Purpose. To distinguish between normal (SV-HUC-1) and cancerous (TCCSUP) human urothelial cell lines using microelectrical impedance spectroscopy (μEIS). Materials and Methods. Two types of μEIS devices were designed and used in combination to measure the impedance of SV-HUC-1 and TCCSUP cells flowing through the channels of the devices. The first device (μEIS-OF) was designed to determine the optimal frequency at which the impedance of two cell lines is most distinguishable. The μEIS-OF trapped the flowing cells and measured their impedance at a frequency ranging from 5 kHz to 1 MHz. The second device (μEIS-RT) was designed for real-time impedance measurement of the cells at the optimal frequency. The impedance was measured instantaneously as the cells passed the sensing electrodes of μEIS-RT. Results. The optimal frequency, which maximized the average difference of the amplitude and phase angle between the two cell lines (p<0.001), was determined to be 119 kHz. The real-time impedance of the cell lines was measured at 119 kHz; the two cell lines differed significantly in terms of amplitude and phase angle (p<0.001). Conclusion. The μEIS-RT can discriminate SV-HUC-1 and TCCSUP cells by measuring the impedance at the optimal frequency determined by the μEIS-OF.

The ex-vivo discrimination between human normal and cancer renal tissues was confirmed using μEoN (micro electrical impedance spectroscopy-on-a-needle) by measuring and comparing the electrical impedances in the frequency domain. To quantify the extent of discrimination between dissimilar tissues and to determine the optimal frequency at which the discrimination capability is at a maximum, discrimination index (DI) was employed for both magnitude and phase. The highest values of DI for the magnitude and phase were 5.15 at 1 MHz and 3.57 at 1 kHz, respectively. The mean magnitude and phase measured at the optimal frequency for normal tissues were 5013.40 ± 94.39 Ω and −68.54 ± 0.72°, respectively; those for cancer tissues were 4165.19 ± 70.32 Ω and −64.10 ± 0.52°, respectively. A statistically significant difference (p< 0.05) between the two tissues was observed at all the investigated frequencies. To extract the electrical properties (resistance and capacitance) of these bio-tissues through curve fitting with experimental results, an equivalent circuit was proposed based on the μEoN structure on the condition that the μEoN was immersed in the bio-tissues. The average and standard deviation of the extracted resistance and capacitance for the normal tissues were 6.22 ± 0.24 kΩ and 280.21 ± 32.25 pF, respectively, and those for the cancer tissues were 5.45 ± 0.22 kΩ and 376.32 ± 34.14 pF, respectively. The electrical impedance was higher in the normal tissues compared with the cancer tissues. The μEoN could clearly discriminate between normal and cancer tissues by comparing the results at the optimal frequency (magnitude and phase) and those of the curve fitting (extracted resistance and capacitance).

A micro electrical impedance spectroscopy (EIS)-on-a-needle for depth profiling (μEoN-DP) with a selective passivation layer (SPL) on a hypodermic needle was recently fabricated to measure the electrical impedance of biotissues along with the penetration depths. The SPL of the μEoN-DP enabled the sensing interdigitated electrodes (IDEs) to contribute predominantly to the measurement by reducing the relative influence of the connection lines on the sensor output. The discrimination capability of the μEoN-DP was verified using phosphate-buffered saline (PBS) at various concentration levels. The resistance and capacitance extracted through curve fitting were similar to those theoretically estimated based on the mixing ratio of PBS and deionized water; the maximum discrepancies were 8.02% and 1.85%, respectively. Depth profiling was conducted using four-layered porcine tissue to verify the effectiveness of the discrimination capability of the μEoN-DP. The magnitude and phase between dissimilar porcine tissues (fat and muscle) were clearly discriminated at the optimal frequency of 1 MHz. Two kinds of simulations, one with SPL and the other with complete passivation layer (CPL), were performed, and it was verified that the SPL was advantageous over CPL in the discrimination of biotissues in terms of sensor output.