Single-cell Impedance Research Articles

Microfluidic-Based Electrical Operation and Measurement Methods in Single-Cell Analysis.

Cellular heterogeneity plays a significant role in understanding biological processes, such as cell cycle and disease progression. Microfluidics has emerged as a versatile tool for manipulating single cells and analyzing their heterogeneity with the merits of precise fluid control, small sample consumption, easy integration, and high throughput. Specifically, integrating microfluidics with electrical techniques provides a rapid, label-free, and non-invasive way to investigate cellular heterogeneity at the single-cell level. Here, we review the recent development of microfluidic-based electrical strategies for single-cell manipulation and analysis, including dielectrophoresis- and electroporation-based single-cell manipulation, impedance- and AC electrokinetic-based methods, and electrochemical-based single-cell detection methods. Finally, the challenges and future perspectives of the microfluidic-based electrical techniques for single-cell analysis are proposed.

Biophysical phenotyping of single-cell based on impedance and application for individualized precision medicine

Single-cell biophysical characterization based on impedance measurement is an advantageous approach due to its label-free, high-efficiency, cost-effective and real-time capability. Biophysical phenotyping can yield timely and rich information on physiological and pathological state of cells for disease diagnosis, drug screening, precision medicine, etc. However, precise measurement on single-cell impedance is challenging, particularly hard to figure out the detailed biophysical parameters of single cell due to coupling and complexity of impedance model. Here, we propose an analytic determination method to decode single-cell electrophysiological parameters (including cell-substrate interface capacitance, cell membrane capacitance, cell membrane conductivity, and cytoplasm conductivity) from the impedances measured at optimized frequencies by using analytic solution rather than spectrum fitting. With this simple and fast analytic solution method, the physiological parameters of single cell in natural adhesion state can be accurately determined in real time. We validate this cell parameter determination method in monitoring the change of cell adhesion under hydraulic effects and exploring electrophysiological differences among MCF-7, HeLa, Huh7, and MDA-MB-231 cell lines. Particularly, we apply the approach to optimize tumor treating fields (TTFields) therapy, realizing individualized precision medicine. Our work provides an accurate and efficient approach for characterizing single-cell biophysical properties with real-time, in-situ, label-free, and less invasive advantages.

Leukocyte differential based on an imaging and impedance flow cytometry of microfluidics coupled with deep neural networks.

The differential of leukocytes functions as the first indicator in clinical examinations. However, microscopic examinations suffered from key limitations of low throughputs in classifying leukocytes while commercially available hematology analyzers failed to provide quantitative accuracies in leukocyte differentials. A home-developed imaging and impedance flow cytometry of microfluidics was used to capture fluorescent images and impedance variations of single cells traveling through constrictional microchannels. Convolutional and recurrent neural networks were adopted for data processing and feature extractions, which were then fused by a support vector machine to realize the four-part differential of leukocytes. The classification accuracies of the four-part leukocyte differential were quantified as 95.4% based on fluorescent images plus the convolutional neural network, 90.3% based on impedance variations plus the recurrent neural network, and 99.3% on the basis of fluorescent images, impedance variations, and deep neural networks. Based on single-cell fluorescent imaging and impedance variations coupled with deep neural networks, the four-part leukocyte differential can be realized with almost 100% accuracy.

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.

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.

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.

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.

Supervised learning on impedance cytometry data for label-free biophysical distinction of pancreatic cancer cells versus their associated fibroblasts under gemcitabine treatment

Chemotherapy failure in pancreatic cancer patients is widely attributed to cancer cell reprogramming towards drug resistance by cancer associated fibroblasts (CAFs), which are the abundant cell type in the tumor microenvironment. Association of drug resistance to specific cancer cell phenotypes within multicellular tumors can advance isolation protocols for enabling cell-type specific gene expression markers to identify drug resistance. This requires the distinction of drug resistant cancer cells versus CAFs, which is challenging since permeabilization of CAF cells during drug treatment can cause non-specific uptake of cancer cell-specific stains. Cellular biophysical metrics, on the other hand, can provide multiparametric information to assess the gradual alteration of target cancer cells towards drug resistance, but these phenotypes need to be distinguished versus CAFs. Using pancreatic cancer cells and CAFs from a metastatic patient-derived tumor that exhibits cancer cell drug resistance under CAF co-culture, the biophysical metrics from multifrequency single-cell impedance cytometry are utilized for distinction of the subpopulation of viable cancer cells versus CAFs, before and after gemcitabine treatment. This is accomplished through supervised machine learning after training the model using key impedance metrics for cancer cells and CAFs from transwell co-cultures, so that an optimized classifier model can recognize each cell type and predict their respective proportions in multicellular tumor samples, before and after gemcitabine treatment, as validated by their confusion matrix and flow cytometry assays. In this manner, an aggregate of the distinguishing biophysical metrics of viable cancer cells after gemcitabine treatment in co-cultures with CAFs can be used in longitudinal studies, to classify and isolate the drug resistant subpopulation for identifying markers.

