A Python module for programmatic access to TrypTag genome-wide subcellular protein localisation data in Trypanosoma brucei

Abstract

Protein subcellular localisation is informative for understanding potential protein function, particularly in highly structured unicellular eukaryotes. Microscopy is especially powerful for interrogating localisation, providing high resolution single cell data about where a protein resides. We previously generated the TrypTag dataset - a genome-wide protein localisation resource for the human unicellular parasite Trypanosoma brucei using fluorescent protein tagging. This is a puissant dataset due to its scale: Originally captured with high content image analysis in mind, it is a formidable resource for machine learning or artificial intelligence tool development and testing. Here, we describe a Python module for programmatic access to this data rich resource. Images of each tagged cell line, together with segmented cell masks, can be accessed arbitrarily by gene ID and tagging terminus, the database can be searched by protein localisation, and tools are provided to assist foundational image analysis of individual T. brucei cell cycle stage and morphology. We stress-tested this tool by using it to examine a key feature of T. brucei morphogenesis during division: The old and newly formed flagellum and associated organelles tend to have different protein compositions, and using the TrypTag toolkit we show that there is extensive age-based differential content of these organelles while the daughter nuclei completely lack such asymmetry.