Multi-scale characterization and analysis of cellular viscoelastic mechanical phenotypes by atomic force microscopy

被引:1
|
作者
Zeng, Yi [1 ,2 ]
Liu, Xianping [3 ]
Wang, Zuobin [1 ,2 ,4 ,5 ]
Gao, Wei [6 ,7 ]
Zhang, Shengli [1 ,2 ]
Wang, Ying [1 ,2 ]
Liu, Yunqing [6 ]
Yu, Haiyue [1 ,2 ]
机构
[1] Changchun Univ Sci & Technol, Int Res Ctr Nano Handling & Mfg China, Changchun 130022, Peoples R China
[2] Changchun Univ Sci & Technol, Minist Educ, Key Lab Cross Scale Micro & Nano Mfg, Changchun, Peoples R China
[3] Univ Warwick, Sch Engn, CV4 7AL AC, Coventry, England
[4] Univ Bedfordshire, JR3CN, Luton, Beds, England
[5] Univ Bedfordshire, IRAC, Luton, Beds, England
[6] Changchun Univ Sci & Technol, Sch Elect & Informat Engn, Changchun, Peoples R China
[7] Changchun Univ, Sch Elect Informat & Engn, Changchun, Peoples R China
关键词
atomic force microscope; indentation parameters; liver cell; machine learning; viscoelasticity; SUBSTRATE STIFFNESS; AFM INDENTATION; CANCER; CELLS; BIOMECHANICS;
D O I
10.1002/jemt.24505
中图分类号
R602 [外科病理学、解剖学]; R32 [人体形态学];
学科分类号
100101 ;
摘要
The viscoelasticity of cells serves as a biomarker that reveals changes induced by malignant transformation, which aids the cytological examinations. However, differences in the measurement methods and parameters have prevented the consistent and effective characterization of the viscoelastic phenotype of cells. To address this issue, nanomechanical indentation experiments were conducted using an atomic force microscope (AFM). Multiple indentation methods were applied, and the indentation parameters were gradually varied to measure the viscoelasticity of normal liver cells and cancerous liver cells to create a database. This database was employed to train machine-learning algorithms in order to analyze the differences in the viscoelasticity of different types of cells and as well as to identify the optimal measurement methods and parameters. These findings indicated that the measurement speed significantly influenced viscoelasticity and that the classification difference between the two cell types was most evident at 5 mu m/s. In addition, the precision and the area under the receiver operating characteristic curve were comparatively analyzed for various widely employed machine-learning algorithms. Unlike previous studies, this research validated the effectiveness of measurement parameters and methods with the assistance of machine-learning algorithms. Furthermore, the results confirmed that the viscoelasticity obtained from the multiparameter indentation measurement could be effectively used for cell classification.Research Highlights This study aimed to analyze the viscoelasticity of liver cancer cells and liver cells. Different nano-indentation methods and parameters were used to measure the viscoelasticity of the two kinds of cells. The neural network algorithm was used to reverse analyze the dataset, and the methods and parameters for accurate classification and identification of cells are successfully found. The morphology of cells was obtained by atomic force microscopy, and the viscoelastic characteristics of cells were obtained by indentation experiments of relaxation and creep. image
引用
收藏
页码:1157 / 1167
页数:11
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