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Chen S, Zhang S, Zhu R. Biophysical phenotyping of single-cell based on impedance and application for individualized precision medicine. Biosens Bioelectron 2024; 259:116410. [PMID: 38781697 DOI: 10.1016/j.bios.2024.116410] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2023] [Revised: 05/03/2024] [Accepted: 05/19/2024] [Indexed: 05/25/2024]
Abstract
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.
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Affiliation(s)
- Shengjie Chen
- State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University, Beijing, 100084, China
| | - Shengsen Zhang
- State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University, Beijing, 100084, China
| | - Rong Zhu
- State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University, Beijing, 100084, China.
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2
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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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3
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Zhu S, Zhu Z, Ni C, Zhou Z, Chen Y, Tang D, Guo K, Yang S, Liu K, Ni Z, Xiang N. Liquid Biopsy Instrument for Ultra-Fast and Label-Free Detection of Circulating Tumor Cells. RESEARCH (WASHINGTON, D.C.) 2024; 7:0431. [PMID: 39050821 PMCID: PMC11266806 DOI: 10.34133/research.0431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Accepted: 06/27/2024] [Indexed: 07/27/2024]
Abstract
Rapid diagnosis and real-time monitoring are of great important in the fight against cancer. However, most available diagnostic technologies are time-consuming and labor-intensive and are commonly invasive. Here, we describe CytoExam, an automatic liquid biopsy instrument designed based on inertial microfluidics and impedance cytometry, which uses a deep learning algorithm for the analysis of circulating tumor cells (CTCs). In silico and in vitro experiments demonstrated that CytoExam could achieve label-free detection of CTCs in the peripheral blood of cancer patients within 15 min. The clinical applicability of CytoExam was also verified using peripheral blood samples from 10 healthy donors and >50 patients with breast, colorectal, or lung cancer. Significant differences in the number of collected cells and predicted CTCs were observed between the 2 groups, with variations in the dielectric properties of the collected cells from cancer patients also being observed. The ultra-fast and minimally invasive features of CytoExam may pave the way for new paths for cancer diagnosis and scientific research.
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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
- School of Electrical and Automation Engineering, and Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing,
Nanjing Normal University, Nanjing 210023, 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
| | - Chen Ni
- 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
| | - Yao Chen
- School of Mechanical Engineering, and Jiangsu Key Laboratory for Design and Manufacture of Micro-Nano Biomedical Instruments,
Southeast University, Nanjing 211189, China
| | - Dezhi Tang
- School of Mechanical Engineering, and Jiangsu Key Laboratory for Design and Manufacture of Micro-Nano Biomedical Instruments,
Southeast University, Nanjing 211189, China
| | - Kefan Guo
- 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
| | - Kang Liu
- 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
| | - 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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4
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Chen Y, Ni C, Zhang X, Ni Z, Xiang N. High-Throughput Sorting and Single-Cell Mechanotyping by Hydrodynamic Sorting-Mechanotyping Cytometry. SMALL METHODS 2024; 8:e2301195. [PMID: 38213022 DOI: 10.1002/smtd.202301195] [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: 09/05/2023] [Revised: 12/28/2023] [Indexed: 01/13/2024]
Abstract
The existence of many background blood cells hinders the accurate identification of circulating tumor cells (CTCs) in the blood of cancer patients. To unlock this limitation, a hydrodynamic sorting-mechanotyping cytometry (HSMC) integrated with a sorting-concentration chip and a detection chip is proposed for simultaneously achieving the high-throughput cell sorting and the multi-parameter mechanotyping of the sorted tumor cells. The HSMC adopts the spiral inertial microfluidics for label-free sorting of cells in a high-throughput manner, allowing the efficient enrichment of tumor cells from the large background blood cells. Then, the sorted cells are concentrated by the concentration unit and finally passed through the detection unit for hydrodynamic deformation. The HSMC has a high throughput for sorting and detection and can successfully reveal the differences in the cellular mechanical properties. After characterizing and optimizing the single chips, the identification of white blood cells (WBCs) and three types of tumor cells (A549, MCF-7, and MDA-MB-231 cells) is successfully achieved. The identification accuracies for WBCs and different tumor cells are all larger than 94%, while the highest identification accuracy is up to 99.2%. This study envisions that the HSMC will offer an avenue for the analysis of single cell intrinsic mechanics in clinical medicine.
