Characterization of Single Yeast Cell Phenotypes Using Microfluidic Impedance Cytometry and Optical Imaging

Impedance-Based Multimodal Electrical-Mechanical Intrinsic Flow Cytometry

Reflecting various physiological states and phenotypes of single cells, intrinsic biophysical characteristics (e.g., mechanical and electrical properties) are reliable and important, label-free biomarkers for characterizing single cells. However, single-modal mechanical or electrical properties alone are not specific enough to characterize single cells accurately, and it has been long and challenging to couple the conventionally image-based mechanical characterization and impedance-based electrical characterization. In this work, the spatial-temporal characteristics of impedance sensing signal are leveraged, and an impedance-based multimodal electrical-mechanical flow cytometry framework for on-the-fly high-dimensional intrinsic measurement is proposed, that is, Young's modulus E, fluidity β, radius r, cytoplasm conductivity σi , and specific membrane capacitance Csm , of single cells. With multimodal high-dimensional characterization, the electrical-mechanical flow cytometry can better reveal the difference in cell types, demonstrated by the experimental results with three types of cancer cells (HepG2, MCF-7, and MDA-MB-468) with 93.4% classification accuracy and pharmacological perturbations of the cytoskeleton (fixed and Cytochalasin B treated cells) with 95.1% classification accuracy. It is envisioned that multimodal electrical-mechanical flow cytometry provides a new perspective for accurate label-free single-cell intrinsic characterization.

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.

Machine learning implementation strategy in imaging and impedance flow cytometry

Imaging and impedance flow cytometry is a label-free technique that has shown promise as a potential replacement for standard flow cytometry. This is due to its ability to provide rich information and archive high-throughput analysis. Recently, significant efforts have been made to leverage machine learning for processing the abundant data generated by those techniques, enabling rapid and accurate analysis. Harnessing the power of machine learning, imaging and impedance flow cytometry has demonstrated its capability to address various complex phenotyping scenarios. Herein, we present a comprehensive overview of the detailed strategies for implementing machine learning in imaging and impedance flow cytometry. We initiate the discussion by outlining the commonly employed setup to acquire the data (i.e., image or signal) from the cell. Subsequently, we delve into the necessary processes for extracting features from the acquired image or signal data. Finally, we discuss how these features can be utilized for cell phenotyping through the application of machine learning algorithms. Furthermore, we discuss the existing challenges and provide insights for future perspectives of intelligent imaging and impedance flow cytometry.

A review on intelligent impedance cytometry systems: Development, applications and advances

Impedance cytometry is a well-established technique for counting and analyzing single cells, with several advantages, such as convenience, high throughput, and no labeling required. A typical experiment consists of the following steps: single-cell measurement, signal processing, data calibration, and particle subtype identification. At the beginning of this article, we compared commercial and self-developed options extensively and provided references for developing reliable detection systems, which are necessary for cell measurement. Then, a number of typical impedance metrics and their relationships to biophysical properties of cells were analyzed with respect to the impedance signal analysis. Given the rapid advances of intelligent impedance cytometry in the past decade, this article also discussed the development of representative machine learning-based approaches and systems, and their applications in data calibration and particle identification. Finally, the remaining challenges facing the field were summarized, and potential future directions for each step of impedance detection were discussed.

The monitoring of oil production process by deep learning based on morphology in oleaginous yeasts

BACKGROUND

Monitoring jar fermenter-cultured microorganisms in real time is important for controlling productivity of bioproducts in large-scale cultivation settings. Morphological data is used to understand the growth and fermentation states of these microorganisms during monitoring. Oleaginous yeasts are used for their high productivity of single-cell oils but the relationship between lipid productivity and morphology has not been elucidated in these organisms.

RESULTS

In this study, we investigated the relationship between the morphology of oleaginous yeasts (Lipomyces starkeyi and Rhodosporidium toruloides were used) and their cultivation state in a large-scale cultivation setting using a real-time monitoring system. We combined this with deep learning by feeding a large amount of high-definition cell images obtained from the monitoring system to a deep learning algorithm. Our results showed that the cell images could be grouped into 7 distinct groups and that a strong correlation existed between each group and its biochemical activity (growth and oil-productivity).

CONCLUSIONS

This is the first report describing the morphological variations of oleaginous yeasts in a large-scale cultivation, and describes a promising new avenue for improving productivity of microorganisms in large-scale cultivation through the use of a real-time monitoring system combined with deep learning.

