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

Label-free high-throughput impedance-activated cell sorting

Cell sorting holds broad applications in fields such as early cancer diagnosis, cell differentiation studies, drug screening, and single-cell sequencing. However, achieving high-throughput and high-purity in label-free single-cell sorting is challenging. To overcome this issue, we propose a label-free, high-throughput, and high-accuracy impedance-activated cell sorting system based on impedance detection and dual membrane pumps. Leveraging the low-latency characteristics of FPGA, the system facilitates real-time dual-frequency single-cell impedance detection with high-throughput (5 × 104 cells per s) for HeLa, MDA-MB-231, and Jurkat cells. Furthermore, the system accomplishes low-latency (less than 0.3 ms), label-free, high-throughput (1000 particles per s) and high-accuracy (almost 99%) single-particle sorting using FPGA-based high-precision sort-timing prediction. In experiments with Jurkat and MDA-MB-231 cells, the system achieved a throughput of up to 1000 cells per s, maintaining a pre-sorting purity of 28.57% and increasing post-sorting purity to 97.09%. These findings indicate that our system holds significant potential for applications in label-free, high-throughput cell sorting.

Three-dimensional inertial focusing based impedance cytometer enabling high-accuracy characterization of electrical properties of tumor cells

The differences in the cross-sectional positions of cells in the detection area have a severe negative impact on achieving accurate characterization of the impedance spectra of cells. Herein, we proposed a three-dimensional (3D) inertial focusing based impedance cytometer integrating sheath fluid compression and inertial focusing for the high-accuracy electrical characterization and identification of tumor cells. First, we studied the effects of the particle initial position and the sheath fluid compression on particle focusing. Then, the relationship of the particle height and the signal-to-noise ratio (SNR) of the impedance signal was explored. The results showed that efficient single-line focusing of 7-20 μm particles close to the electrodes was achieved and impedance signals with a high SNR and a low coefficient of variation (CV) were obtained. Finally, the electrical properties of three types of tumor cells (A549, MDA-MB-231, and UM-UC-3 cells) were accurately characterized. Machine learning algorithms were implemented to accurately identify tumor cells based on the amplitude and phase opacities at multiple frequencies. Compared with traditional two-dimensional (2D) inertial focusing, the identification accuracy of A549, MDA-MB-231, and UM-UC-3 cells using our 3D inertial focusing increased by 57.5%, 36.4% and 36.6%, respectively. The impedance cytometer enables the detection of cells with a wide size range without causing clogging and obtains high SNR signals, improving applicability to different complex biological samples and cell identification accuracy.

A microfluidic impedance cytometry device for robust identification of H. pluvialis

H. pluvialis contains rich oleic acid and astaxanthin, which have important applications in the fields of biodiesel and biomedicine. Detection of live H. pluvialis is the prerequisite to obtaining oleic acid and astaxanthin. For this purpose, we successfully developed a reliable microfluidic impedance cytometry for the identification of live H. pluvialis. Firstly, we established a simulation model for detecting H. pluvialis based on their morphology and studied the effect of medium conductivity on the impedance of H. pluvialis at different frequencies. From the simulations, we determined that the optimal solution conductivity for the detection of H. pluvialis was 1500 μS cm-1 and studied the frequency responses of the impedance of H. pluvialis. Secondly, we fabricated the microchannels and stainless-steel detection electrodes and assembled them into microfluidic impedance cytometry. The frequency dependence of live and dead H. pluvialis was explored under different frequencies, and live and dead H. pluvialis were distinguished at a frequency of 1 MHz. The impedance of live H. pluvialis at the frequency of 1 MHz ranges from 33.73 to 52.23 Ω, while that of dead ones ranges from 13.05 to 19.59 Ω. Based on these findings, we accomplished the identification and counting of live H. pluvialis in the live and dead sample solutions. Furthermore, we accomplished the identification and counting of live H. pluvialis in the mixed samples containing Euglena and H. pluvialis. This approach possesses the promising capacity to serve as a robust tool in the identification of target microalgae, addressing a challenge in the fields of biodiesel and biomedicine.

A Miniature Modular Fluorescence Flow Cytometry System

Fluorescence flow cytometry is a powerful instrument to distinguish cells or particles labelled with high-specificity fluorophores. However, traditional flow cytometry is complex, bulky, and inconvenient for users to adjust fluorescence channels. In this paper, we present a modular fluorescence flow cytometry (M-FCM) system in which fluorescence channels can be flexibly arranged. Modules for particle focusing and fluorescence detection were developed. After hydrodynamical focusing, the cells were measured in the detection modules, which were integrated with in situ illumination and fluorescence detection. The signal-to-noise ratio of the detection reached to 33.2 dB. The crosstalk among the fluorescence channels was eliminated. The M-FCM system was applied to evaluate cell viability in drug screening, agreeing well with the commercial cytometry. The modular cytometry presents several outstanding features: flexibility in setting fluorescence channels, cost efficiency, compact construction, ease of operation, and the potential to upgrade for multifunctional measurements. The modular cytometry provides a multifunctional platform for various biophysical measurements, e.g., electrical impedance and refractive-index detection. The proposed work paves an innovative avenue for the multivariate analysis of cellular characteristics.

AI-Powered Microfluidics: Shaping the Future of Phenotypic Drug Discovery

Phenotypic drug discovery (PDD), which involves harnessing biological systems directly to uncover effective drugs, has undergone a resurgence in recent years. The rapid advancement of artificial intelligence (AI) over the past few years presents numerous opportunities for augmenting phenotypic drug screening on microfluidic platforms, leveraging its predictive capabilities, data analysis, efficient data processing, etc. Microfluidics coupled with AI is poised to revolutionize the landscape of phenotypic drug discovery. By integrating advanced microfluidic platforms with AI algorithms, researchers can rapidly screen large libraries of compounds, identify novel drug candidates, and elucidate complex biological pathways with unprecedented speed and efficiency. This review provides an overview of recent advances and challenges in AI-based microfluidics and their applications in drug discovery. We discuss the synergistic combination of microfluidic systems for high-throughput screening and AI-driven analysis for phenotype characterization, drug-target interactions, and predictive modeling. In addition, we highlight the potential of AI-powered microfluidics to achieve an automated drug screening system. Overall, AI-powered microfluidics represents a promising approach to shaping the future of phenotypic drug discovery by enabling rapid, cost-effective, and accurate identification of therapeutically relevant compounds.

Integration of acoustic micromixing with cyclic olefin copolymer microfluidics for enhanced lab-on-a-chip applications in nanoscale liposome synthesis

The integration of acoustic wave micromixing with microfluidic systems holds great potential for applications in biomedicine and lab-on-a-chip technologies. Polymers such as cyclic olefin copolymer (COC) are increasingly utilized in microfluidic applications due to its unique properties, low cost, and versatile fabrication methods, and incorporating them into acoustofluidics significantly expands their potential applications. In this work, for the first time, we demonstrated the integration of polymer microfluidics with acoustic micromixing utilizing oscillating sharp edge structures to homogenize flowing fluids. The sharp edge mixing platform was entirely composed of COC fabricated in a COC-hydrocarbon solvent swelling based microfabrication process. As an electrical signal is applied to a piezoelectric transducer bonded to the micromixer, the sharp edges start to oscillate generating vortices at its tip, mixing the fluids. A 2D numerical model was implemented to determine the optimum microchannel dimensions for experimental mixing assessment. The system was shown to successfully mix fluids at flow rates up to 150µl h-1and has a modest effect even at the highest tested flow rate of 600µl h-1. The utility of the fabricated sharp edge micromixer was demonstrated by the synthesis of nanoscale liposomes.

