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

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.

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.

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.

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.

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.

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.

Recent Approaches to Design and Analysis of Electrical Impedance Systems for Single Cells Using Machine Learning

Individual cells have many unique properties that can be quantified to develop a holistic understanding of a population. This can include understanding population characteristics, identifying subpopulations, or elucidating outlier characteristics that may be indicators of disease. Electrical impedance measurements are rapid and label-free for the monitoring of single cells and generate large datasets of many cells at single or multiple frequencies. To increase the accuracy and sensitivity of measurements and define the relationships between impedance and biological features, many electrical measurement systems have incorporated machine learning (ML) paradigms for control and analysis. Considering the difficulty capturing complex relationships using traditional modelling and statistical methods due to population heterogeneity, ML offers an exciting approach to the systemic collection and analysis of electrical properties in a data-driven way. In this work, we discuss incorporation of ML to improve the field of electrical single cell analysis by addressing the design challenges to manipulate single cells and sophisticated analysis of electrical properties that distinguish cellular changes. Looking forward, we emphasize the opportunity to build on integrated systems to address common challenges in data quality and generalizability to save time and resources at every step in electrical measurement of single cells.

piRT-IFC: Physics-informed real-time impedance flow cytometry for the characterization of cellular intrinsic electrical properties

Real-time transformation was important for the practical implementation of impedance flow cytometry. The major obstacle was the time-consuming step of translating raw data to cellular intrinsic electrical properties (e.g., specific membrane capacitance Csm and cytoplasm conductivity σcyto). Although optimization strategies such as neural network-aided strategies were recently reported to provide an impressive boost to the translation process, simultaneously achieving high speed, accuracy, and generalization capability is still challenging. To this end, we proposed a fast parallel physical fitting solver that could characterize single cells' Csm and σcyto within 0.62 ms/cell without any data preacquisition or pretraining requirements. We achieved the 27000-fold acceleration without loss of accuracy compared with the traditional solver. Based on the solver, we implemented physics-informed real-time impedance flow cytometry (piRT-IFC), which was able to characterize up to 100,902 cells' Csm and σcyto within 50 min in a real-time manner. Compared to the fully connected neural network (FCNN) predictor, the proposed real-time solver showed comparable processing speed but higher accuracy. Furthermore, we used a neutrophil degranulation cell model to represent tasks to test unfamiliar samples without data for pretraining. After being treated with cytochalasin B and N-Formyl-Met-Leu-Phe, HL-60 cells underwent dynamic degranulation processes, and we characterized cell's Csm and σcyto using piRT-IFC. Compared to the results from our solver, accuracy loss was observed in the results predicted by the FCNN, revealing the advantages of high speed, accuracy, and generalizability of the proposed piRT-IFC.

Altered physical phenotypes of leukemia cells that survive chemotherapy treatment

The recurrence of cancer following chemotherapy treatment is a major cause of death across solid and hematologic cancers. In B-cell acute lymphoblastic leukemia (B-ALL), relapse after initial chemotherapy treatment leads to poor patient outcomes. Here we test the hypothesis that chemotherapy-treated versus control B-ALL cells can be characterized based on cellular physical phenotypes. To quantify physical phenotypes of chemotherapy-treated leukemia cells, we use cells derived from B-ALL patients that are treated for 7 days with a standard multidrug chemotherapy regimen of vincristine, dexamethasone, and L-asparaginase (VDL). We conduct physical phenotyping of VDL-treated versus control cells by tracking the sequential deformations of single cells as they flow through a series of micron-scale constrictions in a microfluidic device; we call this method Quantitative Cyclical Deformability Cytometry. Using automated image analysis, we extract time-dependent features of deforming cells including cell size and transit time (TT) with single-cell resolution. Our findings show that VDL-treated B-ALL cells have faster TTs and transit velocity than control cells, indicating that VDL-treated cells are more deformable. We then test how effectively physical phenotypes can predict the presence of VDL-treated cells in mixed populations of VDL-treated and control cells using machine learning approaches. We find that TT measurements across a series of sequential constrictions can enhance the classification accuracy of VDL-treated cells in mixed populations using a variety of classifiers. Our findings suggest the predictive power of cell physical phenotyping as a complementary prognostic tool to detect the presence of cells that survive chemotherapy treatment. Ultimately such complementary physical phenotyping approaches could guide treatment strategies and therapeutic interventions. Insight box Cancer cells that survive chemotherapy treatment are major contributors to patient relapse, but the ability to predict recurrence remains a challenge. Here we investigate the physical properties of leukemia cells that survive treatment with chemotherapy drugs by deforming individual cells through a series of micron-scale constrictions in a microfluidic channel. Our findings reveal that leukemia cells that survive chemotherapy treatment are more deformable than control cells. We further show that machine learning algorithms applied to physical phenotyping data can predict the presence of cells that survive chemotherapy treatment in a mixed population. Such an integrated approach using physical phenotyping and machine learning could be valuable to guide patient treatments.