KEY POINTS

• A real-time monitoring system followed the morphological change of oleaginous yeasts. • Deep learning grouped them into 7 distinct groups based on their morphology. • A correlation between the cultivation state and the shape of the yeast was observed.

Identification of Single Yeast Budding Using Impedance Cytometry with a Narrow Electrode Span

Impedance cytometry is wildly used in single-cell detection, and its sensitivity is essential for determining the status of single cells. In this work, we focus on the effect of electrode gap on detection sensitivity. Through comparing the electrode span of 1 µm and 5 µm, our work shows that narrowing the electrode span could greatly improve detection sensitivity. The mechanism underlying the sensitivity improvement was analyzed via numerical simulation. The small electrode gap (1 µm) allows the electric field to concentrate near the detection area, resulting in a high sensitivity for tiny particles. This finding is also verified with the mixture suspension of 1 µm and 3 µm polystyrene beads. As a result, the electrodes with 1 µm gap can detect more 1 µm beads in the suspension than electrodes with 5 µm gap. Additionally, for single yeast cells analysis, it is found that impedance cytometry with 1 µm electrodes gap can easily distinguish budding yeast cells, which cannot be realized by the impedance cytometry with 5 µm electrodes gap. All experimental results support that narrowing the electrode gap is necessary for tiny particle detection, which is an important step in the development of submicron and nanoscale impedance cytometry.

A real-time monitoring system for automatic morphology analysis of yeast cultivation in a jar fermenter

The monitoring of microbial cultivation in real time and controlling their cultivation aid in increasing the production yield of useful material in a jar fermenter. Common sensors such as dissolved oxygen (DO) and pH can easily provide general-purpose indexes but do not reveal the physiological states of microbes because of the complexity of measuring them in culture conditions. It is well known from microscopic observations that the microbial morphology changes in response to the intracellular state or extracellular environment. Recently, studies have focused on rapid and quantitative image analysis techniques using machine learning or deep learning for gleaning insights into the morphological, physiological or gene expression information in microbes. During image analysis, it is necessary to retrieve high-definition images to analyze the microbial morphology in detail. In this study, we have developed a microfluidic device with a high-speed camera for the microscopic observation of yeast, and have constructed a system capable of generating their morphological information in real-time and at high definition. This system was connected to a jar fermenter, which enabled the automatic sampling for monitoring the cultivation. We successfully acquired high-definition images of over 10,000 yeast cells in about 2.2 s during ethanol fermentation automatically for over 168 h. We recorded 33,600 captures containing over 1,680,000 cell images. By analyzing these images, the morphological changes of yeast cells through ethanol fermentation could be captured, suggesting the expansion of the application of this system in controlling microbial fermentation using the morphological information generated. KEY POINTS: • Enables real-time visualization of microbes in a jar fermenter using microscopy. • Microfluidic device for acquiring high-definition images. • Generates a large amount of image data by using a high-speed camera.

Modified Red Blood Cells as Multimodal Standards for Benchmarking Single-Cell Cytometry and Separation Based on Electrical Physiology

Biophysical cellular information at single-cell sensitivity is becoming increasingly important within analytical and separation platforms that associate the cell phenotype with markers of disease, infection, and immunity. Frequency-modulated electrically driven microfluidic measurement and separation systems offer the ability to sensitively identify single cells based on biophysical information, such as their size and shape, as well as their subcellular membrane morphology and cytoplasmic organization. However, there is a lack of reliable and reproducible model particles with well-tuned subcellular electrical phenotypes that can be used as standards to benchmark the electrical physiology of unknown cell types or to benchmark dielectrophoretic separation metrics of novel device strategies. Herein, the application of red blood cells (RBCs) as multimodal standard particles with systematically modulated subcellular electrophysiology and associated fluorescence level is presented. Using glutaraldehyde fixation to vary membrane capacitance and by membrane resealing after electrolyte penetration to vary interior cytoplasmic conductivity and fluorescence in a correlated manner, each modified RBC type can be identified at single-cell sensitivity based on phenomenological impedance metrics and fitted to dielectric models to compute biophysical information. In this manner, single-cell impedance data from unknown RBC types can be mapped versus these model RBC types for facile determination of subcellular biophysical information and their dielectrophoretic separation conditions, without the need for time-consuming algorithms that often require unknown fitting parameters. Such internal standards for biophysical cytometry can advance in-line phenotypic recognition strategies.

