Classification of cell types using a microfluidic device for mechanical and electrical measurement on single cells
Classification of cell types using a microfluidic device for mechanical and electrical measurement on single cells
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DOI：
10.1039/c1lc20473d
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发表时间：
2011-01-01
期刊：
影响因子：
6.1
通讯作者：
Sun, Yu
中科院分区：
文献类型：
--
作者：
Chen, Jian;Zheng, Yi;Sun, Yu
本文介绍了一种利用单个细胞的机械和电气测量进行细胞类型分类的微流控系统。细胞通过收缩通道连续吸入，同时测量细胞伸长和阻抗曲线。通过收缩通道的细胞传递时间和阻抗振幅比被量化为细胞的力学和电学性能指标。利用微流控装置和测量系统对成骨细胞（n = 206）和骨细胞（n = 217）进行了表征，结果表明，与骨细胞相比，成骨细胞具有更大的细胞伸长长度（64.51 +/- 14.98 μ m vs. 39.78 +/- 7.16 μ m）、更长的传递时间（1.84 +/- 1.48 s vs. 0.94 +/- 1.07 s）和更高的阻抗振幅比（1.198 +/- 0.071 vs. 1.099 +/- 0.038）。将基于神经网络的模式识别应用于细胞类型分类，对成骨细胞和骨细胞的分类成功率分别为69.8%（仅传递时间）、85.3%（仅阻抗幅值比）和93.7%（传递时间和阻抗幅值比均作为神经网络输入）。该系统还用于检测具有相似大小分布（细胞伸长长度分别为51.47 +/- 11.33 μ m和50.09 +/- 9.70 μ m）的EMT6 （n = 747）和EMT6/AR1.0细胞（n = 770，经阿霉素处理的EMT6）。研究了电池尺寸对传输时间和阻抗振幅比的影响。细胞分类成功率分别为51.3%（单独考虑细胞伸长）、57.5%（单独考虑传递时间）、59.6%（单独考虑阻抗振幅比）和70.2%（同时考虑传递时间和阻抗振幅比）。这些初步结果表明，当生物力学和生物电参数结合使用时，可以提供比单独使用电或机械参数更高的细胞分类成功率。
This paper presents a microfluidic system for cell type classification using mechanical and electrical measurements on single cells. Cells are aspirated continuously through a constriction channel with cell elongations and impedance profiles measured simultaneously. The cell transit time through the constriction channel and the impedance amplitude ratio are quantified as cell's mechanical and electrical property indicators. The microfluidic device and measurement system were used to characterize osteoblasts (n = 206) and osteocytes (n = 217), revealing that osteoblasts, compared with osteocytes, have a larger cell elongation length (64.51 +/- 14.98 mu m vs. 39.78 +/- 7.16 mu m), a longer transit time (1.84 +/- 1.48 s vs. 0.94 +/- 1.07 s), and a higher impedance amplitude ratio (1.198 +/- 0.071 vs. 1.099 +/- 0.038). Pattern recognition using the neural network was applied to cell type classification, resulting in classification success rates of 69.8% (transit time alone), 85.3% (impedance amplitude ratio alone), and 93.7% (both transit time and impedance amplitude ratio as input to neural network) for osteoblasts and osteocytes. The system was also applied to test EMT6 (n = 747) and EMT6/AR1.0 cells (n = 770, EMT6 treated by doxorubicin) that have a comparable size distribution (cell elongation length: 51.47 +/- 11.33 mu m vs. 50.09 +/- 9.70 mu m). The effects of cell size on transit time and impedance amplitude ratio were investigated. Cell classification success rates were 51.3% (cell elongation alone), 57.5% (transit time alone), 59.6% (impedance amplitude ratio alone), and 70.2% (both transit time and impedance amplitude ratio). These preliminary results suggest that biomechanical and bioelectrical parameters, when used in combination, could provide a higher cell classification success rate than using electrical or mechanical parameter alone.