Abstract
Interference in cell cycle progression has been noted as one of the important properties of anticancer drugs. In this study, we developed the cell cycle prediction model using high-content imaging data of recipient cells after drug exposure and DNA-staining with a low-toxic DNA dye, SiR-DNA. For this purpose, we exploited HeLa and MCF7 cells introduced with a fluorescent ubiquitination-based cell cycle indicator (Fucci). Fucci-expressing cancer cells were subjected to high-content imaging analysis using OperettaCLS after 36-h exposure to anticancer drugs; the nuclei were segmented, and the morphological and intensity properties of each nucleus characterized by SiR-DNA staining were calculated using imaging analysis software, Harmony. For the use of training, we classified cells into each phase of the cell cycle using the Fucci system. Training data (n = 7500) and validation data (n = 2500) were randomly sampled and the binary classification prediction models for G1, early S, and S/G2/M phases of the cell cycle were developed using four supervised machine learning algorithms. We selected random forest as the model with the best performance through 10-fold cross-validation; the accuracy rate was approximately 75%-87%. Regarding feature importance, variables expected to be biologically related to the cell cycle, for example, signal intensity and nuclear size, were highly ranked, suggesting the validity of the model. These results showed that the cell cycle can be predicted in cancer cells by simply exploiting the current prediction model using fluorescent images of DNA-staining dye, and the model could be applied for the use of future ex vivo drug sensitivity diagnosis.