Neural network-enhanced real-time impedance flow cytometry for single-cell intrinsic characterization

Single-cell impedance flow cytometry (IFC) is emerging as a label-free and non-invasive method for characterizing the electrical properties and revealing sample heterogeneity. At present, most IFC studies utilize phenomenological parameters (e.g., impedance amplitude, phase and opacity) to characterize single cells instead of intrinsic biophysical metrics (e.g., radius r, cytoplasm conductivity σ_i and specific membrane capacitance C_sm). Intrinsic parameters are normally calculated off-line by time-consuming model-fitting methods. Here, we propose to employ neural network (NN)-enhanced IFC to achieve both real-time single-cell intrinsic characterization and intrinsic parameter-based cell classification at high throughput. Three intrinsic parameters (r, σ_i and C_sm) can be obtained online and in real-time via a trained NN at 0.3 ms per single-cell event, achieving significant improvement in calculation speed. Experiments involving four cancer cells and one lymphocyte cell demonstrated 91.5% classification accuracy in the cell type for a test group of 9751 cell samples. By performing a viability assay, we provide evidence that the IFC test per se would not substantially affect the cell property. We envision that the NN-enhanced real-time IFC will provide a new platform for high-throughput, real-time and online cell intrinsic electrical characterization.

Assessment of the electrical penetration of cell membranes using four-frequency impedance cytometry

The electrical penetration of the cell membrane is vital for determining the cell interior via impedance cytometry. Herein, we propose a method for determining the conductivity of the cell membrane through the tilting levels of impedance pulses. When electrical penetration occurs, a high-frequency current freely passes through the cell membrane; thus, the intracellular distribution can directly act on the high-frequency impedance pulses. Numerical simulation shows that an uneven intracellular component distribution can affect the tilting levels of impedance pulses, and the tilting levels start increasing when the cell membrane is electrically penetrated. Experimental evidence shows that higher detection frequencies (>7 MHz) lead to a wider distribution of the tilting levels of impedance pulses when measuring cell populations with four-frequency impedance cytometry. This finding allows us to determine that a detection frequency of 7 MHz is able to pass through the membrane of Euglena gracilis (E. gracilis) cells. Additionally, we provide a possible application of four-frequency impedance cytometry in the biomass monitoring of single E. gracilis cells. High-frequency impedance (≥7 MHz) can be applied to monitor these biomass changes, and low-frequency impedance (<7 MHz) can be applied to track the corresponding biovolume changes. Overall, this work demonstrates an easy determination method for the electrical penetration of the cell membrane, and the proposed platform is applicable for the multiparameter assessment of the cell state during cultivation.

Inertial microfluidics for high-throughput cell analysis and detection: a review

Since it was first proposed in 2007, inertial microfluidics has been extensively studied in terms of theory, design, fabrication, and application. In recent years, with the rapid development of microfabrication technologies, a variety of channel structures that can focus, concentrate, separate, and capture bioparticles or fluids have been designed and manufactured to extend the range of potential biomedical applications of inertial microfluidics. Due to the advantages of high throughput, simplicity, and low device cost, inertial microfluidics is a promising candidate for rapid sample processing, especially for large-volume samples with low-abundance targets. As an approach to cellular sample pretreatment, inertial microfluidics has been widely employed to ensure downstream cell analysis and detection. In this review, a comprehensive summary of the application of inertial microfluidics for high-throughput cell analysis and detection is presented. According to application areas, the recent advances can be sorted into label-free cell mechanical phenotyping, sheathless flow cytometric counting, electrical impedance cytometer, high-throughput cellular image analysis, and other methods. Finally, the challenges and prospects of inertial microfluidics for cell analysis and detection are summarized.

Electro-optical classification of pollen grains via microfluidics and machine learning

In aerobiological monitoring and agriculture there is a pressing need for accurate, label-free and automated analysis of pollen grains, in order to reduce the cost, workload and possible errors associated to traditional approaches. Methods: We propose a new multimodal approach that combines electrical sensing and optical imaging to classify pollen grains flowing in a microfluidic chip at a throughput of 150 grains per second. Electrical signals and synchronized optical images are processed by two independent machine learning-based classifiers, whose predictions are then combined to provide the final classification outcome. Results: The applicability of the method is demonstrated in a proof-of-concept classification experiment involving eight pollen classes from different taxa. The average balanced accuracy is 78.7 % for the electrical classifier, 76.7 % for the optical classifier and 84.2 % for the multimodal classifier. The accuracy is 82.8 % for the electrical classifier, 84.1 % for the optical classifier and 88.3 % for the multimodal classifier. Conclusion: The multimodal approach provides better classification results with respect to the analysis based on electrical or optical features alone. Significance: The proposed methodology paves the way for automated multimodal palynology. Moreover, it can be extended to other fields, such as diagnostics and cell therapy, where it could be used for label-free identification of cell populations in heterogeneous samples.