Microfluidic characterization of single-cell biophysical properties and the applications in cancer diagnosis

Single-cell biophysical properties play a crucial role in regulating cellular physiological states and functions, demonstrating significant potential in the fields of life sciences and clinical diagnostics. Therefore, over the last few decades, researchers have developed various detection tools to explore the relationship between the biophysical changes of biological cells and human diseases. With the rapid advancement of modern microfabrication technology, microfluidic devices have quickly emerged as a promising platform for single-cell analysis offering advantages including high-throughput, exceptional precision, and ease of manipulation. Consequently, this paper provides an overview of the recent advances in microfluidic analysis and detection systems for single-cell biophysical properties and their applications in the field of cancer. The working principles and latest research progress of single-cell biophysical property detection are first analyzed, highlighting the significance of electrical and mechanical properties. The development of data acquisition and processing methods for real-time, high-throughput, and practical applications are then discussed. Furthermore, the differences in biophysical properties between tumor and normal cells are outlined, illustrating the potential for utilizing single-cell biophysical properties for tumor cell identification, classification, and drug response assessment. Lastly, we summarize the limitations of existing microfluidic analysis and detection systems in single-cell biophysical properties, while also pointing out the prospects and future directions of their applications in cancer diagnosis and treatment.

On the compatibility of single-cell microcarriers (nanovials) with microfluidic impedance cytometry

We investigate for the first time the compatibility of nanovials with microfluidic impedance cytometry (MIC). Nanovials are suspendable crescent-shaped single-cell microcarriers that enable specific cell adhesion, the creation of compartments for undisturbed cell growth and secretion, as well as protection against wall shear stress. MIC is a label-free single-cell technique that characterizes flowing cells based on their electrical fingerprints and it is especially targeted to cells that are naturally in suspension. Combining nanovial technology with MIC is intriguing as it would represent a robust framework for the electrical analysis of single adherent cells at high throughput. Here, as a proof-of-concept, we report the MIC analysis of mesenchymal stromal cells loaded in nanovials. The electrical analysis is supported by numerical simulations and validated by means of optical analysis. We demonstrate that the electrical diameter can discriminate among free cells, empty nanovials, cell-loaded nanovials, and clusters, thus grounding the foundation for the use of nanovials in MIC. Furthermore, we investigate the potentiality of MIC to assess the electrical phenotype of cells loaded in nanovials and we draw directions for future studies.

Microwave Flow Cytometric Detection and Differentiation of Escherichia coli

Label-free measurement and analysis of single bacterial cells are essential for food safety monitoring and microbial disease diagnosis. We report a microwave flow cytometric sensor with a microstrip sensing device with reduced channel height for bacterial cell measurement. Escherichia coli B and Escherichia coli K-12 were measured with the sensor at frequencies between 500 MHz and 8 GHz. The results show microwave properties of E. coli cells are frequency-dependent. A LightGBM model was developed to classify cell types at a high accuracy of 0.96 at 1 GHz. Thus, the sensor provides a promising label-free method to rapidly detect and differentiate bacterial cells. Nevertheless, the method needs to be further developed by comprehensively measuring different types of cells and demonstrating accurate cell classification with improved machine-learning techniques.

Microfluidic impedance cytometry with flat-end cylindrical electrodes for accurate and fast analysis of marine microalgae

Marine microalgae play an increasingly significant role in addressing the issues of environmental monitoring and disease treatment, making the analysis of marine microalgae at the single-cell level an essential technique. For this, we put forward accurate and fast microfluidic impedance cytometry to analyze microalgal cells by assembling two cylindrical electrodes and microchannels to form a three-dimensional detection zone. Firstly, we established a mathematical model of microalgal cell detection based on Maxwell's mixture theory and numerically investigated the effects of the electrode gap, microalgal positions, and ion concentrations of the solution on detection to optimize detection conditions. Secondly, 80 μm stainless steel wires were used to construct flat-ended cylindrical electrodes and were then inserted into two collinear channels fabricated using standard photolithography techniques to form a spatially uniform electric field to promote the detection throughput and sensitivity. Thirdly, based on the validation of this method, we measured the impedance of living Euglena and Haematococcus pluvialis to study parametric influences, including ion concentration, cell density and electrode gap. The throughput of this method was also investigated, which reached 1800 cells per s in the detection of Haematococcus pluvialis. Fourthly, we analyzed live and dead Euglena to prove the ability of this method to detect the physiological status of cells and obtained impedances of 124.3 Ω and 31.0 Ω with proportions of 15.9% and 84.1%, respectively. Finally, this method was engineered for the analysis of marine microalgae, measuring living Euglena with an impedance of 159.61 Ω accounting for 3.9%, dead Euglena with an impedance of 36.43 Ω accounting for 10.1% and Oocystis sp. with an impedance of 55.00 Ω accounting for about 81.0%. This method could provide a reliable tool to analyze marine microalgae for monitoring the marine environment and treatment of diseases owing to its outstanding advantages of low cost, high throughput and high corrosion resistance.

Photocell-Based Optofluidic Device for Clogging-Free Cell Transit Time Measurements

Measuring the transit time of a cell forced through a bottleneck is one of the most widely used techniques for the study of cell deformability in flow. It in turn provides an accessible and rapid way of obtaining crucial information regarding cell physiology. Many techniques are currently being investigated to reliably retrieve this time, but their translation to diagnostic-oriented devices is often hampered by their complexity, lack of robustness, and the bulky external equipment required. Herein, we demonstrate the benefits of coupling microfluidics with an optical method, like photocells, to measure the transit time. We exploit the femtosecond laser irradiation followed by chemical etching (FLICE) fabrication technique to build a monolithic 3D device capable of detecting cells flowing through a 3D non-deformable constriction which is fully buried in a fused silica substrate. We validated our chip by measuring the transit times of pristine breast cancer cells (MCF-7) and MCF-7 cells treated with Latrunculin A, a drug typically used to increase their deformability. A difference in transit times can be assessed without the need for complex external instrumentation and/or demanding computational efforts. The high throughput (4000-10,000 cells/min), ease of use, and clogging-free operation of our device bring this approach much closer to real scenarios.

