Single-cell microfluidic impedance cytometry: from raw signals to cell phenotypes using data analytics.
Single-cell microfluidic impedance cytometry: from raw signals to cell phenotypes using data analytics.
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DOI：
10.1039/d0lc00840k
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发表时间：
2021-01-05
期刊：
影响因子：
6.1
通讯作者：
Caselli F
中科院分区：
文献类型：
--
作者：
Honrado C;Bisegna P;Swami NS;Caselli F
利用微流控阻抗细胞术对单细胞进行生物物理分析，是一种基于细胞电生理对细胞系统异质性进行分层的无标记高通量分析方法。新兴的应用范围从基础生命科学和药物评估研究到护理点诊断和精确医学。最近，新的芯片设计和数据分析策略正在为多参数细胞表征和亚群区分奠定基础，这对于了解生物功能、跟踪疾病进展和监测微系统中的细胞行为至关重要。在这篇教程综述中，我们介绍了从阻抗细胞仪数据中阐明细胞和亚细胞特征的方法的比较综述，涵盖了设备设计、数据分析(即信号处理、介电模型、种群聚集)和表型应用的相关主题。我们特别强调该技术令人兴奋的最新发展(2017-2020年)，并对未来的挑战和方向提供我们的观点。它与微流控分离、传感器科学和机器学习的协同应用可以形成一个基本的工具包，用于无标记量化和亚群的分离，以分层不同的生物系统。微流控阻抗细胞术用于细胞系统异质性分层的芯片设计和数据分析综述
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
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影响因子：
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