Colocalization Analysis And Particle Tracking In Multi-Channel Fluorescence Microscopy Images

Observing the dynamic characteristics of proteins by multichannel fluorescence microscopy allows studying the interactions between different subcellular structures. We introduce a probabilistic approach for tracking and colocalization analysis of viral proteins in two-channel microscopy image sequences. Our approach is based on particle filters and the Kalman filter, and performs tracking and colocalization analysis jointly. The transition probability matrix is adjusted dynamically. We have applied our approach to two-channel synthetic data and real live-cell fluorescence microscopy images of hepatitis C virus proteins required for the assembly of infectious virus particles. It turned out that the approach accurately determines the colocalization state of the observed particles and robustly tracks the particles.