Project Summary Tracking living cells in video sequences is a fundamental task in many fields of science, including biochemistry, bioinformatics, cell biology, and genetics. Manually linking cells is extremely time-consuming and not feasible in large-scale analysis. Automatic approaches can compute cell links by measuring how close two instances of a cell are, or how similar they look. These techniques work well with video acquired at a relatively high frame rate, but, unfortunately, acquiring images at high frame rates affects cells negatively. Too frequent imaging not only causes phototoxicity, leading to experimental artifacts, but also photobleaching, leading to the inability to measure quantities of interest over time. In addition, during image acquisition, the environment temperature and air quality are typically less controlled, which could also contribute to cytotoxicity. Moreover, when performing high- throughput live-cell imaging, the lower the acquisition rate, the more cells/plates can be imaged, and, consequently, the more experimental treatments can be applied and studied. If reducing the acquisition rate is beneficial for all these reasons, it severely affects the accuracy of cell tracking algorithms. To this end, we propose a new class of cell tracking approaches based on cell movement predictions. Instead of comparing cells based on their similarity, we propose to predict where every cell will move in the next frame. This will allow for searching the occurrence of such cells, even if the next frame was acquired after an extended period. The new approach will be investigated using a newly generated dataset for low frame rate cell tracking (Aim 1). Cell displacement will be predicted by using a new Recurrent Neural Network designed for the task (Aim 2). Cell tracking algorithms will be defined re-evaluating existing approaches under low-frame rate constraints when using cell displacement information (Aim 3). While current approaches require image acquisition to occur at least every 5-15 minutes, we will investigate the feasibility of cell tracking on images acquired at intervals of up to 2 hours. If successful, our research will allow to accurately track cells in low frame rate video sequences without the need for specialized tools or equipment.