The Influence of Electrode Design on Detecting the Effects of Ferric Ammonium Citrate (FAC) on Pre-Osteoblast through Electrical Cell-Substrate Impedance Sensing (ECIS)

Detection sensitivity is a crucial factor in the application of ECIS sensors. For these biosensors, the electrode configuration has a direct impact on sensitivity, yet few studies on monopolar electrodes have been reported. In this study, ECIS sensor arrays, which have a series of working electrode configuration with a wide diameter range and different electrode number, were fabricated to monitor living osteoblast-like MC3T3-E1 cells. The experimental results revealed that when the electrode diameter was larger than 25 μm, electrodes with smaller diameter and number yielded higher impedance values and generated more impedance shift to cell status change. The membrane capacitance obtained by equivalent circuit fitting was at the same level. When the electrode diameter was even smaller, the results in detection of cell monolayer were opposite, and there was no distinct relationship between impedance and membrane capacitance shift to cell status change and electrode geometry. The proposed sensor chip, allowing for a sustained and stable detection of cellular impedance, provides the basis for the selection of the electrode configuration of monopolar electrodes. The test results of electrodes with a diameter of 25 μm and lower indicated the possibility of single cell impedance measurement, which can provide unique insight into the heterogeneous electrical behavior of cells, and, in this case, the electrode size should be close to the cell size.

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.

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.

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.

Toward five-part differential of leukocytes based on electrical impedances of single cells and neural network.

The five-part differential of leukocytes plays key roles in the diagnosis of a variety of diseases and is realized by optical examinations of single cells, which is prone to various artifacts due to chemical treatments. The classification of leukocytes based on electrical impedances without cell treatments has not been demonstrated because of limitations in approaches of impedance acquisition and data processing. In this study, based on treatment-free single-cell impedance profiles collected from impedance flow cytometry leveraging constriction microchannels, two types of neural pattern recognition were conducted for comparisons with the purpose of realizing the five-part differential of leukocytes. In the first approach, 30 features from impedance profiles were defined manually and extracted automatically, and then a feedforward neural network was conducted, producing a classification accuracy of 84.9% in the five-part leukocyte differential. In the second approach, a customized recurrent neural network was developed to process impedance profiles directly and based on deep learning, a classification accuracy of 97.5% in the five-part leukocyte differential was reported. These results validated the feasibility of the five-part leukocyte differential based on label-free impedance profiles of single cells and thus provide a new perspective of differentiating white blood cells based on impedance flow cytometry.

Single-cell assessment of the modulation of macrophage activation by ex vivo intervertebral discs using impedance cytometry

Measurement of macrophage activation and its modulation for immune regulation is of great interest to arrest inflammatory responses associated with degeneration of intervertebral discs that cause chronic back pain, and with transplants that face immune rejection. Due to the phenotypic plasticity of macrophages that serve multiple immune functions, the net disease outcome is determined by a balance of subpopulations with competing functions, highlighting the need for single-cell methods to quantify heterogeneity in their activation phenotypes. However, since macrophage activation can follow several signaling pathways, cytometry after fluorescent staining of markers with antibodies does not often provide dose-dependent information on activation dynamics. We present high throughput single-cell impedance cytometry for multiparametric measurement of biophysical changes to individual macrophages for quantifying activation in a dose and duration dependent manner, without relying on a particular signaling pathway. Impedance phase metrics measured at two frequencies and the electrical diameter from impedance magnitude at lower frequencies are used in tandem to benchmark macrophage activation by degenerated discs against that from lipopolysaccharide stimulation at varying dose and duration levels, so that reversal of the activation state by curcumin can be ascertained. This label-free single-cell measurement method can form the basis for platforms to screen therapies for inflammation, thereby addressing the chronic problem of back pain.

Cost-effective portable microfluidic impedance cytometer for broadband impedance cell analysis based on viscoelastic focusing.

