Quantitative characterization of cell physiological state based on dynamical cell mechanics for drug efficacy indication

Shuang Ma; Junfeng Wu; Zhihua Liu; Rong He; Yuechao Wang; Lianqing Liu; Tianlu Wang; Wenxue Wang

doi:10.1016/j.jpha.2023.03.002

Volume 13 Issue 4

Apr. 2023

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Article Navigation > Journal of Pharmaceutical Analysis > 2023 > 13(4): 388-402

Shuang Ma, Junfeng Wu, Zhihua Liu, Rong He, Yuechao Wang, Lianqing Liu, Tianlu Wang, Wenxue Wang. Quantitative characterization of cell physiological state based on dynamical cell mechanics for drug efficacy indication[J]. Journal of Pharmaceutical Analysis, 2023, 13(4): 388-402. doi: 10.1016/j.jpha.2023.03.002

Citation:

Shuang Ma, Junfeng Wu, Zhihua Liu, Rong He, Yuechao Wang, Lianqing Liu, Tianlu Wang, Wenxue Wang. Quantitative characterization of cell physiological state based on dynamical cell mechanics for drug efficacy indication[J]. Journal of Pharmaceutical Analysis, 2023, 13(4): 388-402. doi: 10.1016/j.jpha.2023.03.002

Citation:

Shuang Ma, Junfeng Wu, Zhihua Liu, Rong He, Yuechao Wang, Lianqing Liu, Tianlu Wang, Wenxue Wang. Quantitative characterization of cell physiological state based on dynamical cell mechanics for drug efficacy indication[J]. Journal of Pharmaceutical Analysis, 2023, 13(4): 388-402. doi: 10.1016/j.jpha.2023.03.002

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Quantitative characterization of cell physiological state based on dynamical cell mechanics for drug efficacy indication

doi: 10.1016/j.jpha.2023.03.002

Shuang Ma^a,b,c,
Junfeng Wu^a,b,c,
Zhihua Liu^a,b,
Rong He^d,
Yuechao Wang^a,b,
Lianqing Liu^a,b,
Tianlu Wang^{e
,
,},
Wenxue Wang^{a,b
,
,}

a. State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China;
b. Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China;
c. University of Chinese Academy of Sciences, Beijing 100049, China;
d. Department of Thoracic Surgery, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang 110042, China;
e. Department of Radiotherapy, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang 110042, China

Funds:

This work was supported by the National Natural Science Foundation of China (Grant Nos: U1908215, 61925307, 62003338, and 61933008), CAS Project for Young Scientists in Basic Research (Grant No: YSBR-041), Liaoning Revitalization Talents Program (Grant No: XLYC2002014), Natural Science Foundation of Liaoning Province of China (Grant No: 2020-ZLLH-47), and Joint fund of Science & Technology Department of Liaoning Province and State Key Laboratory of Robotics, China (Grant No: 2019-KF-01-01).

Received Date: Nov. 14, 2022
Accepted Date: Mar. 07, 2023
Rev Recd Date: Mar. 06, 2023
Publish Date: Mar. 13, 2023

Abstract

Abstract

Cell mechanics is essential to cell development and function, and its dynamics evolution reflects the physiological state of cells. Here, we investigate the dynamical mechanical properties of single cells under various drug conditions, and present two mathematical approaches to quantitatively characterizing the cell physiological state. It is demonstrated that the cellular mechanical properties upon the drug action increase over time and tend to saturate, and can be mathematically characterized by a linear time-invariant dynamical model. It is shown that the transition matrices of dynamical cell systems significantly improve the classification accuracies of the cells under different drug actions. Furthermore, it is revealed that there exists a positive linear correlation between the cytoskeleton density and the cellular mechanical properties, and the physiological state of a cell in terms of its cytoskeleton density can be predicted from its mechanical properties by a linear regression model. This study builds a relationship between the cellular mechanical properties and the cellular physiological state, adding information for evaluating drug efficacy.
- Cellular mechanical properties,
- Cytoskeleton,
- Drug efficacy evaluation,
- Cell system modelling,
- Linear regression

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References(50)

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