Numerical study of dielectrophoresis-modified inertial migration for overlapping sized cell separation

Enhancing cell separation in a hybrid spiral dielectrophoretic microchannel: Numerical insights and optimal operating conditions

Reliable separation of circulating tumor cells from blood cells is crucial for early cancer diagnosis and prognosis. Many conventional microfluidic platforms take advantage of the size difference between particles for their separation, which renders them impractical for sorting overlapping-sized cells. To address this concern, a hybrid inertial-dielectrophoretic microfluidic chip is proposed in this work for continuous and single-stage separation of lung cancer cell line A549 cells from white blood cells of overlapping size. The working mechanism of the proposed spiral microchannel embedded with planar interdigitated electrodes is validated against the experimental results. A numerical investigation is carried out over a range of flow conditions and electric field intensity to determine the separation efficiency and migration characteristics of the cell mixture. The results demonstrate the unique capability of the proposed microchannel to achieve high-throughput separation of cells at low applied voltages in both vertical and lateral directions. A significant lateral separation distance between the CTCs and the WBCs has been achieved, which allows for high-resolution and effective separation of cells. The separation resolution can be controlled by adjusting the strength of the applied electric field. Furthermore, the results demonstrate that the lateral separation distance is maximum at a voltage termed the critical voltage, which increases with the increase in the flow rate. The proposed microchannel and the developed technique can provide valuable insight into the development of a tunable and robust medical device for effective and high-throughput separation of cancer cells from the WBCs.

Microfluidic platform for omics analysis on single cells with diverse morphology and size: A review

BACKGROUND

Microfluidic techniques have emerged as powerful tools in single-cell research, facilitating the exploration of omics information from individual cells. Cell morphology is crucial for gene expression and physiological processes. However, there is currently a lack of integrated analysis of morphology and single-cell omics information. A critical challenge remains: what platform technologies are the best option to decode omics data of cells that are complex in morphology and size?

RESULTS

This review highlights achievements in microfluidic-based single-cell omics and isolation of cells based on morphology, along with other cell sorting methods based on physical characteristics. Various microfluidic platforms for single-cell isolation are systematically presented, showcasing their diversity and adaptability. The discussion focuses on microfluidic devices tailored to the distinct single-cell isolation requirements in plants and animals, emphasizing the significance of considering cell morphology and cell size in optimizing single-cell omics strategies. Simultaneously, it explores the application of microfluidic single-cell sorting technologies to single-cell sequencing, aiming to effectively integrate information about cell shape and size.

SIGNIFICANCE AND NOVELTY

The novelty lies in presenting a comprehensive overview of recent accomplishments in microfluidic-based single-cell omics, emphasizing the integration of different microfluidic platforms and their implications for cell morphology-based isolation. By underscoring the pivotal role of the specialized morphology of different cells in single-cell research, this review provides robust support for delving deeper into the exploration of single-cell omics data.

Separation of CTCs from WBCs using DEP-assisted inertial manipulation: A numerical study

Isolation and detection of circulating tumor cells (CTCs) hold significant importance for the early diagnosis of cancer and the assessment of therapeutic strategies. However, the scarcity of CTCs among peripheral blood cells presents a major challenge to their detection. Additionally, a similar size range between CTCs and white blood cells (WBCs) makes conventional microfluidic platforms inadequate for the isolation of CTCs. To overcome these challenges, in this study, a novel inertial-dielectrophoretic microfluidic channel for size-independent, single-stage separation of CTCs from WBCs has been presented. The proposed device utilizes a spiral microchannel embedded with interdigitated electrodes. A numerical model is developed and validated to investigate the influence of various parameters related to the channel design, fluid flow, and electrode configuration. It was found that optimal separation of CTCs could be obtained at a relatively low voltage, termed the critical voltage. Furthermore, at the critical voltage of 7.5 V, the hybrid microchannel is demonstrated to be capable of separating CTCs from different WBC subtypes including granulocytes, monocytes, T-, and B-lymphocytes. The unique capabilities of the hybrid spiral microchannel allow for this size-independent isolation of CTCs from a mixture of WBCs. Overall, the proposed technique can be readily utilized for continuous and high-throughput separation of cancer cells.