Convolutional Neural Network-Driven Impedance Flow Cytometry for Accurate Bacterial Differentiation

Impedance flow cytometry (IFC) has been demonstrated to be an efficient tool for label-free bacterial investigation to obtain the electrical properties in real time. However, the accurate differentiation of different species of bacteria by IFC technology remains a challenge owing to the insignificant differences in data. Here, we developed a convolutional neural networks (ConvNet) deep learning approach to enhance the accuracy and efficiency of the IFC toward distinguishing various species of bacteria. First, more than 1 million sets of impedance data (comprising 42 characteristic features for each set) of various groups of bacteria were trained by the ConvNet model. To improve the efficiency for data analysis, the Spearman correlation coefficient and the mean decrease accuracy of the random forest algorithm were introduced to eliminate feature interaction and extract the opacity of impedance related to the bacterial wall and membrane structure as the predominant features in bacterial differentiation. Moreover, the 25 optimized features were selected with differentiation accuracies of >96% for three groups of bacteria (bacilli, cocci, and vibrio) and >95% for two species of bacilli (Escherichia coli and Salmonella enteritidis), compared to machine learning algorithms (complex tree, linear discriminant, and K-nearest neighbor algorithms) with a maximum accuracy of 76.4%. Furthermore, bacterial differentiation was achieved on spiked samples of different species with different mixing ratios. The proposed ConvNet deep learning-assisted data analysis method of IFC exhibits advantages in analyzing a huge number of data sets with capacity for extracting predominant features within multicomponent information and will bring about progress and advances in the fields of both biosensing and data analysis.

Advance of microfluidic flow cytometry enabling high-throughput characterization of single-cell electrical and structural properties

This paper reported a micro flow cytometer capable of high-throughput characterization of single-cell electrical and structural features based on constrictional microchannels and deep neural networks. When single cells traveled through microchannels with constricted cross-sectional areas, they effectively blocked concentrated electric field lines, producing large impedance variations. Meanwhile, the traveling cells were confined within the cross-sectional areas of the constrictional microchannels, enabling the capture of high-quality images without losing focuses. Then single-cell features from impedance profiles and optical images were extracted from customized recurrent and convolution networks (RNN and CNN), which were further fused for cell-type classification based on support vector machines (SVM). As a demonstration, two leukemia cell lines (e.g., HL60 vs. Jurkat) were analyzed, producing high-classification accuracies of 99.3% based on electrical features extracted from Long Short-Term Memory (LSTM) of RNN, 96.7% based on structural features extracted from Resnet18 of CNN and 100.0% based on combined features enabled by SVM. The microfluidic flow cytometry developed in this study may provide a new perspective for the field of single-cell analysis.

Dielectrophoretic enrichment of live chemo-resistant circulating-like pancreatic cancer cells from media of drug-treated adherent cultures of solid tumors

Due to low numbers of circulating tumor cells (CTCs) in liquid biopsies, there is much interest in enrichment of alternative circulating-like mesenchymal cancer cell subpopulations from in vitro tumor cultures for utilization within molecular profiling and drug screening. Viable cancer cells that are released into the media of drug-treated adherent cancer cell cultures exhibit anoikis resistance or anchorage-independent survival away from their extracellular matrix with nutrient sources and waste sinks, which serves as a pre-requisite for metastasis. The enrichment of these cell subpopulations from tumor cultures can potentially serve as an in vitro source of circulating-like cancer cells with greater potential for scale-up in comparison with CTCs. However, these live circulating-like cancer cell subpopulations exhibit size overlaps with necrotic and apoptotic cells in the culture media, which makes it challenging to selectively enrich them, while maintaining them in their suspended state. We present optimization of a flowthrough high frequency (1 MHz) positive dielectrophoresis (pDEP) device with sequential 3D field non-uniformities that enables enrichment of the live chemo-resistant circulating cancer cell subpopulation from an in vitro culture of metastatic patient-derived pancreatic tumor cells. Central to this strategy is the utilization of single-cell impedance cytometry with gates set by supervised machine learning, to optimize the frequency for pDEP, so that live circulating cells are selected based on multiple biophysical metrics, including membrane physiology, cytoplasmic conductivity and cell size, which is not possible using deterministic lateral displacement that is solely based on cell size. Using typical drug-treated samples with low levels of live circulating cells (<3%), we present pDEP enrichment of the target subpopulation to ∼44% levels within 20 minutes, while rejecting >90% of dead cells. This strategy of utilizing single-cell impedance cytometry to guide the optimization of dielectrophoresis has implications for other complex biological samples.

Investigation of geometry-dependent sensing characteristics of microfluidic for single-cell 3D localization

Flow cytometry-based measurement techniques have been widely used for single-cell characterizations, such as impedance, size, and dielectric properties. However, in the measurement process, the reliability of the output measurement signal directly affects the ability of the microsystem to judge the characteristics of single cells. Here, we designed a multiple nonparallel electrode structure for single-cell 3D localization. The performance of the structures was studied by analyzing the changes in electric field strength and the output differential current. The effects of microchannel height, sensing electrode distance, electrode inclination angle, and electrode width on output signals are investigated by analyzing the current change and electric field strength of single cells passing from the center of the microchannel. The numerical simulation results indicate that, when the microchannel height is 20 µm, the distance of the sensing electrodes is 100 µm, the inclination angle is 30°, the electrode width is 20 µm, and the optimal signal quality can be obtained. Reducing the height of the flow channel and shortening the sensing electrode spacing can significantly improve the signal amplitude. When the channel height is 20 µm, the signal intensity increases by 80% than that of 30 µm. The signal intensity of induced current with the sensing electrode spacing of 100 µm is 42% higher than that with the spacing of 120 µm. We analyzed the presence of multiple independent cells and adherent cells in the detection area and demonstrated through simulation that the signal changes caused by multi-cells can be superimposed by multiple single-cell signals. The induced current signal intensity of the same volume of cells with an ellipticity of 1 is 49% lower than that of cells with an ellipticity of 4. Based on the numerical investigation, we expect that the optimal geometry structure design will aid in the development of better performance signal cell impedance cytometry microsystems.

Electrical micro flow cytometry with LSTM and its application in leukocyte differential

This paper developed an electrical micro flow cytometry to realize leukocyte differentials leveraging a constrictional microchannel and a deep neural network. Firstly, purified granulocytes, lymphocytes or monocytes traveled through the constrictional microchannel with a cross-sectional area marginally larger than individual cells and produced large impedance variations by blocking focused electric field lines. By optimizing key elements (e.g., normalization, learning rate, batch size and neuron number) of the recurrent neural network (RNN), electrical results of purified leukocytes were analyzed to establish a leukocyte differential system with a classification accuracy of 95.2%. Then the leukocyte mixtures were forced to travel through the same constrictional microchannel, producing mixed impedance profiles which were classified into granulocytes, lymphocytes and monocytes based on the aforementioned differential system. As to the classification results, two leukocyte mixtures from the same donor were processed, producing comparable classification results, which were 57% versus 59% of granulocytes, 37% versus 34% of lymphocytes and 6% versus 7% of monocytes. These results validated the established classification system based on the constrictional microchannel and the recurrent neural network, providing a new perspective of differentiating white blood cells by electrical flow cytometry.