An easy-fabricated and disposable polymer-film microfluidic impedance cytometer for cell sensing

We report here an easy-fabricated and disposable polymer-film microfluidic impedance cytometer (PMIC) integrated with inertial focusing and parallel facing electrodes for cell sensing. The cells are first focused in an asymmetric serpentine channel, and then their impedance signals are measured when passing through the electrode region. The proposed PMIC device is the first impedance cytometer that is fabricated into a flexible sheet (with a thickness of 0.45 mm) by using the materials of commonly-available ITO-coated polymer films and double-sided adhesive tapes, the whole fabrication process is shortened from traditional 3-4 days to less than 5 min by using UV laser cutting. To verify the feasibility of our device for cell sensing, we explore the focusing behaviors of three differently sized particles and two types of tumor cells, and analyze their impedance signals. The results show that our device is capable of obtaining impedance information on numbers, diameters, and longitudinal positions of cells. We envision that our PMIC device is promising in label-free cell sensing owning to the advantages of low cost, small footprint, and simple fabrication.

Biochip with multi-planar electrodes geometry for differentiation of non-spherical bioparticles in a microchannel

A biosensor capable of differentiating cells or other microparticles based on morphology finds significant biomedical applications. Examples may include morphological determination in the cellular division process, differentiation of bacterial cells, and cellular morphological variation in inflammation and cancer etc. Here, we present a novel integrated multi-planar microelectrodes geometry design that can distinguish a non-spherical individual particle flowing along a microchannel based on its electrical signature. We simulated multi-planar electrodes design in COMSOL Multiphysics and have shown that the changes in electrical field intensity corresponding to multiple particle morphologies can be distinguished. Our initial investigation has shown that top-bottom electrodes configuration produces significantly enhanced signal strength for a spherical particle as compared to co-planar configuration. Next, we integrated the co-planar and top-bottom configurations to develop a multi-planar microelectrode design capable of electrical impedance measurement at different spatial planes inside a microchannel by collecting multiple output signatures. We tested our integrated multi-planar electrode design with particles of different elliptical morphologies by gradually changing spherical particle dimensions to the non-spherical. The computed electrical signal ratio of non-spherical to spherical particle shows a very good correlation to predict the particle morphology. The biochip sensitivity is also found be independent of orientation of the particle flowing in the microchannel. Our integrated design will help develop the technology that will allow morphological analysis of various bioparticles in a microfluidic channel in the future.

Microfluidic impedance cytometry for single-cell sensing: Review on electrode configurations

Single-cell analysis has gained considerable attention for disease diagnosis, drug screening, and differentiation monitoring. Compared to the well-established flow cytometry, which uses fluorescent-labeled antibodies, microfluidic impedance cytometry (MIC) offers a simple, label-free, and noninvasive method for counting, classifying, and monitoring cells. Superior features including a small footprint, low reagent consumption, and ease of use have also been reported. The MIC device detects changes in the impedance signal caused by cells passing through the sensing/electric field zone, which can extract information regarding the size, shape, and dielectric properties of these cells. According to recent studies, electrode configuration has a remarkable effect on detection accuracy, sensitivity, and throughput. With the improvement in microfabrication technology, various electrode configurations have been reported for improving detection accuracy and throughput. However, the various electrode configurations of MIC devices have not been reviewed. In this review, the theoretical background of the impedance technique for single-cell analysis is introduced. Then, two-dimensional, three-dimensional, and liquid electrode configurations are discussed separately; their sensing mechanisms, fabrication processes, advantages, disadvantages, and applications are also described in detail. Finally, the current limitations and future perspectives of these electrode configurations are summarized. The main aim of this review is to offer a guide for researchers on the ongoing advancement in electrode configuration designs.

