A microfluidic system for cell type classification based on cellular size-independent electrical properties

Tutorial on impedance and dielectric spectroscopy for single-cell characterisation on microfluidic platforms: theory, practice, and recent advances

Cell analysis plays an important role in disease diagnosis. However, many characterisation techniques are labour intensive, expensive and time-consuming. Impedance and dielectric spectroscopy (IDS) offers a new approach by using varying electrical current and electric field propagation responses to probe cell physiology. This review aims to explore the theoretical foundations, practical applications, and advancements in IDS for single-cell analysis, particularly when integrated with microfluidic technologies. It highlights recent developments in electrode configurations, calibration techniques, and data analysis methodologies, emphasising their importance in enhancing sensitivity and selectivity. The review identifies key trends, including the shift towards high-throughput and precise single-cell analysis, and discusses the challenges and potential solutions in this field. The implications of these findings suggest significant near-future advances in biomedical research, diagnostics, and therapeutic monitoring. This paper serves as a comprehensive reference for researchers in different fields to make a bridge between theoretical research and practical implementation in single-cell analysis.

Acousto-dielectric tweezers for size-insensitive manipulation and biophysical characterization of single cells

The intrinsic biophysical properties of cells, such as mechanical, acoustic, and electrical properties, are valuable indicators of a cell's function and state. However, traditional single-cell biophysical characterization methods are hindered by limited measurable properties, time-consuming procedures, and complex system setups. This study presents acousto-dielectric tweezers that leverage the balance between controllable acoustophoretic and dielectrophoretic forces applied on cells through surface acoustic waves and alternating current electric fields, respectively. Particularly, the balanced acoustophoretic and dielectrophoretic forces can trap cells at equilibrium positions independent of the cell size to differentiate between various cell-intrinsic mechanical, acoustic, and electrical properties. Experimental results show our mechanism has the potential for applications in single-cell analysis, size-insensitive cell separation, and cell phenotyping, which are all primarily based on cells' intrinsic biophysical properties. Our results also show the measured equilibrium position of a cell can inversely determine multiple biophysical properties, including membrane capacitance, cytoplasm conductivity, and acoustic contrast factor. With these features, our acousto-dielectric tweezing mechanism is a valuable addition to the resources available for biophysical property-based biological and medical research.

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.

Classification between Normal and Cancerous Human Urothelial Cells by Using Micro-Dimensional Electrochemical Impedance Spectroscopy Combined with Machine Learning

Although the high incidence and recurrence rates of urothelial cancer of the bladder (UCB) are heavy burdens, a noninvasive tool for effectively detecting UCB as an alternative to voided urine cytology, which has low sensitivity, is yet to be reported. Herein, we propose an intelligent discrimination method between normal (SV-HUC-1) and cancerous (TCCSUP) urothelial cells by using a combination of micro-dimensional electrochemical impedance spectroscopy (µEIS) with machine learning (ML) for a noninvasive and high-accuracy UCB diagnostic tool. We developed a unique valved flow cytometry, equipped with a pneumatic valve to increase sensitivity without cell clogging. Since contact between a cell and electrodes is tight with a high volume fraction, the electric field can be effectively confined to the cell. This enables the proposed sensor to highly discriminate different cell types at frequencies of 10, 50, 100, 500 kHz, and 1 MHz. A total of 236 impedance spectra were applied to six ML models, and systematic comparisons of the ML models were carried out. The hyperparameters were estimated by conducting a grid search or Bayesian optimization. Among the ML models, random forest strongly discriminated between SV-HUC-1 and TCCSUP, with an accuracy of 91.7%, sensitivity of 92.9%, precision of 92.9%, specificity of 90%, and F1-score of 93.8%.

Microfluidics for Neuronal Cell and Circuit Engineering

The widespread adoption of microfluidic devices among the neuroscience and neurobiology communities has enabled addressing a broad range of questions at the molecular, cellular, circuit, and system levels. Here, we review biomedical engineering approaches that harness the power of microfluidics for bottom-up generation of neuronal cell types and for the assembly and analysis of neural circuits. Microfluidics-based approaches are instrumental to generate the knowledge necessary for the derivation of diverse neuronal cell types from human pluripotent stem cells, as they enable the isolation and subsequent examination of individual neurons of interest. Moreover, microfluidic devices allow to engineer neural circuits with specific orientations and directionality by providing control over neuronal cell polarity and permitting the isolation of axons in individual microchannels. Similarly, the use of microfluidic chips enables the construction not only of 2D but also of 3D brain, retinal, and peripheral nervous system model circuits. Such brain-on-a-chip and organoid-on-a-chip technologies are promising platforms for studying these organs as they closely recapitulate some aspects of in vivo biological processes. Microfluidic 3D neuronal models, together with 2D in vitro systems, are widely used in many applications ranging from drug development and toxicology studies to neurological disease modeling and personalized medicine. Altogether, microfluidics provide researchers with powerful systems that complement and partially replace animal models.

