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Sub-cellular localisation of proteins is an essential post-translational regulatory mechanism that can be assayed using high-throughput mass spectrometry (MS). These MS-based spatial proteomics experiments enable us to pinpoint the sub-cellular distribution of thousands of proteins in a specific system under controlled conditions. Recent advances in high-throughput MS methods have yielded a plethora of experimental spatial proteomics data for the cell biology community. Yet, there are many third-party data sources, such as immunofluorescence microscopy or protein annotations and sequences, which represent a rich and vast source of complementary information. We present a unique transfer learning classification framework that utilises a nearest-neighbour or support vector machine system, to integrate heterogeneous data sources to considerably improve on the quantity and quality of sub-cellular protein assignment. We demonstrate the utility of our algorithms through evaluation of five experimental datasets, from four different species in conjunction with four different auxiliary data sources to classify proteins to tens of sub-cellular compartments with high generalisation accuracy. We further apply the method to an experiment on pluripotent mouse embryonic stem cells to classify a set of previously unknown proteins, and validate our findings against a recent high resolution map of the mouse stem cell proteome. The methodology is distributed as part of the open-source Bioconductor pRoloc suite for spatial proteomics data analysis. ; LMB was supported by a BBSRC Tools and Resources Development Fund (Award BB/K00137X/1) and a Wellcome Trust Technology Development Grant (108441/Z/15/Z). LG was supported by the European Union 7th Framework Program (PRIME-XS project, grant agreement number 262067) and a BBSRC Strategic Longer and Larger Award (Award BB/L002817/1). DW and OK acknowledge funding from the European Union (PRIME-XS, GA 262067) and Deutsche Forschungsgemeinschaft (KO-2313/6-1). ; This is the final version of the article. It first appeared from PLOS via https://doi.org/10.1371/journal.pcbi.1004920

Knowledge of the subcellular distribution of proteins is vital for understanding cellular mechanisms. Capturing the subcellular proteome in a single experiment has proven challenging, with studies focusing on specific compartments or assigning proteins to subcellular niches with low resolution and/or accuracy. Here we introduce hyperLOPIT, a method that couples extensive fractionation, quantitative high-resolution accurate mass spectrometry with multivariate data analysis. We apply hyperLOPIT to a pluripotent stem cell population whose subcellular proteome has not been extensively studied. We provide localization data on over 5,000 proteins with unprecedented spatial resolution to reveal the organization of organelles, sub-organellar compartments, protein complexes, functional networks and steady-state dynamics of proteins and unexpected subcellular locations. The method paves the way for characterizing the impact of post-transcriptional and post-translational modification on protein location and studies involving proteome-level locational changes on cellular perturbation. An interactive open-source resource is presented that enables exploration of these data. ; The authors thank Andreas Hühmer, Philip Remes, Jesse Canterbury and Graeme McAlister of Thermo Fisher Scientific, San Jose, CA, USA, for their advice regarding operation of the Orbitrap Fusion. We also thank Mike Deery for assistance with checking sample integrity on the mass spectrometers in the Cambridge Centre for Proteomics on equipment purchased via a Wellcome Trust grant (099135/Z/12/Z ), and Brian Hendrich of the Wellcome Trust-MRC Stem Cell Institute in Cambridge and Sean Munro of the MRC Laboratory of Molecular Biology in Cambridge for insightful comments about the data. AC was supported by BBSRC grant (BB/D526088/1). C.M.M. and L.G. were supported by European Union 7th Framework Program (PRIMEXS project, grant agreement number 262067), L.M.B was supported by a BBSRC Tools and Resources Development Fund (Award BB/K00137X/1), and P.C.H. was ...

Knowledge of the subcellular distribution of proteins is vital for understanding cellular mechanisms. Capturing the subcellular proteome in a single experiment has proven challenging, with studies focusing on specific compartments or assigning proteins to subcellular niches with low resolution and/or accuracy. Here we introduce hyperLOPIT, a method that couples extensive fractionation, quantitative high-resolution accurate mass spectrometry with multivariate data analysis. We apply hyperLOPIT to a pluripotent stem cell population whose subcellular proteome has not been extensively studied. We provide localization data on over 5,000 proteins with unprecedented spatial resolution to reveal the organization of organelles, sub-organellar compartments, protein complexes, functional networks and steady-state dynamics of proteins and unexpected subcellular locations. The method paves the way for characterizing the impact of post-transcriptional and posttranslational modification on protein location and studies involving proteome-level locational changes on cellular perturbation. An interactive open-source resource is presented that enables exploration of these data. ; Wellcome Trust grant (099135/Z/12/Z ), Wellcome Trust-MRC Stem Cell Institute in Cambridge and Sean Munro of the MRC Laboratory of Molecular Biology in Cambridge for insightful comments about the data. A.C. was supported by BBSRC grant (BB/D526088/1). European Union 7th Framework Program (PRIMEXS project, grant agreement number 262067), BBSRC Tools and Resources Development Fund (Award BB/K00137X/1), ERC Advanced Investigator, Onassis Public Benefit Foundation, the Foundation for Education and European Culture (IPEP) and the Embiricos Trust Scholarship of Jesus College Cambridge. Commonwealth Split Site. ERASMUS Placement. ; http://www.nature.com/ncomms ; hb2015

This is the author accepted manuscript. The final version is available from Wiley via http://dx.doi.org/10.1002/stem.2067 ; During mammalian pre-implantation development, the cells of the blastocyst's inner cell mass differentiate into the epiblast and primitive endoderm lineages, which give rise to the fetus and extra-embryonic tissues, respectively. Extra-embryonic endoderm differentiation can be modeled in vitro by induced expression of GATA transcription factors in mouse embryonic stem cells. Here we use this GATA-inducible system to quantitatively monitor the dynamics of global proteomic changes during the early stages of this differentiation event and also investigate the fully differentiated phenotype, as represented by embryo-derived extra-embryonic endoderm (XEN) cells. Using mass spectrometry-based quantitative proteomic profiling with multivariate data analysis tools, we reproducibly quantified 2,336 proteins across three biological replicates and have identified clusters of proteins characterized by distinct, dynamic temporal abundance profiles. We first used this approach to highlight novel marker candidates of the pluripotent state and extra-embryonic endoderm differentiation. Through functional annotation enrichment analysis, we have shown that the downregulation of chromatin-modifying enzymes, the re-organization of membrane trafficking machinery and the breakdown of cell-cell adhesion are successive steps of the extra-embryonic differentiation process. Thus, applying a range of sophisticated clustering approaches to a time-resolved proteomic dataset has allowed the elucidation of complex biological processes which characterize stem cell differentiation and could establish a general paradigm for the investigation of these processes. ; This work was supported by the European Union 7th Framework Program (PRIME-XS project grant number 262067 to K.S.L., L.G and C.M.M), the Biotechnology and Biological Sciences Research Council (BBSRC grant number BB/L002817/1 to K.S.L and L.G.), as well as a HFSP ...