Biomedical Engineering Department, Faculty of Engineering, Helwan University, Cairo, Egypt.
Center of Nanoelectronics and Devices (CND), The American University in Cairo (AUC) and Zewail City of Science and Technology, Cairo, Egypt.
Sci Rep. 2022 Aug 16;12(1):13839. doi: 10.1038/s41598-022-18005-1.
The characterization and tracking of biological cells using biosensors are necessary for many scientific fields, specifically cell culture monitoring. Capacitive sensors offer a great solution due to their ability to extract many features such as the biological cells' position, shape, and capacitance. Through this study, a CMOS-based biochip that consists of a matrix of capacitive sensors (CSM), utilizing a ring oscillator-based pixel readout circuit (PRC), is designed and simulated to track and characterize a single biological cell based on its aforementioned different features. The proposed biochip is simulated to characterize a single Hepatocellular carcinoma cell (HCC) and a single normal liver cell (NLC). COMSOL Multiphysics was used to extract the capacitance values of the HCC and NLC and test the CSM's performance at different distances from the analyte. The PRC's ability to detect the extracted capacitance values of the HCC and NLC is evaluated using Virtuoso Analog Design Environment. A novel algorithm is developed to animate and predict the location and shape of the tested biological cell depending on CSM's capacitance readings simultaneously using MATLAB R2022a script. The results of both models, the measured capacitance from CSM and the correlated frequency from the readout circuit, show the biochip's ability to characterize and distinguish between HCC and NLC.
使用生物传感器对生物细胞进行特征描述和跟踪对于许多科学领域都是必要的,特别是细胞培养监测。电容传感器由于能够提取生物细胞的位置、形状和电容等许多特征,因此是一个很好的解决方案。通过这项研究,设计并模拟了一种基于 CMOS 的生物芯片,该生物芯片由电容传感器矩阵(CSM)组成,利用基于环形振荡器的像素读出电路(PRC),基于其上述不同特征来跟踪和描述单个生物细胞。该生物芯片被模拟来描述单个肝癌细胞(HCC)和单个正常肝细胞(NLC)。使用 COMSOL Multiphysics 提取 HCC 和 NLC 的电容值,并在与分析物不同距离处测试 CSM 的性能。使用 Virtuoso Analog Design Environment 评估 PRC 检测 HCC 和 NLC 提取电容值的能力。使用 MATLAB R2022a 脚本开发了一种新算法,根据 CSM 的电容读数同时动画化和预测测试生物细胞的位置和形状。这两个模型的结果,即 CSM 测量的电容和读出电路相关的频率,都表明了该生物芯片能够对 HCC 和 NLC 进行特征描述和区分。