Single Cell Classification Using Statistical Learning on Mechanical Properties Measured by MEMS Tweezers
Single Cell Classification Using Statistical Learning on Mechanical Properties Measured by MEMS Tweezers
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DOI：
10.1109/mems51670.2022.9699466
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发表时间：
2022-01
期刊：
影响因子：
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通讯作者：
Bahram Ahmadian;Deborah Mbujamba;J. Gerbedoen;M. Kumemura;H. Fujita;D. Collard;Sophie Dabo-Niang;Chann Lagadec;M. Tarhan
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文献类型：
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作者：
Bahram Ahmadian;Deborah Mbujamba;J. Gerbedoen;M. Kumemura;H. Fujita;D. Collard;Sophie Dabo-Niang;Chann Lagadec;M. Tarhan
细胞群体是异质的，因此呈现出广泛的特性作为转移潜力。但是，将稀有细胞用于临床应用需要对单个细胞进行精确分类。在这里，我们提出了一种单细胞的多参数分析，使用统计学习技术对它们进行分类，并预测每个细胞的亚群，尽管它们可能具有相似的特征。我们使用MEMS镊子在受控环境中分析来自两种不同乳腺癌细胞系的单细胞的机械特性（刚度，粘度和大小），并运行监督学习方法来预测它们所属的群体。这种无标记的方法是区分临床应用的稀有细胞亚群的重要一步。
Cell population is heterogenous and so presents a wide range of properties as metastatic potential. But using rare cells for clinical applications requires precise classification of individual cells. Here, we propose a multi-parameter analysis of single cells to classify them using statistical learning techniques and to predict the sub-population of each cell, although they may have close characteristics. We used MEMS tweezers to analyze mechanical properties (stiffness, viscosity, and size) of single cells from two different breast cancer cell lines in a controlled environment and run supervised learning methods to predict the population they belong to. This label-free method is a significant step forward to distinguish rare cell sub-populations for clinical applications.