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 (sigma(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 sigma(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 mu F cm(-2) vs. 2.00 +/- 0.60 mu F cm(-2), and sigma(cytoplasm) of 0.90 +/- 0.19 S m(-1) vs. 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% (sigma(cytoplasm)), and 74.4% (C-specific membrane and sigma(cytoplasm)), suggesting that these two tumor cell lines can be classified based on their electrical properties.