Use of Discrete Wavelet Transform to Assess Impedance Fluctuations Obtained from Cellular Micromotion

Electric cell–substrate impedance sensing (ECIS) is an attractive method for monitoring cell behaviors in tissue culture in real time. The time series impedance fluctuations of the cell-covered electrodes measured by ECIS are the phenomena accompanying cellular micromotion as cells continually rearrange their cell–cell and cell–substrate adhesion sites. Accurate assessment of these fluctuations to extract useful information from raw data is important for both scientific and practical purposes. In this study, we apply discrete wavelet transform (DWT) to analyze the concentration-dependent effect of cytochalasin B on human umbilical vein endothelial cells (HUVECs). The sampling rate of the impedance time series is 1 Hz and each data set consists of 2048 points. Our results demonstrate that, in the Daubechies (db) wavelet family, db1 is the optimal mother wavelet function for DWT-based analysis to assess the effect of cytochalasin B on HUVEC micromotion. By calculating the energy, standard deviation, variance, and signal magnitude area of DWT detail coefficients at level 1, we are able to significantly distinguish cytotoxic concentrations of cytochalasin B as low as 0.1 μM, and in a concentration-dependent manner. Furthermore, DWT-based analysis indicates the possibility to decrease the sampling rate of the micromotion measurement from 1 Hz to 1/16 Hz without decreasing the discerning power. The statistical measures of DWT detail coefficients are effective methods for determining both the sampling rate and the number of individual samples for ECIS-based micromotion assays.

[1]  M. Mandal,et al.  Wavelet-based multiscale analysis of bioimpedance data measured by electric cell-substrate impedance sensing for classification of cancerous and normal cells. , 2015, Physical review. E, Statistical, nonlinear, and soft matter physics.

[2]  C.E. Shannon,et al.  Communication in the Presence of Noise , 1949, Proceedings of the IRE.

[3]  J. D. Janssen,et al.  Assessment of energy expenditure for physical activity using a triaxial accelerometer. , 1994, Medicine and science in sports and exercise.

[4]  V. V. van Hinsbergh,et al.  Localized RhoA GTPase activity regulates dynamics of endothelial monolayer integrity. , 2013, Cardiovascular research.

[5]  Binil Starly,et al.  Electrical Cell‐Substrate Impedance Spectroscopy Can Monitor Age‐Grouped Human Adipose Stem Cell Variability During Osteogenic Differentiation , 2016, Stem cells translational medicine.

[6]  C. Hertweck,et al.  The chemistry and biology of cytochalasans. , 2010, Natural product reports.

[7]  Ivar Giaever,et al.  A morphological biosensor for mammalian cells , 1993, Nature.

[8]  C. Renken,et al.  Impedance analysis of GPCR-mediated changes in endothelial barrier function: overview and fundamental considerations for stable and reproducible measurements , 2015, Pflügers Archiv - European Journal of Physiology.

[9]  A. Medl,et al.  Time Frequency and Wavelets in Biomedical Signal Processing , 1998, IEEE Engineering in Medicine and Biology Magazine.

[10]  Yongming Huang,et al.  Time Sequence Learning for Electrical Impedance Tomography Using Bayesian Spatiotemporal Priors , 2020, IEEE Transactions on Instrumentation and Measurement.

[11]  Soumen Kumar Das,et al.  Bioimpedimetric analysis in conjunction with growth dynamics to differentiate aggressiveness of cancer cells , 2018, Scientific Reports.

[12]  Benjamin Thierry,et al.  Cellular micromotion monitored by long-range surface plasmon resonance with optical fluctuation analysis. , 2015, Analytical chemistry.

[13]  Tse-Hua Tung,et al.  Application of ECIS to Assess FCCP-Induced Changes of MSC Micromotion and Wound Healing Migration , 2019, Sensors.

[14]  Szi-Wen Chen,et al.  A computational modeling and analysis in cell biological dynamics using electric cell-substrate impedance sensing (ECIS). , 2012, Biosensors & bioelectronics.

[15]  F. Hlawatsch,et al.  Linear and quadratic time-frequency signal representations , 1992, IEEE Signal Processing Magazine.

[16]  I. Giaever,et al.  Monitoring fibroblast behavior in tissue culture with an applied electric field. , 1984, Proceedings of the National Academy of Sciences of the United States of America.

[17]  Szi-Wen Chen,et al.  A quantitative cell modeling and wound-healing analysis based on the Electric Cell-substrate Impedance Sensing (ECIS) method , 2016, Comput. Biol. Medicine.

[18]  J. Wegener,et al.  Impedance-Based Assays Along the Life Span of Adherent Mammalian Cells In Vitro: From Initial Adhesion to Cell Death , 2019, Bioanalytical Reviews.

[19]  Chun-Min Lo,et al.  Detecting effects of low levels of cytochalasin B in 3T3 fibroblast cultures by analysis of electrical noise obtained from cellular micromotion. , 2009, Biosensors & bioelectronics.

[20]  Andreas Janshoff,et al.  Cell motility probed by noise analysis of thickness shear mode resonators. , 2006, Analytical chemistry.

[21]  Yi-Ting Lai,et al.  Assessing in vitro cytotoxicity of cell micromotion by Hilbert-Huang Transform , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[22]  C. Lo,et al.  Monitoring motion of confluent cells in tissue culture. , 1993, Experimental cell research.

[23]  S. Carter.  Effects of Cytochalasins on Mammalian Cells , 1967, Nature.

[24]  Madalena Costa,et al.  Multiscale entropy analysis of biological signals. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[25]  C. Lo,et al.  Effects of electrode diameter on the detection sensitivity and frequency characteristics of electric cell-substrate impedance sensing , 2019, Sensors and Actuators B: Chemical.

[26]  Chun-Min Lo,et al.  Use of electric cell-substrate impedance sensing to assess in vitro cytotoxicity. , 2009, Biosensors & bioelectronics.

[27]  I. Giaever,et al.  Micromotion of mammalian cells measured electrically. , 1991, Proceedings of the National Academy of Sciences of the United States of America.

[28]  Ada Wai-Chee Fu,et al.  Efficient time series matching by wavelets , 1999, Proceedings 15th International Conference on Data Engineering (Cat. No.99CB36337).

[29]  David Schneider,et al.  Cytotoxicity of metal and semiconductor nanoparticles indicated by cellular micromotility. , 2009, ACS nano.

[30]  M. Mathie,et al.  Detection of daily physical activities using a triaxial accelerometer , 2003, Medical and Biological Engineering and Computing.

[31]  Joachim Wegener,et al.  Label‐free profiling of cell dynamics: A sequence of impedance‐based assays to estimate tumor cell invasiveness in vitro , 2017, Experimental cell research.

[32]  George Karabatis,et al.  Discrete wavelet transform-based time series analysis and mining , 2011, CSUR.

[33]  Michael R. Chernick,et al.  Wavelet Methods for Time Series Analysis , 2001, Technometrics.