Deep-Learning Terahertz Single-Cell Metabolic Viability Study

文献类型： 外文期刊

第一作者： Yang, Ning

作者： Yang, Ning;Shi, Qian;Wei, Mingji;Wang, Wencong;Xiao, Yi;Xia, Muming;Zhang, Xingcai;Cai, Xiaolu;Zhang, Xiaodong;Mao, Hanping;Pan, Xiaoqing;Zou, Xiaobo;Guo, Ming

作者机构：

关键词： cell viability; deep learning; terahertz; cell apoptosis; absorption spectrum; spectroscopy; machine learning

期刊名称：ACS NANO （ 影响因子：17.1； 五年影响因子：17.1 ）

ISSN： 1936-0851

年卷期： 2023 年 17 卷 21 期

页码：

收录情况： SCI

摘要： Cell viability assessment is critical, yet existing assessments are not accurate enough. We report a cell viability evaluation method based on the metabolic ability of a single cell. Without culture medium, we measured the absorption of cells to terahertz laser beams, which could target a single cell. The cell viability was assessed with a convolution neural classification network based on cell morphology. We established a cell viability assessment model based on the THz-AS (terahertz-absorption spectrum) results as y = a = (x - b)(c) , where x is the terahertz absorbance and y is the cell viability, and a, b, and c are the fitting parameters of the model. Under water stress the changes in terahertz absorbance of cells corresponded one-to-one with the apoptosis process, and we propose a cell 0 viability definition as terahertz absorbance remains unchanged based on the cell metabolic mechanism. Compared with typical methods, our method is accurate, label-free, contact-free, and almost interference-free and could help visualize the cell apoptosis process for broad applications including drug screening.

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