Machine learning classification of cellular states based on the impedance features derived from microfluidic single-cell impedance flow cytometry

Mitosis is a crucial biological process where a parental cell undergoes precisely controlled functional phases and divides into two daughter cells. Some drugs can inhibit cell mitosis, for instance, the anti-cancer drugs interacting with the tumor cell proliferation and leading to mitosis arrest at a specific phase or cell death eventually. Combining machine learning with microfluidic impedance flow cytometry (IFC) offers a concise way for label-free and high-throughput classification of drug-treated cells at single-cell level. IFC-based single-cell analysis generates a large amount of data related to the cell electrophysiology parameters, and machine learning helps establish correlations between these data and specific cell states. This work demonstrates the application of machine learning for cell state classification, including the binary differentiations between the G1/S and apoptosis states and between the G2/M and apoptosis states, as well as the classification of three subpopulations comprising a subgroup insensitive to the drug beyond the two drug-induced states of G2/M arrest and apoptosis. The impedance amplitudes and phases used as input features for the model training were extracted from the IFC-measured datasets for the drug-treated tumor cells. The deep neural network (DNN) model was exploited here with the structure (e.g., hidden layer number and neuron number in each layer) optimized for each given cell type and drug. For the H1650 cells, we obtained an accuracy of 78.51% for classification between the G1/S and apoptosis states and 82.55% for the G2/M and apoptosis states. For HeLa cells, we achieved a high accuracy of 96.94% for classification between the G2/M and apoptosis states, both of which were induced by taxol treatment. Even higher accuracy approaching 100% was achieved for the vinblastine-treated HeLa cells for the differentiation between the viable and non-viable states, and between the G2/M and apoptosis states. We also demonstrate the capability of the DNN model for high-accuracy classification of the three subpopulations in a complete cell sample treated by taxol or vinblastine.

An impedance flow cytometry with integrated dual microneedle for electrical properties characterization of single cell

Electrical characteristics of living cells have been proven to reveal important details about their internal structure, charge distribution and composition changes in the cell membrane, as well as the extracellular context. An impedance flow cytometry is a common approach to determine the electrical properties of a cell, having the advantage of label-free and high throughput. However, the current techniques are complex and costly for the fabrication process. For that reason, we introduce an integrated dual microneedle-microchannel for single-cell detection and electrical properties extraction. The dual microneedles utilized a commercially available tungsten needle coated with parylene. When a single cell flows through the parallel-facing electrode configuration of the dual microneedle, the electrical impedance at multiple frequencies is measured. The impedance measurement demonstrated the differential of normal red blood cells (RBCs) with three different sizes of microbeads at low and high frequencies, 100 kHz and 2 MHz, respectively. An electrical equivalent circuit model (ECM) was used to determine the unique membrane capacitance of individual cells. The proposed technique demonstrated that the specific membrane capacitance of an RBC is 9.42 mF/m-2, with the regression coefficients, ρ at 0.9895. As a result, this device may potentially be used in developing countries for low-cost single-cell screening and detection.

Microfluidic Blood Separation: Key Technologies and Critical Figures of Merit

Blood is a complex sample comprised mostly of plasma, red blood cells (RBCs), and other cells whose concentrations correlate to physiological or pathological health conditions. There are also many blood-circulating biomarkers, such as circulating tumor cells (CTCs) and various pathogens, that can be used as measurands to diagnose certain diseases. Microfluidic devices are attractive analytical tools for separating blood components in point-of-care (POC) applications. These platforms have the potential advantage of, among other features, being compact and portable. These features can eventually be exploited in clinics and rapid tests performed in households and low-income scenarios. Microfluidic systems have the added benefit of only needing small volumes of blood drawn from patients (from nanoliters to milliliters) while integrating (within the devices) the steps required before detecting analytes. Hence, these systems will reduce the associated costs of purifying blood components of interest (e.g., specific groups of cells or blood biomarkers) for studying and quantifying collected blood fractions. The microfluidic blood separation field has grown since the 2000s, and important advances have been reported in the last few years. Nonetheless, real POC microfluidic blood separation platforms are still elusive. A widespread consensus on what key figures of merit should be reported to assess the quality and yield of these platforms has not been achieved. Knowing what parameters should be reported for microfluidic blood separations will help achieve that consensus and establish a clear road map to promote further commercialization of these devices and attain real POC applications. This review provides an overview of the separation techniques currently used to separate blood components for higher throughput separations (number of cells or particles per minute). We present a summary of the critical parameters that should be considered when designing such devices and the figures of merit that should be explicitly reported when presenting a device's separation capabilities. Ultimately, reporting the relevant figures of merit will benefit this growing community and help pave the road toward commercialization of these microfluidic systems.

Single-cell impedance cytometry of anticancer drug-treated tumor cells exhibiting mitotic arrest state to apoptosis using low-cost silver-PDMS microelectrodes

Chemotherapeutic drugs such as paclitaxel and vinblastine interact with microtubules and thus induce complex cell states of mitosis arrest at the G2/M phase followed by apoptosis dependent on drug exposure time and concentration. Microfluidic impedance cytometry (MIC), as a label-free and high-throughput technology for single-cell analysis, has been applied for viability assay of cancer cells post drug exposure at fixed time and dosage, yet verification of this technique for varied tumor cell states after anticancer drug treatment remains a challenge. Here we present a novel MIC device and for the first time perform impedance cytometry on carcinoma cells exhibiting progressive states of G2/M arrest followed by apoptosis related to drug concentration and exposure time, after treatments with paclitaxel and vinblastine, respectively. Our results from impedance cytometry reveal increased amplitude and negative phase shift at low frequency as well as higher opacity for HeLa cells under G2/M mitotic arrest compared to untreated cells. The cells under apoptosis, on the other hand, exhibit opposite changes in these electrical parameters. Therefore, the impedance features differentiate the HeLa cells under progressive states post anticancer drug treatment. We also demonstrate that vinblastine poses a more potent drug effect than paclitaxel especially at low concentrations. Our device is fabricated using a unique sacrificial layer-free soft lithography process as compared to the existing MIC device, which gives rise to readily aligned parallel microelectrodes made of silver-PDMS embedded in PDMS channel sidewalls with one molding step. Our results uncover the potential of the MIC device, with a fairly simple and low-cost fabrication process, for cellular state screening in anticancer drug therapy.

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.

Correlating Antibiotic-Induced Dysbiosis to Clostridioides difficile Spore Germination and Host Susceptibility to Infection Using an Ex Vivo Assay

Antibiotic-induced microbiota disruption and its persistence create conditions for dysbiosis and colonization by opportunistic pathogens, such as those causing Clostridioides difficile (C. difficile) infection (CDI), which is the most severe hospital-acquired intestinal infection. Given the wide differences in microbiota across hosts and in their recovery after antibiotic treatments, there is a need for assays to assess the influence of dysbiosis and its recovery dynamics on the susceptibility of the host to CDI. Germination of C. difficile spores is a key virulence trait for the onset of CDI, which is influenced by the level of primary vs secondary bile acids in the intestinal milieu that is regulated by the microbiota composition. Herein, the germination of C. difficile spores in fecal supernatant from mice that are subject to varying degrees of antibiotic treatment is utilized as an ex vivo assay to predict intestinal dysbiosis in the host based on their susceptibility to CDI, as determined by in vivo CDI metrics in the same mouse model. Quantification of spore germination down to lower detection limits than the colony-forming assay is achieved by using impedance cytometry to count single vegetative bacteria that are identified based on their characteristic electrical physiology for distinction vs aggregated spores and cell debris in the media. As a result, germination can be quantified at earlier time points and with fewer spores for correlation to CDI outcomes. This sets the groundwork for a point-of-care tool to gauge the susceptibility of human microbiota to CDI after antibiotic treatments.

