Machine Learning-based Single-cell Analysis Using Microfluidic Impedance Flow Cytometer
Main Article Content
Abstract
Single-cell analysis offers a more comprehensive approach to disease diagnosis compared to conventional methods. Electrical properties at a cellular level have been established as reliable biomarkers, enabling the identification of variations between individual cells. In this work we introduce a machine learning-based methodology for analyzing electrical impedance signals obtained from a microfluidic biosensor system for biological cell analysis. The proposed model is designed to detect and enumerate CD4 T-lymphocytes (CD4), which are a critical component of the immune system, through a microfluidic impedance flow cytometer. By identifying and analyzing the bioelectrical signal characteristics of CD4 cells as they traverse the sensing region, the machine learning models provide accurate cell enumeration while also estimating the size distribution of cell populations within the sample. A signal classification framework is employed to isolate cell signals from background noise, followed by the application and evaluation of various machine learning algorithms to optimize performance. The proposed method demonstrates improved accuracy and speed in cellular analysis compared to traditional techniques such as flow cytometry. Moreover, this method presents a significant potential for applications in cell analysis, addressing the demand for point-of-care diagnostics and enhancing the efficiency of biological diagnostics.
Keywords:
Biosensor, impedance sensing, microfluidic, single-cell analysis, machine learning.
References
[1] T. Sun, H. Morgan, Single-cell Microfluidic Impedance Cytometry: A Review, Microfluid Nanofluidics, Vol. 8, No. 4, 2010, pp. 423-443, https//doi.org/1007/S10404-010-0580-9.
[2] C. Petchakup et al., Advances in Single Cell Impedance Cytometry for Biomedical Applications, Micromachines Vol. 8, No. 3, 2017, pp. 87, https//doi.org/10.3390/MI8030087.
[3] K. Cheung, S. Gawad, P. Renaud, Impedance Spectroscopy Flow Cytometry: On-chip Label-Free Cell Differentiation, Cytometry A, Vol. 65, No. 2, 2005, pp. 124-132, https//doi.org/10.1002/CYTO.A.20141.
[4] M. E. Piyasena S. W. Graves, The Intersection of Flow Cytometry with Microfluidics Microfabrication, Lab Chip, Vol. 14, No. 6,2014, pp. 1044-1059, https//doi.org/10.1039/C3LC51152A.
[5] D. R. Gossett et al., Label-Free Cell Separation Sorting in Microfluidic Systems, Anal Bioanal Chem, Vol. 397, No. 8, 2010, pp. 3249-3267, https//doi.org/10.1007/S00216-010-3721-9.
[6] J. Chen, C. Xue, Y. Zhao, D. Chen, M. H. Wu, J. Wang, Microfluidic Impedance Flow Cytometry Enabling High-Throughput Single-Cell Electrical Property Characterization, International Journal of Molecular Sciences 2015, Vol. 16, No. 5, 2015, pp. 9804-9830, https//doi.org/10.3390/Ijms16059804.
[7] S. Kumari et al., Microfluidic Platforms for Single Cell Analysis: Applications in Cellular Manipulation Optical Biosensing, Chemosensors 2023, Vol. 11, No. 2, 2023, pp. 107, https//doi.org/10.3390/Chemosensors11020107.
[8] Y. Manmana, K. Yamada, D. Citterio, Paper-Based Microfluidics for Point-of-care Medical Diagnostics,
2024, pp. 443-493, https//doi.org/10.1007/978-981-97-6540-9_13.
[9] C. Honrado, P. Bisegna, N. S. Swami, F. Caselli, Single-Cell Microfluidic Impedance Cytometry: from Raw Signals to Cell Phenotypes Using Data Analytics, Lab Chip, Vol. 21, No. 1, 2021, pp. 22-54, https//doi.org/10.1039/D0lc00840k.
[10] D. Vloemans, L. V.an Hileghem, H. Ordutowski, F. D. Dosso, D. Spasic, J. Lammertyn, Self-Powered Microfluidics for Point-Of-Care Solutions: from Sampling to Detection of Proteins Nucleic Acids, Methods Mol Biol, Vol. 2804, 2024, pp. 3-50, https//doi.org/10.1007/978-1-0716-3850-7_1.