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Affiliation(s)
- Yao Chen
- School of Mechanical Engineering, and Jiangsu Key Laboratory for Design and Manufacture of Micro-Nano Biomedical Instruments, Southeast University, Nanjing, 211189, China
| | - Chen Ni
- 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
| | - Zhonghua Ni
- 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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Zhang Y, Wang Y, Yin H, Wang J, Liu N, Zhong S, Li L, Zhang Q, Yue T. Strain sensor on a chip for quantifying the magnitudes of tensile stress on cells. MICROSYSTEMS & NANOENGINEERING 2024; 10:88. [PMID: 38919164 PMCID: PMC11196625 DOI: 10.1038/s41378-024-00719-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Revised: 04/08/2024] [Accepted: 04/23/2024] [Indexed: 06/27/2024]
Abstract
During cardiac development, mechanotransduction from the in vivo microenvironment modulates cardiomyocyte growth in terms of the number, area, and arrangement heterogeneity. However, the response of cells to different degrees of mechanical stimuli is unclear. Organ-on-a-chip, as a platform for investigating mechanical stress stimuli in cellular mimicry of the in vivo microenvironment, is limited by the lack of ability to accurately quantify externally induced stimuli. However, previous technology lacks the integration of external stimuli and feedback sensors in microfluidic platforms to obtain and apply precise amounts of external stimuli. Here, we designed a cell stretching platform with an in-situ sensor. The in-situ liquid metal sensors can accurately measure the mechanical stimulation caused by the deformation of the vacuum cavity exerted on cells. The platform was applied to human cardiomyocytes (AC16) under cyclic strain (5%, 10%, 15%, 20 and 25%), and we found that cyclic strain promoted cell growth induced the arrangement of cells on the membrane to gradually unify, and stabilized the cells at 15% amplitude, which was even more effective after 3 days of culture. The platform's precise control and measurement of mechanical forces can be used to establish more accurate in vitro microenvironmental models for disease modeling and therapeutic research.
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Affiliation(s)
- Yuyin Zhang
- School of Mechatronics Engineering and Automation, Shanghai University, Shanghai, China
| | - Yue Wang
- School of Future Technology, Shanghai University, Shanghai, China
- Key Laboratory of Advanced Manufacturing Technology of Zhejiang Province, School of Mechanical Engineering, Zhejiang University, Hangzhou, China
| | - Hongze Yin
- School of Mechatronics Engineering and Automation, Shanghai University, Shanghai, China
| | - Jiahao Wang
- School of Mechatronics Engineering and Automation, Shanghai University, Shanghai, China
| | - Na Liu
- School of Mechatronics Engineering and Automation, Shanghai University, Shanghai, China
- Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, Shanghai University, Shanghai, China
- Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai, China
| | - Songyi Zhong
- School of Mechatronics Engineering and Automation, Shanghai University, Shanghai, China
- School of Future Technology, Shanghai University, Shanghai, China
| | - Long Li
- School of Mechatronics Engineering and Automation, Shanghai University, Shanghai, China
- Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, Shanghai University, Shanghai, China
| | - Quan Zhang
- School of Mechatronics Engineering and Automation, Shanghai University, Shanghai, China
- School of Future Technology, Shanghai University, Shanghai, China
- Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, Shanghai University, Shanghai, China
| | - Tao Yue
- School of Mechatronics Engineering and Automation, Shanghai University, Shanghai, China
- School of Future Technology, Shanghai University, Shanghai, China
- Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, Shanghai University, Shanghai, China
- Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai, China
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6
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Zhu J, Pan S, Chai H, Zhao P, Feng Y, Cheng Z, Zhang S, Wang W. Microfluidic Impedance Cytometry Enabled One-Step Sample Preparation for Efficient Single-Cell Mass Spectrometry. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2024; 20:e2310700. [PMID: 38483007 DOI: 10.1002/smll.202310700] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 03/05/2024] [Indexed: 06/27/2024]
