On-chip label-free determination of cell survival rate

Integrating impedance cytometry with other microfluidic tools towards multifunctional single-cell analysis platforms

Microfluidic impedance cytometry (MIC) is a label-free technique that characterizes individual flowing particles/cells based on their interaction with a multifrequency electric field. The technique has been successfully applied in different scenarios including life-science research, diagnostics, and environmental monitoring. The aim of this review is to illustrate the fascinating opportunities enabled by the integration of MIC with other microfluidic tools. Specifically, we identify five categories according to their synergistic advantage: (i) improving the multiparametric characterization capability, (ii) enabling on-chip sample preparation steps, (iii) stimulating the sample, (iv) sample carrying/confinement, and (v) impedance-activated sample sorting. We discuss examples from each category, highlighting integration challenges and promising perspectives for next-generation multifunctional systems.

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.

On-Chip Label-Free Sorting of Living and Dead Cells

With the emergence of various cutting-edge micromachining technologies, lab on a chip is growing rapidly, but it is always a challenge to realize the on-chip separation of living cells from cell samples without affecting cell activity and function. Herein, we report a novel on-chip label-free method for sorting living and dead cells by integrating the hypertonic stimulus and tilted-angle standing surface acoustic wave (T-SSAW) technologies. On a self-designed microfluidic chip, the hypertonic stimulus is used to distinguish cells by producing volume differences between living and dead cells, while T-SSAW is used to separate living and dead cells according to the cell volume difference. Under the optimized operation conditions, for the sample containing 50% of living human umbilical vein endothelial cells (HUVECs) and 50% of dead HUVECs treated with paraformaldehyde, the purity of living cells after the first separation can reach approximately 80%, while after the second separation, it can be as high as 93%; furthermore, the purity of living cells after separation increases with the initial proportion of living cells. In addition, the chip we designed is safe for cells and can robustly handle cell samples with different cell types or different causes of cell death. This work provides a new design of a microfluidic chip for label-free sorting of living and dead cells, greatly promoting the multi-functionality of lab on a chip.

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

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

Microfluidic flow cytometry for blood-based biomarker analysis

Flow cytometry has proven its capability for rapid and quantitative analysis of individual cells and the separation of targeted biological samples from others. The emerging microfluidics technology makes it possible to develop portable microfluidic diagnostic devices for point-of-care testing (POCT) applications. Microfluidic flow cytometry (MFCM), where flow cytometry and microfluidics are combined to achieve similar or even superior functionalities on microfluidic chips, provides a powerful single-cell characterisation and sorting tool for various biological samples. In recent years, researchers have made great progress in the development of the MFCM including focusing, detecting, and sorting subsystems, and its unique capabilities have been demonstrated in various biological applications. Moreover, liquid biopsy using blood can provide various physiological and pathological information. Thus, biomarkers from blood are regarded as meaningful circulating transporters of signal molecules or particles and have great potential to be used as non (or minimally)-invasive diagnostic tools. In this review, we summarise the recent progress of the key subsystems for MFCM and its achievements in blood-based biomarker analysis. Finally, foresight is offered to highlight the research challenges faced by MFCM in expanding into blood-based POCT applications, potentially yielding commercialisation opportunities.

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.

Label-free classification of dead and live colonic adenocarcinoma cells based on 2D light scattering and deep learning analysis

The measurement of cell viability plays an essential role in the area of cell biology. At present, the common methods for cell viability assay mainly on the responses of cells to different dyes. However, the additional steps of cell staining will consequently cause time-consuming and laborious efforts. Furthermore, the process of cell staining is invasive and may cause internal structure damage of cells, restricting their reuse in subsequent experiments. In this work, we proposed a label-free method to classify live and dead colonic adenocarcinoma cells by 2D light scattering combined with the deep learning algorithm. The deep convolutional network of YOLO-v3 was used to identify and classify light scattering images of live and dead HT29 cells. This method achieved an excellent sensitivity (93.6%), specificity (94.4%), and accuracy (94%). The results showed that the combination of 2D light scattering images and deep neural network may provide a new label-free method for cellular analysis.

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.

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.