[11] S. A. Graham, E. Segal, Lab-on-a-chip Devices for Point-of-care Medical Diagnostics, Adv Biochem Eng Biotechnol, Vol. 179, 2022, pp. 247-265, https//doi.org/10.1007/10_2020_127/Figures/9.
[12] F. Piorino, A. T. Patterson, M. P. Styczynski, Low-cost, Point-of-care Biomarker Quantification, Curr Opin Biotechnol, Vol. 76, 2022, pp. 102738, https//doi.org/10.1016/J.Copbio.2022.102738.
[13] A. McDavid et al., Data Exploration, Quality Control and Testing in Single-cell Qpcr-based Gene Expression Experiments, Bioinformatics, Vol. 29, No. 4, 2013, pp. 461-467, https//doi.org/10.1093/Bioinformatics/Bts714.
[14] J. Chen, C. Xue, Y. Zhao, D. Chen, M. H. Wu, J. Wang, Microfluidic Impedance Flow Cytometry Enabling High-Throughput Single-Cell Electrical Property Characterization, International Journal of Molecular Sciences Vol. 16, No. 5, 2015, pp. 9804-9830, https//doi.org/10.3390/IJMS16059804.
[15] K. Cheung, S. Gawad, and P. Renaud, Impedance Spectroscopy Flow Cytometry: On-chip Label-free Cell Differentiation, Cytometry Part A, Vol. 65A, No. 2, 2005, pp. 124-132, https//doi.org/10.1002/CYTO.A.20141.
[16] K. C. Cheung et al., Microfluidic Impedance-based Flow Cytometry, Cytometry Part A, Vol. 77A, No. 7, 2010, pp. 648-666, https//doi.org/10.1002/CYTO.A.20910.
[17] C. Honrado, P. Bisegna, N. S. Swami, F. Caselli, Single-cell Microfluidic Impedance Cytometry: from Raw Signals to Cell Phenotypes Using Data Analytics, Lab Chip, Vol. 21, No. 1, 2021, pp. 22-54, https//doi.org/10.1039/D0LC00840K.
[18] T. Sun, H. Morgan, Single-cell Microfluidic Impedance Cytometry: A Review, Microfluid Nanofluidics, Vol. 8, No. 4, 2010, pp. 423-443, https//doi.org/10.1007/S10404-010-0580-9/Tables/1.
[19] G. Wu et al., Optimizing Microfluidic Impedance Cytometry by Bypass Electrode Layout Design, Biosensors (Basel), Vol. 14, No. 4, 2024, pp. 204, https//doi.org/10.3390/Bios14040204/S1.
[20] T. A. Nguyen, T. I. Yin, G. Urban, A Cell Impedance Sensor Chip for Cancer Cells Detection with Single Cell Resolution, Proceedings of Ieee Sensors, 2013, https//doi.org/10.1109/Icsens.2013.6688160.
[21] C. Ferguson, Y. Zhang, C. Palego, X. Cheng, Recent Approaches to Design and Analysis of Electrical Impedance Systems for Single Cells Using Machine Learning, Sensors, Vol. 23, No. 13, 2023, pp. 5990, https//doi.org/10.3390/S23135990.
[22] Z. Gao, Y. Li, Enhancing Single-cell Biology Through Advanced AI-powered Microfluidics, Biomicrofluidics, Vol. 17, No. 5, 2023, https//doi.org/10.1063/5.0170050/2914114.
[23] J. Wei et al., Machine Learning Classification of Cellular States Based on the Impedance Features Derived from Microfluidic Single-cell Impedance Flow Cytometry, Biomicrofluidics, Vol. 18, No. 1, 2024, https//doi.org/10.1063/5.0181287/3061531.