Design and 3D modeling investigation of a microfluidic electrode array for electrical impedance measurement of single yeast cells

High-resolution microscopic imaging may cause intensive image processing and potential impact of light irradiation on yeast replicative lifespan (RLS). Electrical impedance spectroscopy (EIS) could be alternatively used to perform high-throughput and label-free yeast RLS assays. Prior to fabricating EIS-integrated microfluidic devices for yeast RLS determination, systematic modeling and theoretical investigation are crucial for device design and optimization. Here, we report three-dimensional (3D) finite-element modeling and simulations of EIS measurement in a microfluidic single yeast in-situ impedance array (SYIIA), which is designed by patterning an electrode matrix underneath a cell-trapping array. SYIIA was instantiated and modeled as a 5 × 5 sensing array comprising 25 units for cell immobilization, culturing, and time-lapse EIS recording. Simulations of yeast growing and budding in a sensing unit demonstrated that EIS signals enable the characterization of cell growth and daughter-cell dissections. In the 5 × 5 sensing array, simulation results indicated that when monitoring a target cell, daughter dissections in its surrounding traps may induce variations of the recorded EIS signals, which could cause mistakes in identifying target daughter-cell dissections. To eliminate the mis-identifications, electrode array pitch was optimized. Therefore, the results could conduct the design and optimization of microfluidic electrode-array-integrated devices for high-throughput and accurate yeast RLS assays.

Electrical properties characterization of single yeast cells by dielectrophoretic motion and electro-rotation

The electrical parameters of single cells are label-free and intrinsic properties that can reflect the physiological characteristics. In recent years, many measurement methods based on impedance spectroscopy and rotation spectrum analysis have been developed. However, most of these works need to measure the response at whole frequency range to obtain DEP spectra and estimate the electrical parameters by fitting method, which are time-consuming and limit the measurement throughput. Therefore, improving the measurement throughput for single cells is an essential problem to be solved addressed. In this paper we present a microfluidic chip that combines dielectrophoretic motion and electro-rotation technology for single-cell electrical properties characterization. Since the movement and rotation speed of single cell in mediums are related to the electrical parameters of itself, electric signals and medium, the electrical properties can be obtained by measuring and analyzing the movement trajectory and rotation speed of the cell. Numerical simulations were performed to analyze the electric field distribution of the chip under different signal configurations, which predict the movement trajectory and rotation state, and determine the values of electric field on the cells. Based on the simulation results, cell focusing, dielectrophoretic motion and electro-rotation were successfully realized. By analyzing the movement trajectory and rotation speed, the conductivity of wall and the permittivity of membrane of yeast cells were characterized. The measurement method avoids the time-consuming of the traditional rotational spectra method, and can realize rapid and efficiency and single-cell electrical characterization.

Label-Free Quantification of Cell Cycle Synchronicity of Human Neural Progenitor Cells Based on Electrophysiology Phenotypes

The ability to coax human-induced pluripotent stem cells (hiPSCs) into human neural progenitor cells (hNPCs) can lead to novel drug discovery and transplant therapy platforms for neurological diseases. Since hNPCs can form organoids that mimic brain development, there is emerging interest in their label-free characterization for controlling cell composition to optimize organoid formation in three-dimensional (3D) cultures. However, this requires the ability to quantify hNPCs in heterogeneous samples with subpopulations of similar phenotype. Using high-throughput (>6000 cells per condition), single-cell impedance cytometry, we present the utilization of electrophysiology for quantification of hNPC subpopulations that are altered in cell cycle synchronicity by camptothecin (CPT) exposure. Electrophysiology phenotypes are determined from impedance magnitude and phase metrics for distinguishing each cell cycle phase, as validated by flow cytometry, for a wide range of subpopulation proportions. Using multishell dielectric models for each cell cycle phase, electrophysiology alterations with CPT dose could be predicted. This label-free detection strategy can prevent loss of cell viability to speed the optimization of cellular compositions for organoid development.