Biosensors and machine learning for enhanced detection, stratification, and classification of cells: a review

Biological cells, by definition, are the basic units which contain the fundamental molecules of life of which all living things are composed. Understanding how they function and differentiating cells from one another, therefore, is of paramount importance for disease diagnostics as well as therapeutics. Sensors focusing on the detection and stratification of cells have gained popularity as technological advancements have allowed for the miniaturization of various components inching us closer to Point-of-Care (POC) solutions with each passing day. Furthermore, Machine Learning has allowed for enhancement in the analytical capabilities of these various biosensing modalities, especially the challenging task of classification of cells into various categories using a data-driven approach rather than physics-driven. In this review, we provide an account of how Machine Learning has been applied explicitly to sensors that detect and classify cells. We also provide a comparison of how different sensing modalities and algorithms affect the classifier accuracy and the dataset size required.

Inherent Single-Cell Bioelectrical Parameters of Thousands of Neutrophils, Eosinophils and Basophils Derived from Impedance Flow Cytometry

Single-cell bioelectrical properties are commonly used for blood cell phenotyping in a label-free manner. However, previously reported inherent single-cell bioelectrical parameters (e.g., diameter D_c , specific membrane capacitance C_sm and cytoplasmic conductivity σ_cy ) of neutrophils, eosinophils and basophils were obtained from only tens of individual cells with limited statistical significance. In this study, granulocytes were separated into neutrophils, eosinophils and basophils based on fluorescent flow cytometry, which were further aspirated through a constriction-microchannel impedance flow cytometry for electrical property characterization. Based on this microfluidic impedance flow cytometry, single-cell values of D_c , C_sm and σ_cy were measured as 10.25 ± 0.66 μm, 2.17 ± 0.30 μF/cm² , and 0.37 ± 0.05 S/m for neutrophils (n_cell = 9 442); 9.73 ± 0.51 μm, 2.07 ± 0.19 μF/cm² , and 0.30 ± 0.04 S/m for eosinophils (n_cell = 2 982); 9.75 ± 0.49 μm, 2.06 ± 0.17 μF/cm² , and 0.31 ± 0.04 S/m for basophils (n_cell = 5 377). Based on these inherent single-cell bioelectrical parameters, neural pattern recognition was conducted, producing classification rates of 80.8% (neutrophil vs. eosinophil), 77.7% (neutrophil vs. basophil) and 59.3% (neutrophil vs. basophil). These results indicate that as inherent single-cell bioelectrical parameters, D_c , C_sm and σ_cy can be used to classify neutrophils from eosinophils or basophils to some extent while they cannot be used to effectively distinguish eosinophils from basophils.

Inherent Bioelectrical Parameters of Hundreds of Thousands of Single Leukocytes Based on Impedance Flow Cytometry

As label-free biomarkers, bioelectrical properties of single cells have been widely used in hematology analyzers for 3-part differential of leukocytes, in which, however, instrument dependent bioelectrical parameters (e.g., DC/AC impedance values) rather than inherent bioelectrical parameters (e.g., diameter D_c , specific membrane capacitance C_sm and cytoplasmic conductivity σ_cy ) were used, leading to poor comparisons among different instruments. In order to address this issue, this study collected inherent bioelectrical parameters from hundreds of thousands of white blood cells based on a home-developed impedance flow cytometry with corresponding 3-part differential of leukocytes realized. More specifically, leukocytes were separated into three major subtypes of granulocytes, monocytes and lymphocytes based on density gradient centrifugation. Then these separated cells were aspirated through a constriction-microchannel based impedance flow cytometry where inherent bioelectrical parameters of D_c , C_sm and σ_cy were quantified as 9.8 ± 0.7 μm, 2.06 ± 0.26 μF/cm² , and 0.34 ± 0.05 S/m for granulocytes (n_cell = 134 829); 10.4 ± 1.0 μm, 2.45 ± 0.48 μF/cm² , and 0.42 ± 0.08 S/m for monocytes (n_cell = 40 226); 8.0 ± 0.5 μm, 2.23 ± 0.34 μF/cm² , and 0.35 ± 0.08 S/m for lymphocytes (n_cell = 129 193). Based on these inherent bioelectrical parameters, neural pattern recognition was conducted, producing a high "classification accuracy" of 93.5% in classifying these three subtypes of leukocytes. These results indicate that as inherent bioelectrical parameters, D_c , C_sm and σ_cy can be used to electrically phenotype white blood cells in a label-free manner.

