Application of Computational Language-Based Image Analysis to Investigate Intracellular Signaling

Cell migration is important for morphogenesis, cancer cell migration, and wound healing. Prediction of cell migration direction has numerous possibilities to reveal fine details, eventually contributing to the diagnosis of incurable diseases like Tumor, Cancer, Alzheimer's, and also can help in various computer-aided disease diagnoses. We focused on developing a MATLAB-based custom algorithm for analyzing sequential images of cells and a function of morphological properties that maintain the directionality of the cells. Several parameters of cell movement such as velocity, protrusion and retraction rates, and intensity distribution indicating localization of protein activity were calculated from simple cell migration images. A correlation is proposed in order to predict the direction of cells after a predetermined time-lapse period. The correlation curates the possibility to propose a function of high-intensity region-based hotspot areas, intensity and cell perimeter which relates the centroid movement of the concerned cell images. About 75% of total data was found to be following one of the relationships, which can be used for future study of cell migration. This study aims to illustrate the capability of MATLAB as an efficient and versatile image analysis tool and find a function of the morphological properties of cells to determine the directional cell migration.