Understanding protein multi- and trans-localisation at the full proteome level

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In biology, localisation is function. Cells display a complex sub-cellular structure, where each of these niches are characterised by specific biochemical conditions and fulfil dedicated functions. A protein must be localised to its intended sub-cellular niche to meet its interaction partners and be functionally active. Hence, being able to systematically measure the locations of proteins, and in particular the full proteome, a field coined spatial proteomics, is of major interest in cell biology.To further depict an accurate view of the spatial sub-cellular landscape of proteins, they are known to display more than one sub-cellular location, and to traffic between different such niches. The former phenomenon is termed multi-localisation and the second one, whether initiated by normal biological triggers, pathological cellular states, or external stimuli such as changes in the cell nutrients or effect of a drug, is called trans-localisation. Finally, the mis-localisation of proteins have been associated with cellular dis-function and diseases such as cancer.The most information-rich datasets for proteome-wise spatial proteomics are generated using high accuracy mass-spectrometry, a technique that allows to identify and quantify the proteome content in complex biological samples. These datasets are high quality rich sources of data that have been mined using a variety of robust supervised statistical machine learning methods which have shown to yield valuable protein-organelle predictions. In particular, the applicants recently published hyperLOPIT, a technological advance enabling to obtain exquisite spatial resolution. Using this groundbreaking technology on mouse embryonic stem cells, they identified the localisation of 7000 proteins with unprecedented spatial resolution, uncovering the organisation of organelles, sub-organellar compartments, protein complexes, functional networks, and the steady-state dynamics of proteins including unexpected sub-cellular locations.In this proposal, we aim to complement contemporary spatial proteomics data with state-of-the-art statistical routines to reliably identify multi- and trans-localisation events at the full proteome level. These new tools, which will complement our existing open-source spatial proteomics suite of software, will enable the proteomics and cell biology community to mine spatial proteomics data to new depths, identifying subtle yet biologically important patterns such as proteins with mixed localisation and proteins that change localisations upon perturbation, in a robust and statistically sound way. We will also develop dedicated visualisation platforms to highlight the outputs of our analysis pipelines and enable interactive exploration of the multidimensional spatial data. We will apply these tools ourselves on a wide range of spatial proteomics datasets from various different biological systems of interest. To guarantee broad exposure of our work, the datasets we will analyse and the spatial patterns we will infer will further be disseminated through community databases, in particular the SpatialMap.org online resource.