Use of high frequency electrorotation to identify cytoplasmic changes in cells non-disruptively

In this paper we demonstrate how the use of frequencies ranging from 50 kHz to 5 GHz in the analysis of cells by electrorotation can open the path to the identification of differences not detectable by conventional set-ups. Earlier works usually reported electrorotation devices operating below 20 MHz, limiting the response obtained to properties associated with the cell membrane. Those devices are thus unable to resolve the physiological properties in the cytoplasm. We used microwave-based technology to extend the frequency operation to 5 GHz. At high frequencies (from tens of MHz to GHz), the electromagnetic signal passes through the membrane and allows probing the cytoplasm. This enables several applications, such as cell classification, and viability analysis. Additionally, the use of conventional microfabrication techniques reduces the cost and complexity of analysis, compared to other non-invasive methods. We demonstrated the potential of this set-up by identifying two different populations of T-lymphocytes not distinguishable through visual assessment. We also assessed the effect of calcein on cell cytoplasmic properties and used it as a controlled experiment to demonstrate the possibility of this method to detect changes happening predominantly in the cytoplasm.

Full-electric microfluidic platform to capture, analyze and selectively release single cells

Current single-cell technologies require large and expensive equipment, limiting their use to specialized labs. In this paper, we present for the first time a microfluidic device which demonstrates a combined method for full-electric cell capturing, analyzing, and selectively releasing with single-cell resolution. All functionalities are experimentally demonstrated on Saccharomyces cerevisiae. Our microfluidic platform consists of traps centered around a pair of individually accessible coplanar electrodes, positioned under a microfluidic channel. Using this device, we validate our novel Two-Voltage method for trapping single cells by positive dielectrophoresis (pDEP). Cells are attracted to the trap when a high voltage (VH) is applied. A low voltage (VL) holds the already trapped cell in place without attracting additional cells, allowing full control over the number of trapped cells. After trapping, the cells are analyzed by broadband electrochemical impedance spectroscopy. These measurements allow the detection of single cells and the extraction of cell parameters. Additionally, these measurements show a strong correlation between average phase change and cell size, enabling the use of our system for size measurements in biological applications. Finally, our device allows selectively releasing trapped cells by turning off the pDEP signal in their trap. The experimental results show the techniques potential as a full-electric single-cell analysis tool with potential for miniaturization and automation which opens new avenues towards small-scale, high throughput single-cell analysis and sorting lab-on-CMOS devices.

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.

Rapid Screening of Urinary Tract Infection Using Microfluidic Inertial-Impedance Cytometry

Urinary tract infection (UTI) diagnosis based on urine culture for bacteriuria analysis is time-consuming and often leads to wastage of hospital resources due to false-positive UTI cases. Direct cellular phenotyping (e.g., RBCs, neutrophils, epithelial cells) of urine samples remains a technical challenge as low cell concentrations, and urine characteristics (conductivities, pH, microbes) can affect the accuracy of cell measurements. In this work, we report a microfluidic inertial-impedance cytometry technique for label-free rapid (<5 min) neutrophil sorting and impedance profiling from urine directly. Based on size-based inertial focusing effects, neutrophils are isolated, concentrated, and resuspended in saline (buffer exchange) to improve consistency in impedance-based single-cell analysis. We first observed that both urine pH and the presence of bacteria can affect neutrophil high-frequency impedance measurements possibly due to changes in nucleus morphology as neutrophils undergo NETosis and phagocytosis, respectively. As a proof-of-concept for clinical testing, we report for the first time, rapid UTI testing based on multiparametric impedance profiling of putative neutrophils (electrical size, membrane properties, and distribution) in urine samples from non-UTI (n = 20) and UTI patients (n = 20). A significant increase in cell count was observed in UTI samples, and biophysical parameters were used to develop a UTI classifier with an area under the receiver operating characteristic curve of 0.84. Overall, the developed platform facilitates rapid culture-free urine screening which can be further developed to assess disease severity in UTI and other urologic diseases based on neutrophil electrical signatures.

Opportunities in optical and electrical single-cell technologies to study microbial ecosystems

New techniques are revolutionizing single-cell research, allowing us to study microbes at unprecedented scales and in unparalleled depth. This review highlights the state-of-the-art technologies in single-cell analysis in microbial ecology applications, with particular attention to both optical tools, i.e., specialized use of flow cytometry and Raman spectroscopy and emerging electrical techniques. The objectives of this review include showcasing the diversity of single-cell optical approaches for studying microbiological phenomena, highlighting successful applications in understanding microbial systems, discussing emerging techniques, and encouraging the combination of established and novel approaches to address research questions. The review aims to answer key questions such as how single-cell approaches have advanced our understanding of individual and interacting cells, how they have been used to study uncultured microbes, which new analysis tools will become widespread, and how they contribute to our knowledge of ecological interactions.

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.

Acoustophoretic Characterization and Separation of Blood Cells in Acoustic Impedance Gradients

Single-cell phenotyping based on biophysical properties is a promising tool to distinguish cell types and their response to a given condition, and charting such properties also enables optimization of cell separations. Isoacoustic focusing, where cells migrate to their points of zero acoustic contrast in an acoustic impedance gradient, added the effective acoustic impedance of cells to the directory of biophysical properties that can be utilized to categorize or separate cells. This study investigates isoacoustic focusing in a stop-flow regime and shows how cells migrate towards their isoacoustic point. We introduce a numerical model that we use to estimate the acoustic energy density in acoustic impedance gradient media by tracking particles of known properties, and we investigate the effect of acoustic streaming. From the measured trajectories of cells combined with fluorescence intensity images of the slowly diffusing gradient, we read out the effective acoustic impedance of neutrophils and K562 cancer cells. Finally, we propose suitable acoustic impedance gradients that lead to a high degree separation of neutrophils and K562 cells in a continuous-flow configuration.

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.

