Zhao Lili, Zhao Meng, Yang Yu, Gu Yajun, Zheng Fang, Wang Xuan, Zheng Zhiyuan, Sun Xuguo
Department of Laboratory Science, School of Laboratory Medicine, Tianjin Medical University, Tianjin 300203, P.R. China.
Key Laboratory of Computer Vision and System of Ministry of Education, School of Computer Science and Engineering, Tianjin University of Technology, Tianjin 300384, P.R. China.
Oncol Lett. 2019 May;17(5):4532-4544. doi: 10.3892/ol.2019.10118. Epub 2019 Mar 6.
The detection of tumor cells and clusters in pleural effusion assists in the diagnosis of lung cancer. The proportion of tumor cells and clusters to the total number of cells in each patient varies substantially due to individual differences and the severity of the disease. The identification of one tumor cell or cluster from a large number of pleural effusions is the main challenge for hydrothorax tumor cell detection techniques. In the present study, by using A549 lung cancer and Met-5A mesothelial cell lines, a label-free microfluidic chip based on cell cluster size was designed. By setting the parameters of the chip, individual cells and clusters were able to enter different microfluidic channels. Subsequent to non-specific staining, the recovered components were stained using acridine orange (AO). A charge-coupled device camera was used to captured images of the cell, and the features of these cells were analyzed in their R and G channels using Matlab software to establish the characteristics and finally differentiate between the tumor and non-tumor cell or clusters. According to the results, when inlet A and B were under a velocity of 10 and 8.5 ml/h, respectively, the tumor cell clusters were successfully collected through microfluidic channels III-V, with a recovery rate of ~80%. Subsequent to staining with AO, the feature values in the R and G channels were identified, and initial differentiation was achieved. The present study combined the microfluidic chip, which is based on cluster size, with a computer identification method for pleural effusion. The successful differentiation of tumor cell clusters from non-tumor clusters provides the basis for the identification of tumor clusters in hydrothorax.
检测胸腔积液中的肿瘤细胞和细胞团有助于肺癌的诊断。由于个体差异和疾病严重程度不同,每位患者肿瘤细胞和细胞团在细胞总数中所占的比例差异很大。从大量胸腔积液中识别出一个肿瘤细胞或细胞团是胸腔积液肿瘤细胞检测技术面临的主要挑战。在本研究中,利用A549肺癌细胞系和Met-5A间皮细胞系,设计了一种基于细胞团大小的无标记微流控芯片。通过设置芯片参数,单个细胞和细胞团能够进入不同的微流控通道。非特异性染色后,回收的成分用吖啶橙(AO)染色。使用电荷耦合器件相机采集细胞图像,并利用Matlab软件在其R和G通道中分析这些细胞的特征,以确定其特性,最终区分肿瘤细胞与非肿瘤细胞或细胞团。结果显示,当入口A和B的流速分别为10和8.5 ml/h时,肿瘤细胞团通过微流控通道III-V成功收集,回收率约为80%。用AO染色后,确定了R和G通道中的特征值,并实现了初步区分。本研究将基于细胞团大小的微流控芯片与胸腔积液的计算机识别方法相结合。肿瘤细胞团与非肿瘤细胞团的成功区分,为胸腔积液中肿瘤细胞团的识别提供了依据。