Fig. 1. HyperLOPIT was employed to resolve the spatial proteome of T. bruceiand T. congolense BSF and PCF.

A Outline of the experimental workflow to implement hyperLOPIT on African trypanosomes: first cells were harvested and lysed by nitrogen cavitation releasing subcellular compartments. Soluble proteins and crude membranes were separated on an iodixanol cushion followed by pelleting of the membranous fraction, which was then resolved by density gradient centrifugation. After collection, fractions were analysed by western blotting, strategically pooled and prepared for multiplexed quantitative proteomics with LC-SPS-MS³ analysis. Peptide identification and quantification were performed in ProteomeDiscoverer with further processing including aggregation and normalisation performed in R. Datasets were inspected visually (t-SNE) and with unsupervised clustering (HDBSCAN). Unsupervised clustering guided the curation of marker proteins allied with Novelty-TAGM for further discovery in T. congolense. Resultant marker proteins were used in the classification of uncharacterised proteins using TAGM-MAP. Further analysis was performed with TAGM-MCMC for insight into proteins unclassified with TAGM-MAP that may reside in multiple locations. Image created with BioRender.com. B Three hyperLOPIT experimental iterations (#1–3) were conducted and data from all iterations were concatenated per cell type. Venn diagrams show that the final datasets contain approximately 5500 proteins per cell-type for which there is 33-plex quantitative data. C Dimensionality reduction via t-SNE projection facilitates visualisation of each 33-dimensional dataset in two-dimensions which reveals structure within the data. Each point represents a protein.