Autoregressive parametric modeling combined ANOVA approach for label-free-based cancerous and normal cells discrimination

Label free based methods received huge interest in the field of bio cell characterizations because they do not cause any cell damage nor contribute any change in its compositions. This work takes a close outlook of cancerous cells discrimination from normal cells utilizing parametric modeling approach. Autoregressive (AR) modeling technique is used to fit the measured optical transmittance profiles of both cancer and normal cells. The transmitted light intensity, when passes through the cells, gets affected by their intercellular compositions and membrane properties. In this study, four types of cells: lung-cancerous and normal, liver-cancerous and normal, were suspended in their corresponding medium and their transmission characteristics were collected and processed. The AR coefficients of each type of the cell were analyzed with the statistical technique called Analysis of variance (ANOVA), which provided the significant coefficients. The poles extracted from the significant coefficients resulted in an improved demarcation for normal and cancer cells. These outcomes can be further utilized for cell classification using statistical tools.

Classification of tumor subtypes leveraging constriction-channel based impedance flow cytometry and optical imaging

As label-free biomarkers, electrical properties of single cells have been widely used for cell-type classification and cell-status evaluation. However, as intrinsic bioelectrical markers, previously reported membrane capacitance and cytoplasmic resistance (e.g., specific membrane capacitance C_{specific membrane} and cytoplasmic conductivity σ_cytoplasm ) of tumor subtypes were derived from tens of single cells, lacking statistical significance due to low cell numbers. In this study, tumor subtypes were constructed based on phenotype (treatment with 4-methylumbelliferone) or genotype (knockdown of ROCK1) modifications and then aspirated through a constriction-channel based impedance flow cytometry to characterize single-cell C_{specific membrane} and σ_cytoplasm . Thousands of single tumor cells with phenotype modifications were measured, resulting in significant differences in 1.64 ± 0.43 μF/cm² vs. 1.55 ± 0.47 μF/cm² of C_{specific membrane} and 0.96 ± 0.37 S/m vs. 1.24 ± 0.47 S/m of σ_cytoplasm for 95C cells (792 cells of 95C-control vs. 1529 cells of 95C-pheno-mod); 2.56 ± 0.88 μF/cm² vs. 2.33 ± 0.56 μF/cm² of C_{specific membrane} and 0.83 ± 0.18 S/m vs. 0.93 ± 0.25 S/m of σ_cytoplasm for H1299 cells (962 cells of H1299-control vs. 637 cells of H1299-pheno-mod). Furthermore, thousands of single tumor cells with genotype modifications were measured, resulting in significant differences in 3.82 ± 0.92 vs. 3.18 ± 0.47 μF/cm² of C_{specific membrane} and 0.47 ± 0.05 vs. 0.52 ± 0.05 S/m of σ_cytoplasm (1100 cells of A549-control vs. 1100 cells of A549-geno-mod). These results indicate that as intrinsic bioelectrical markers, specific membrane capacitance and cytoplasmic conductivity can be used to classify tumor subtypes.

Monitoring Single S. cerevisiae Cells with Multifrequency Electrical Impedance Spectroscopy in an Electrode-Integrated Microfluidic Device

This chapter describes an electrode-integrated microfluidic system with multiple functions of manipulating and monitoring single S. cerevisiae cells. In this system, hydrodynamic trapping and negative dielectrophoretic (nDEP) releasing of S. cerevisiae cells are implemented, providing a flexible method for single-cell manipulation. The multiplexing microelectrodes also enable sensitive electrical impedance spectroscopy (EIS) to discern the number of immobilized cells, classify different orientations of captured cells, as well as detect potential movements of immobilized single yeast cells during the overall recording duration by using principal component analysis (PCA) in data mining. The multifrequency EIS measurements can, therefore, obtain sufficient information of S. cerevisiae cells at single-cell level.

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

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

Identifying Cancer Targets Based on Machine Learning Methods via Chou's 5-steps Rule and General Pseudo Components

In recent years, the successful implementation of human genome project has made people realize that genetic, environmental and lifestyle factors should be combined together to study cancer due to the complexity and various forms of the disease. The increasing availability and growth rate of 'big data' derived from various omics, opens a new window for study and therapy of cancer. In this paper, we will introduce the application of machine learning methods in handling cancer big data including the use of artificial neural networks, support vector machines, ensemble learning and naïve Bayes classifiers.

Use of an Insulation Layer on the Connection Tracks of a Biosensor with Coplanar Electrodes to Increase the Normalized Impedance Variation

New technologies, such as biosensors and lab-on-a-chip, are reducing time consumption and costs for the detection and characterization of biological cells. One challenge is to detect and characterize cells and bacteria one by one or at a very low concentration. In this case, measurements have very low variations that can be difficult to detect. In this article, the use of an insulation layer on the connection tracks of a biosensor with coplanar electrodes is proposed to improve a biosensor previously developed. The impedance spectroscopy technique was used to analyze the influence of the insulation layer on the cutoff frequencies and on the normalized impedance variation. This solution does not induce changes in the cutoff frequencies, though it permits improving the normalized impedance variations, compared to the same biosensor without the insulation layer.

