Open AccessDiscrimination of different cell monolayers before and after exposure to nanosecond pulsed electric fields based on Cole–Cole and multivariate analysis
Author(s) -
Fukun Shi,
Jie Zhuang,
Juergen F. Kolb
Publication year - 2019
Publication title -
journal of physics. d, applied physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.857
H-Index - 198
eISSN - 1361-6463
pISSN - 0022-3727
DOI - 10.1088/1361-6463/ab40d7
Subject(s) - principal component analysis , nanosecond , monolayer , extracellular , cancer cell , chemistry , biophysics , cell , intracellular , cancer , multivariate analysis , multivariate statistics , dielectric spectroscopy , biology , biochemistry , medicine , mathematics , electrode , statistics , genetics , physics , laser , optics , electrochemistry
Normal and cancer cells, which were grown in monolayers, were investigated and discriminated by electrical bioimpedance spectroscopy (EBIS) before and after exposures to nanosecond pulsed electric fields (nsPEFs). Bioimpedance data were analysed with a Cole–Cole model and the principal component analysis (PCA). Normal and cancer cells could be clearly distinguished from each other either from Cole parameters ( R 0 , α , τ ) or from two dominant principal components. The trend of changes for Cole parameters indicated distinctively different post-nsPEF-effects between normal and cancer cells. PCA was also able to distinguish characteristic impedance spectra 30 min after exposures. The first principal component suggested that post-nsPEF-effects for normal cells were revealed especially at lower frequencies. The results indicated further that the extracellular resistance, which is dominated by cell–cell connections, might be an important factor with respect to selective nsPEF-effects on cancer cells that are organized in a monolayer or a tissue, respectively. Accordingly, the results support the application of EBIS as an early, non-invasive, label-free, and time-saving approach for the classification of cells to provide in particular predictive information on the success of cancer treatments with nsPEFs.