Atomic Force Microscopy Detects the Difference in Cancer Cells of Different Neoplastic Aggressiveness via Machine Learning
Atomic Force Microscopy Detects the Difference in Cancer Cells of Different Neoplastic Aggressiveness via Machine Learning
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DOI：
10.1002/anbr.202000116
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发表时间：
2021-08-01
影响因子：
3.4
通讯作者：
Sokolov, Igor
中科院分区：
文献类型：
--
作者：
Prasad, Siona;Rankine, Alex;Sokolov, Igor
报道了一种基于原子力显微镜（AFM）的新方法，该方法在ring模式（RM）下工作，以区分两种表现出不同程度肿瘤侵袭性的相似人类结肠上皮癌细胞系。基于单细胞图像识别细胞系的分类准确率可高达94%（受试者工作特征曲线下面积为0.99）。对比RM和常规成像通道的精度，可以看出RM通道具有较高的精度。用传统的原子力显微镜压痕法对细胞进行了研究，从而获得了细胞力学和细胞外膜的信息。虽然在压痕法中也可以看到两种细胞系之间存在统计学上的显著差异，但它在单细胞水平上提供的细胞系识别精度小于68% （ROC曲线下面积为0.73）。因此，AFM细胞成像在识别细胞表型方面比传统的AFM压痕法更准确。所有获得的细胞数据都收集在固定细胞上，并使用机器学习方法进行分析。讨论了观察到的分类的生物物理原因。
A novel method based on atomic force microscopy (AFM) working in Ringing mode (RM) to distinguish between two similar human colon epithelial cancer cell lines that exhibit different degrees of neoplastic aggressiveness is reported on. The classification accuracy in identifying the cell line based on the images of a single cell can be as high as 94% (the area under the receiver operating characteristic [ROC] curve is 0.99). Comparing the accuracy using the RM and the regular imaging channels, it is seen that the RM channels are responsible for the high accuracy. The cells are also studied with a traditional AFM indentation method, which gives information about cell mechanics and the pericellular coat. Although a statistically significant difference between the two cell lines is also seen in the indentation method, it provides the accuracy of identifying the cell line at the single-cell level less than 68% (the area under the ROC curve is 0.73). Thus, AFM cell imaging is substantially more accurate in identifying the cell phenotype than the traditional AFM indentation method. All the obtained cell data are collected on fixed cells and analyzed using machine learning methods. The biophysical reasons for the observed classification are discussed.