Microsystem Advances through Integration with Artificial Intelligence

Microfluidics is a rapidly growing discipline that involves studying and manipulating fluids at reduced length scale and volume, typically on the scale of micro- or nanoliters. Under the reduced length scale and larger surface-to-volume ratio, advantages of low reagent consumption, faster reaction kinetics, and more compact systems are evident in microfluidics. However, miniaturization of microfluidic chips and systems introduces challenges of stricter tolerances in designing and controlling them for interdisciplinary applications. Recent advances in artificial intelligence (AI) have brought innovation to microfluidics from design, simulation, automation, and optimization to bioanalysis and data analytics. In microfluidics, the Navier-Stokes equations, which are partial differential equations describing viscous fluid motion that in complete form are known to not have a general analytical solution, can be simplified and have fair performance through numerical approximation due to low inertia and laminar flow. Approximation using neural networks trained by rules of physical knowledge introduces a new possibility to predict the physicochemical nature. The combination of microfluidics and automation can produce large amounts of data, where features and patterns that are difficult to discern by a human can be extracted by machine learning. Therefore, integration with AI introduces the potential to revolutionize the microfluidic workflow by enabling the precision control and automation of data analysis. Deployment of smart microfluidics may be tremendously beneficial in various applications in the future, including high-throughput drug discovery, rapid point-of-care-testing (POCT), and personalized medicine. In this review, we summarize key microfluidic advances integrated with AI and discuss the outlook and possibilities of combining AI and microfluidics.

Selectable encapsulated cell quantity in droplets via label-free electrical screening and impedance-activated sorting

Single-cell encapsulation in droplets has become a powerful tool in immunotherapy, medicine discovery, and single-cell analysis, thanks to its capability for cell confinement in picoliter volumes. However, the purity and throughput of single-cell droplets are limited by random encapsulation process, which resuts in a majority of empty and multi-cells droplets. Herein we introduce the first label-free selectable cell quantity encapsulation in droplets sorting system to overcome this problem. The system utilizes a simple and reliable electrical impedance based screening (98.9% of accuracy) integrated with biocompatible acoustic sorting to select single-cell droplets, achieving 90.3% of efficiency and up to 200 ​Hz of throughput, by removing multi-cells (∼60% of rejection) and empty droplets (∼90% of rejection). We demonstrate the use of the droplet sorting to improve the throughput of single-cell encapsulation by ∼9-fold compared to the conventional random encapsulation process.

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.

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.