Influence of Electrode Connection Tracks on Biological Cell Measurements by Impedance Spectroscopy

The limit of detection of a biological sensor is an important parameter because, when it is optimized, it allows the detection of a reduced number of biological cells and the reduction of the detection time. This parameter can be improved upon with a reduction in electrode size, but the rate of detection is similarly reduced as well. To avoid this problem, we propose a sensor matrix composed of 20 × 20 µm² coplanar square electrodes with a standard clean room manufacturing process. However, it was observed that the exposition of electrode connection tracks to the solution reduces the normalized impedance variation. In this pursuit, we propose in this paper an analysis of electrode connection tracks on the normalized impedance variation and cutoff frequencies to biological cell measurements by impedance spectroscopy. The experimental results were obtained using the E4990A Keysight impedance analyser (Keysight Technologies, Santa Rosa, CA, USA) with a frequency band ranging from 100 Hz to 12 MHz, thus allowing for good measurement accuracy. Therefore, it was found that, for the measurements between the electrodes with 9 µm of connection tracks in contact with the solution, the normalized impedance variation was from 3.7% to 4.2% for different measurements, while, for the electrodes with 40 µm of connection tracks in contact with the solution, the normalized impedance variation was from 1.8% to 2.1% for different measurements.

Crossing constriction channel-based microfluidic cytometry capable of electrically phenotyping large populations of single cells

This paper presents a crossing constriction channel-based microfluidic system for high-throughput characterization of specific membrane capacitance (Csm) and cytoplasm conductivity (σcy) of single cells. In operations, cells in suspension were forced through the major constriction channel and instead of invading the side constriction channel, they effectively sealed the side constriction channel, which led to variations in impedance data. Based on an equivalent circuit model, these raw impedance data were translated into Csm and σcy. As a demonstration, the developed microfluidic system quantified Csm (3.01 ± 0.92 μF cm-2) and σcy (0.36 ± 0.08 S m-1) of 100 000 A549 cells, which could generate reliable results by properly controlling cell positions during their traveling in the crossing constriction channels. Furthermore, the developed microfluidic impedance cytometry was used to distinguish paired low- and high-metastatic carcinoma cell types of SACC-83 (ncell = ∼100 000) and SACC-LM cells (ncell = ∼100 000), distinguishing significant differences in both Csm (3.16 ± 0.90 vs. 2.79 ± 0.67 μF cm-2) and σcy (0.36 ± 0.06 vs.0.41 ± 0.08 S m-1). As high-throughput microfluidic impedance cytometry, this technique may add a new marker-free dimension to flow cytometry in single-cell analysis.

Biophysical phenotyping of single cells using a differential multiconstriction microfluidic device with self-aligned 3D electrodes

Precise measurement of mechanical and electrical properties of single cells can yield useful information on the physiological and pathological state of cells. In this work, we develop a differential multiconstriction microfluidic device with self-aligned 3D electrodes to simultaneously characterize the deformability, electrical impedance and relaxation index of single cells at a high throughput manner (>430 cell/min). Cells are pressure-driven to flow through a series of sequential microfluidic constrictions, during which deformability, electrical impedance and relaxation index of single cells are extracted simultaneously from impedance spectroscopy measurements. Mechanical and electrical phenotyping of untreated, Cytochalasin B treated and N-Ethylmaleimide treated MCF-7 breast cancer cells demonstrate the ability of our system to distinguish different cell populations purely based on these biophysical properties. In addition, we quantify the classification of different cell types using a back propagation neural network. The trained neural network yields the classification accuracy of 87.8% (electrical impedance), 70.1% (deformability), 42.7% (relaxation index) and 93.3% (combination of electrical impedance, deformability and relaxation index) with high sensitivity (93.3%) and specificity (93.3%) for the test group. Furthermore, we have demonstrated the cell classification of a cell mixture using the presented biophysical phenotyping technique with the trained neural network, which is in quantitative agreement with the flow cytometric analysis using fluorescent labels. The developed concurrent electrical and mechanical phenotyping provide great potential for high-throughput and label-free single cell analysis.

Developments in label-free microfluidic methods for single-cell analysis and sorting

