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 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 tool-kit for label-free quantification and isolation of subpopulations to stratify heterogeneous biosystems.

Automated summary

Microfluidic impedance cytometry is a label-free and high-throughput method for stratifying cellular systems based on electrophysiology, with applications ranging from life science research to precision medicine. Novel chip designs and data analytic strategies are laying the foundation for multiparametric cell characterization and subpopulation distinction, essential for understanding biological function and disease progression. Emerging developments in the technique, such as device design, data analytics, and phenotyping applications, offer exciting prospects for future challenges and directions in label-free quantification and isolation of subpopulations in heterogeneous biosystems.