Deep-Learning Terahertz Single-Cell Metabolic Viability Study
Ning Yang, Qian Shi, Mingji Wei, Yi Xiao, Muming Xia, Xiaolu Cai, Xiaodong Zhang, Xiaodong Zhang, Wencong Wang, Xiaoqing Pan, Hanping Mao, Xiaobo Zou, Ming Guo, Xingcai Zhang, Xingcai Zhang
Abstract
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.