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Tang T, Julian T, Ma D, Yang Y, Li M, Hosokawa Y, Yalikun Y. A review on intelligent impedance cytometry systems: Development, applications and advances. Anal Chim Acta 2023; 1269:341424. [PMID: 37290859 DOI: 10.1016/j.aca.2023.341424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 05/22/2023] [Accepted: 05/23/2023] [Indexed: 06/10/2023]
Abstract
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.
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Affiliation(s)
- Tao Tang
- Division of Materials Science, Nara Institute of Science and Technology, 8916-5 Takayamacho, Ikoma, Nara, 630-0192, Japan; Department of Biomedical Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583, Singapore
| | - Trisna Julian
- Division of Materials Science, Nara Institute of Science and Technology, 8916-5 Takayamacho, Ikoma, Nara, 630-0192, Japan
| | - Doudou Ma
- Center for Biosystems Dynamics Research (BDR), RIKEN, 1-3 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Yang Yang
- Institute of Deep-Sea Science and Engineering, Chinese Academy of Sciences, Sanya, Hainan, 572000, PR China
| | - Ming Li
- School of Engineering, Macquarie University, Sydney, 2109, Australia
| | - Yoichiroh Hosokawa
- Division of Materials Science, Nara Institute of Science and Technology, 8916-5 Takayamacho, Ikoma, Nara, 630-0192, Japan
| | - Yaxiaer Yalikun
- Division of Materials Science, Nara Institute of Science and Technology, 8916-5 Takayamacho, Ikoma, Nara, 630-0192, Japan; Center for Biosystems Dynamics Research (BDR), RIKEN, 1-3 Yamadaoka, Suita, Osaka, 565-0871, Japan.
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Zhu S, Zhang X, Zhou Z, Han Y, Xiang N, Ni Z. Microfluidic impedance cytometry for single-cell sensing: Review on electrode configurations. Talanta 2021; 233:122571. [PMID: 34215067 DOI: 10.1016/j.talanta.2021.122571] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 05/25/2021] [Accepted: 05/27/2021] [Indexed: 10/21/2022]
Abstract
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.
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Affiliation(s)
- Shu Zhu
- School of Mechanical Engineering, And Jiangsu Key Laboratory for Design and Manufacture of Micro-Nano Biomedical Instruments, Southeast University, Nanjing, 211189, China
| | - Xiaozhe Zhang
- School of Mechanical Engineering, And Jiangsu Key Laboratory for Design and Manufacture of Micro-Nano Biomedical Instruments, Southeast University, Nanjing, 211189, China
| | - Zheng Zhou
- School of Mechanical Engineering, And Jiangsu Key Laboratory for Design and Manufacture of Micro-Nano Biomedical Instruments, Southeast University, Nanjing, 211189, China
| | - Yu Han
- School of Mechanical Engineering, And Jiangsu Key Laboratory for Design and Manufacture of Micro-Nano Biomedical Instruments, Southeast University, Nanjing, 211189, China
| | - Nan Xiang
- School of Mechanical Engineering, And Jiangsu Key Laboratory for Design and Manufacture of Micro-Nano Biomedical Instruments, Southeast University, Nanjing, 211189, China.
| | - Zhonghua Ni
- School of Mechanical Engineering, And Jiangsu Key Laboratory for Design and Manufacture of Micro-Nano Biomedical Instruments, Southeast University, Nanjing, 211189, China.
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Honrado C, Bisegna P, Swami NS, Caselli F. Single-cell microfluidic impedance cytometry: from raw signals to cell phenotypes using data analytics. LAB ON A CHIP 2021; 21:22-54. [PMID: 33331376 PMCID: PMC7909465 DOI: 10.1039/d0lc00840k] [Citation(s) in RCA: 79] [Impact Index Per Article: 26.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
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.
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Affiliation(s)
- Carlos Honrado
- Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, VA 22904, USA.
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Daguerre H, Solsona M, Cottet J, Gauthier M, Renaud P, Bolopion A. Positional dependence of particles and cells in microfluidic electrical impedance flow cytometry: origin, challenges and opportunities. LAB ON A CHIP 2020; 20:3665-3689. [PMID: 32914827 DOI: 10.1039/d0lc00616e] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
Microfluidic electrical impedance flow cytometry is now a well-known and established method for single-cell analysis. Given the richness of the information provided by impedance measurements, this non-invasive and label-free approach can be used in a wide field of applications ranging from simple cell counting to disease diagnostics. One of its major limitations is the variation of the impedance signal with the position of the cell in the sensing area. Indeed, identical particles traveling along different trajectories do not result in the same data. The positional dependence can be considered as a challenge for the accuracy of microfluidic impedance cytometers. On the other hand, it has recently been regarded by several groups as an opportunity to estimate the position of particles in the microchannel and thus take a further step in the logic of integrating sensors in so-called "Lab-on-a-chip" devices. This review provides a comprehensive overview of the physical grounds of the positional dependence of impedance measurements. Then, both the developed strategies to reduce position influence in impedance-based assays and the recent reported technologies exploiting that dependence for the integration of position detection in microfluidic devices are reviewed.