Advancements in microfluidic technologies have led to the development of many new tools for both the characterization and sorting of single cells without the need for exogenous labels. Label-free microfluidics reduce the preparation time, reagents needed, and cost of conventional methods based on fluorescent or magnetic labels. Furthermore, these devices enable analysis of cell properties such as mechanical phenotype and dielectric parameters that cannot be characterized with traditional labels. Some of the most promising technologies for current and future development toward label-free, single-cell analysis and sorting include electronic sensors such as Coulter counters and electrical impedance cytometry; deformation analysis using optical traps and deformation cytometry; hydrodynamic sorting such as deterministic lateral displacement, inertial focusing, and microvortex trapping; and acoustic sorting using traveling or standing surface acoustic waves. These label-free microfluidic methods have been used to screen, sort, and analyze cells for a wide range of biomedical and clinical applications, including cell cycle monitoring, rapid complete blood counts, cancer diagnosis, metastatic progression monitoring, HIV and parasite detection, circulating tumor cell isolation, and point-of-care diagnostics. Because of the versatility of label-free methods for characterization and sorting, the low-cost nature of microfluidics, and the rapid prototyping capabilities of modern microfabrication, we expect this class of technology to continue to be an area of high research interest going forward. New developments in this field will contribute to the ongoing paradigm shift in cell analysis and sorting technologies toward label-free microfluidic devices, enabling new capabilities in biomedical research tools as well as clinical diagnostics. This article is categorized under: Diagnostic Tools > Biosensing Diagnostic Tools > Diagnostic Nanodevices.

Microfluidic dielectrophoresis illuminates the relationship between microbial cell envelope polarizability and electrochemical activity

Electrons can be transported from microbes to external insoluble electron acceptors (e.g., metal oxides or electrodes in an electrochemical cell). This process is known as extracellular electron transfer (EET) and has received considerable attention due to its applications in environmental remediation and energy conversion. However, the paucity of rapid and noninvasive phenotyping techniques hinders a detailed understanding of microbial EET mechanisms. Most EET phenotyping techniques assess microorganisms based on their metabolism and growth in various conditions and/or performance in electrochemical systems, which requires large sample volumes and cumbersome experimentation. Here, we use microfluidic dielectrophoresis to show a strong correlation between bacterial EET and surface polarizability. We analyzed surface polarizabilities for wild-type strains and cytochrome-deletion mutants of two model EET microbes, Geobacter sulfurreducens and Shewanella oneidensis, and for Escherichia coli strains heterologously expressing S. oneidensis EET pathways in various growth conditions. Dielectrophoretic phenotyping is achieved with small cell culture volumes (~100 μl) in a short amount of time (1 to 2 min per strain). Our work demonstrates that cell polarizability is diminished in response to deletions of crucial outer-membrane cytochromes and enhanced due to additions of EET pathways. Results of this work hold exciting promise for rapid screening of direct EET or other cell envelope phenotypes using cell polarizability as a proxy, especially for microbes difficult to cultivate in laboratory conditions.

Membrane capacitance of thousands of single white blood cells

As label-free biomarkers, the electrical properties of single cells are widely used for cell type classification and cellular status evaluation. However, as intrinsic cellular electrical markers, previously reported membrane capacitances (e.g. specific membrane capacitance C_spec and total membrane capacitance C_mem) of white blood cells were derived from tens of single cells, lacking statistical significance due to low cell numbers. In this study, white blood cells were first separated into granulocytes and lymphocytes by density gradient centrifugation and were then aspirated through a microfluidic constriction channel to characterize both C_spec and C_mem Thousands of granulocytes (n_cell = 3327) and lymphocytes (n_cell = 3302) from 10 healthy blood donors were characterized, resulting in C_spec values of 1.95 ± 0.22 µF cm^-2 versus 2.39 ± 0.39 µF cm^-2 and C_mem values of 6.81 ± 1.09 pF versus 4.63 ± 0.57 pF. Statistically significant differences between granulocytes and lymphocytes were located for both C_spec and C_mem In addition, neural network-based pattern recognition was used to classify white blood cells, producing successful classification rates of 78.1% for C_spec and 91.3% for C_mem, respectively. These results indicate that as intrinsic bioelectrical markers, membrane capacitances may contribute to the classification of white blood cells.

Mechanical property characterization of hundreds of single nuclei based on microfluidic constriction channel

As label-free biomarkers, the mechanical properties of nuclei are widely treated as promising biomechanical markers for cell type classification and cellular status evaluation. However, previously reported mechanical parameters were derived from only around 10 nuclei, lacking statistical significances due to low sample numbers. To address this issue, nuclei were first isolated from SW620 and A549 cells, respectively, using a chemical treatment method. This was followed by aspirating them through two types of microfluidic constriction channels for mechanical property characterization. In this study, hundreds of nuclei were characterized, producing passage times of 0.5 ± 1.2 s for SW620 nuclei in type I constriction channel (n = 153), 0.045 ± 0.047 s for SW620 nuclei in type II constriction channel (n = 215) and 0.50 ± 0.86 s for A549 nuclei in type II constriction channel. In addition, neural network based pattern recognition was used to classify the nuclei isolated from SW620 and A549 cells, producing successful classification rates of 87.2% for diameters of nuclei, 85.5% for passage times of nuclei and 89.3% for both passage times and diameters of nuclei. These results indicate that the characterization of the mechanical properties of nuclei may contribute to the classification of different tumor cells.

