Development of a noise elimination electrical impedance spectroscopy (neEIS) system for single cell identification
Development of a noise elimination electrical impedance spectroscopy (neEIS) system for single cell identification
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DOI：
10.1016/j.sbsr.2020.100381
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发表时间：
2020-12
影响因子：
5.3
通讯作者：
A. K. Tran;D. Kawashima;Michiko Sugarawa;H. Obara;K. Okeyo;M. Takei
中科院分区：
文献类型：
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作者：
A. K. Tran;D. Kawashima;Michiko Sugarawa;H. Obara;K. Okeyo;M. Takei
提出了一种用于单细胞识别的消噪阻抗谱系统，该系统具有无细胞钳位结构和细胞位置独立校准的特点。无单元夹紧结构的作用是使单个单元能够在eeis系统中自由定位。单元位置独立校准包括三个步骤；阻抗测量，电极电池常数计算，和单电池介电特性估计。单元位置独立校准能够消除测量过程中与离子和电双层相关的噪声的影响。我们演示了应用neeis系统来区分三种类型的MRC-5人肺成纤维细胞；野生型（WT）、GFP融合组蛋白修饰（HT）和GFP转染（GFPT）的MRC-5细胞悬浮在蔗糖培养基中。为了进一步评估eeis系统，我们用数值方法模拟了电势分布和单电池介电性能的均方根误差。得到的电势分布清晰地表征了单细胞组分的内部变化。此外，我们确定了eeis系统能够在消除噪声后准确识别单细胞，平均均方根误差小于1.05%。综上所述，这些结果表明，所提出的neeis系统具有提高生物传感器性能的潜力。因此，本研究将为高精度单细胞鉴定的生物传感器系统的开发带来新的见解，并启发基于eeis系统的早期胎儿肺部疾病诊断设备的开发。
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 intoneEIS 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 ofneEIS 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 theneEIS 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 theneEIS 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 proposedneEIS 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 theneEIS system.