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Ni C, Yang M, Yang S, Zhu Z, Chen Y, Jiang L, Xiang N. Three-dimensional inertial focusing based impedance cytometer enabling high-accuracy characterization of electrical properties of tumor cells. LAB ON A CHIP 2024. [PMID: 39132910 DOI: 10.1039/d4lc00523f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/13/2024]
Abstract
The differences in the cross-sectional positions of cells in the detection area have a severe negative impact on achieving accurate characterization of the impedance spectra of cells. Herein, we proposed a three-dimensional (3D) inertial focusing based impedance cytometer integrating sheath fluid compression and inertial focusing for the high-accuracy electrical characterization and identification of tumor cells. First, we studied the effects of the particle initial position and the sheath fluid compression on particle focusing. Then, the relationship of the particle height and the signal-to-noise ratio (SNR) of the impedance signal was explored. The results showed that efficient single-line focusing of 7-20 μm particles close to the electrodes was achieved and impedance signals with a high SNR and a low coefficient of variation (CV) were obtained. Finally, the electrical properties of three types of tumor cells (A549, MDA-MB-231, and UM-UC-3 cells) were accurately characterized. Machine learning algorithms were implemented to accurately identify tumor cells based on the amplitude and phase opacities at multiple frequencies. Compared with traditional two-dimensional (2D) inertial focusing, the identification accuracy of A549, MDA-MB-231, and UM-UC-3 cells using our 3D inertial focusing increased by 57.5%, 36.4% and 36.6%, respectively. The impedance cytometer enables the detection of cells with a wide size range without causing clogging and obtains high SNR signals, improving applicability to different complex biological samples and cell identification accuracy.
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Affiliation(s)
- Chen Ni
- School of Mechanical Engineering, and, Jiangsu Key Laboratory for Design and Manufacture of Micro-Nano Biomedical Instruments, Southeast University, Nanjing, 211189, China.
| | - Mingqi Yang
- School of Mechanical Engineering, and, Jiangsu Key Laboratory for Design and Manufacture of Micro-Nano Biomedical Instruments, Southeast University, Nanjing, 211189, China.
| | - Shuai Yang
- School of Mechanical Engineering, and, Jiangsu Key Laboratory for Design and Manufacture of Micro-Nano Biomedical Instruments, Southeast University, Nanjing, 211189, China.
| | - Zhixian Zhu
- School of Mechanical Engineering, and, Jiangsu Key Laboratory for Design and Manufacture of Micro-Nano Biomedical Instruments, Southeast University, Nanjing, 211189, China.
| | - Yao Chen
- School of Mechanical Engineering, and, Jiangsu Key Laboratory for Design and Manufacture of Micro-Nano Biomedical Instruments, Southeast University, Nanjing, 211189, China.
| | - Lin Jiang
- 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.
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Panklang N, Techaumnat B, Tanthanuch N, Chotivanich K, Horprathum M, Nakano M. On-Chip Impedance Spectroscopy of Malaria-Infected Red Blood Cells. SENSORS (BASEL, SWITZERLAND) 2024; 24:3186. [PMID: 38794040 PMCID: PMC11125259 DOI: 10.3390/s24103186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/03/2024] [Revised: 04/30/2024] [Accepted: 05/10/2024] [Indexed: 05/26/2024]
Abstract
Malaria is a disease that affects millions of people worldwide, particularly in developing countries. The development of accurate and efficient methods for the detection of malaria-infected cells is crucial for effective disease management and control. This paper presents the electrical impedance spectroscopy (EIS) of normal and malaria-infected red blood cells. An EIS microfluidic device, comprising a microchannel and a pair of coplanar electrodes, was fabricated for single-cell measurements in a continuous manner. Based on the EIS results, the aim of this work is to discriminate Plasmodium falciparum-infected red blood cells from the normal ones. Different from typical impedance spectroscopy, our measurement was performed for the cells in a low-conductivity medium in a frequency range between 50 kHz and 800 kHz. Numerical simulation was utilized to study the suitability parameters of the microchannel and electrodes for the EIS experiment over the measurement frequencies. The measurement results have shown that by using the low-conductivity medium, we could focus on the change in the conductance caused by the presence of a cell in the sensing electrode gap. The results indicated a distinct frequency spectrum of the conductance between the normal and infected red blood cells, which can be further used for the detection of the disease.
