Background and Objective

The dielectrophoresis (DEP) technique is increasingly being recognised as a potentially valuable tool for non-contact manipulation of numerous cells as well as for biological single cell analysis with non-invasive characterisation of a cell's electrical properties. Several studies have attempted to track multiple cells to characterise their cellular DEP mobility. However, they encountered difficulties in simultaneously tracking the movement of a large number of individual cells in a bright-field image sequence because of interference from the background electrode pattern. Consequently, this present study aims to develop an automatic system for imaging-based characterisation of cellular DEP mobility, which enables the simultaneous tracking of several hundred of cells inside a microfluidic device.

Methods

: The proposed method for segmentation and tracking of cells consists of two main stages: pre-processing and particle centre localisation. In the pre-processing stage, background subtraction and contrast enhancement were performed to distinguish the cell region from the background image. In the particle centre localisation stage, the unmarked cell was automatically detected via graph-cut algorithm-based K-means clustering.

Results

: Our algorithm enabled segmentation and tracking of numerous Michigan Cancer Foundation-7 (MCF-7) cell trajectories while the DEP force was oscillated between positive and negative. The cell tracking accuracy and cell count capability was at least 90% of the total number of cells with the newly developed algorithm. In addition, the cross-over frequency was measured by analysing the segmented and tracked trajectory data of the cellular movements caused by the positive and negative DEP force. The measured cross-over frequency was compared with previous results. The multi-cellular movements investigation based on the measured cross-over frequency was repeated until the viability of cells was unchanged in the same environment as in a microfluidic device. The results were statistically consistent, indicating that the developed algorithm was reliable for the investigation of DEP cellular mobility.

Conclusion

: This study developed a powerful platform to simultaneously measure the DEP-induced trajectories of numerous cells, and to investigate in a robust, efficient, and accurate manner the DEP properties at both the single cell and cell ensemble level.

中文翻译：

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一种新颖的自动分段和跟踪方法，可从单个细胞轨迹测量细胞介电泳迁移率，以进行高通量分析。

背景与目的

介电电泳（DEP）技术越来越被认为是潜在的有价值的工具，可用于无接触操作多个细胞以及用于具有电特性的非侵入性表征的生物单细胞分析。数项研究已尝试追踪多个细胞以表征其细胞DEP迁移性。然而，由于来自背景电极图案的干扰，他们在同时跟踪明场图像序列中大量单个细胞的运动时遇到困难。因此，本研究旨在开发一种基于系统的细胞DEP迁移性表征的自动系统，该系统能够同时跟踪微流控设备内部的数百个细胞。

方法

：建议的细胞分割和跟踪方法包括两个主要阶段：预处理和粒子中心定位。在预处理阶段，进行背景扣除和对比度增强以将细胞区域与背景图像区分开。在粒子中心定位阶段，通过基于图割算法的K均值聚类自动检测未标记的细胞。

结果

：当DEP力在正负之间振荡时，我们的算法可以对许多密歇根癌症基金会7（MCF-7）细胞轨迹进行分割和跟踪。使用新开发的算法，细胞跟踪精度和细胞计数能力至少为细胞总数的90％。另外，通过分析由正和负DEP力引起的细胞运动的分段和跟踪轨迹数据来测量交叉频率。将测得的交叉频率与以前的结果进行比较。重复基于测得的交叉频率的多细胞运动研究，直到在与微流体装置相同的环境中细胞的活力不变。结果在统计上是一致的，

结论

：这项研究开发了一个强大的平台，可同时测量DEP诱导的许多细胞的轨迹，并以健壮，高效和准确的方式研究单细胞和细胞集合水平的DEP特性。