Optimized hybrid dielectrophoretic microchip for separation of bioparticles

Point-of-care diagnostics requires a smart separation of particles and/or cells. In this work, the multiorifice fluid fractionation as a passive method and dielectrophoresis-based actuator as an active tool are combined to offer a new device for size-based particle separation. The main objective of the combination of these two well-established techniques is to improve the performance of the multiorifice fluid fractionation by taking advantage of dielectrophoresis-based actuator for separating particles. Initially, by using numerical simulations, the effect of using dielectrophoresis-based actuator in multiorifice fluid fractionation on the separation of particles was investigated, and the size of the device was optimized by 25% compared to a device without dielectrophoresis-based actuator. Also, adding dielectrophoresis-based actuator to multiorifice fluid fractionation can extend the range of flow rates needed for separation. In the absence of dielectrophoresis-based actuator, the separation took place only when the flow rate is 100 μL/min, in the presence of dielectrophoresis-based actuator (20 Vp-p), the separation happened in flow rates ranging from 70 to 120 μL/min.

Continuous CTC separation through a DEP-based contraction-expansion inertial microfluidic channel

The efficient isolation of viable and intact circulating tumor cells (CTCs) from blood is critical for the genetic analysis of cancer cells, prediction of cancer progression, development of drugs, and evaluation of therapeutic treatments. While conventional cell separation devices utilize the size difference between CTCs and other blood cells, they fail to separate CTCs from white blood cells (WBCs) due to significant size overlap. To overcome this issue, we present a novel approach that combines curved contraction-expansion (CE) channels with dielectrophoresis (DEP) and inertial microfluidics to isolate CTCs from WBCs regardless of size overlap. This label-free and continuous separation method utilizes dielectric properties and size variation of cells for the separation of CTCs from WBCs. The results demonstrate that the proposed hybrid microfluidic channel can effectively isolate A549 CTCs from WBCs regardless of their size with a throughput of 300 μL/min, achieving a high separation distance of 233.4 μm at an applied voltage of 50 Vp-p . The proposed method allows for the modification of cell migration characteristics by controlling the number of CE sections of the channel, applied voltage, applied frequency, and flow rate. With its unique features of a single-stage separation, simple design, and tunability, the proposed method provides a promising alternative to the existing label-free cell separation techniques and may have a wide range of applications in biomedicine.

Microparticle separation using dielectrophoresis-assisted inertial microfluidics: A GPU-accelerated immersed boundary-lattice Boltzmann simulation

In this study, the migration of microparticles towards the inertial equilibrium positions in a straight microchannel with a square cross section in the presence of an inhomogeneous oscillating electric field was examined. The dynamics of microparticles were simulated using the immersed boundary-lattice Boltzmann method of fluid-structure interaction simulation. Moreover, the lattice Boltzmann Poisson solver was applied to calculate the electric field required for calculation of the dielectrophoretic force using the equivalent dipole moment approximation. These numerical methods were implemented on a single GPU coupled with the AA pattern of storing distribution functions in memory to speed up the computationally demanding simulation of microparticles dynamics. In the absence of an electric field, spherical polystyrene microparticles migrate to four symmetric stable equilibrium positions corresponding to the sidewalls of the square cross-sectional microchannel. The equilibrium distance from the sidewall was increased by increasing the particle size. The equilibrium positions near electrodes disappeared and particles migrated to the other equilibrium positions far from the electrodes by the application of the high-frequency oscillatory electric field at voltages beyond a threshold value. Finally, a two-step dielectrophoresis-assisted inertial microfluidics methodology was introduced for particle separation based on the crossover frequencies and the observed threshold voltages of different particles. The proposed method exploited the synergistic effect of dielectrophoresis and inertial microfluidics methods to remove their limitations, allowing the separation of a broad range of polydisperse particle mixtures with a single device in a short time.

Deterministic Lateral Displacement (DLD) Analysis Tool Utilizing Machine Learning towards High-Throughput Separation

Deterministic lateral displacement (DLD) is a microfluidic method for the continuous separation of particles based on their size. There is growing interest in using DLD for harvesting circulating tumor cells from blood for further assays due to its low cost and robustness. While DLD is a powerful tool and development of high-throughput DLD separation devices holds great promise in cancer diagnostics and therapeutics, much of the experimental data analysis in DLD research still relies on error-prone and time-consuming manual processes. There is a strong need to automate data analysis in microfluidic devices to reduce human errors and the manual processing time. In this work, a reliable particle detection method is developed as the basis for the DLD separation analysis. Python and its available packages are used for machine vision techniques, along with existing identification methods and machine learning models. Three machine learning techniques are implemented and compared in the determination of the DLD separation mode. The program provides a significant reduction in video analysis time in DLD separation, achieving an overall particle detection accuracy of 97.86% with an average computation time of 25.274 s.