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Affiliation(s)
- Nitipong Panklang
- Department of Electrical Engineering, Faculty of Engineering, Rajamangala University of Technology Thanyaburi, Pathum Thani 12110, Thailand;
| | - Boonchai Techaumnat
- Department of Electrical Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok 10330, Thailand
- Micro/Nano-Electro-Mechanical Integrated System Research Unit, Faculty of Engineering, Chulalongkorn University, Bangkok 10330, Thailand
| | - Nutthaphong Tanthanuch
- Department of Electrical and Computer Engineering, Faculty of Engineering, Thammasat School of Engineering, Thammasat University, Pathum Thani 12120, Thailand;
| | - Kesinee Chotivanich
- Cell and Tissue Culture Resources Unit, Department of Clinical Tropical Medicine, Faculty of Tropical Medicine, Mahidol University, Bangkok 10400, Thailand;
| | - Mati Horprathum
- Spectroscopic and Sensing Devices Research Group, NECTEC, NSTDA, Pathum Thani 12120, Thailand;
| | - Michihiko Nakano
- Faculty of Information Science and Electrical Engineering, Kyushu University, Fukuoka 819-0395, Japan;
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Wang YK, Shi BW, Zhao JM, Wang YX, Jiang YF, Yang GL, Gao XD, Qiang T. Highly Sensitive and Linear Resonator-Based Biosensor for White Blood Cell Counting: Feasible Measurement Method and Intrinsic Mechanism Exploration. BIOSENSORS 2024; 14:180. [PMID: 38667173 PMCID: PMC11048127 DOI: 10.3390/bios14040180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Revised: 04/04/2024] [Accepted: 04/05/2024] [Indexed: 04/28/2024]
Abstract
Since different quantities of white blood cells (WBCs) in solution possess an adaptive osmotic pressure of cells, the WBCs themselves and in solution have similar concentrations, resulting in them having similar dielectric properties. Therefore, a microwave sensor could have difficulty in sensing the quantity variation when WBCs are in solution. This paper presents a highly sensitive, linear permittivity-inspired microwave biosensor for WBCs, counting through the evaporation method. Such a measurement method is proposed to record measurements after the cell solution is dripped onto the chip and is completely evaporated naturally. The proposed biosensor consists of an air-bridged asymmetric differential inductor and a centrally located circular fork-finger capacitor fabricated on a GaAs substrate using integrated passive fabrication technology. It is optimized to feature a larger sensitive area and improved Q-factor, which increases the effective area of interaction between cells and the electromagnetic field and facilitates the detection of their changes in number. The sensing relies on the dielectric properties of the cells and the change in the dielectric constant for different concentrations, and the change in resonance properties, which mainly represents the frequency shift, corresponds to the macroscopic change in the concentration of the cells. The microwave biosensors are used to measure biological samples with concentrations ranging from 0.25 × 106 to 8 × 106 cells per mL in a temperature (26.00 ± 0.40 °C) and humidity (54.40 ± 3.90 RH%) environment. The measurement results show a high sensitivity of 25.06 Hz/cells·mL-1 with a highly linear response of r2 = 0.99748. In addition, a mathematical modeling of individual cells in suspension is performed to estimate the dielectric constant of individual cells and further explain the working mechanism of the proposed microwave biosensor.
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Affiliation(s)
- Yi-Ke Wang
- School of Internet of Things Engineering, Institute of Advanced Technology, Jiangnan University, Wuxi 214122, China; (Y.-K.W.); (B.-W.S.); (J.-M.Z.); (Y.-X.W.); (Y.-F.J.)
| | - Bo-Wen Shi
- School of Internet of Things Engineering, Institute of Advanced Technology, Jiangnan University, Wuxi 214122, China; (Y.-K.W.); (B.-W.S.); (J.-M.Z.); (Y.-X.W.); (Y.-F.J.)
| | - Jun-Ming Zhao
- School of Internet of Things Engineering, Institute of Advanced Technology, Jiangnan University, Wuxi 214122, China; (Y.-K.W.); (B.-W.S.); (J.-M.Z.); (Y.-X.W.); (Y.-F.J.)
| | - Yan-Xiong Wang
- School of Internet of Things Engineering, Institute of Advanced Technology, Jiangnan University, Wuxi 214122, China; (Y.-K.W.); (B.-W.S.); (J.-M.Z.); (Y.-X.W.); (Y.-F.J.)
| | - Yan-Feng Jiang
- School of Internet of Things Engineering, Institute of Advanced Technology, Jiangnan University, Wuxi 214122, China; (Y.-K.W.); (B.-W.S.); (J.-M.Z.); (Y.-X.W.); (Y.-F.J.)
| | - Gang-Long Yang
- State Key Laboratory of Biochemical Engineering, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, China;
- Key Laboratory of Biopharmaceutical Preparation and Delivery, Chinese Academy of Sciences, Beijing 100190, China
| | - Xiao-Dong Gao
- School of Biotechnology, The Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Tian Qiang
- School of Internet of Things Engineering, Institute of Advanced Technology, Jiangnan University, Wuxi 214122, China; (Y.-K.W.); (B.-W.S.); (J.-M.Z.); (Y.-X.W.); (Y.-F.J.)
- School of Biotechnology, The Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China
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Wei J, Gao W, Yang X, Yu Z, Su F, Han C, Xing X. Machine learning classification of cellular states based on the impedance features derived from microfluidic single-cell impedance flow cytometry. BIOMICROFLUIDICS 2024; 18:014103. [PMID: 38274201 PMCID: PMC10807927 DOI: 10.1063/5.0181287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 12/19/2023] [Indexed: 01/27/2024]
Abstract
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.
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Affiliation(s)
- Jian Wei
- College of Information Science and Technology, Beijing University of Chemical Technology, No. 15 North 3rd Ring Road, Chaoyang District, Beijing 100029, China
| | - Wenbing Gao
- College of Information Science and Technology, Beijing University of Chemical Technology, No. 15 North 3rd Ring Road, Chaoyang District, Beijing 100029, China
| | - Xinlong Yang
- College of Information Science and Technology, Beijing University of Chemical Technology, No. 15 North 3rd Ring Road, Chaoyang District, Beijing 100029, China
| | - Zhuotong Yu
- College of Information Science and Technology, Beijing University of Chemical Technology, No. 15 North 3rd Ring Road, Chaoyang District, Beijing 100029, China
| | - Fei Su
- Department of Integrative Oncology, China-Japan Friendship Hospital, No. 2 Yinghuayuan East Street, Chaoyang District, Beijing 100029, China
| | - Chengwu Han
- Department of Clinical Laboratory, China-Japan Friendship Hospital, No. 2 Yinghuayuan East Street, Chaoyang District, Beijing 100029, China
| | - Xiaoxing Xing
- College of Information Science and Technology, Beijing University of Chemical Technology, No. 15 North 3rd Ring Road, Chaoyang District, Beijing 100029, China
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