Das Debanjan, Shiladitya Kumar, Biswas Karabi, Dutta Pranab Kumar, Parekh Aditya, Mandal Mahitosh, Das Soumen
Department of Electrical Engineering, IIT Kharagpur, India.
School of Medical Science and Technology, IIT Kharagpur, India.
Phys Rev E Stat Nonlin Soft Matter Phys. 2015 Dec;92(6):062702. doi: 10.1103/PhysRevE.92.062702. Epub 2015 Dec 7.
The paper presents a study to differentiate normal and cancerous cells using label-free bioimpedance signal measured by electric cell-substrate impedance sensing. The real-time-measured bioimpedance data of human breast cancer cells and human epithelial normal cells employs fluctuations of impedance value due to cellular micromotions resulting from dynamic structural rearrangement of membrane protrusions under nonagitated condition. Here, a wavelet-based multiscale quantitative analysis technique has been applied to analyze the fluctuations in bioimpedance. The study demonstrates a method to classify cancerous and normal cells from the signature of their impedance fluctuations. The fluctuations associated with cellular micromotion are quantified in terms of cellular energy, cellular power dissipation, and cellular moments. The cellular energy and power dissipation are found higher for cancerous cells associated with higher micromotions in cancer cells. The initial study suggests that proposed wavelet-based quantitative technique promises to be an effective method to analyze real-time bioimpedance signal for distinguishing cancer and normal cells.
本文介绍了一项利用通过电细胞基质阻抗传感测量的无标记生物阻抗信号来区分正常细胞和癌细胞的研究。人类乳腺癌细胞和人类上皮正常细胞的实时测量生物阻抗数据利用了在非搅拌条件下膜突起的动态结构重排导致的细胞微运动所引起的阻抗值波动。在此，基于小波的多尺度定量分析技术已被应用于分析生物阻抗的波动。该研究展示了一种根据癌细胞和正常细胞阻抗波动特征对它们进行分类的方法。与细胞微运动相关的波动在细胞能量、细胞功率耗散和细胞矩方面进行了量化。发现癌细胞的细胞能量和功率耗散更高，这与癌细胞中更高的微运动相关。初步研究表明，所提出的基于小波的定量技术有望成为一种分析实时生物阻抗信号以区分癌细胞和正常细胞的有效方法。
