Identification and Characterization of Rare Cells through Electrokinetic Cell Oscillations

The goal of this project is to demonstrate an electrokinetic tool for label-free detection, isolation, characterization and analysis of rare cells based on their biomolecular profiles.

Different normal cells and rare cells undergo different cellular processes based on their unique biomolecular profile. Consequently they modulate the overall electrical properties of the cells and their ability to polarize under external electric fields. For normal cells, the dielectric properties are well characterized based on their internal and surface biomarkers. Hence they can be used to electrically control and isolate them in biological samples. However, apriori knowledge of rare cells such as cancer stem cells, circulating tumor cells, progenitor cells, etc, are not well-known. Recent scientific advances have demonstrated the proof of concept in detecting and isolating rare cell population with the little known knowledge of their cell surface proteins and markers. However, the ability to distinguish between endothelial stem cells and endothelial progenitor cells are very marginal using present day methods.

Here we present a successful label-free lab-on-chip tool which could eventually be used as an add-on module to existing methods to identify and characterize based on their biomolecular profile. The platform utilizes size-matched microelectrodes to apply gradient electric fields to the system and microfluidic channels for sample fluid flow. Cells under study are suspended in conductive medium to facilitate differential polarization inside and outside of the cell. As a proof of concept, we demonstrate the applicability of this platform tool for cancer stem cells which have demonstrated positive activity of aldehyde dehydrogenase (ALDH) isozymes. In this study, we used a simulated ratiometric mixture of ALDH+ and ALDH- cell lines. The cells were derived from synthetically cultured lung cancer cell lines enriched for cancer stem cells. ALDH+ cells have a unique biomolecular profile that affects its polarizability which results in resonance behavior at harmonic frequencies.

Upon characterization through electrokinetic oscillatory tool, we were able to perform cell counting and successfully distinguish cancer cells that were ALDH+ and ALDH- cells. Image processing algorithms were used to characterize the results in less than 15 minutes post introduction of input sample. Statistical modeling were performed to estimate the probability of success rate. In conclusion, the developed tool was able to identify and isolate target cells with significance of p<0.05, leading to efficiency of separation upto 90%. In conclusion, we present a method to identify rare cells based on their unique oscillatory behavior as a function of their biomolecular profile.