In-situ non-invasive detection of cellular reactive oxygen species by integrating Raman spectrum and bidirectional gated recurrent unit models

Cellular reactive oxygen species (ROS), a key parameter involved in cell metabolism, signaling, and apoptosis, whose detection is necessary to achieve in a variety of biological processes. However, current ROS detection methods, including fluorescence, colorimetry, and electrochemical methods, are difficult to achieve in-situ non-invasive detection due to their reliance on invasive probes or destructive sampling. In this study, we propose an in-situ non-invasive ROS detection integrating the Raman spectrum and a bidirectional gated recurrent unit (Bi-GRU) deep learning model during HepG2 cell apoptosis. The Bi-GRU model leverages bidirectional gating mechanisms to capture long-term dependencies in Raman spectra while incorporating both forward and backward spectral information for enhanced feature extraction. After training with spectral data of HepG2 cells in various apoptotic states, the R² (coefficient of determination) of the Bi-GRU model reaches 0.8511, which outperforms that of traditional methods such as KNN (0.2607), PLS (0.4720), and RNN (0.7724). In the present study, we not only realized the in-situ and non-invasive cellular ROS detection but also expanded the application of artificial intelligence in the field of cellular medicine. Importantly, this will provide a new research idea for further understanding the physiological state of cells and the mechanism of drug action.