Development of microfluidic impedance cytometry enabling the quantification of specific membrane capacitance and cytoplasm conductivity from 100,000 single cells

This paper presents a new microfluidic impedance cytometry with crossing constriction microchannels, enabling the characterization of cellular electrical markers (e.g., specific membrane capacitance (C_sm) and cytoplasm conductivity (σ_cy)) in large cell populations (~ 100,000 cells) at a rate greater than 100 cells/s. Single cells were aspirated continuously through the major constriction channel with a proper sealing of the side constriction channel. An equivalent circuit model was developed and the measured impedance values were translated to C_sm and σ_cy. Neural network was used to classify different cell populations where classification success rates were calculated. To evaluate the developed technique, different tumour cell lines, and the effects of epithelial-mesenchymal transitions on tumour cells were examined. Significant differences in both C_sm and σ_cy were found for H1299 and HeLa cell lines with a classification success rate of 90.9% in combination of the two parameters. Meanwhile, tumour cells A549 showed significant decreases in both C_sm and σ_cy after epithelial-mesenchymal transitions with a classification success rate of 76.5%. As a high-throughput microfluidic impedance cytometry, this technique can add a new marker-free dimension to flow cytometry in single-cell analysis.

A microfluidic flow cytometer enabling absolute quantification of single-cell intracellular proteins

Quantification of single-cell proteomics provides key insights into cellular heterogeneity while conventional flow cytometry cannot provide absolute quantification of intracellular proteins of single cells due to the lack of calibration approaches. This paper presents a constriction channel (with a cross sectional area smaller than cells) based microfluidic flow cytometer, capable of collecting copy numbers of specific intracellular proteins. In this platform, single cells stained with fluorescence labelled antibodies were forced to squeeze through the constriction channel with the fluorescence intensities quantified and since cells fully filled the constriction channel during the squeezing process, solutions with fluorescence labelled antibodies were flushed into the constriction channel to obtain calibration curves. By combining raw fluorescence data and calibration curves, absolute quantification of intracellular proteins was realized. As a demonstration, copy numbers of beta-actin of single tumour cells were quantified to be 0.90 ± 0.30 μM (A549, n_cell = 14 228), 2.34 ± 0.70 μM (MCF 10A, n_cell = 2455), and 0.98 ± 0.65 μM (Hep G2, n_cell = 6945). The travelling time for individual cells was quantified to be roughly 10 ms and thus a throughput of 100 cells per s can be achieved. This microfluidic system can be used to quantify the copy numbers of intracellular proteins in a high-throughput manner, which may function as an enabling technique in the field of single-cell proteomics.

Electrical Property Characterization of Neural Stem Cells in Differentiation

Electrical property characterization of stem cells could be utilized as a potential label-free biophysical approach to evaluate the differentiation process. However, there has been a lack of technology or tools that can quantify the intrinsic cellular electrical markers (e.g., specific membrane capacitance (Cspecific membrane) and cytoplasm conductivity (σcytoplasm)) for a large amount of stem cells or differentiated cells. In this paper, a microfluidic platform enabling the high-throughput quantification of Cspecific membrane and σcytoplasm from hundreds of single neural stem cells undergoing differentiation was developed to explore the feasibility to characterize the neural stem cell differentiation process without biochemical staining. Experimental quantification using biochemical markers (e.g., Nestin, Tubulin and GFAP) of neural stem cells confirmed the initiation of the differentiation process featured with gradual loss in cellular stemness and increased cell markers for neurons and glial cells. The recorded electrical properties of neural stem cells undergoing differentiation showed distinctive and unique patterns: 1) in the suspension culture before inducing differentiation, a large distribution and difference in σcytoplasm among individual neural stem cells was noticed, which indicated heterogeneity that may result from the nature of suspension culture of neurospheres; and 2) during the differentiation in adhering monolayer culture, significant changes and a large difference in Cspecific membrane were located indicating different expressions of membrane proteins during the differentiation process, and a small distribution difference in σcytoplasm was less significant that indicated the relatively consistent properties of cytoplasm during the culture. In summary, significant differences in Cspecific membrane and σcytoplasm were observed during the neural stem cell differentiation process, which may potentially be used as label-free biophysical markers to monitor this process.

Single-Cell Electrical Phenotyping Enabling the Classification of Mouse Tumor Samples

Single-cell electrical phenotyping (e.g., specific membrane capacitance (Cm) and cytoplasm conductivity (σp)) has long been regarded as potential label-free biophysical markers in tumor status evaluation. However, previous studies only reported the differentiation of tumor cell lines without classifying real tumor samples using cellular electrical properties. In this study, two types of mouse tumor models were constructed by injecting two types of tumor cell lines (A549 and H1299), respectively. Then tumor portions were retrieved for immunohistochemistry studies and single-cell electrical phenotyping based on home-developed microfluidic platforms. Immunohistochemistry results of tumor samples confirmed the adenocarcinoma and large-cell carcinoma characteristics for A549 and H1299 based tumor samples, respectively. Meanwhile, cellular Cm and σp were characterized as 2.25 ± 0.50 μF/cm(2) and 0.96 ± 0.20 S/m for A549 based tumor samples (ncell = 1336, Mouse I, II, III) and 1.76 ± 0.54 μF/cm(2) and 1.35 ± 0.28 S/m for H1299 based tumor samples (ncell = 1442, Mouse IV, V, VI). Significant differences in Cm and σp were observed between these two types of tumor samples, validating the feasibility of using Cm and σp for mouse tumor classification.