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Affiliation(s)
- Hugo Daguerre
- FEMTO-ST Institute, CNRS, Univ. Bourgogne Franche-Comté, AS2M Department, 24 rue Alain Savary, F-25000 Besançon, France.
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Honrado C, McGrath JS, Reale R, Bisegna P, Swami NS, Caselli F. A neural network approach for real-time particle/cell characterization in microfluidic impedance cytometry. Anal Bioanal Chem 2020; 412:3835-3845. [PMID: 32189012 DOI: 10.1007/s00216-020-02497-9] [Citation(s) in RCA: 54] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2019] [Revised: 01/30/2020] [Accepted: 02/06/2020] [Indexed: 11/26/2022]
Abstract
Microfluidic applications such as active particle sorting or selective enrichment require particle classification techniques that are capable of working in real time. In this paper, we explore the use of neural networks for fast label-free particle characterization during microfluidic impedance cytometry. A recurrent neural network is designed to process data from a novel impedance chip layout for enabling real-time multiparametric analysis of the measured impedance data streams. As demonstrated with both synthetic and experimental datasets, the trained network is able to characterize with good accuracy size, velocity, and cross-sectional position of beads, red blood cells, and yeasts, with a unitary prediction time of 0.4 ms. The proposed approach can be extended to other device designs and cell types for electrical parameter extraction. This combination of microfluidic impedance cytometry and machine learning can serve as a stepping stone to real-time single-cell analysis and sorting. Graphical Abstract.
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Affiliation(s)
- Carlos Honrado
- Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, VA, 22904, USA
| | - John S McGrath
- Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, VA, 22904, USA
| | - Riccardo Reale
- Department of Civil Engineering and Computer Science, University of Rome Tor Vergata, Via del Politecnico 1, 00133, Rome, Italy
| | - Paolo Bisegna
- Department of Civil Engineering and Computer Science, University of Rome Tor Vergata, Via del Politecnico 1, 00133, Rome, Italy
| | - Nathan S Swami
- Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, VA, 22904, USA.
| | - Frederica Caselli
- Department of Civil Engineering and Computer Science, University of Rome Tor Vergata, Via del Politecnico 1, 00133, Rome, Italy.
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Reale R, De Ninno A, Businaro L, Bisegna P, Caselli F. High-throughput electrical position detection of single flowing particles/cells with non-spherical shape. LAB ON A CHIP 2019; 19:1818-1827. [PMID: 30997463 DOI: 10.1039/c9lc00071b] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
We present an innovative impedance cytometer for the measurement of the cross-sectional position of single particles or cells flowing in a microchannel. As predicted by numerical simulations and experimentally validated, the proposed approach is applicable to particles/cells with either spherical or non-spherical shape. In particular, the optics-free high-throughput position detection of individual flowing red blood cells (RBCs) is demonstrated and applied to monitor RBCs hydrodynamic focusing under different sheath flow conditions. Moreover, the device provides multiparametric information useful for lab-on-a-chip applications, including particle inter-arrival times and velocity profile, as well as RBCs mean corpuscular volume, distribution width and electrical opacity.
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Affiliation(s)
- Riccardo Reale
- Department of Civil Engineering and Computer Science, University of Rome Tor Vergata, 00133 Rome, Italy.
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Reale R, De Ninno A, Businaro L, Bisegna P, Caselli F. A simple electrical approach to monitor dielectrophoretic focusing of particles flowing in a microchannel. Electrophoresis 2019; 40:1400-1407. [PMID: 30661234 DOI: 10.1002/elps.201800423] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2018] [Revised: 12/20/2018] [Accepted: 01/02/2019] [Indexed: 02/01/2023]
Abstract
This paper reports an impedance-based system for the quantitative assessment of dielectrophoretic (DEP) focusing of single particles flowing in a microchannel. Particle lateral positions are detected in two electrical sensing zones placed before and after a DEP-focusing region, respectively. In each sensing zone, particle lateral positions are estimated using the unbalance between the opposite pulses of a differential current signal obtained with a straightforward coplanar electrode configuration. The system is used to monitor the focusing of polystyrene beads of 7 or 10 μm diameter, under various conditions of DEP field intensities and flow rates that produce different degrees of focusing. This electrical approach represents a simple and valuable alternative to optical methods for monitoring of particle focusing systems.
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Affiliation(s)
- Riccardo Reale
- Department of Civil Engineering and Computer Science, University of Rome Tor Vergata, Rome, Italy
| | - Adele De Ninno
- Department of Civil Engineering and Computer Science, University of Rome Tor Vergata, Rome, Italy
| | - Luca Businaro
- Institute for Photonics and Nanotechnologies, Italian National Research Council, Rome, Italy
| | - Paolo Bisegna
- Department of Civil Engineering and Computer Science, University of Rome Tor Vergata, Rome, Italy
| | - Federica Caselli
- Department of Civil Engineering and Computer Science, University of Rome Tor Vergata, Rome, Italy
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