Abstract
Quantitatively tracking subcellular protein puncta over time is critical for understanding protein functions and related cellular processes, as many proteins assemble into discrete protein puncta when performing functions. Monitoring these protein puncta over an extended period enables the quantitation of their dynamic behaviors, such as repositioning, remodeling, and inter-object interactions such as fusion and split. These characteristics are essential for deciphering the underlying regulatory mechanisms of protein function. However, tracking protein puncta is challenging due to these clusters undergoing rapid and complex temporal changes. To address these challenges, a comprehensive tool 'ProTrack3D' is developed. It implements a multi-stage multi-object tracking workflow specifically designed to handle the complexity of dynamic protein puncta. The segmentation stage can adopt any advanced deep neural network to detect protein puncta. The Tracking stage follows individual puncta over time and detects birth, death, split and fusion events. The Life Path Reconstruction stage visualizes the life history of protein puncta using tree structures. Applying ProTrack3D tool to the 4D fluorescence microscopy images of developing Drosophila embryos confirms the effectiveness of the method implemented at each stage. Compared with existing tracking tools, our tracking algorithm achieves significant improvement in tracking performance by utilizing deep feature maps which encode rich spatial and intensity information. By integrating all stages of tracking and offering quantitative analysis functions with a user-friendly graphical interface, ProTrack3D enables researchers with basic computer skills to perform segmentation, tracking, and analysis of protein puncta in fluorescence microscopy images, thus facilitating the broader accessibility and usability of advanced protein tracking techniques. ProTrack3D is available for distribution via GitHub at https://github.com/ramugautam1/ProTrack3D.
