL-5803 cells (n_cell = 489) and CCL-185 cells (n _cell = 487) were used to evaluate this technique, producing a C _{specific membrane} of 1.63 ± 0.52 μF cm^-2vs. 2.00 ± 0.60 μF cm^-2, and σ_cytoplasm of 0.90 ± 0.19 S m^-1vs. 0.73 ± 0.17 S m^-1. Neural network-based pattern recognition was used to classify CRL-5803 and CCL-185 cells, producing success rates of 65.4% (C_{specific membrane}), 71.4% (σ_cytoplasm), and 74.4% (C_{specific membrane} and σ_cytoplasm), suggesting that these two tumor cell lines can be classified based on their electrical properties.