Interphase cell cycle staging using deep learning

The progression of cells through the cell cycle is a tightly regulated process and is known to be key in maintaining normal tissue architecture and function. Disruption of these orchestrated phases will result in alterations that can lead to many diseases including cancer. Regrettably, reliable automatic tools to evaluate the cell cycle stage of individual cells are still lacking, in particular at interphase. Therefore, the development of new tools for a proper classification are urgently needed and will be of critical importance for cancer prognosis and predictive therapeutic purposes. Thus, in this work, we aimed to investigate three deep learning approaches for interphase cell cycle staging in microscopy images: 1) joint detection and cell cycle classification of nuclei patches; 2) detection of cell nuclei patches followed by classification of the cycle stage; 3) detection and segmentation of cell nuclei followed by classification of cell cycle staging. Our methods were applied to a dataset of microscopy images of nuclei stained with DAPI. The best results (0.908 F1-Score) were obtained with approach 3 in which the segmentation step allows for an intensity normalization that takes into account the intensities of all nuclei in a given image. These results show that for a correct cell cycle staging it is important to consider the relative intensities of the nuclei. Herein, we have developed a new deep learning method for interphase cell cycle staging at single cell level with potential implications in cancer prognosis and therapeutic strategies.