Single-cell microfluidic impedance cytometry: from raw signals to cell phenotypes using data analytics

The biophysical analysis of single-cells by microfluidic impedance cytometry is emerging as a label-free and high-throughput means to stratify the heterogeneity of cellular systems based on their electrophysiology. Emerging applications range from fundamental life-science and drug assessment research to point-of-care diagnostics and precision medicine. Recently, novel chip designs and data analytic strategies are laying the foundation for multiparametric cell characterization and subpopulation distinction, which are essential to understand biological function, follow disease progression and monitor cell behaviour in microsystems. In this tutorial review, we present a comparative survey of the approaches to elucidate cellular and subcellular features from impedance cytometry data, covering the related subjects of device design, data analytics (i.e., signal processing, dielectric modelling, population clustering), and phenotyping applications. We give special emphasis to the exciting recent developments of the technique (timeframe 2017-2020) and provide our perspective on future challenges and directions. Its synergistic application with microfluidic separation, sensor science and machine learning can form an essential toolkit for label-free quantification and isolation of subpopulations to stratify heterogeneous biosystems.

Double-peak signal features in microfluidic impedance flow cytometry enable sensitive measurement of cell membrane capacitance

The probing of individual cells at specific frequency regimes in a microfluidic impedance flow cytometer led to the observation of unusual "double peak" features in the reactive component of the resulting signal. The phenomenon was restricted to the lower frequencies (400-800 kHz) of the β-dispersion regime and its occurrence was facilitated by the co-planar microelectrode geometry in the device. To understand the reasons for this anomalous behaviour, the system was modelled using COMSOL. The simulated model agreed well with experimental observations and provided insight into the origins of this signal profile and the effect of various parameters on its behaviour. One of the most significant observations of this study was the high sensitivity of the features in the "double peak" profile to changes in cell membrane capacitance (CMC), compared to conventional "single peaks" of reactive impedance. This was consequently exploited to accurately distinguish populations of normal and glutaraldehyde treated erythrocytes based on variations in their CMC, indicating a drastic decrease in the CMC of treated cells. Additionally, we demonstrate the applicability of using this double peak effect to identify cell populations within a mixture of PBMCs. This study is an improvement over conventional approaches of measuring CMC via impedance flow cytometry by enabling the measurement of both cell size and cell membrane properties at a single frequency rather than using multiple frequencies. Using a single frequency significantly simplifies the system and reduces the associated costs. Additionally, this technique enables the measurement of CMC at relatively low frequencies.

Positional dependence of particles and cells in microfluidic electrical impedance flow cytometry: origin, challenges and opportunities

Microfluidic electrical impedance flow cytometry is now a well-known and established method for single-cell analysis. Given the richness of the information provided by impedance measurements, this non-invasive and label-free approach can be used in a wide field of applications ranging from simple cell counting to disease diagnostics. One of its major limitations is the variation of the impedance signal with the position of the cell in the sensing area. Indeed, identical particles traveling along different trajectories do not result in the same data. The positional dependence can be considered as a challenge for the accuracy of microfluidic impedance cytometers. On the other hand, it has recently been regarded by several groups as an opportunity to estimate the position of particles in the microchannel and thus take a further step in the logic of integrating sensors in so-called "Lab-on-a-chip" devices. This review provides a comprehensive overview of the physical grounds of the positional dependence of impedance measurements. Then, both the developed strategies to reduce position influence in impedance-based assays and the recent reported technologies exploiting that dependence for the integration of position detection in microfluidic devices are reviewed.

High-throughput label-free characterization of viable, necrotic and apoptotic human lymphoma cells in a coplanar-electrode microfluidic impedance chip

The study and the characterization of cell death mechanisms are fundamental in cell biology research. Traditional death/viability assays usually involve laborious sample preparation and expensive equipment or reagents. In this work, we use electrical impedance spectroscopy as a label-free methodology to characterize viable, necrotic and apoptotic human lymphoma U937 cells. A simple three-electrode coplanar layout is used in a differential measurement scheme and thousands of cells are measured at high-throughput (≈200 cell/s). Tailored signal processing enables accurate and robust cell characterization without the need for cell focusing systems. The results suggest that, at low frequency (0.5 MHz), signal magnitude enables the discrimination between viable/necrotic cells and cell fragments, whereas phase information allows discriminating between viable cells and necrotic cells. At higher frequency (10 MHz) two subpopulations of cell fragments are distinguished. This work substantiates the prominent role of electrical impedance spectroscopy for the development of next-generation cell viability assays.

