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This study addresses the variations observed in electrorotation measurements due to cell positioning and movement. Electrorotation provides a non-disruptive method for inferring the electrical properties of individual cells. However, its widespread adoption is hindered by significant variation in the observed speed. By mitigating the impact of positional dependencies and other influencing factors, our methodology opens avenues for broader applications of electrorotation in single-cell analysis without the need for complex setups to trap and retain the cell in place. Our novel approach combines multi-plane imaging with mathematical treatment of rotation data. This method uses a conventional quadrupole chip and lens-free imaging to track cell movement, resulting in a simpler design and set-up. Through numerical simulations incorporating cell coordinates, chip design, and experimental parameters, we calculate the variation in torque for each position. These values serve as the basis for the correction factors. Validation experiments with T-lymphocytes and fibroblasts show that the correction factors reduce electrorotation speed variation due to cell movement, with an average reduction to 21% and 18%, respectively. These corrections also revealed previously concealed changes in cell properties, in response to external stimuli, thereby enhancing the reliability of measurements and enabling broader applications in single-cell analysis.
This study addresses the variations observed in electrorotation measurements due to cell positioning and movement. Electrorotation provides a non-disruptive method for inferring the electrical properties of individual cells. However, its widespread adoption is hindered by significant variation in the observed speed. By mitigating the impact of positional dependencies and other influencing factors, our methodology opens avenues for broader applications of electrorotation in single-cell analysis without the need for complex setups to trap and retain the cell in place. Our novel approach combines multi-plane imaging with mathematical treatment of rotation data. This method uses a conventional quadrupole chip and lens-free imaging to track cell movement, resulting in a simpler design and set-up. Through numerical simulations incorporating cell coordinates, chip design, and experimental parameters, we calculate the variation in torque for each position. These values serve as the basis for the correction factors. Validation experiments with T-lymphocytes and fibroblasts show that the correction factors reduce electrorotation speed variation due to cell movement, with an average reduction to 21% and 18%, respectively. These corrections also revealed previously concealed changes in cell properties, in response to external stimuli, thereby enhancing the reliability of measurements and enabling broader applications in single-cell analysis.
Microfluidic devices have long been useful for both the modeling and diagnostics of numerous diseases. In the past 20 years, they have been increasingly adopted for helping to study those in the family of breast cancer through characterizing breast cancer cells and advancing treatment research in portable and replicable formats. This paper adds to the body of work concerning cancer-focused microfluidics by proposing a simulation of a hypothetical bi-ended three-pronged device with a single channel and 16 electrodes with 8 pairs under different voltage and frequency regimes using COMSOL. Further, a study was conducted to examine the frequencies most effective for ACEO to separate cancer cells and accompanying particles. The study revealed that the frequency of EF has a more significant impact on the separation of particles than the inlet velocity. Inlet velocity variations while holding the frequency of EF constant resulted in a consistent trend showing a direct proportionality between inlet velocity and net velocity. These findings suggest that optimizing the frequency of EF could lead to more effective particle separation and targeted therapeutic interventions for breast cancer. This study hopefully will help to create targeted therapeutic interventions by bridging the disparity between in vitro and in vivo models.
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