A Systematic Study of Size Correlation and Young's Modulus Sensitivity for Cellular Mechanical Phenotyping by Microfluidic Approaches

Cellular mechanical properties are a class of intrinsic biophysical markers for cell state and health. Microfluidic mechanical phenotyping methods have emerged as promising tools to overcome the challenges of low throughput and high demand for manual skills in conventional approaches. In this work, two types of microfluidic cellular mechanical phenotyping methods, contactless hydro-stretching deformability cytometry (lh-DC) and contact constriction deformability cytometry (cc-DC) are comprehensively studied and compared. Polymerized hydrogel beads with defined sizes are used to characterize a strong negative correlation between size and deformability in cc-DC (r = -0.95), while lh-DC presents a weak positive correlation (r = 0.13). Young's modulus sensitivity in cc-DC is size-dependent while it is a constant in lh-DC. Moreover, the deformability assessment for human breast cell line mixture suggests the lh-DC exhibits better differentiation capability of cells with different size distributions, while cc-DC provides higher sensitivity to identify cellular mechanical changes within a single cell line. This work is the first to present a quantitative study and comparison of size correlation and Young's modulus sensitivity of contactless and contact microfluidic mechanical phenotyping methods, which provides guidance to choose the most suitable cellular mechanical phenotyping platform for specific cell analysis applications.

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.

Impedance-Enabled Camera-Free Intrinsic Mechanical Cytometry

Mechanical properties of single cells are important label-free biomarkers normally measured by expensive and complex imaging systems. To unlock this limit and allow mechanical properties comparable across different measurement platforms, camera-free intrinsic mechanical cytometry (CFIMC) is proposed for on-the-fly measurement of two major intrinsic mechanical parameters, that is, Young's modulus E and fluidity β, of single cells. CFIMC adopts a framework that couples the impedance electrodes with the constriction channel spatially, so that the impedance signals contain the dynamic deformability information of the cell squeezing through the constriction channel. Deformation of the cell is thus extracted from the impedance signals and used to derive the intrinsic mechanical parameters. With reasonably high throughput (>500 cells min^-1 ), CFIMC can successfully reveal the mechanical difference in cancer and normal cells (i.e., human breast cell lines MCF-10A, MCF-7, and MDA-MB-231), living and fixed cells, and pharmacological perturbations of the cytoskeleton. It is further found that 1 µM level concentration of Cytochalasin B may be the threshold for the treated cells to induce a significant cytoskeleton effect reflected by the mechanical parameters. It is envisioned that CFIMC provides an alternative avenue for high-throughput and real-time single-cell intrinsic mechanical analysis.

Deciphering impedance cytometry signals with neural networks

Microfluidic impedance cytometry is a label-free technique for high-throughput single-cell analysis. Multi-frequency impedance measurements provide data that allows full characterisation of cells, linking electrical phenotype to individual biophysical properties. To efficiently extract the information embedded in the electrical signals, potentially in real-time, tailored signal processing is needed. Artificial intelligence approaches provide a promising new direction. Here we demonstrate the ability of neural networks to decipher impedance cytometry signals in two challenging scenarios: (i) to determine the intrinsic dielectric properties of single cells directly from raw impedance data streams, (ii) to capture single-cell signals that are hidden in the measured signals of coincident cells. The accuracy of the results and the high processing speed (fractions of ms per cell) demonstrate that neural networks can have an important role in impedance-based single-cell analysis.

Microfluidic Impedance-Deformability Cytometry for Label-Free Single Neutrophil Mechanophenotyping

The intrinsic biophysical states of neutrophils are associated with immune dysfunctions in diseases. While advanced image-based biophysical flow cytometers can probe cell deformability at high throughput, it is nontrivial to couple different sensing modalities (e.g., electrical) to measure other critical cell attributes including cell viability and membrane integrity. Herein, an "optics-free" impedance-deformability cytometer for multiparametric single cell mechanophenotyping is reported. The microfluidic platform integrates hydrodynamic cell pinching, and multifrequency impedance quantification of cell size, deformability, and membrane impedance (indicative of cell viability and activation). A newly-defined "electrical deformability index" is validated by numerical simulations, and shows strong correlations with the optical cell deformability index of HL-60 experimentally. Human neutrophils treated with various biochemical stimul are further profiled, and distinct differences in multimodal impedance signatures and UMAP analysis are observed. Overall, the integrated cytometer enables label-free cell profiling at throughput of >1000 cells min^-1 without any antibodies labeling to facilitate clinical diagnostics.