A Microfluidic Device Integrating Impedance Flow Cytometry and Electric Impedance Spectroscopy for High-Efficiency Single-Cell Electrical Property Measurement

Single-cell impedance measurement is a label-free, noninvasive method for characterizing the electrical properties of single cells. At present, though widely used for impedance measurement, electric impedance flow cytometry (IFC) and electric impedance spectroscopy (EIS) are used alone for most microfluidic chips. In this paper, we present a microfluidic device combining the IFC and EIS techniques for single-cell electrical property measurement. The device uses hydrodynamic constriction to passively trap single cells and uses coplanar electrodes to obtain the impedance spectrum of the trapped cell via EIS and discrete impedance data points of the passing cells via IFC. Through experiment, we verified the individual functionality of IFC and EIS respectively, by revealing through IFC the impedance magnitude difference and quantifying through EIS the area-specific membrane capacitance and cytoplasm conductivity of the three types of cancer cells. We also demonstrated the complementarity of IFC and EIS, which holds for a wide range of the flow rate. We envision that the strategy of combining IFC and EIS provides a new thought in the efforts to enhancing the efficiency of electrical property measurement for single cells.

Particle Self-Aligning, Focusing, and Electric Impedance Microcytometer Device for Label-Free Single Cell Morphology Discrimination and Yeast Budding Analysis

Microfluidic electric impedance flow cytometry (IFC) devices have been applied in single cell analysis, such as cell counting, volume discrimination, cell viability, etc. A cell's shape provides specific information about cellular physiological and pathological conditions, especially in microorganisms such as yeast. In this study, the particle orientation focusing was theoretically analyzed and realized by hydrodynamics. The pulse width (passing time for the particles) of the conductance signal was used to discriminate particle shapes. Spherical and rod-shaped particles with similar volumes/lengths were differentiated by the IFC device, using the impedance pulse parameters of the events. Then, typical late-budding, early budding, and unbudded yeast cells were distinguished by the width, amplitude, and ratio of width to amplitude (R) of the impedance pulse. The pulse amplitude and the R combination gate for identifying the late-budding yeast was estimated through the statistic results. Using the gate, the late-budding rates under different conditions were calculated. Late-budding rates obtained using our method showed a high correlation (R² = 0.83) with the manual cell counting result and represented the budding status of yeast cells under different conditions proficiently. Thus, the late-budding rate calculated using the above method can be used as a qualitative parameter to assess the reproductive performance of yeast and whether a yeast culturing environment is optimal. This IFC device and cell shape discrimination method is very simple and could be applied in the fermentation industry and other microorganisms' discrimination as a rapid analysis technique in the future.

Lab-On-A-Chip Device for Yeast Cell Characterization in Low-Conductivity Media Combining Cytometry and Bio-Impedance

This paper proposes a simple approach to optimize the operating frequency band of a lab-on-a-chip based on bio-impedance cytometry for a single cell. It mainly concerns applications in low-conductivity media. Bio-impedance allows for the characterization of low cell concentration or single cells by providing an electrical signature. Thus, it may be necessary to perform impedance measurements up to several tens of megahertz in order to extract the internal cell signature. In the case of single cells, characterization is performed in a very small volume down to 1 pL. At the same time, measured impedances increase from tens of kilo-ohms for physiological liquids up to several mega-ohms for low conductivity media. This is, for example, the case for water analysis. At frequencies above hundreds of kilohertz, parasitic effects, such as coupling capacitances, can prevail over the impedance of the sample and completely short-circuit measurements. To optimize the sensor under these conditions, a complete model of a cytometry device was developed, including parasitic coupling capacitances of the sensor to take into account all the impedances. It appears that it is possible to increase the pass band by optimizing track geometries and placement without changing the sensing area. This assumption was obtained by measuring and comparing electrical properties of yeast cells in a low-conductivity medium (tap water). Decreased coupling capacitance by a factor higher than 10 was obtained compared with a previous non-optimized sensor, which allowed for the impedance measurement of all electrical properties of cells as small as yeast cells in a low-conductivity medium.

Smart Cell Culture Systems: Integration of Sensors and Actuators into Microphysiological Systems

Technological advances in microfabrication techniques in combination with organotypic cell and tissue models have enabled the realization of microphysiological systems capable of recapitulating aspects of human physiology in vitro with great fidelity. Concurrently, a number of analysis techniques has been developed to probe and characterize these model systems. However, many assays are still performed off-line, which severely compromises the possibility of obtaining real-time information from the samples under examination, and which also limits the use of these platforms in high-throughput analysis. In this review, we focus on sensing and actuation schemes that have already been established or offer great potential to provide in situ detection or manipulation of relevant cell or tissue samples in microphysiological platforms. We will first describe methods that can be integrated in a straightforward way and that offer potential multiplexing and/or parallelization of sensing and actuation functions. These methods include electrical impedance spectroscopy, electrochemical biosensors, and the use of surface acoustic waves for manipulation and analysis of cells, tissue, and multicellular organisms. In the second part, we will describe two sensor approaches based on surface-plasmon resonance and mechanical resonators that have recently provided new characterization features for biological samples, although technological limitations for use in high-throughput applications still exist.