Abstract
Single-cell mass spectrometry (MS) is significant in biochemical analysis and holds great potential in biomedical applications. Efficient sample preparation like sorting (i.e., separating target cells from the mixed population) and desalting (i.e., moving the cells off non-volatile salt solution) is urgently required in single-cell MS. However, traditional sample preparation methods suffer from complicated operation with various apparatus, or insufficient performance. Herein, a one-step sample preparation strategy by leveraging label-free impedance flow cytometry (IFC) based microfluidics is proposed. Specifically, the IFC framework to characterize and sort single-cells is adopted. Simultaneously with sorting, the target cell is transferred from the local high-salinity buffer to the MS-compatible solution. In this way, one-step sorting and desalting are achieved and the collected cells can be directly fed for MS analysis. A high sorting efficiency (>99%), cancer cell purity (≈87%), and desalting efficiency (>99%), and the whole workflow of impedance-based separation and MS analysis of normal cells (MCF-10A) and cancer cells (MDA-MB-468) are verified. As a standalone sample preparation module, the microfluidic chip is compatible with a variety of MS analysis methods, and envisioned to provide a new paradigm in efficient MS sample preparation, and further in multi-modal (i.e., electrical and metabolic) characterization of single-cells.
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Affiliation(s)
- Junwen Zhu
- State Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University, Beijing, 100084, China
| | - Siyuan Pan
- Department of Chemistry, Tsinghua University, Beijing, 100084, China
| | - Huichao Chai
- State Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University, Beijing, 100084, China
| | - Peng Zhao
- State Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University, Beijing, 100084, China
| | - Yongxiang Feng
- State Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University, Beijing, 100084, China
| | - Zhen Cheng
- Department of Automation, Tsinghua University, Beijing, 100084, China
| | - Sichun Zhang
- Department of Chemistry, Tsinghua University, Beijing, 100084, China
| | - Wenhui Wang
- State Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University, Beijing, 100084, China
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Chai H, Zhu J, Feng Y, Liang F, Wu Q, Ju Z, Huang L, Wang W. Capillarity Enabled Large-Array Liquid Metal Electrodes for Compact and High-Throughput Dielectrophoretic Microfluidics. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2310212. [PMID: 38236647 DOI: 10.1002/adma.202310212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 12/30/2023] [Indexed: 01/30/2024]
Abstract
Dielectrophoresis (DEP) particle separation has label-free, well-controllable, and low-damage merits. Sidewall microelectrodes made of liquid metal alloy (LMA) inherits the additional advantage of thick electrodes to generate impactful DEP force. However, existing LMA electrode-based devices lack the ability to integrate large-array electrodes in a compact footprint, severely limiting flow rate and thus throughput. Herein, a facile and versatile method is proposed to integrate high-density thick LMA electrodes in microfluidic devices, taking advantage of the passive control ability of capillary burst valves (CBVs). CBVs with carefully designed burst pressures are co-designed in microfluidic channels, allowing self-assembly of LMA electrode array through simple hand-push injection. The arrayed electrode configuration brings the accumulative DEP deflection effect. Specifically, The fabricated 5000 pairs of sidewall electrodes in a compact chip are demonstrted to achieve ten times higher throughput in DEP deflection. The 5000-electrode-pair device is applied to successfully separate four mixed samples, including human peripheral blood mononuclear cells and A549 cells with the flow rate of 70 µL min-1. It is envisioned that this work can greatly facilitate LMA electrode array fabrication and offer a robust and versatile platform for DEP separation applications.
