Authors

U Dobramysl¹; R Wheeler¹;  ¹ Peter Medawar Building for Pathogen Research, Nuffield Department of Medicine, University of Oxford, UK

Discussion

TrypTag has great potential as a disruptive resource for discovery biology. It comprises 65,474 images containing 4,584,261 cells across 12,459 cell lines which map subcellular protein localisation genome-wide in the Trypanosoma brucei parasite procyclic form. TrypTag is the first genome-wide protein subcellular localisation dataset for a parasite, for a flagellate and for a eukaryote outside of animal/fungi. This makes it a powerful resource for understanding trypanosomatid pathogen biology, evolution of parasitism, flagellar biology and eukaryotic diversity. However, to do so, effective data access is needed.
Here, we describe a python module for seamless access to TrypTag data. It automatically handles fetching and caching localisation and microscopy data, massively simplifying access. A localisation search is simply "result = tryptag.localisation_search('paraflagellar rod')", while loading a field of view of microscopy data is "images = tryptag.open_field('Tb927.7.1920', 'n')". All data is loaded directly from the permanent public Zenodo data depositions supporting our 2023 TrypTag main paper. This easy data access is supported by trypanosome-specific image analysis tools based on our previous work.
Trypanosoma brucei famously has asymmetries in division, even during normal proliferation, where single copy organelles which have duplicated ready for division have differing protein compositions. However fundamental questions remain unanswered, for example are the two daughter nuclei identical or is there an ‘old’ and a ‘new’ nucleus with differing protein composition? This is extremely difficult to identify using any information other than protein localisation. As an example of the power of this python toolkit, we demonstrate a quantitative and statistically-supported high-throughput search for such asymmetrically distributed proteins – all in <50 lines of code. This is just one example of the enormous number of possible analyses, exploiting the highly organised nature of the T. brucei cell, that this python module enables.