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

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

Advance of microfluidic constriction channel system of measuring single-cell cortical tension/specific capacitance of membrane and conductivity of cytoplasm

This paper reported a microfluidic platform which realized the characterization of inherent single-cell biomechanical and bioelectrical parameters simultaneously. Individual cells traveled through a constriction channel with deformation images and impedance variations captured and processed into cortical tension T_c , specific membrane capacitance C_sm , and cytoplasmic conductivity σ_cy based on an equivalent biophysical model. These properties of thousands of individual cells of K562, Jurkat, HL-60, HL-60 treated with paraformaldehyde (PA)/cytochalasin D (CD)/concanavalin A (ConA), granulocytes of Donor 1, Donor 2, and Donor 3 were quantified for the first time. Leveraging T_c , C_sm , and σ_cy , (1) high accuracies of classifying wild-type and processed HL-60 cells (e.g., 93.5% of PA treated vs. CD treated HL-60 cells) were realized, revealing the effectiveness of using these three biophysical parameters in cell-type classification; (2) low accuracies of classifying normal granulocytes from three donors (e.g., 56.4% of Donor 1 vs. 2), indicating comparable parameters for normal granulocytes. In conclusion, this platform can characterize single-cell T_c , C_sm , and σ_cy concurrently and quantify multiple parameters in single-cell analysis.

Systematic Review: Microfluidics and Plasmodium

Malaria affects 228 million people worldwide each year, causing severe disease and worsening the conditions of already vulnerable populations. In this review, we explore how malaria has been detected in the past and how it can be detected in the future. Our primary focus is on finding new directions for low-cost diagnostic methods that unspecialized personnel can apply in situ. Through this review, we show that microfluidic devices can help pre-concentrate samples of blood infected with malaria to facilitate the diagnosis. Importantly, these devices can be made cheaply and be readily deployed in remote locations.

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

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

Automated Cell Mechanical Characterization by On-Chip Sequential Squeezing: From Static to Dynamic

The mechanical properties of cells are harmless biomarkers for cell identification and disease diagnosis. Although many systems have been developed to evaluate the static mechanical properties of cells for biomedical research, their robustness, effectiveness, and cost do not meet clinical requirements or the experiments with a large number of cell samples. In this paper, we propose an approach for on-chip cell mechanical characterization by analyzing the dynamic behavior of cells as they pass through multiple constrictions. The proposed serpentine microfluidic channel consisted of 20 constrictions connected in series and divided into five rows for tracking cell dynamic behavior. Assisted by computer vision, the squeezing time of each cell through five rows of constrictions was automatically collected and filtered to evaluate the cell's mechanical deformability. We observed a decreasing passage time and increasing dynamic deformability of the cells as they passed through the multiple constrictions. The deformability increase rate of the HeLa cells was eight times greater than that of MEF cells. Moreover, the weak correlation between the deformability increase rate and the cell size indicated that cell recognition based on measuring the deformability increase rate could hardly be affected by the cell size variation. These findings showed that the deformability increase rate of the cell under on-chip sequential squeezing as a new index has great potential in cancer cell recognition.

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

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

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

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

Non-invasive acquisition of mechanical properties of cells via passive microfluidic mechanisms: A review

The demand to understand the mechanical properties of cells from biomedical, bioengineering, and clinical diagnostic fields has given rise to a variety of research studies. In this context, how to use lab-on-a-chip devices to achieve accurate, high-throughput, and non-invasive acquisition of the mechanical properties of cells has become the focus of many studies. Accordingly, we present a comprehensive review of the development of the measurement of mechanical properties of cells using passive microfluidic mechanisms, including constriction channel-based, fluid-induced, and micropipette aspiration-based mechanisms. This review discusses how these mechanisms work to determine the mechanical properties of the cell as well as their advantages and disadvantages. A detailed discussion is also presented on a series of typical applications of these three mechanisms to measure the mechanical properties of cells. At the end of this article, the current challenges and future prospects of these mechanisms are demonstrated, which will help guide researchers who are interested to get into this area of research. Our conclusion is that these passive microfluidic mechanisms will offer more preferences for the development of lab-on-a-chip technologies and hold great potential for advancing biomedical and bioengineering research studies.