Rapid Assessment of Susceptibility of Bacteria and Erythrocytes to Antimicrobial Peptides by Single-Cell Impedance Cytometry

Antimicrobial peptides (AMPs) represent a promising class of compounds to fight antibiotic-resistant infections. In most cases, they kill bacteria by making their membrane permeable and therefore exhibit low propensity to induce bacterial resistance. In addition, they are often selective, killing bacteria at concentrations lower than those at which they are toxic to the host. However, clinical applications of AMPs are hindered by a limited understanding of their interactions with bacteria and human cells. Standard susceptibility testing methods are based on the analysis of the growth of a bacterial population and therefore require several hours. Moreover, different assays are required to assess the toxicity to host cells. In this work, we propose the use of microfluidic impedance cytometry to explore the action of AMPs on both bacteria and host cells in a rapid manner and with single-cell resolution. Impedance measurements are particularly well-suited to detect the effects of AMPs on bacteria, due to the fact that the mechanism of action involves perturbation of the permeability of cell membranes. We show that the electrical signatures of Bacillus megaterium cells and human red blood cells (RBCs) reflect the action of a representative antimicrobial peptide, DNS-PMAP23. In particular, the impedance phase at high frequency (e.g., 11 or 20 MHz) is a reliable label-free metric for monitoring DNS-PMAP23 bactericidal activity and toxicity to RBCs. The impedance-based characterization is validated by comparison with standard antibacterial activity assays and absorbance-based hemolytic activity assays. Furthermore, we demonstrate the applicability of the technique to a mixed sample of B. megaterium cells and RBCs, which paves the way to study AMP selectivity for bacterial versus eukaryotic cells in the presence of both cell types.

The next generation of hybrid microfluidic/integrated circuit chips: recent and upcoming advances in high-speed, high-throughput, and multifunctional lab-on-IC systems

Since the field's inception, pioneers in microfluidics have made significant progress towards realizing complete lab-on-chip systems capable of sophisticated sample analysis and processing. One avenue towards this goal has been to join forces with the related field of microelectronics, using integrated circuits (ICs) to perform on-chip actuation and sensing. While early demonstrations focused on using microfluidic-IC hybrid chips to miniaturize benchtop instruments, steady advancements in the field have enabled a new generation of devices that expand past miniaturization into high-performance applications that would not be possible without IC hybrid integration. In this review, we identify recent examples of labs-on-chip that use high-resolution, high-speed, and multifunctional electronic and photonic chips to expand the capabilities of conventional sample analysis. We focus on three particularly active areas: a) high-throughput integrated flow cytometers; b) large-scale microelectrode arrays for stimulation and multimodal sensing of cells over a wide field of view; c) high-speed biosensors for studying molecules with high temporal resolution. We also discuss recent advancements in IC technology, including on-chip data processing techniques and lens-free optics based on integrated photonics, that are poised to further advance microfluidic-IC hybrid chips.

Development of a Novel Design of Microfluidic Impedance Cytometry for Improved Sensitivity and Cell Identification

A long-standing issue for microfluidic impedance cytometry devices is the accuracy in determining the size of cells during counting and measurements. In this paper, we introduce a novel design that produces a homogeneous electric field in the sensing region and demonstrates higher accuracy than traditional designs in cell counting and sizing, reducing the reliance on cell focusing and signal postprocessing. The concept is validated, and the increased accuracy of the device over traditional designs is demonstrated through the use of finite element simulations to generate suitable data sets for particle trajectories and model expected signal variations.

iSort enables automated complex microfluidic droplet sorting in an effort to democratize technology

Fluorescence-activated droplet sorting (FADS) is a widely used microfluidic technique for high-throughput screening. However, it requires highly trained specialists to determine optimal sorting parameters, and this results in a large combinatorial space that is challenging to optimize systematically. Additionally, it is currently challenging to track every single droplet within a screen, leading to compromised sorting and "hidden" false-positive events. To overcome these limitations, we have developed a setup in which the droplet frequency, spacing, and trajectory at the sorting junction are monitored in real time using impedance analysis. The resulting data are used to continuously optimize all parameters automatically and to counteract perturbations, resulting in higher throughput, higher reproducibility, increased robustness, and a beginner-friendly character. We believe this provides a missing piece for the spreading of phenotypic single-cell analysis methods, similar to what we have seen for single-cell genomics platforms.

Multichannel impedance cytometry downstream of cell separation by deterministic lateral displacement to quantify macrophage enrichment in heterogeneous samples

The integration of on-chip biophysical cytometry downstream of microfluidic enrichment for inline monitoring of phenotypic and separation metrics at single-cell sensitivity can allow for active control of separation and its application to versatile sample sets. We present integration of impedance cytometry downstream of cell separation by deterministic lateral displacement (DLD) for enrichment of activated macrophages from a heterogeneous sample, without the problems of biased sample loss and sample dilution caused by off-chip analysis. This required designs to match cell/particle flow rates from DLD separation into the confined single-cell impedance cytometry stage, the balancing of flow resistances across the separation array width to maintain unidirectionality, and the utilization of co-flowing beads as calibrated internal standards for inline assessment of DLD separation and for impedance data normalization. Using a heterogeneous sample with un-activated and activated macrophages, wherein macrophage polarization during activation causes cell size enlargement, on-chip impedance cytometry is used to validate DLD enrichment of the activated subpopulation at the displaced outlet, based on the multiparametric characteristics of cell size distribution and impedance phase metrics. This hybrid platform can monitor separation of specific subpopulations from cellular samples with wide size distributions, for active operational control and enhanced sample versatility.

Performance-enhanced clogging-free viscous sheath constriction impedance flow cytometry

As a label-free and high-throughput single cell analysis platform, impedance flow cytometry (IFC) suffers from clogging caused by a narrow microchannel as mechanical constriction (MC). Current sheath constriction (SC) solutions lack systematic evaluation of the performance and proper guidelines for the sheath fluid. Herein, we hypothesize that the viscosity of the non-conductive liquid is the key to the performance of SC, and propose to employ non-conductive viscous sheath flow in SC to unlock the tradeoff between sensitivity and throughput, while ensuring measurement accuracy. By placing MC and SC in series in the same microfluidic chip, we established an evaluation platform to prove the hypothesis. Through modeling analysis and experiments, we confirmed the accuracy (error < 1.60% ± 4.71%) of SC w.r.t. MC, and demonstrated that viscous non-conductive PEG solution achieved an improved sensitivity (7.92×) and signal-to-noise ratio (1.42×) in impedance measurement, with the accuracy maintained and free of clogging. Viscous SC IFC also shows satisfactory ability to distinguish different types of cancer cells and different subtypes of human breast cancer cells. It is envisioned that viscous SC IFC paves the way for IFC to be really usable in practice with clogging-free, accurate, and sensitive performance.

Machine learning empowered multi-stress level electromechanical phenotyping for high-dimensional single cell analysis

Microfluidics provides a powerful platform for biological analysis by harnessing the ability to precisely manipulate fluids and microparticles with integrated microsensors. Here, we introduce an imaging and impedance cell analyzer (IM2Cell), which implements single cell level impedance analysis and hydrodynamic mechanical phenotyping simultaneously. For the first time, IM2Cell demonstrates the capability of multi-stress level mechanical phenotyping. Specifically, IM2Cell is capable of characterizing cell diameter, three deformability responses, and four electrical properties. It presents high-dimensional information to give insight into subcellular components such as cell membrane, cytoplasm, cytoskeleton, and nucleus. In this work, we first validate imaging and impedance-based cell analyses separately. Then, the two techniques are combined to obtain both imaging and impedance data analyzed by machine learning method, exhibiting an improved prediction accuracy from 83.1% to 95.4% between fixed and living MDA-MB-231 breast cancer cells. Next, IM2Cell demonstrates 91.2% classification accuracy in a mixture of unlabeled MCF-10A, MCF-7, and MDA-MB-231 cell lines. Finally, an application demonstrates the potential of IM2Cell for the deformability studies of peripheral blood mononuclear cells (PBMCs) subpopulations without cumbersome isolation or labeling steps.

