Multi-Channel Colocalization Analysis and Visualization of Viral Proteins in Fluorescence Microscopy Images

Abstract

Automatic analysis of colocalizing biological structures in multi-channel fluorescence microscopy images is an important task to quantify and understand biological processes at high spatial-temporal resolution. Here, we introduce a software suite for colocalization analysis of spot-like objects in multi-channel fluorescence microscopy images. The software suite consists of ColocQuant and ColocJ, and is easy to use for biologists. ColocQuant is a Python-based software with graphical user interface to quantify colocalization of particles in two or three channels. Object-based colocalization is performed by an efficient multi-dimensional graph-based $k$ -d-tree approach, which determines nearest neighbors involved in double or triple colocalization. ColocJ enables efficient and intuitive visualization of the color composition of colocalizations by a Maxwell color triangle and a color ribbon. Colocalization information can be visualized for an entire image or a selected region-of-interest. In addition, global statistics of the particle intensity, particle size, and the number of colocalizations over time are provided. The colocalization analysis results can be exported and used in other software. We illustrate the application of our software suite for multi-channel live cell fluorescence microscopy image sequences of viral proteins in hepatitis C virus infected cells. We performed two-channel and three-channel colocalization analysis.

Keywords

• Biomedical imaging
• microscopy images
• colocalization analysis
• viral proteins