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Review
. 2023 Jun 28;23(13):5990.
doi: 10.3390/s23135990.

Recent Approaches to Design and Analysis of Electrical Impedance Systems for Single Cells Using Machine Learning

Caroline Ferguson  1 , Yu Zhang  1 , Cristiano Palego  2 , Xuanhong Cheng  1   3
Affiliations
Review

Recent Approaches to Design and Analysis of Electrical Impedance Systems for Single Cells Using Machine Learning

Caroline Ferguson et al. Sensors (Basel). .

Abstract

Individual cells have many unique properties that can be quantified to develop a holistic understanding of a population. This can include understanding population characteristics, identifying subpopulations, or elucidating outlier characteristics that may be indicators of disease. Electrical impedance measurements are rapid and label-free for the monitoring of single cells and generate large datasets of many cells at single or multiple frequencies. To increase the accuracy and sensitivity of measurements and define the relationships between impedance and biological features, many electrical measurement systems have incorporated machine learning (ML) paradigms for control and analysis. Considering the difficulty capturing complex relationships using traditional modelling and statistical methods due to population heterogeneity, ML offers an exciting approach to the systemic collection and analysis of electrical properties in a data-driven way. In this work, we discuss incorporation of ML to improve the field of electrical single cell analysis by addressing the design challenges to manipulate single cells and sophisticated analysis of electrical properties that distinguish cellular changes. Looking forward, we emphasize the opportunity to build on integrated systems to address common challenges in data quality and generalizability to save time and resources at every step in electrical measurement of single cells.

Keywords: electrical sensing; impedance cytometry; impedance spectroscopy; machine learning; single-cell analysis.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Summary of individual cell properties that can be measured to distinguish populations.
Figure 2
Figure 2
Structural overview of topics covered over the course of this review.
Figure 3
Figure 3
Schematic demonstrating basic processing in ML classification model training and to determine final performance on a population of treated cells.
Figure 4
Figure 4
Visualization using partition-based graph abstraction (PAGA) of different levels of cell property clustering during the process of differentiation. Reprinted with permission from Ref. [91]. 2020. Genome Biology.
See this image and copyright information in PMC

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