Single cell density prediction based on optically induced electrokinetics (OEK) and machine learning.

Journal: Analytical methods : advancing methods and applications
Published Date:

Abstract

Single cell density is a key indicator for judging cell physiological state, crucial for studying cell function. However, existing measurement methods are often complex and time-consuming, limiting their efficiency in practical applications. To address this, we developed a machine learning-driven single cell density prediction system based on an optically induced electrokinetics (OEK) platform. First, the OEK platform was designed to enable non-invasive electrical manipulation of cells, and cell motion trajectories were obtained using a Depth-from-Defocus (DFD)-based template matching algorithm. Then, the time series of matched frame counts during sedimentation were extracted to characterize feature differences among cells with varying densities. Finally, Bayesian optimization was applied to a gradient boosting machine (GBM) model for parameter tuning and density prediction. The proposed method achieves an of 0.950, a root mean square error (RMSE) of 0.0037 g cm, and a mean absolute error (MAE) of 0.0028 g cm, yielding the lowest prediction errors compared with several mainstream machine learning models and reducing computation time and load compared to our previous method. These results demonstrate the effectiveness of the proposed method, which is expected to improve measurement efficiency and offer a new tool for cell biomedical research.

Authors

  • Xiru Lin
    School of Control Engineering, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China. zhaoyuliang@neuq.edu.cn.
  • Xinyue Zhang
    Department of Radiology, Changhai Hospital.
  • Jinliang Shao
    School of Control Engineering, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China. zhaoyuliang@neuq.edu.cn.
  • Chao Lian
    School of Control Engineering, Northeastern University at Qinhuangdao, Qinhuangdao, 066000, Hebei, China.
  • Ying Wang
    Key Laboratory of Macromolecular Science of Shaanxi Province, School of Chemistry & Chemical Engineering, Shaanxi Normal University, Xi'an, Shaanxi 710062, China.
  • Dongmei Yan
    School of Control Engineering, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China. zhaoyuliang@neuq.edu.cn.
  • Yuliang Zhao
    Department of Control Engineering, Northeastern University, Qinhuangdao, Hebei, 066001, PR China.
  • Wen Jung Li
    Department of Mechanical Engineering, City University of Hong Kong, Hong Kong, China.

Keywords

No keywords available for this article.