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 researc...

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Bibliographic Details
Published inLab on a chip Vol. 21; no. 1; pp. 22 - 54
Main Authors Honrado, Carlos, Bisegna, Paolo, Swami, Nathan S, Caselli, Federica
Format Journal Article
LanguageEnglish
Published England Royal Society of Chemistry 05.01.2021
Subjects
Clustering
Cytometry
Data analysis
Data Science
Electric Impedance
Electrophysiology
Flow Cytometry
Heterogeneity
Impedance
Machine learning
Mathematical analysis
Microfluidic Analytical Techniques
Microfluidics
Phenotype
Signal processing
Toolkits
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Summary: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. Review of chip designs and data analytics to stratify heterogeneity in cellular systems via microfluidic impedance cytometry.
Bibliography:Federica Caselli is Associate Professor of Biomedical Engineering at University of Rome Tor Vergata. She graduated in Medical Engineering, in Mathematics, and obtained her Ph.D. in advanced computational methods in biomechanics. Dr. Caselli's research deals with the development of microfluidic devices for biomedical applications. She seeks novel impedance-based solutions for single-cell biophysical phenotyping and manipulation, by using model-based device design, signal processing and data analytics.
10.1039/d0lc00840k
Carlos Honrado received his Ph.D. from the School of Electronics and Computer Science at the University of Southampton (UK) and he is now further pursuing research on label-free single particle analysis and separation, as a Postdoctoral Research Associate in the Department of Electrical and Computer Engineering at the University of Virginia (USA). His research interests are focused on the development of microfluidic devices for biomedical applications, using label-free and single-cell methods based on AC electrokinetics.
Paolo Bisegna serves as Professor of Mechanics of Materials and Structures at the Department of Civil Engineering and Computer Science, University of Rome Tor Vergata, and as Director of the Structural Engineering Doctoral Program. He received the M.Sc. degree in Engineering, the M.Sc. degree in Mathematics, and the M.D. degree. His research interests include lab-on-a-chip devices, biomechanics, mechanics of materials and structures.
Electronic supplementary information (ESI) available. See DOI
Nathan Swami serves as Professor of Electrical & Computer Engineering at the University of Virginia (UVA), Charlottesville, VA. His research group specializes in label-free microfluidic techniques for biofabrication, electrophysiology-based single-cell analysis and nano-confined systems for biomolecular analysis. Previously, he served on the scientific staff of the MEMS group at Motorola Labs and at Clinical Microsensors, Inc. He seeks to impact diagnostic systems in point-of-care and resource-poor settings.
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ISSN:1473-0197
1473-0189
1473-0189
DOI:10.1039/d0lc00840k