Direct and Label-Free Cell Status Monitoring of Spheroids and Microcarriers Using Microfluidic Impedance Cytometry

3D cellular spheroids/microcarriers (100 µm-1 mm) are widely used in biomanufacturing, and non-invasive biosensors are useful to monitor cell quality in bioprocesses. In this work, a novel microfluidic approach for label-free and continuous-flow monitoring of single spheroid/microcarrier (hydrogel and Cytodex) based on electrical impedance spectroscopy using co-planar Field's metal electrodes is reported. Through numerical simulation and experimental validation, two unique impedance signatures (|Z_LF | (60 kHz), |Z_HF | (1 MHz)) which are optimal for spheroid growth and viability monitoring are identified. Using a closed-loop recirculation system, it is demonstrated that |Z_LF | increases with breast cancer (MCF-7) spheroid biomass, while higher opacity (impedance ratio |Z_HF |/|Z_LF |) indicates cell death due to compromised cell membrane. Anti-cancer drug (paclitaxel)-treated spheroids also exhibit lower |Z_LF | with increased cell dissociation. Interestingly, impedance characterization of adipose-derived mesenchymal stem cell differentiation on Cytodex microcarriers reveals that adipogenic cells (higher intracellular lipid content) exhibit higher impedance than osteogenic cells (more conductive due to calcium ions) for both microcarriers and single cell level. Taken together, the developed platform offers great versatility for multi-parametric analysis of spheroids/microcarriers at high throughput (≈1 particle/s), and can be readily integrated into bioreactors for long-term and remote monitoring of biomass and cell quality.

Self-aligned sequential lateral field non-uniformities over channel depth for high throughput dielectrophoretic cell deflection

Dielectrophoresis (DEP) enables the separation of cells based on subtle subcellular phenotypic differences by controlling the frequency of the applied field. However, current electrode-based geometries extend over a limited depth of the sample channel, thereby reducing the throughput of the manipulated sample (sub-μL min-1 flow rates and <105 cells per mL). We present a flow through device with self-aligned sequential field non-uniformities extending laterally across the sample channel width (100 μm) that are created by metal patterned over the entire depth (50 μm) of the sample channel sidewall using a single lithography step. This enables single-cell streamlines to undergo progressive DEP deflection with minimal dependence on the cell starting position, its orientation versus the field and intercellular interactions. Phenotype-specific cell separation is validated (>μL min-1 flow and >106 cells per mL) using heterogeneous samples of healthy and glutaraldehyde-fixed red blood cells, with single-cell impedance cytometry showing that the DEP collected fractions are intact and exhibit electrical opacity differences consistent with their capacitance-based DEP crossover frequency. This geometry can address the vision of an "all electric" selective cell isolation and cytometry system for quantifying phenotypic heterogeneity of cellular systems.

Label-Free Multivariate Biophysical Phenotyping-Activated Acoustic Sorting at the Single-Cell Level

