Deep Learning based Method for Segmentation, Tracking, and Analysis of Intracellular Proteins and Their Interactions

Ramu Gautam, Yang Jiao, Mo Weng, Mei Yang

Abstract: The in-vivo functions of many proteins highly depend on their ability to assemble into supramolecular complexes at specific subcellular localizations. Such subcellular localizations can change in response to intracellular and extracellular cues, which is an important way to regulate the behavior and morphology of the cell. Therefore, segmentation and tracking of these supramolecular complexes are critical in understanding gene functions and regulations. Proteins that form discrete puncta or clusters can be tracked for a long period of time. However, segmentation and tracking become challenging when protein complexes undergo quick repositioning and remodeling such as assembly, disassembly, fusion, and split. In this paper, we first use deep learning methods to segment and track protein clusters with changing morphology, size, and intensity in hyperdimensional biological images. Using the segmentation and tracking results, we reconstruct the life paths and family tree of the protein clusters by integrating fusion and split events. Based on the family trees, various quantitative analyses can be performed including temporal correlations between the tracked protein and its interacting protein complexes. The effectiveness of the proposed method for analyzing subcellular proteins is confirmed through the evaluation using the two-channel 3D fluorescent microscopy time-lapse videos (5D images) of developing Drosophila embryos.