Identification of Leader Cells in Cell Migration by Filopodia using Computer Vision

Cancer claimed to be one of the leading causes of death in the world, and it is just second to ischemic heart disease. While unintentional injury such as road accident is accounted for third place of leading causes of death globally [1]. According to the World Health Organization, approximately 9.60 million and 1.35 million deaths each year for cancer and road accidents respectively [2, 3]. While the risk of getting cancer or injuries can be greatly reduced by adopting a healthy lifestyle or paying more attention to the environment respectively, however, accidents still always happen. Once it does, the key point to save lives is to provide dedicated treatment at the earliest possible moment. Thus, the development of those treatments becomes essential in medical fields. To develop medicines or other treatments, various technology has been utilized. Compare to the past, today’s medical technology has reached a point where we can observe individual cells or even further smaller scale under various microscopy technology. With the study of the behavior of cells, many diseases and injuries including cancer that was determined to be impossible to be treated in the past became possible today. Among such studies, one of them is collective cell migration, which described as the movement of a group of cells with collective behavior towards the environment and among each other. It is the main pattern of locomotion of collective cells that contribute to cancer spreading and wound healing [4]. Due to that reason, many kinds of researches have been carried out to investigate the method to control the speed of the movement. If the movement can be slow down, cancer spreading will be slow down. In contrast, if the movement can be speed up, the wound healing effect will also speed up accordingly. Moreover, it is observed that special kinds of cells formed at the leading edges of the cells during collective cell migration, they are called leader cells. They act like leaders to the group of cells and are able to regulate the migration [5]. Thus, scientists are carrying out experiments revolving leader cells to investigate if its existence is the main reason for collective cell migration. Solving this enigma allowing us to take one step forward to develop the medicines that can slow down cancer spreading or speed up would healing, which helps to reduce deaths. To study leader cells in collective cell migration, an affirmative issue is the identification of leader cells itself. Thus, the characteristics that can distinguish leader cells with other cells, also called follower cells need to be determined. One of those determinants is the fact that it has been observed that leader cells possess distinctively large amounts of proteins from Rho family of GTPases such as Rac1 and its downstream to compare to the follower cells [6]. By protein staining, the signal of those proteins is stronger at the leading edges. While identifying leader cells by protein staining provide a clear result, the staining process itself is considered invasive and might already have disturbed the behavior of collective cell migration. In this research, a more direct, non-invasive method is proposed to identify leader cells more efficiently, which is utilizing computer vision with deep learning to identify leader cells through filopodia, a piece of basic information from leader cells’ morphology in a faster, more organized manner.