Biophysical markers of cells such as cellular electrical and mechanical properties have been proven as promising label-free biomarkers for studying, characterizing, and classifying different cell types and even their subpopulations. Further analysis or manipulation of specific cell types or subtypes requires accurate isolation of them from the original heterogeneous samples. However, there is currently a lack of cell sorting ability that could actively separate a large number of individual cells at the single-cell level based on their multivariate biophysical makers or phenotypes. In this work, we, for the first time, demonstrate label-free and high-throughput acoustic single-cell sorting activated by the characterization of multivariate biophysical phenotypes. Electrical phenotyping is implemented by single-cell electrical impedance characterization with two pairs of differential sensing electrodes, while mechanical phenotyping is performed by extracting the transit time for the single cell to pass through microconstriction from the recorded impedance signals. A real-time impedance signal processing and triggering algorithm has been developed to identify the target sample population and activate a pulsed highly focused surface acoustic wave for single-cell level sorting. We have demonstrated acoustic single-particle sorting solely based on electrical or mechanical phenotyping. Furthermore, we have applied the developed microfluidic system to sort live MCF-7 cells from a mixture of fixed and live MCF-7 population activated by a combined electrical and mechanical phenotyping at a high throughput >100 cells/s and purity ∼91.8%. This demonstrated ability to analyze and sort cells based on multivariate biophysical phenotyping provides a solution to the current challenges of cell purification that lack specific molecular biomarkers.

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

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

Comparative Study of Prostate Cancer Biophysical and Migratory Characteristics via Iterative Mechanoelectrical Properties (iMEP) and Standard Migration Assays

This study reveals a new microfluidic biosensor consisting of a multi-constriction microfluidic device with embedded electrodes for measuring the biophysical attributes of single cells. The biosensing platform called the iterative mechano-electrical properties (iMEP) analyzer captures electronic records of biomechanical and bioelectrical properties of cells. The iMEP assay is used in conjunction with standard migration assays, such as chemotaxis-based Boyden chamber and scratch wound healing assays, to evaluate the migratory behavior and biophysical properties of prostate cancer cells. The three cell lines evaluated in the study each represent a stage in the standard progression of prostate cancer, while the fourth cell line serves as a normal/healthy counterpart. Neither the scratch assay nor the chemotaxis assay could fully differentiate the four cell lines. Furthermore, there was not a direct correlation between wound healing rate or the migratory rate with the cells' metastatic potential. However, the iMEP assay, through its multiparametric dataset, could distinguish between all four cell line populations with p-value < 0.05. Further studies are needed to determine if iMEP signatures can be used for a wider range of human cells to assess the tumorigenicity of a cell population or the metastatic potential of cancer cells.

Microfluidic impedance cytometry device with N-shaped electrodes for lateral position measurement of single cells/particles

Tracking the lateral position of single cells and particles plays an important role in evaluating the efficiency of microfluidic cell focusing, separation and sorting. In this work, we present an N-shaped electrode-based microfluidic impedance cytometry device for the measurement of the lateral position of single cells and particles in continuous flows. Specifically, a simple analytical expression for determining the particle lateral position is derived from the measured electrical signal and geometry relationship among the positions of the flowing particles, electrodes and microchannel. This microfluidic system is experimentally validated by measuring the lateral positions of 5, 7 and 10 μm diameter beads and human red blood cells (RBCs) flowing in a 200 μm wide channel at varying flow rates up to 59.3 μl min^-1. Statistical analyses show a good correlation (R² = 0.99) and agreement (Bland-Altman analysis) between our results and those obtained by a microscopy imaging method. The resolution of our system reflected by the root-mean-square deviation (RMSD) is 10.3 μm (5.15% of the channel width) for 5 and 10 μm beads, and 11.4 μm (5.7% of the channel width) for RBCs at a flow rate of 42.4 μl min^-1. Compared to the existing impedance-based methods for measuring the particle lateral position, we achieve the highest resolution, highest flow rate and smallest measured particle size (3.6 μm beads). The experimental results of the mixture with 5 and 10 μm beads demonstrate that our device does not merely measure the lateral position of single particles or cells, but also can characterize their physical properties (e.g., size) simultaneously. Furthermore, we demonstrate the position monitoring of sheath flow-induced particle focusing, which is in quantitative agreement with the results by imaging quantification. With the advantages of rapid and accurate processing of electrical signal and high throughput of the impedance flow cytometry, this novel N-shaped electrode-based system can be easily integrated with other microfluidic platforms as a downstream approach for the real-time measurement of the lateral position and physical properties of single cells and particles.