A draft map of the mouse pluripotent stem cell spatial proteome

Andy Christoforou, Claire M. Mulvey, Lisa M. Breckels, Aikaterini Geladaki, Tracey Hurrell, Penelope C. Hayward, Thomas Naake, Laurent Gatto, Rosa Viner, Alfonso Martinez Arias and Kathryn S. Lilley ()
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Andy Christoforou: Cambridge Centre for Proteomics, University of Cambridge
Claire M. Mulvey: Cambridge Centre for Proteomics, University of Cambridge
Lisa M. Breckels: Cambridge Centre for Proteomics, University of Cambridge
Aikaterini Geladaki: Cambridge Centre for Proteomics, University of Cambridge
Tracey Hurrell: Cambridge Centre for Proteomics, University of Cambridge
Penelope C. Hayward: University of Cambridge
Thomas Naake: Cambridge Centre for Proteomics, University of Cambridge
Laurent Gatto: Cambridge Centre for Proteomics, University of Cambridge
Rosa Viner: Thermo Fisher Scientific
Alfonso Martinez Arias: University of Cambridge
Kathryn S. Lilley: Cambridge Centre for Proteomics, University of Cambridge

Nature Communications, 2016, vol. 7, issue 1, 1-12

Abstract: Abstract 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.

Date: 2016
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DOI: 10.1038/ncomms9992

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