Combining dielectrophoresis and computer vision for precise and fully automated single-cell handling and analysis

Combined Dielectric-Optical Characterization of Single Cells Using Dielectrophoresis-Imaging Flow Cytometry

In this paper, we present a microfluidic flow cytometer for simultaneous imaging and dielectric characterization of individual biological cells within a flow. Utilizing a combination of dielectrophoresis (DEP) and high-speed imaging, this system offers a dual-modality approach to analyze both cell morphology and dielectric properties, enhancing the ability to analyze, characterize, and discriminate cells in a heterogeneous population. A high-speed camera is used to capture images of and track multiple cells in real-time as they flow through a microfluidic channel. A wide channel is used, enabling analysis of many cells in parallel. A coplanar electrode array perpendicular to cell flow is incorporated at the bottom of the channel to perform dielectrophoresis-based dielectric characterization. A frequency-dependent voltage applied to the array produces a non-uniform electric field, translating cells to higher or lower velocity depending on their dielectric polarizability. In this paper, we demonstrate how cell size, obtained by optical imaging, and DEP response, obtained by particle tracking, can be used to discriminate viable and non-viable Chinese hamster ovary cells in a heterogeneous cell culture. Multiphysics electrostatic-fluid dynamics simulation is used to develop a relationship between cell incoming velocity, differential velocity, size, and the cell's polarizability, which can subsequently be used to evaluate its physiological state. Measurement of a mixture of polystyrene microspheres is used to evaluate the accuracy of the cytometer.

On-chip dielectrophoretic single-cell manipulation

Bioanalysis at a single-cell level has yielded unparalleled insight into the heterogeneity of complex biological samples. Combined with Lab-on-a-Chip concepts, various simultaneous and high-frequency techniques and microfluidic platforms have led to the development of high-throughput platforms for single-cell analysis. Dielectrophoresis (DEP), an electrical approach based on the dielectric property of target cells, makes it possible to efficiently manipulate individual cells without labeling. This review focusses on the engineering designs of recent advanced microfluidic designs that utilize DEP techniques for multiple single-cell analyses. On-chip DEP is primarily effectuated by the induced dipole of dielectric particles, (i.e., cells) in a non-uniform electric field. In addition to simply capturing and releasing particles, DEP can also aid in more complex manipulations, such as rotation and moving along arbitrary predefined routes for numerous applications. Correspondingly, DEP electrodes can be designed with different patterns to achieve different geometric boundaries of the electric fields. Since many single-cell analyses require isolation and compartmentalization of individual cells, specific microstructures can also be incorporated into DEP devices. This article discusses common electrical and physical designs of single-cell DEP microfluidic devices as well as different categories of electrodes and microstructures. In addition, an up-to-date summary of achievements and challenges in current designs, together with prospects for future design direction, is provided.

Computer vision meets microfluidics: a label-free method for high-throughput cell analysis

In this paper, we review the integration of microfluidic chips and computer vision, which has great potential to advance research in the life sciences and biology, particularly in the analysis of cell imaging data. Microfluidic chips enable the generation of large amounts of visual data at the single-cell level, while computer vision techniques can rapidly process and analyze these data to extract valuable information about cellular health and function. One of the key advantages of this integrative approach is that it allows for noninvasive and low-damage cellular characterization, which is important for studying delicate or fragile microbial cells. The use of microfluidic chips provides a highly controlled environment for cell growth and manipulation, minimizes experimental variability and improves the accuracy of data analysis. Computer vision can be used to recognize and analyze target species within heterogeneous microbial populations, which is important for understanding the physiological status of cells in complex biological systems. As hardware and artificial intelligence algorithms continue to improve, computer vision is expected to become an increasingly powerful tool for in situ cell analysis. The use of microelectromechanical devices in combination with microfluidic chips and computer vision could enable the development of label-free, automatic, low-cost, and fast cellular information recognition and the high-throughput analysis of cellular responses to different compounds, for broad applications in fields such as drug discovery, diagnostics, and personalized medicine.