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Affiliation(s)
- Huichao Chai
- State Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University, Beijing, 100084, P. R. China
| | - Junwen Zhu
- State Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University, Beijing, 100084, P. R. China
| | - Yongxiang Feng
- State Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University, Beijing, 100084, P. R. China
| | - Fei Liang
- State Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University, Beijing, 100084, P. R. China
| | - Qiyan Wu
- The First Medical Center of PLA General Hospital, Beijing, 100853, P. R. China
| | - Zhongjian Ju
- The First Medical Center of PLA General Hospital, Beijing, 100853, P. R. China
| | - Liang Huang
- School of Instrument Science and Opto-Electronics Engineering, Hefei University of Technology, Hefei, 230009, P. R. China
| | - Wenhui Wang
- State Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University, Beijing, 100084, P. R. China
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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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9
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Zhang S, Han Z, Qi H, Liu S, Liu B, Sun C, Feng Z, Sun M, Duan X. Convolutional Neural Network-Driven Impedance Flow Cytometry for Accurate Bacterial Differentiation. Anal Chem 2024; 96:4419-4429. [PMID: 38448396 DOI: 10.1021/acs.analchem.3c04421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/08/2024]
Abstract
Impedance flow cytometry (IFC) has been demonstrated to be an efficient tool for label-free bacterial investigation to obtain the electrical properties in real time. However, the accurate differentiation of different species of bacteria by IFC technology remains a challenge owing to the insignificant differences in data. Here, we developed a convolutional neural networks (ConvNet) deep learning approach to enhance the accuracy and efficiency of the IFC toward distinguishing various species of bacteria. First, more than 1 million sets of impedance data (comprising 42 characteristic features for each set) of various groups of bacteria were trained by the ConvNet model. To improve the efficiency for data analysis, the Spearman correlation coefficient and the mean decrease accuracy of the random forest algorithm were introduced to eliminate feature interaction and extract the opacity of impedance related to the bacterial wall and membrane structure as the predominant features in bacterial differentiation. Moreover, the 25 optimized features were selected with differentiation accuracies of >96% for three groups of bacteria (bacilli, cocci, and vibrio) and >95% for two species of bacilli (Escherichia coli and Salmonella enteritidis), compared to machine learning algorithms (complex tree, linear discriminant, and K-nearest neighbor algorithms) with a maximum accuracy of 76.4%. Furthermore, bacterial differentiation was achieved on spiked samples of different species with different mixing ratios. The proposed ConvNet deep learning-assisted data analysis method of IFC exhibits advantages in analyzing a huge number of data sets with capacity for extracting predominant features within multicomponent information and will bring about progress and advances in the fields of both biosensing and data analysis.
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Affiliation(s)
- Shuaihua Zhang
- State Key Laboratory of Precision Measuring Technology & Instruments, College of Precision Instrument and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China
| | - Ziyu Han
- State Key Laboratory of Precision Measuring Technology & Instruments, College of Precision Instrument and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China
| | - Hang Qi
- State Key Laboratory of Precision Measuring Technology & Instruments, College of Precision Instrument and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China
| | - Siyuan Liu
- State Key Laboratory of Precision Measuring Technology & Instruments, College of Precision Instrument and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China
| | - Bohua Liu
- State Key Laboratory of Precision Measuring Technology & Instruments, College of Precision Instrument and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China
| | - Chongling Sun
- State Key Laboratory of Precision Measuring Technology & Instruments, College of Precision Instrument and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China
| | - Zhe Feng
- Wuqing District Center for Disease Control and Prevention, Tianjin 301700, China
| | - Meiqing Sun
- Wuqing District Center for Disease Control and Prevention, Tianjin 301700, China
| | - Xuexin Duan
- State Key Laboratory of Precision Measuring Technology & Instruments, College of Precision Instrument and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China
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10
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Wang T, Fang Q, Huang L. Investigation of geometry-dependent sensing characteristics of microfluidic for single-cell 3D localization. THE REVIEW OF SCIENTIFIC INSTRUMENTS 2024; 95:014102. [PMID: 38197766 DOI: 10.1063/5.0172520] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 12/17/2023] [Indexed: 01/11/2024]
Abstract
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.