Single-cell impedance analysis can provide valuable information for characterizing and discriminating cells. In this paper, a cost-effective portable microfluidic impedance cytometer (MIC) was proposed to realize the broadband impedance analysis of cells by using maximum length sequence (MLS) and viscoelastic focusing. The MIC comprised a microfluidic chip with a straight microchannel, two indium tin oxide (ITO) electrodes, and a home-made platform performing maximum length sequence technique. The viscoelastic focusing enabled cells to focus into a single train to eliminate the influence of cell position variation on acquired electrical signals and allow the cells to pass through the detection region one by one. The MLS technique realized the fast broadband impedance detection of single cells at a low hardware cost. The impedance data under multiplex frequencies was obtained to uncover the dielectric properties of white blood cells (WBCs) and MCF-7 cancer cells. The machine learning was used to train the impedance data and to identify cell types. The results indicated that 98.98% of MCF-7cells and 98.65% of WBCs were correctly identified. Our MIC showed a potential to be developed as a cost-effective and portable device for point-of-care testing of circulating tumor cells from patients' peripheral blood.

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.

Extraction of Single Cell Impedance From Battery Module Measurement by Simulation-Based De-Embedding

Batteries are a fundamental part of many modern electric systems. As a result, battery modeling is increasingly important for the prediction of signal and power integrity (SIPI) as well as electromagnetic compatibility (EMC). Conventional battery module modeling requires knowledge of the integrated cells first, which is usually obtained by measurement on single cells. However, if individual cells are not accessible, the single cell&#x0027;s impedance needs to be extracted from measurement of the complete module. This work describes two solutions to this problem, which are both based on 3D electromagnetic (EM) simulation of the battery module with surrogate cell models to obtain S-parameters which describe coupling effects inherent to the modules&#x0027;s geometry. By either fitting of the simulated module impedance to the measured data on circuit schematic level, or by numerical multiport de-embedding, the single cell impedance is extracted. Considered frequencies range from 9 kHz to 1 GHz.

Investigation of the sensitivity of operating temperature for water distribution of fuel cell stack based on electrochemical impedance spectroscopy

The water content of fuel cell can be controlled by adjusting working conditions. Although the water content cannot be directly observed, the electrochemical impedance spectroscopy (EIS) data can be used to characterise the internal water content. In this paper, the EIS data of fuel cell are obtained through experiments. According to the results, changing the operating temperature has a significant impact on the low-frequency impedance. The temperature should be changed to avoid dehydration or flooding at different current density. The impedance of single cell shows that although the optimised temperature improves performance, there is still an imbalance of water content among the cells. This article also compares the effect of controlling relative humidity and working temperature. The comparison shows that the two solutions have close effects on the impedance. Considering various factors, controlling working temperature is an effective method of fuel cell water management.

Automated biophysical classification of apoptotic pancreatic cancer cell subpopulations by using machine learning approaches with impedance cytometry.

Unrestricted cell death can lead to an immunosuppressive tumor microenvironment, with dysregulated apoptotic signaling that causes resistance of pancreatic cancer cells to cytotoxic therapies. Hence, modulating cell death by distinguishing the progression of subpopulations under drug treatment from viable towards early apoptotic, late apoptotic, and necrotic states is of interest. While flow cytometry after fluorescent staining can monitor apoptosis with single-cell sensitivity, the background of non-viable cells within non-immortalized pancreatic tumors from xenografts can confound distinction of the intensity of each apoptotic state. Based on single-cell impedance cytometry of drug-treated pancreatic cancer cells that are obtained from tumor xenografts with differing levels of gemcitabine sensitivity, we identify the biophysical metrics that can distinguish and quantify cellular subpopulations at the early apoptotic versus late apoptotic and necrotic states, by using machine learning methods to train for the recognition of each phenotype. While supervised learning has previously been used for classification of datasets with known classes, our advancement is the utilization of optimal positive controls for each class, so that clustering by unsupervised learning and classification by supervised learning can occur on unknown datasets, without human interference or manual gating. In this manner, automated biophysical classification can be used to follow the progression of apoptotic states in each heterogeneous drug-treated sample, for developing drug treatments to modulate cancer cell death and advance longitudinal analysis to discern the emergence of drug resistant phenotypes.

Single-Cell Impedance Cytometry of Anticancer Drug-Treated Hela Cells Exhibiting Progressive States from Mitotic Arrest to Apoptosis Using Monolithic Silver-Pdms Microelectrodes

Single-Cell Impedance Cytometry of Anticancer Drug-Treated Hela Cells Exhibiting Progressive States from Mitotic Arrest to Apoptosis Using Monolithic Silver-Pdms Microelectrodes