The impact of sphingosine kinase inhibitor-loaded nanoparticles on bioelectrical and biomechanical properties of cancer cells

Cancer progression and physiological changes within the cells are accompanied by alterations in the biophysical properties. Therefore, the cell biophysical properties can serve as promising markers for cancer detection and physiological activities. To aid in the investigation of the biophysical markers of cells, a microfluidic chip has been developed which consists of a constriction channel and embedded microelectrodes. Single-cell impedance magnitudes at four frequencies and entry and travel times are measured simultaneously during their transit through the constriction channel. This microchip provides a high-throughput, label-free, automated assay to identify biophysical signatures of malignant cells and monitor the therapeutic efficacy of drugs. Here, we monitored the dynamic cellular biophysical properties in response to sphingosine kinase inhibitors (SphKIs), and compared the effectiveness of drug delivery using poly lactic-co-glycolic acid (PLGA) nanoparticles (NPs) loaded with SphKIs versus conventional delivery. Cells treated with SphKIs showed significantly higher impedance magnitudes at all four frequencies. The bioelectrical parameters extracted using a model also revealed that the highly aggressive breast cells treated with SphKIs shifted electrically towards that of a less malignant phenotype; SphKI-treated cells exhibited an increase in cell-channel interface resistance and a significant decrease in specific membrane capacitance. Furthermore, SphKI-treated cells became slightly more deformable as measured by a decrease in their channel entry and travel times. We observed no significant difference in the bioelectrical changes produced by SphKI delivered conventionally or with NPs. However, NPs-packaged delivery of SphKI decreased the cell deformability. In summary, this study showed that while the bioelectrical properties of the cells were dominantly affected by SphKIs, the biomechanical properties were mainly changed by the NPs.

Single Cell Electrical Characterization Techniques

Electrical properties of living cells have been proven to play significant roles in understanding of various biological activities including disease progression both at the cellular and molecular levels. Since two decades ago, many researchers have developed tools to analyze the cell’s electrical states especially in single cell analysis (SCA). In depth analysis and more fully described activities of cell differentiation and cancer can only be accomplished with single cell analysis. This growing interest was supported by the emergence of various microfluidic techniques to fulfill high precisions screening, reduced equipment cost and low analysis time for characterization of the single cell’s electrical properties, as compared to classical bulky technique. This paper presents a historical review of single cell electrical properties analysis development from classical techniques to recent advances in microfluidic techniques. Technical details of the different microfluidic techniques are highlighted, and the advantages and limitations of various microfluidic devices are discussed.

Microfluidic impedance flow cytometry enabling high-throughput single-cell electrical property characterization

This article reviews recent developments in microfluidic impedance flow cytometry for high-throughput electrical property characterization of single cells. Four major perspectives of microfluidic impedance flow cytometry for single-cell characterization are included in this review: (1) early developments of microfluidic impedance flow cytometry for single-cell electrical property characterization; (2) microfluidic impedance flow cytometry with enhanced sensitivity; (3) microfluidic impedance and optical flow cytometry for single-cell analysis and (4) integrated point of care system based on microfluidic impedance flow cytometry. We examine the advantages and limitations of each technique and discuss future research opportunities from the perspectives of both technical innovation and clinical applications.

Malaria detection using inertial microfluidics

Diagnosis of malaria at the early stage of infection is challenging due to the difficulty in detecting low abundance parasites from blood. Molecular methods such as real-time polymerase chain reaction (qPCR) can be especially useful for detecting low parasitemia levels due to their high sensitivity and their ability to recognize different malarial species and strains. Unfortunately, the accuracy of qPCR-based malaria detection can be compromised by many factors, including the limited specificity of primers, presence of PCR inhibitors in blood serum and DNA contamination from nucleated blood cells. Here, we use a label-free, shear-modulated inertial microfluidic system to enrich malaria parasites from blood so as to facilitate a more reliable and specific PCR-based malaria detection. This technique capitalizes on cell focusing behaviors in high aspect ratio microchannels coupled with pinched flow dynamics to isolate ring-stage malaria parasites from lysed blood containing white blood cells (WBCs). In this high aspect ratio (ratio of the channel height to the width) platform, the high shear rate along the channel width causes the dispersed WBCs at the inlet to migrate and align into two streams near the channel sidewalls while the malaria parasites remain unfocused. Sensitive detection of parasites at spiked densities ranging from 10(3) to 10(4)Plasmodium falciparum parasites per mL (~2-10 per μL) has been demonstrated; they have also been quantified in whole blood using qPCR. This is approximately 100-fold more sensitive than the gold standard conventional microscopy analysis of thick blood smears. The simplicity of this device makes it ideal for integration with an automatic system for ultra-fast and accurate detection despite low levels of parasitemia. It can also help in malaria screening and elimination efforts.

