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In this paper, the translational motion and self-rotational behaviors of the Raji cells, a type of B-cell lymphoma cell, in an optically induced, non-rotational, electric field have been characterized by utilizing a digitally programmable and optically activated microfluidics chip with the assistance of an externally applied AC bias potential. The crossover frequency spectrum of the Raji cells was studied by observing the different linear translation responses of these cells to the positive and negative optically induced dielectrophoresis force generated by a projected light pattern. This digitally projected spot served as the virtual electrode to generate an axisymmetric and non-uniform electric field. Then, the membrane capacitance of the Raji cells could be directly measured. Furthermore, Raji cells under this condition also exhibited a self-rotation behavior. The repeatable and controlled self-rotation speeds of the Raji cells to the externally applied frequency and voltage were systematically investigated and characterized via computer-vision algorithms. The self-rotational speed of the Raji cells reached a maximum value at 60 kHz and demonstrated a quadratic relationship with respect to the applied voltage. Furthermore, optically projected patterns of four orthogonal electrodes were also employed as the virtual electrodes to manipulate the Raji cells. These results demonstrated that Raji cells located at the center of the four electrode pattern could not be self-rotated. Instead any Raji cells that deviated from this center area would also self-rotate. Most importantly, the Raji cells did not exhibit the self-rotational behavior after translating and rotating with respect to the center of any two adjacent electrodes. The spatial distributions of the electric field generated by the optically projected spot and the pattern of four electrodes were also modeled using a finite element numerical simulation. These simulations validated that the electric field distributions were non-uniform and non-rotational. Hence, the non-uniform electric field must play a key role in the self-rotation of the Raji cells. As a whole, this study elucidates an optoelectric-coupled microfluidics-based mechanism for cellular translation and self-rotation that can be used to extract the dielectric properties of the cells without using conventional metal-based microelectrodes. This technique may provide a simpler method for label-free identification of cancerous cells with many associated clinical applications.

Cell dielectric properties, a type of intrinsic property of cells, can be used as electrophysiological biomarkers that offer a label-free way to characterize cell phenotypes and states, purify clinical samples, and identify target cancer cells. Here, we present a review of the determination of cell dielectric properties using alternating current (AC) electrokinetic-based microfluidic mechanisms, including electro-rotation (ROT) and dielectrophoresis (DEP). The review covers theoretically how ROT and DEP work to extract cell dielectric properties. We also dive into the details of differently structured ROT chips, followed by a discussion on the determination of cell dielectric properties and the use of these properties in bio-related applications. Additionally, the review offers a look at the future challenges facing the AC electrokinetic-based microfluidic platform in terms of acquiring cell dielectric parameters. Our conclusion is that this platform will bring biomedical and bioengineering sciences to the next level and ultimately achieve the shift from lab-oriented research to real-world applications.

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We present an image-matching-based automated algorithm capable of accurately determining the self-rotational speed of cancer cells in an optically-induced electrokinetics-based microfluidic chip. To automatically track a specific cell in a video featuring more than one cell, a background subtraction technique was used. To determine the rotational speeds of cells, a reference frame was automatically selected and curve fitting was performed to improve the stability and accuracy. Results show that the algorithm was able to accurately calculate the self-rotational speeds of cells up to ~150 rpm. In addition, the algorithm could be used to determine the motion trajectories of the cells. Potential applications for the developed algorithm include the differentiation of cell morphology and characterization of cell electrical properties.