[24] C. Honrado, J. S. McGrath, R. Reale, P. Bisegna, N. S. Swami, F. Caselli, A Neural Network Approach for Real-Time Particle/Cell Characterization in Microfluidic Impedance Cytometry, Anal Bioanal Chem, Vol. 412, No. 16, 2020, pp. 3835-3845, https//doi.org/10.1007/S00216-020-02497-9/Figures/5.
[25] J. Wei et al., Machine Learning Classification of Cellular States Based on the Impedance Features Derived From Microfluidic Single-cell Impedance Flow Cytometry, Biomicrofluidics, Vol. 18, No. 1, 2024, https//doi.org/10.1063/5.0181287/3061531.
[26] P. T. Huong et al., A Novel Approach to Detect CD4 T-Lymphocytes Using a Microfluidic Chip and Compact Signal Processing Circuit, 2024, https//doi.org/10.21203/RS.3.RS-5171054/V1.
[2] C. Petchakup et al., Advances in Single Cell Impedance Cytometry for Biomedical Applications, Micromachines Vol. 8, No. 3, 2017, pp. 87, https//doi.org/10.3390/MI8030087.
[3] K. Cheung, S. Gawad, P. Renaud, Impedance Spectroscopy Flow Cytometry: On-chip Label-Free Cell Differentiation, Cytometry A, Vol. 65, No. 2, 2005, pp. 124-132, https//doi.org/10.1002/CYTO.A.20141.
[4] M. E. Piyasena S. W. Graves, The Intersection of Flow Cytometry with Microfluidics Microfabrication, Lab Chip, Vol. 14, No. 6,2014, pp. 1044-1059, https//doi.org/10.1039/C3LC51152A.
[5] D. R. Gossett et al., Label-Free Cell Separation Sorting in Microfluidic Systems, Anal Bioanal Chem, Vol. 397, No. 8, 2010, pp. 3249-3267, https//doi.org/10.1007/S00216-010-3721-9.
[6] J. Chen, C. Xue, Y. Zhao, D. Chen, M. H. Wu, J. Wang, Microfluidic Impedance Flow Cytometry Enabling High-Throughput Single-Cell Electrical Property Characterization, International Journal of Molecular Sciences 2015, Vol. 16, No. 5, 2015, pp. 9804-9830, https//doi.org/10.3390/Ijms16059804.
[7] S. Kumari et al., Microfluidic Platforms for Single Cell Analysis: Applications in Cellular Manipulation Optical Biosensing, Chemosensors 2023, Vol. 11, No. 2, 2023, pp. 107, https//doi.org/10.3390/Chemosensors11020107.
[8] Y. Manmana, K. Yamada, D. Citterio, Paper-Based Microfluidics for Point-of-care Medical Diagnostics,
2024, pp. 443-493, https//doi.org/10.1007/978-981-97-6540-9_13.
[9] C. Honrado, P. Bisegna, N. S. Swami, F. Caselli, Single-Cell Microfluidic Impedance Cytometry: from Raw Signals to Cell Phenotypes Using Data Analytics, Lab Chip, Vol. 21, No. 1, 2021, pp. 22-54, https//doi.org/10.1039/D0lc00840k.
[10] D. Vloemans, L. V.an Hileghem, H. Ordutowski, F. D. Dosso, D. Spasic, J. Lammertyn, Self-Powered Microfluidics for Point-Of-Care Solutions: from Sampling to Detection of Proteins Nucleic Acids, Methods Mol Biol, Vol. 2804, 2024, pp. 3-50, https//doi.org/10.1007/978-1-0716-3850-7_1.
[11] S. A. Graham, E. Segal, Lab-on-a-chip Devices for Point-of-care Medical Diagnostics, Adv Biochem Eng Biotechnol, Vol. 179, 2022, pp. 247-265, https//doi.org/10.1007/10_2020_127/Figures/9.
[12] F. Piorino, A. T. Patterson, M. P. Styczynski, Low-cost, Point-of-care Biomarker Quantification, Curr Opin Biotechnol, Vol. 76, 2022, pp. 102738, https//doi.org/10.1016/J.Copbio.2022.102738.