Floating-Electrode-Enabled Impedance Cytometry for Single-Cell 3D Localization

As a label-free, low-cost, and noninvasive tool, impedance measurement has been widely used in single-cell characterization analysis. However, due to the tiny volume of cells, the uncertainty of the spatial position in the microchannel will bring measurement errors in single-cell electrical parameters. To overcome the issue, we designed a novel microdevice configured with a coplanar differential electrode structure to accurately resolve the spatial position of single cells without constraining techniques such as additional sheath fluids or narrow microchannels. The device precisely localizes single cells by measuring the induced current generated by the combined action of the floating electrode and the differential electrodes when single cells flow through the electrode-sensing area. The device was experimentally validated by measuring 6 μm yeast cells and 10 μm particles, achieving spatial localization with a resolution down to 2.1 μm (about 5.3% of the channel width) in lateral direction and 1.2 μm (about 5.9% of the channel height) in the vertical direction at a flow rate of 1.2 μL/min. In addition, by comparing measurement of yeast cells and particles, it was demonstrated that the device not only localizes the single cells or particles but also simultaneously characterizes their status properties such as velocity and size. The device offers a competitive electrode configuration in impedance cytometry with the advantages of simple structure, low cost, and high throughput, promising cell localization and thus electrical characterization.

Concepts, electrode configuration, characterization, and data analytics of electric and electrochemical microfluidic platforms: a review

Microfluidic cytometry (MC) and electrical impedance spectroscopy (EIS) are two important techniques in biomedical engineering. Microfluidic cytometry has been utilized in various fields such as stem cell differentiation and cancer metastasis studies, and provides a simple, label-free, real-time method for characterizing and monitoring cellular fates. The impedance microdevice, including impedance flow cytometry (IFC) and electrical impedance spectroscopy (EIS), is integrated into MC systems. IFC measures the impedance of individual cells as they flow through a microfluidic device, while EIS measures impedance changes during binding events on electrode regions. There have been significant efforts to improve and optimize these devices for both basic research and clinical applications, based on the concepts, electrode configurations, and cell fates. This review outlines the theoretical concepts, electrode engineering, and data analytics of these devices, and highlights future directions for development.

Nanobubble size distribution measurement by interactive force apparatus under an electric field

Nanobubbles have been applied in many fields, such as environmental cleaning, material production, agriculture, and medicine. However, the measured nanobubble sizes differed among the measurement methods, such as dynamic light scattering, particle trajectory, and resonance mass methods. Additionally, the measurement methods were limited with respect to the bubble concentration, refractive index of liquid, and liquid color. Here, a novel interactive force measurement method for bulk nanobubble size measurement was developed by measuring the force between two electrodes filled with bulk nanobubble-containing liquid under an electric field when the electrode distance was changed in the nm scale with piezoelectric equipment. The nanobubble size was measured with a bubble gas diameter and also an effective water thin film layer covered with a gas bubble that was estimated to be approximately 10 nm based on the difference between the median diameter of the particle trajectory method and this method. This method could also be applied to the solid particle size distribution measurement in a solution.

Microfluidic Impedance Cytometry for Single-Cell Particulate Inorganic Carbon:Particulate Organic Carbon Measurements of Calcifying Algae

Calcifying algae, like coccolithophores, greatly contribute to the oceanic carbon cycle and are therefore of particular interest for ocean carbon models. They play a key role in two processes that are important for the effective CO2 flux: The organic carbon pump (photosynthesis) and the inorganic carbon pump (calcification). The relative contribution of calcification and photosynthesis can be measured in algae by the amount of particulate inorganic carbon (PIC) and particulate organic carbon (POC). A microfluidic impedance cytometer is presented, enabling non-invasive and high-throughput assessment of the calcification state of single coccolithophore cells. Gradual modification of the exoskeleton by acidification results in a strong linear fit (R 2 = 0.98) between the average electrical phase and the PIC:POC ratio of the coccolithophore Emiliania huxleyi 920/9. The effect of different CO2 treatments on the PIC:POC ratio, however, is inconclusive, indicating that there is no strong effect observed for this particular strain. Lower PIC:POC ratios in cultures that grew to higher cell densities are found, which are also recorded with the impedance-based PIC:POC sensor. The development of this new quantification tool for small volumes paves the way for high-throughput analysis while applying multi-variable environmental stressors to support projections of the future marine carbon cycle.

Supervised machine learning in microfluidic impedance flow cytometry for improved particle size determination

The assessment of particle and cell size in electrical microfluidic flow cytometers has become common practice. Nevertheless, in flow cytometers with coplanar electrodes accurate determination of particle size is difficult, owing to the inhomogeneous electric field. Pre-defined signal templates and compensation methods have been introduced to correct for this positional dependence, but are cumbersome when dealing with irregular signal shapes. We introduce a simple and accurate post-processing method without the use of pre-defined signal templates and compensation functions using supervised machine learning. We implemented a multiple linear regression model and show an average reduction of the particle diameter variation by 37% with respect to an earlier processing method based on a feature extraction algorithm and compensation function. Furthermore, we demonstrate its application in flow cytometry by determining the size distribution of a population of small (4.6 ± 0.9 μm) and large (5.9 ± 0.8 μm) yeast cells. The improved performance of this coplanar, two electrode chip enables precise cell size determination in easy to fabricate impedance flow cytometers.

A Simple Micromilled Microfluidic Impedance Cytometer with Vertical Parallel Electrodes for Cell Viability Analysis

Microfluidic impedance cytometry has been demonstrated as an effective platform for single cell analysis, taking advantage of microfabricated features and dielectric cell sensing methods. In this study, we present a simple microfluidic device to improve the sensitivity, accuracy, and throughput of single suspension cell viability analysis using vertical sidewall electrodes fabricated by a widely accessible negative manufacturing method. A microchannel milled through a 75 µm platinum wire, which was embedded into poly-methyl-methacrylate (PMMA), created a pair of parallel vertical sidewall platinum electrodes. Jurkat cells were interrogated in a custom low-conductivity buffer (1.2 ± 0.04 mS/cm) to reduce current leakage and increase device sensitivity. Confirmed by live/dead staining and electron microscopy, a single optimum excitation frequency of 2 MHz was identified at which live and dead cells were discriminated based on the disruption in the cell membrane associated with cell death. At this frequency, live cells were found to exhibit changes in the impedance phase with no appreciable change in magnitude, while dead cells displayed the opposite behavior. Correlated with video microscopy, a computational algorithm was created that could identify cell detection events and determine cell viability status by application of a mathematical correlation method.