A high-throughput flow cytometry-on-a-CMOS platform for single-cell dielectric spectroscopy at microwave frequencies

This work presents a microfluidics-integrated label-free flow cytometry-on-a-CMOS platform for the characterization of the cytoplasm dielectric properties at microwave frequencies. Compared with MHz impedance cytometers, operating at GHz frequencies offers direct intracellular permittivity probing due to electric fields penetrating through the cellular membrane. To overcome the detection challenges at high frequencies, the spectrometer employs on-chip oscillator-based sensors, which embeds simultaneous frequency generation, electrode excitation, and signal detection capabilities. By employing an injection-locking phase-detection technique, the spectrometer offers state-of-the-art sensitivity, achieving a less than 1 aFrms capacitance detection limit (or 5 ppm in frequency-shift) at a 100 kHz noise filtering bandwidth, enabling high throughput (>1k cells per s), with a measured cellular SNR of more than 28 dB. With CMOS/microfluidics co-design, we distribute four sensing channels at 6.5, 11, 17.5, and 30 GHz in an arrayed format whereas the frequencies are selected to center around the water relaxation frequency at 18 GHz. An issue in the integration of CMOS and microfluidics due to size mismatch is also addressed through introducing a cost-efficient epoxy-molding technique. With 3-D hydrodynamic focusing microfluidics, we perform characterization on four different cell lines including two breast cell lines (MCF-10A and MDA-MB-231) and two leukocyte cell lines (K-562 and THP-1). After normalizing the higher frequency signals to the 6.5 GHz ones, the size-independent dielectric opacity shows a differentiable distribution at 17.5 GHz between normal (0.905 ± 0.160, mean ± std.) and highly metastatic (1.033 ± 0.107) breast cells with p ≪ 0.001.

Cultivation and quantitative single-cell analysis of Saccharomyces cerevisiae on a multifunctional microfluidic device

Here, we present a multifunctional microfluidic device whose integrative design enables to combine cell culture studies and quantitative single cell biomolecule analysis. The platform consists of 32 analysis units providing two key features; first, a micrometer-sized trap for hydrodynamic capture of a single Saccharomyces cerevisiae (S. cerevisiae) yeast cell; second, a convenient double-valve configuration surrounding the trap. Actuating of the outer valve with integrated opening results in a partial isolation in a volume of 11.8 nL, i.e. the cell surrounding fluid can be exchanged diffusion-based without causing shear stress or cell loss. Actuation of the inner ring-shaped valve isolates the trapped cell completely in a small analysis volume of 230 pL. The device was used to determine the growth rate of yeast cells (S. cerevisiae) under under optimum and oxidative stress conditions. In addition, we successfully quantified the cofactor beta-nicotinamide adenine dinucleotide phosphate (NAD(P)H) in single and few cells exposed to the different microenvironments. In conclusion, the microdevice enables to analyze the influence of an external stress factor on the cellular fitness in a fast and more comprehensive way as cell growth and intracellular biomolecule levels can be investigated.

Coplanar electrode microfluidic chip enabling accurate sheathless impedance cytometry

Microfluidic impedance cytometry offers a simple non-invasive method for single-cell analysis. Coplanar electrode chips are especially attractive due to ease of fabrication, yielding miniaturized, reproducible, and ultimately low-cost devices. However, their accuracy is challenged by the dependence of the measured signal on particle trajectory within the interrogation volume, that manifests itself as an error in the estimated particle size, unless any kind of focusing system is used. In this paper, we present an original five-electrode coplanar chip enabling accurate particle sizing without the need for focusing. The chip layout is designed to provide a peculiar signal shape from which a new metric correlating with particle trajectory can be extracted. This metric is exploited to correct the estimated size of polystyrene beads of 5.2, 6 and 7 μm nominal diameter, reaching coefficient of variations lower than the manufacturers' quoted values. The potential impact of the proposed device in the field of life sciences is demonstrated with an application to Saccharomyces cerevisiae yeast.