[13] A. McDavid et al., Data Exploration, Quality Control and Testing in Single-cell Qpcr-based Gene Expression Experiments, Bioinformatics, Vol. 29, No. 4, 2013, pp. 461-467, https//doi.org/10.1093/Bioinformatics/Bts714.
[14] J. Chen, C. Xue, Y. Zhao, D. Chen, M. H. Wu, J. Wang, Microfluidic Impedance Flow Cytometry Enabling High-Throughput Single-Cell Electrical Property Characterization, International Journal of Molecular Sciences Vol. 16, No. 5, 2015, pp. 9804-9830, https//doi.org/10.3390/IJMS16059804.
[15] K. Cheung, S. Gawad, and P. Renaud, Impedance Spectroscopy Flow Cytometry: On-chip Label-free Cell Differentiation, Cytometry Part A, Vol. 65A, No. 2, 2005, pp. 124-132, https//doi.org/10.1002/CYTO.A.20141.
[16] K. C. Cheung et al., Microfluidic Impedance-based Flow Cytometry, Cytometry Part A, Vol. 77A, No. 7, 2010, pp. 648-666, https//doi.org/10.1002/CYTO.A.20910.
[17] C. Honrado, P. Bisegna, N. S. Swami, F. Caselli, Single-cell Microfluidic Impedance Cytometry: from Raw Signals to Cell Phenotypes Using Data Analytics, Lab Chip, Vol. 21, No. 1, 2021, pp. 22-54, https//doi.org/10.1039/D0LC00840K.
[18] T. Sun, H. Morgan, Single-cell Microfluidic Impedance Cytometry: A Review, Microfluid Nanofluidics, Vol. 8, No. 4, 2010, pp. 423-443, https//doi.org/10.1007/S10404-010-0580-9/Tables/1.
[19] G. Wu et al., Optimizing Microfluidic Impedance Cytometry by Bypass Electrode Layout Design, Biosensors (Basel), Vol. 14, No. 4, 2024, pp. 204, https//doi.org/10.3390/Bios14040204/S1.
[20] T. A. Nguyen, T. I. Yin, G. Urban, A Cell Impedance Sensor Chip for Cancer Cells Detection with Single Cell Resolution, Proceedings of Ieee Sensors, 2013, https//doi.org/10.1109/Icsens.2013.6688160.
[21] C. Ferguson, Y. Zhang, C. Palego, X. Cheng, Recent Approaches to Design and Analysis of Electrical Impedance Systems for Single Cells Using Machine Learning, Sensors, Vol. 23, No. 13, 2023, pp. 5990, https//doi.org/10.3390/S23135990.
[22] Z. Gao, Y. Li, Enhancing Single-cell Biology Through Advanced AI-powered Microfluidics, Biomicrofluidics, Vol. 17, No. 5, 2023, https//doi.org/10.1063/5.0170050/2914114.
[23] J. Wei et al., Machine Learning Classification of Cellular States Based on the Impedance Features Derived from Microfluidic Single-cell Impedance Flow Cytometry, Biomicrofluidics, Vol. 18, No. 1, 2024, https//doi.org/10.1063/5.0181287/3061531.
[24] C. Honrado, J. S. McGrath, R. Reale, P. Bisegna, N. S. Swami, F. Caselli, A Neural Network Approach for Real-Time Particle/Cell Characterization in Microfluidic Impedance Cytometry, Anal Bioanal Chem, Vol. 412, No. 16, 2020, pp. 3835-3845, https//doi.org/10.1007/S00216-020-02497-9/Figures/5.
[25] J. Wei et al., Machine Learning Classification of Cellular States Based on the Impedance Features Derived From Microfluidic Single-cell Impedance Flow Cytometry, Biomicrofluidics, Vol. 18, No. 1, 2024, https//doi.org/10.1063/5.0181287/3061531.
[26] P. T. Huong et al., A Novel Approach to Detect CD4 T-Lymphocytes Using a Microfluidic Chip and Compact Signal Processing Circuit, 2024, https//doi.org/10.21203/RS.3.RS-5171054/V1.