Single-Cell Classification Based on Population Nucleus Size Combining Microwave Impedance Spectroscopy and Machine Learning

Many recent efforts in the diagnostic field address the accessibility of cancer diagnosis. Typical histological staining methods identify cancer cells visually by a larger nucleus with more condensed chromatin. Machine learning (ML) has been incorporated into image analysis for improving this process. Recently, impedance spectrometers have been shown to generate all-inclusive lab-on-a-chip platforms to detect nucleus abnormities. In this paper, a wideband electrical sensor and data analysis paradigm that can identify nuclear changes shows the realization of a single-cell microfluidic device to detect nuclei of altered sizes. To model cells of altered nucleus, Jurkat cells were treated to enlarge or shrink their nucleus followed by broadband sensing to obtain the S-parameters of single cells. The ability to deduce important frequencies associated with nucleus size is demonstrated and used to improve classification models in both binary and multiclass scenarios, despite a heterogeneous and overlapping cell population. The important frequency features match those predicted in a double-shell circuit model published in prior work, demonstrating a coherent new analytical technique for electrical data analysis. The electrical sensing platform assisted by ML with impressive accuracy of cell classification looks forward to a label-free and flexible approach to cancer diagnosis.

High-Efficiency Single-Cell Electrical Impedance Spectroscopy

Single-cell impedance measurement is label free and noninvasive in characterizing the electrical properties of single cells. At present, though widely used for impedance measurement, electrical impedance flow cytometry (IFC) and electrical impedance spectroscopy (EIS) are used alone for most microfluidic chips. Here, we describe high-efficiency single-cell electrical impedance spectroscopy, which combines in one chip the IFC and EIS techniques for high-efficiency single-cell electrical property measurement. We envision that the strategy of combining IFC and EIS provides a new thought in the efforts to enhance the efficiency of electrical property measurement for single cells.

Droplet Detection and Sorting System in Microfluidics: A Review

Since being invented, droplet microfluidic technologies have been proven to be perfect tools for high-throughput chemical and biological functional screening applications, and they have been heavily studied and improved through the past two decades. Each droplet can be used as one single bioreactor to compartmentalize a big material or biological population, so millions of droplets can be individually screened based on demand, while the sorting function could extract the droplets of interest to a separate pool from the main droplet library. In this paper, we reviewed droplet detection and active sorting methods that are currently still being widely used for high-through screening applications in microfluidic systems, including the latest updates regarding each technology. We analyze and summarize the merits and drawbacks of each presented technology and conclude, with our perspectives, on future direction of development.

Parallel Impedance Cytometry for Real-Time Screening of Bacterial Single Cells from Nano- to Microscale

The benefits of impedance cytometry include high-throughput and label-free detection, while long-term calibration is required to remove the effects of the detection circuits. This study presents a novel impedance cytometry system, called parallel impedance cytometry, to simplify the calibration and analysis of the impedance signals. Furthermore, target objects can be detected even when benchmarked against similar objects. Parallel dual microchannels allow the simultaneous detection of reference and target particles in two separate microchannels, without the premixing of reference and target suspensions. The impedance pulses of both can appear separately on the opposite sides of the same time series, which have been verified via simulation and experimental results. Raw impedance signals can easily distinguish target particles from reference ones. Polystyrene beads with different sizes ranging from nano- to microscale (e.g., 500, 750 nm, 1, 2, 3, and 4.5 μm) confirm the nanosensitivity of the system. In addition, the detection of antibiotic-treated Escherichia coli cells demonstrates that our system can be used for the quantitative assessment of the dielectric properties of individual cells, as well as for the proportion of susceptible cells. Through benchmarking against untreated E. coli cells in the other channel, our method enables the discrimination of susceptible cells from others and the comparison of susceptible and insusceptible cells in the target suspension. Those findings indicate that the parallel impedance cytometry can greatly facilitate the measurement and calibration of the impedances of various particles or cells and provide a means to compare their dielectric properties.

Microfluidic Systems for Blood and Blood Cell Characterization

A laboratory blood test is vital for assessing a patient's health and disease status. Advances in microfluidic technology have opened the door for on-chip blood analysis. Currently, microfluidic devices can reproduce myriad routine laboratory blood tests. Considerable progress has been made in microfluidic cytometry, blood cell separation, and characterization. Along with the usual clinical parameters, microfluidics makes it possible to determine the physical properties of blood and blood cells. We review recent advances in microfluidic systems for measuring the physical properties and biophysical characteristics of blood and blood cells. Added emphasis is placed on multifunctional platforms that combine several microfluidic technologies for effective cell characterization. The combination of hydrodynamic, optical, electromagnetic, and/or acoustic methods in a microfluidic device facilitates the precise determination of various physical properties of blood and blood cells. We analyzed the physical quantities that are measured by microfluidic devices and the parameters that are determined through these measurements. We discuss unexplored problems and present our perspectives on the long-term challenges and trends associated with the application of microfluidics in clinical laboratories. We expect the characterization of the physical properties of blood and blood cells in a microfluidic environment to be considered a standard blood test in the future.

A new approach for accurate determination of particle sizes in microfluidic impedance cytometry

In microfluidic impedance cytometry, the change in impedance is recorded as an individual cell passes through a channel between electrodes deposited on its walls, and the particle size is inferred from the amplitude of the impedance signal using calibration. However, because the current density is nonuniform between electrodes of finite width, there could be an error in the particle size measurement because of uncertainty about the location of the particle in the channel cross section. Here, a correlation is developed relating the particle size to the signal amplitude and the velocity of the particle through the channel. The latter is inferred from the time interval between the two extrema in the impedance curve as the particle passes through a channel with cross-sectional dimensions of 50 μm (width) × 30 μm (height) with two pairs of parallel facing electrodes. The change in impedance is predicted using 3D COMSOL finite-element simulations, and a theoretical correlation that is independent of particle size is formulated to correct the particle diameter for variations in the cross-sectional location. With this correlation, the standard deviation in the experimental data is reduced by a factor of two to close to the standard deviation reported in the manufacturer specifications.

On-Chip Single-Cell Bioelectrical Analysis for Identification of Cell Electrical Phenotyping in Response to Sequential Electric Signal Modulation

In recent years, an interesting biomarker called membrane breakdown voltage has been examined using artificial planar lipid bilayers. Even though they have great potential to identify cell electrical phenotyping for distinguishing similar cell lines or cells under different physiological conditions, the biomarker has not been evaluated in the context of living cell electrical phenotyping. Herein, we present a single-cell analysis platform to continuously measure the electric response in a large number of cells in parallel using electric frequency and voltage variables. Using this platform, we measured the direction of cell displacement and transparent cell image alteration as electric polarization of the cell responds to signal modulation, extracting the dielectrophoretic crossover frequency and membrane breakdown voltage for each cell, and utilizing the measurement results in the same spatiotemporal environment. We developed paired parameters using the dielectrophoretic crossover frequency and membrane breakdown voltage for each cell and evaluated the paired parameter efficiency concerning the identification of two different breast cancer cells and cell drug response. Moreover, we showed that the platform was able to identify cell electrical phenotyping, which was generated by subtle changes in cholesterol depletion-induced cell membrane integrity disruption when the paired parameter was used. Our platform introduced in this paper is extremely useful for facilitating more accurate and efficient evaluation of cell electrical phenotyping in a variety of applications, such as cell biology and drug discovery.