We propose a noise elimination electrical impedance spectroscopy (neEIS) system for single cell identification whose characteristics are cell-free clamp structure and cell-position independent calibration. The function of the cell-free clamp structure is to enable a single cell to be freely located into neEIS system. The cell-position independent calibration consists of three steps; impedance measurement, electrode cell constant calculation, and single cell dielectric properties estimation. The cell-position independent calibration enables elimination the influence of noise associated with ions as well as electrical double layer during the measurement process. We demonstrate an application of neEIS system to discriminate three types of MRC-5 human lung fibroblasts; wild type (WT), GFP-fused histone (HT) modification, and GFP transfected (GFPT) MRC-5 cells suspended in a sucrose medium. To further evaluate the neEIS system, we simulated the electrical potential distribution and root means square error of single cell dielectric properties numerically. As a result, we obtained electric potential distribution which clearly characterized the internal change of single cell components. Moreover, we determined that the neEIS system is capable of accurate single cell identification after noises elimination with less than 1.05% average root means square error. Taken together, these results demonstrate that the proposed neEIS system has the potential to improve the performance of biosensors. Thus, this study will bring a new insight into the development of a biosensor system for high accuracy single cell identification as well as inspire the development of a diagnostic device for early fetal lung diseases based on the neEIS system.

我们提出了一种用于单细胞识别的噪声消除电阻抗光谱（ne EIS）系统，其特征是无细胞钳位结构和与细胞位置无关的校准。无细胞夹紧结构的功能是使单个细胞可自由定位到NE EIS系统。细胞位置无关的校准包括三个步骤：阻抗测量，电极电池常数计算和单电池介电特性估计。单元位置独立校准可消除在测量过程中与离子相关的噪声以及双电层的影响。我们演示ne的应用EIS系统可区分三种类型的MRC-5人肺成纤维细胞；悬浮在蔗糖培养基中的野生型（WT），GFP融合组蛋白（HT）修饰和GFP转染（GFPT）MRC-5细胞。为了进一步评估ne EIS系统，我们通过数值模拟了单电池介电特性的电势分布和均方根误差。结果，我们获得了电位分布，该电位分布清楚地表征了单电池组件的内部变化。此外，我们确定ne EIS系统在消除噪声后能够准确地进行单细胞识别，平均均方根误差小于1.05％。总之，这些结果表明，该NEEIS系统具有改善生物传感器性能的潜力。因此，本研究将为用于高精度单细胞鉴定的生物传感器系统的开发带来新的见识，并激发基于ne EIS系统的早期胎儿肺部疾病诊断设备的开发。