Cellular subpopulations identified using an ensemble average of multiple dielectrophoresis measurements

While the average value measurement approach can successfully analyze and predict the general behavior and biophysical properties of an isogenic cell population, it fails when significant differences among individual cells are generated in the population by intracellular changes such as the cell cycle, or different cellular responses to certain stimuli. Detecting such single-cell differences in a cell population has remained elusive. Here, we describe an easy-to-implement and generalizable platform that measures the dielectrophoretic cross-over frequency of individual cells by decreasing measurement noise with a stochastic method and computing ensemble average statistics. This platform enables multiple, real-time, label-free detection of individual cells with significant dielectric variations over time within an isogenic cell population. Using a stochastic method in combination with the platform, we distinguished cell subpopulations from a mixture of drug-untreated and -treated isogenic cells. Furthermore, we demonstrate that our platform can identify drug-treated isogenic cells with different recovery rates.