A novel automatic segmentation and tracking method to measure cellular dielectrophoretic mobility from individual cell trajectories for high throughput assay

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.

[1]  Fabian Rudolf,et al.  Accurate cell segmentation in microscopy images using membrane patterns , 2014, Bioinform..

[2]  Hongyuan Jiang,et al.  High-Throughput Separation, Trapping, and Manipulation of Single Cells and Particles by Combined Dielectrophoresis at a Bipolar Electrode Array. , 2018, Analytical chemistry.

[3]  Srinivas Velugotla,et al.  Dielectrophoresis based discrimination of human embryonic stem cells from differentiating derivatives. , 2012, Biomicrofluidics.

[4]  Chen-Ho Wang,et al.  Dielectrophoresis-based cellular microarray chip for anticancer drug screening in perfusion microenvironments. , 2011, Lab on a chip.

[5]  X. Gidrol,et al.  Comprehensive Analysis of Human Cells Motion under an Irrotational AC Electric Field in an Electro-Microfluidic Chip , 2014, PloS one.

[6]  G. Fowles,et al.  Introduction to modern optics , 1968 .

[7]  R. Pethig.  Review article-dielectrophoresis: status of the theory, technology, and applications. , 2010, Biomicrofluidics.

[8]  Soo Hyeon Kim,et al.  Label-free single-cell separation and imaging of cancer cells using an integrated microfluidic system , 2017, Scientific Reports.

[9]  Sung Hoon Kim,et al.  Non-Linear Cellular Dielectrophoretic Behavior Characterization Using Dielectrophoretic Tweezers-Based Force Spectroscopy inside a Microfluidic Device , 2018, Sensors.

[10]  Woo-Jin Chang,et al.  Automated Dielectrophoretic Tweezers-Based Force Spectroscopy System in a Microfluidic Device , 2017, Sensors.

[11]  Eva M. Schmelz,et al.  Selective concentration of human cancer cells using contactless dielectrophoresis , 2011, Electrophoresis.

[12]  Pedro Quelhas,et al.  MobilityAnalyser: A novel approach for automatic quantification of cell mobility on periodic patterned substrates using brightfield microscopy images , 2018, Comput. Methods Programs Biomed..

[13]  M. Gandhi,et al.  Dielectrophoretic Microfluidic Chip Enables Single-Cell Measurements for Multidrug Resistance in Heterogeneous Acute Myeloid Leukemia Patient Samples. , 2016, Analytical chemistry.

[14]  Frédérique Kermarrec,et al.  Label-Free Electric Monitoring of Human Cancer Cells as a Potential Diagnostic Tool. , 2016, Analytical chemistry.

[15]  Soumya K. Srivastava,et al.  Dielectric characterization of bioparticles via electrokinetics: The past, present, and the future , 2019 .

[16]  Quantitative characterization for dielectrophoretic behavior of biological cells using optical tweezers , 2014 .

[17]  Olga Veksler,et al.  Fast Approximate Energy Minimization via Graph Cuts , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  Joel Voldman,et al.  Rapid dielectrophoretic characterization of single cells using the dielectrophoretic spring. , 2013, Lab on a chip.

[19]  Wenfeng Liang,et al.  Determination of Cell Membrane Capacitance and Conductance via Optically Induced Electrokinetics. , 2017, Biophysical journal.

[20]  Peng Qiu,et al.  Accurately tracking single-cell movement trajectories in microfluidic cell sorting devices , 2018, PloS one.

[21]  F. Molinari,et al.  Movement of giant lipid vesicles induced by millimeter wave radiation change when they contain magnetic nanoparticles , 2018, Drug Delivery and Translational Research.

[22]  Chun-Ping Jen,et al.  Single-Cell Electric Lysis on an Electroosmotic-Driven Microfluidic Chip with Arrays of Microwells , 2012, Sensors.

[23]  Zhizhong Zhang,et al.  Flexible Trapping and Manipulation of Single Cells on a Chip by Modulating Phases and Amplitudes of Electrical Signals Applied onto Microelectrodes. , 2019, Analytical chemistry.

[24]  Rama Chellappa,et al.  Segmentation of Nuclei From 3D Microscopy Images of Tissue via Graphcut Optimization , 2016, IEEE Journal of Selected Topics in Signal Processing.

[25]  Michael P Hughes,et al.  Dielectrophoretic assay of bacterial resistance to antibiotics. , 2003, Physics in medicine and biology.

[26]  Bo Peng,et al.  Parameter Selection for Graph Cut Based Image Segmentation , 2008, BMVC.

[27]  Amar Mitiche,et al.  Multiregion Image Segmentation by Parametric Kernel Graph Cuts , 2011, IEEE Transactions on Image Processing.

[28]  Ulrich G. Hofmann,et al.  Reduction of periodic noise in Fourier domain optical coherence tomography images by frequency domain filtering , 2012 .

[29]  Young-Jin Jung,et al.  Selective position of individual cells without lysis on a circular window array using dielectrophoresis in a microfluidic device , 2017 .

[30]  Soo Hyeon Kim,et al.  Electroactive microwell arrays for highly efficient single-cell trapping and analysis. , 2011, Small.

[31]  J. Voldman,et al.  Multiparameter cell-tracking intrinsic cytometry for single-cell characterization. , 2018, Lab on a chip.

[32]  Yu-Chun Kung,et al.  Tunnel Dielectrophoresis for Tunable, Single-Stream Cell Focusing in Physiological Buffers in High-Speed Microfluidic Flows. , 2016, Small.

[33]  Gyudo Lee,et al.  Real-Time Analysis of Cellular Response to Small-Molecule Drugs within a Microfluidic Dielectrophoresis Device. , 2015, Analytical chemistry.

[34]  Meltem Elitaş,et al.  Dielectrophoresis as a single cell characterization method for bacteria , 2017 .

[35]  A-Andrew D Jones,et al.  Microfluidic dielectrophoresis illuminates the relationship between microbial cell envelope polarizability and electrochemical activity , 2019, Science Advances.

[36]  J. Tapamo,et al.  Automatic Segmentation of Cell Images by Improved Graph Cut-Based Approach , 2016 .

[37]  Hongyuan Jiang,et al.  Large-Scale Single Particle and Cell Trapping based on Rotating Electric Field Induced-Charge Electroosmosis. , 2016, Analytical chemistry.

[38]  Zubair Khan,et al.  Automatic detection and counting of circular shaped overlapped objects using circular hough transform and contour detection , 2016, 2016 12th World Congress on Intelligent Control and Automation (WCICA).