On-chip label-free cell classification based directly on off-axis holograms and spatial-frequency-invariant deep learning

We present a rapid label-free imaging flow cytometry and cell classification approach based directly on raw digital holograms. Off-axis holography enables real-time acquisition of cells during rapid flow. However, classification of the cells typically requires reconstruction of their quantitative phase profiles, which is time-consuming. Here, we present a new approach for label-free classification of individual cells based directly on the raw off-axis holographic images, each of which contains the complete complex wavefront (amplitude and quantitative phase profiles) of the cell. To obtain this, we built a convolutional neural network, which is invariant to the spatial frequencies and directions of the interference fringes of the off-axis holograms. We demonstrate the effectiveness of this approach using four types of cancer cells. This approach has the potential to significantly improve both speed and robustness of imaging flow cytometry, enabling real-time label-free classification of individual cells.

Automatic trajectory control of single cells using dielectrophoresis based on visual feedback

This paper deals with the automatic control of the trajectory of T-lymphocytes using dielectrophoretic (DEP) actuation. Dielectrophoresis is a physical phenomenon induced by a non-uniform electric field enabling application of a force on a dielectric object. In most of the cases, it is used in a passive way. The electric field is in a steady state and the force applied on the cells depends on the cell's characteristics and position inside the channel. These systems are limited as cells with similar characteristics will undergo the same forces. To overcome this issue, active devices where the electric field changes over time were developed. However, the voltages that should be applied to generate the desired electric field are mostly computed offline using finite element methods. Thus, there is a low number of devices using automatic approaches with dielectrophoretic actuation where the electric field is computed and updated in real time based on the current position of the cell. We propose here an experimental bench used to study the automatic trajectory control of cells by dielectrophoresis. The computation of the dielectrophoretic force is done online with a model based on the Fourier series depending on the cell's characteristics, position and electric field. This model allows the use of a controller based on visual feedback running at 120 Hz to control the position of cells inside a microfluidic chip. As cells are sensitive to the electric field, the controller limits the norm of the electric field while maximizing the gradient to maximize the DEP force. Experiments have been performed and T-lymphocytes were successfully steered along several types of trajectories at a speed of five times their size per second. The mean error along those trajectories is below 2 μm. The viability of the cells has been checked after the experiments and confirms that this active DEP actuation does not harm the cells.

Continuous microfluidic flow-through protocol for selective and image-activated electroporation of single cells

Electroporation of cells is a widely-used tool to transport molecules such as proteins or nucleic acids into cells or to extract cellular material. However, bulk methods for electroporation do not offer the possibility to selectively porate subpopulations or single cells in heterogeneous cell samples. To achieve this, either presorting or complex single-cell technologies are required currently. In this work, we present a microfluidic flow protocol for selective electroporation of predefined target cells identified in real-time by high-quality microscopic image analysis of fluorescence and transmitted light. While traveling through the microchannel, the cells are focused by dielectrophoretic forces into the microscopic detection area, where they are classified based on image analysis techniques. Finally, the cells are forwarded to a poration electrode and only the target cells are pulsed. By processing a heterogenically stained cell sample, we were able to selectively porate only target cells (green-fluorescent) while non-target cells (blue-fluorescent) remained unaffected. We achieved highly selective poration with >90% specificity at average poration rates of >50% and throughputs of up to 7200 cells per hour.

