ProteinCoLoc streamlines Bayesian analysis of colocalization in microscopic images

Abstract

Colocalization, the spatial overlap of molecular entities, is often key to support their involvement in common functions. Existing colocalization tools, however, face limitations, particularly because of their basic statistical analysis and their low-throughput manual entry processes making them unsuitable for automation and potentially introducing bias. These shortcomings underscore the need for user-friendly tools streamlining colocalization assessments and enabling their robust and automated quantitative analyses. We have developed ProteinCoLoc, an innovative software designed for automated high-throughput colocalization analyses and incorporating advanced statistical features such as Bayesian modelling, automatic background detection and localised correlation analysis. ProteinCoLoc rationalises colocalization assessments without manual input, comes with a user-friendly graphical user interface and provides various analytics allowing to study and locally quantify colocalization. This easy-to-use application presents numerous advantages, including a direct comparison with controls employing a Bayesian model and the analysis of local correlation patterns, while reducing hands-on time through automatic background detection. The software was validated while studying the colocalization pattern of two proteins forming a stable complex: the huntingtin protein (HTT) and its partner huntingtin-associated protein 40 (HAP40). Our results showcase the software’s capacity to quantitatively assess colocalizations. ProteinCoLoc is available both as a Julia package and as a compiled software (https://github.com/ma-seefelder/ProteinCoLoc).