TIRF protein tracking and analysis
A bioimage processing workflow that detects, tracks and analyzes protein dynamics in TIRF microscopy images.
This project was done in group in Bioimage Informatics (2023 Spring) taught by Dr. Sage Daniel and Dr. Seitz Arne.
Total internal reflection fluorescence (TIRF) microscopy is an advance technique for quantifying fluorescence signal in a hundred-nanometer thin axial section. In the context of particle tracking, it provides high contrast images of how particles diffuse, interact, and traffic within the plane.
Together with Kuzey Aydin and Chang Liu, we built a particle tracking workflow for TIRF microscopy images. Specifically, I focused on the development of particle detection and trajectory linking, and my teammates worked on trajectory analysis.
- Particle detection: particles in our raw data feature diverse moving patterns, including elongated movement and burst-like movement. To detect these particles accordingly, a particle detection algorithm was developed based on a series of preprocessing steps (i.e., exponential correction, DoG filter, gaussian blur, tophat filter) and feature-dependent particle localization (i.e., either local max or autothresholding).
- Trajectory linking: following particle detection, particles were linked from frame to frame to form trajectories. A loss function was defined based on distance, grayscale intensity, and additionally particle orientation. The orientation was determined by a time convolution filter.
Please refer to the GitHub repository for more details.