Simultaneous characterization of instantaneous Young's modulus and specific membrane capacitance of single cells using a microfluidic system

This paper presents a microfluidics-based approach capable of continuously characterizing instantaneous Young's modulus (E(instantaneous)) and specific membrane capacitance (C(specific membrane)) of suspended single cells. In this method, cells were aspirated through a constriction channel while the cellular entry process into the constriction channel was recorded using a high speed camera and the impedance profiles at two frequencies (1 kHz and 100 kHz) were simultaneously measured by a lock-in amplifier. Numerical simulations were conducted to model cellular entry process into the constriction channel, focusing on two key parameters: instantaneous aspiration length (L(instantaneous)) and transitional aspiration length (L(transitional)), which was further translated to E(instantaneous). An equivalent distribution circuit model for a cell travelling in the constriction channel was used to determine C(specific membrane). A non-small-cell lung cancer cell line 95C (n = 354) was used to evaluate this technique, producing E(instantaneous) of 2.96 ± 0.40 kPa and Cspecific membrane of 1.59 ± 0.28 μF/cm2. As a platform for continuous and simultaneous characterization of cellular E(instantaneous) and C(specific membrane), this approach can facilitate a more comprehensive understanding of cellular biophysical properties.

Resonance-enhanced microfluidic impedance cytometer for detection of single bacteria

This paper reports on a novel impedance-based cytometer, which can detect and characterize sub-micrometer particles and cells passing through a microfluidic channel. The cytometer incorporates a resonator, which is constructed by means of a discrete inductor in series with the measurement electrodes in the microfluidic channel. The use of a resonator increases the sensitivity of the system in comparison to state-of-the-art devices. We demonstrate the functionality and sensitivity of the cytometer by discriminating E. coli and B. subtilis from beads of similar sizes by means of the resonance-enhanced phase shift of the current through the microfluidic channel. The phase shift can be correlated to size and dielectric properties of the measured objects.

Tumor cell characterization and classification based on cellular specific membrane capacitance and cytoplasm conductivity

This paper reports a microfluidic system that enables the characterization of tumor cell electrical properties where cells were aspirated through a constriction channel (cross-section area smaller than that of biological cells) with cellular impedance profiles measured and translated to specific membrane capacitance (Cspecific membrane) and cytoplasm conductivity (σcytoplasm). Two batches of H1299 cells were quantified by the microfluidic platform with different constriction channel cross-section areas, recording no differences with statistical significance (p<0.001) in both Cspecific membrane (1.63±0.52 vs. 1.65±0.43 μF/cm(2)) and σcytoplasm (0.90±0.19 vs. 0.92±0.15S/m), and thus confirming the reliability of the microfluidic platform. For paired high- and low-metastatic carcinoma strains 95D (ncell=537) and 95C cells (ncell=486), significant differences in both Cspecific membrane (2.00±0.43 vs. 1.62±0.39 μF/cm(2)) and σcytoplasm (0.88±0.46 vs. 1.25±0.35S/m) were observed. Statistically significant difference only in Cspecific membrane (2.00±0.43 vs. 1.58±0.30 μF/cm(2)) was observed for 95D cells (ncell=537) and 95D CCNY-KD cells with single oncogene CCNY down regulation (ncell=479, CCNY is a membrane-associated protein). In addition, statistically significant difference only in σcytoplasm (0.73±0.17 vs. 1.01±0.17S/m) was observed for A549 cells (ncell=487) and A549 CypA-KD cells with single oncogene CypA down regulation (ncell=597, CypA is a cytosolic protein). These results validated the developed microfluidic platform for Cspecific membrane and σcytoplasm quantification and confirmed the feasibility of using Cspecific membrane and σcytoplasm for tumor cell classification.

Biologically inspired intelligent decision making: a commentary on the use of artificial neural networks in bioinformatics

Artificial neural networks (ANNs) are a class of powerful machine learning models for classification and function approximation which have analogs in nature. An ANN learns to map stimuli to responses through repeated evaluation of exemplars of the mapping. This learning approach results in networks which are recognized for their noise tolerance and ability to generalize meaningful responses for novel stimuli. It is these properties of ANNs which make them appealing for applications to bioinformatics problems where interpretation of data may not always be obvious, and where the domain knowledge required for deductive techniques is incomplete or can cause a combinatorial explosion of rules. In this paper, we provide an introduction to artificial neural network theory and review some interesting recent applications to bioinformatics problems.