High-precision, low-complexity, high-resolution microscopy-based cell sorting

Continuous flow cell sorting based on image analysis is a powerful concept that exploits spatially-resolved features in cells, such as subcellular protein localisation or cell and organelle morphology, to isolate highly specialised cell types that were previously inaccessible to biomedical research, biotechnology, and medicine. Recently, sorting protocols have been proposed that achieve impressive throughput by combining ultra-high flow rates with sophisticated imaging and data processing protocols. However, moderate image quality and high complex experimental setups still prevent the full potential of image-activated cell sorting from being a general-purpose tool. Here, we present a new low-complexity microfluidic approach based on high numerical aperture wide-field microscopy and precise dielectrophoretic cell handling. It provides high-quality images with unprecedented resolution in image-activated cell sorting (i.e., 216 nm). In addition, it also allows long image processing times of several hundred milliseconds for thorough image analysis, while ensuring reliable and low-loss cell processing. Using our approach, we sorted live T cells based on subcellular localisation of fluorescence signals and demonstrated that purities above 80% are possible while targeting maximum yields and sample volume throughputs in the range of μl min^-1. We were able to recover 85% of the target cells analysed. Finally, we ensure and quantify the full vitality of the sorted cells cultivating the cells for a period of time and through colorimetric viability tests.

Dielectrophoretic Traps for Efficient Bead and Cell Trapping and Formation of Aggregates of Controlled Size and Composition

We present a microfluidic dielectrophoretic-actuated system designed to trap chosen single-cell and form controlled cell aggregates. A novel method is proposed to characterize the efficiency of the dielectrophoretic trapping, considering the flow speed but also the heat generated by the traps as limiting criteria in cell-safe manipulation. Two original designs with different manufacturing processes are experimentally compared. The most efficient design is selected and the cell membrane integrity is monitored by fluorescence imaging to guarantee a safe-cell trapping. Design rules are suggested to adapt the traps to multiple-cells trapping and are experimentally validated as we formed aggregates of controlled size and composition with two different types of cells. We provide hereby a simple manufactured tool allowing the controlled manipulation of particles for the composition of multicellular assemblies.

Trapping and releasing of single microparticles and cells in a microfluidic chip

A microfluidic device was designed and fabricated to capture single microparticles and cells by using hydrodynamic force and selectively release the microparticles and cells of interest via negative dielectrophoresis by activating selected individual microelectrodes. The trap microstructure was optimized based on numerical simulation of the electric field as well as the flow field. The capture and selective release functions of the device were verified by multi-types microparticles with different diameters and K562 cells. The capture efficiencies/release efficiencies were 95.55% ± 0.43%/96.41% ± 1.08% and 91.34% ± 0.01%/93.67% ± 0.36% for microparticles and cells, respectively. By including more traps and microelectrodes, the device can achieve high throughput and realize the visual separation of microparticles/cells of interest in a large number of particle/cell groups.

Magnetophoretic Micro-Distributor for Controlled Clustering of Cells

Cell clustering techniques are important to produce artificial cell clusters for in vitro models of intercellular mechanisms at the single-cell level. The analyses considering physical variables such as the shape and size of cells have been very limited. In addition, the precise manipulation of cells and control of the physical variables are still challenging. In this paper, a magnetophoretic device consisting of a trampoline micromagnet and active elements that enable the control of individual selective jumping motion and positioning of a micro-object is proposed. Based on a numerical simulation under various conditions, automatic separation or selective clustering of micro-objects according to their sizes is performed by parallel control and programmable manipulation. This method provides efficient control of the physical variables of cells and grouping of cells with the desired size and number, which can be a milestone for a better understanding of the intercellular dynamics between clustered cells at the single-cell level for future cell-on-chip applications.

Label-free discrimination and selection of cancer cells from blood during flow using holography-induced dielectrophoresis

We present a method for label-free imaging and sorting of cancer cells in blood, which is based on a dielectrophoretic microfluidic chip and label-free interferometric phase microscopy. The chip used for imaging has been embedded with dielectrophoretic electrodes, and therefore it can be used to sort the cells based on the decisions obtained during the cell flow by the label-free quantitative imaging method. Hence, we obtained a real-time, automatic, label-free imaging flow cytometry with the ability to sort the cells during flow. To validate our model, we combined into the label-free imaging interferometer a fluorescence imaging channel that indicated the correctness of the label-free sorting. We have achieved above 98% classification success and 69% sorting accuracy at flow rates of 4 to 7 μL hr^-1 . In the future, this method is expected to help in label-free sorting of circulating tumor cells in blood following an initial state-of-the-art cell enrichment.