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Affiliation(s)
- Tan Wang
- Anhui Province Key Laboratory of Measuring Theory and Precision Instrument and School of Instrument Science and Opto-Electronics Engineering, Hefei University of Technology, Hefei 230009, China
| | - Qiang Fang
- Anhui Province Key Laboratory of Measuring Theory and Precision Instrument and School of Instrument Science and Opto-Electronics Engineering, Hefei University of Technology, Hefei 230009, China
| | - Liang Huang
- Anhui Province Key Laboratory of Measuring Theory and Precision Instrument and School of Instrument Science and Opto-Electronics Engineering, Hefei University of Technology, Hefei 230009, China
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Zhou Z, Ni C, Zhu Z, Chen Y, Ni Z, Xiang N. High-throughput adjustable deformability cytometry utilizing elasto-inertial focusing and virtual fluidic channel. LAB ON A CHIP 2023; 23:4528-4539. [PMID: 37766593 DOI: 10.1039/d3lc00591g] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/29/2023]
Abstract
Cell mechanical properties provide a label-free marker for indicating cell states and disease processes. Although microfluidic deformability cytometry has demonstrated great potential and successes in mechanical phenotyping in recent years, its universal applicability for characterizing multiple sizes of cells using a single device has not been realized. Herein, we propose high-throughput adjustable deformability cytometry integrated with three-dimensional (3D) elasto-inertial focusing and a virtual fluidic channel. By properly adjusting the flow ratio of the sample and sheath, the virtual fluidic channel in a wide solid channel can generate a strong shear force in the normal direction of the flow velocity and simultaneously squeeze cells from both sides to induce significant cell deformation. The combination of elasto-inertial focusing and a virtual fluidic channel provides a great hydrodynamic symmetrical force for inducing significant and homogeneous cell deformation. In addition, our deformability cytometry system not only achieves rapid and precise cell deformation, but also allows the adjustable detection of multiple sizes of cells at a high throughput of up to 3000 cells per second. The mini-bilateral segmentation network (mini-BiSeNet) was developed to identify cells and extract features quickly. The classification of different cell populations (A549, MCF-7, MDA-MB-231, and WBCs) was carried out based on the cell size and deformation. By applying deep learning to cell classification, a high accuracy reaching approximately 90% was achieved. We also revealed the potential of our deformability cytometry for characterizing pleural effusions. The flexibility of our deformability cytometry holds promise for the mechanical phenotyping and detection of various biological samples.
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Affiliation(s)
- Zheng Zhou
- School of Mechanical Engineering, and, Jiangsu Key Laboratory for Design and Manufacture of Micro-Nano Biomedical Instruments, Southeast University, Nanjing, 211189, China.
| | - Chen Ni
- 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.
| | - Zhonghua Ni
- 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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Julian T, Tang T, Hosokawa Y, Yalikun Y. Machine learning implementation strategy in imaging and impedance flow cytometry. BIOMICROFLUIDICS 2023; 17:051506. [PMID: 37900052 PMCID: PMC10613093 DOI: 10.1063/5.0166595] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 10/06/2023] [Indexed: 10/31/2023]
Abstract
Imaging and impedance flow cytometry is a label-free technique that has shown promise as a potential replacement for standard flow cytometry. This is due to its ability to provide rich information and archive high-throughput analysis. Recently, significant efforts have been made to leverage machine learning for processing the abundant data generated by those techniques, enabling rapid and accurate analysis. Harnessing the power of machine learning, imaging and impedance flow cytometry has demonstrated its capability to address various complex phenotyping scenarios. Herein, we present a comprehensive overview of the detailed strategies for implementing machine learning in imaging and impedance flow cytometry. We initiate the discussion by outlining the commonly employed setup to acquire the data (i.e., image or signal) from the cell. Subsequently, we delve into the necessary processes for extracting features from the acquired image or signal data. Finally, we discuss how these features can be utilized for cell phenotyping through the application of machine learning algorithms. Furthermore, we discuss the existing challenges and provide insights for future perspectives of intelligent imaging and impedance flow cytometry.
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Affiliation(s)
- Trisna Julian
- Division of Materials Science, Nara Institute of Science and Technology, 8916-5 Takayamacho, Ikoma, Nara 630-0192, Japan
| | - Tao Tang
- Department of Biomedical Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore 117583, Singapore
| | - Yoichiroh Hosokawa
- Division of Materials Science, Nara Institute of Science and Technology, 8916-5 Takayamacho, Ikoma, Nara 630-0192, Japan
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