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首页> 外文期刊>Biosensors & Bioelectronics: The International Journal for the Professional Involved with Research, Technology and Applications of Biosensers and Related Devices >Development of microfluidic impedance cytometry enabling the quantification of specific membrane capacitance and cytoplasm conductivity from 100,000 single cells
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Development of microfluidic impedance cytometry enabling the quantification of specific membrane capacitance and cytoplasm conductivity from 100,000 single cells

机译:显微流体阻抗细胞术的研制能够从100,000个单细胞定量特定膜电容和细胞质电导率的定量

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This paper presents a new microfluidic impedance cytometry with crossing constriction microchannels, enabling the characterization of cellular electrical markers (e.g., specific membrane capacitance (C-sm) and cytoplasm conductivity (sigma(cy))) in large cell populations (similar to 100,000 cells) at a rate greater than 100 cells/s. Single cells were aspirated continuously through the major constriction channel with a proper sealing of the side constriction channel. An equivalent circuit model was developed and the measured impedance values were translated to C-sm and sigma(cy). Neural network was used to classify different cell populations where classification success rates were calculated. To evaluate the developed technique, different tumour cell lines, and the effects of epithelial-mesenchymal transitions on tumour cells were examined. Significant differences in both C-sm and sigma(cy), were found for H1299 and HeLa cell lines with a classification success rate of 90.9% in combination of the two parameters. Meanwhile, tumour cells A549 showed significant decreases in both C-sm and sigma(cy) after epithelial-mesenchymal transitions with a classification success rate of 76.5%. As a high-throughput microfluidic impedance cytometry, this technique can add a new marker-free dimension to flow cytometry in single-cell analysis.
机译:本文提出了一种具有交叉收缩微通道的新微流体阻抗细胞术,使得细胞电气标记的表征(例如,特定膜电容(C-SM)和细胞质电导率(Sigma(Cy)))中的大细胞群(类似于100,000个细胞)以大于100个细胞/秒的速率。通过具有主要密封侧收缩通道的主要收缩通道连续吸出单个细胞。开发了等效电路模型,并将测量的阻抗值转换为C-SM和Sigma(Cy)。神经网络用于对计算成功率的不同小区群进行分类。为了评估开发的技术,不同的肿瘤细胞系,以及上皮间充质转变对肿瘤细胞的影响。对于H1299和HeLa细胞系,发现C-SM和Sigma(Cy)的显着差异,其两种参数的分类成功率为90.9%。同时,肿瘤细胞A549在上皮 - 间充质转换后表现出C-SM和Sigma(Cy)的显着降低,其分类成功率为76.5%。作为高通量的微流体阻抗细胞术,该技术可以在单细胞分析中添加新的无标记尺寸以流式细胞术。

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