Dynamic proteomic profiling of extra-embryonic endoderm differentiation in mouse embryonic stem cells

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 grant (RGP0029/2010) and a European Research Council (ERC) Advanced Investigator grant to A.M.A.. C.S was supported by an EMBO long term fellowship and a Marie Curie IEF. L.T.Y.C. and K.K.N. were supported by the Medical Research Council (MRC, UK, MC_UP_1202/9) and the March of Dimes Foundation (FY11-436). We also thank Professor Steve Oliver and Dr. A.K.Hadjantonakis for helpful discussions and advice.This is the author accepted manuscript. The final version is available from Wiley via http://dx.doi.org/10.1002/stem.206

Dynamic Proteomic Profiling of Extra-Embryonic Endoderm Differentiation in Mouse Embryonic Stem Cells.

During mammalian preimplantation 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 (XEN) 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 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 XEN differentiation. Through functional annotation enrichment analysis, we have shown that the downregulation of chromatin-modifying enzymes, the reorganization 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 grant (RGP0029/2010) and a European Research Council (ERC) Advanced Investigator grant to A.M.A.. C.S was supported by an EMBO long term fellowship and a Marie Curie IEF. L.T.Y.C. and K.K.N. were supported by the Medical Research Council (MRC, UK, MC_UP_1202/9) and the March of Dimes Foundation (FY11-436). We also thank Professor Steve Oliver and Dr. A.K.Hadjantonakis for helpful discussions and advice.This is the author accepted manuscript. The final version is available from Wiley via http://dx.doi.org/10.1002/stem.206

Spatiotemporal proteomic profiling of the pro-inflammatory response to lipopolysaccharide in the THP-1 human leukaemia cell line.

Protein localisation and translocation between intracellular compartments underlie almost all physiological processes. The hyperLOPIT proteomics platform combines mass spectrometry with state-of-the-art machine learning to map the subcellular location of thousands of proteins simultaneously. We combine global proteome analysis with hyperLOPIT in a fully Bayesian framework to elucidate spatiotemporal proteomic changes during a lipopolysaccharide (LPS)-induced inflammatory response. We report a highly dynamic proteome in terms of both protein abundance and subcellular localisation, with alterations in the interferon response, endo-lysosomal system, plasma membrane reorganisation and cell migration. Proteins not previously associated with an LPS response were found to relocalise upon stimulation, the functional consequences of which are still unclear. By quantifying proteome-wide uncertainty through Bayesian modelling, a necessary role for protein relocalisation and the importance of taking a holistic overview of the LPS-driven immune response has been revealed. The data are showcased as an interactive application freely available for the scientific community

Learning from Heterogeneous Data Sources: An Application in Spatial Proteomics.

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.100492

Additional Precursor Purification in Isobaric Mass Tagging Experiments by Traveling Wave Ion Mobility Separation (TWIMS)

Despite the increasing popularity of data-independent acquisition workflows, data-dependent acquisition (DDA) is still the prevalent method of LC–MS-based proteomics. DDA is the basis of isobaric mass tagging technique, a powerful MS2 quantification strategy that allows coanalysis of up to 10 proteomics samples. A well-documented limitation of DDA, however, is precursor coselection, whereby a target peptide is coisolated with other ions for fragmentation. Here, we investigated if additional peptide purification by traveling wave ion mobility separation (TWIMS) can reduce precursor contamination using a mixture of <i>Saccharomyces cerevisiae</i> and HeLa proteomes. In accordance with previous reports on FAIMS-Orbitrap instruments, we find that TWIMS provides a remarkable improvement (on average 2.85 times) in the signal-to-noise ratio for sequence ions. We also report that TWIMS reduces reporter ions contamination by around one-third (to 14–15% contamination) and even further (to 6–9%) when combined with a narrowed quadrupole isolation window. We discuss challenges associated with applying TWIMS purification to isobaric mass tagging experiments, including correlation between ion <i>m</i>/<i>z</i> and drift time, which means that coselected peptides are expected to have similar mobility. We also demonstrate that labeling results in peptides having more uniform <i>m</i>/<i>z</i> and drift time distributions than observed for unlabeled peptides. Data are available via ProteomeXchange with identifier PXD001047

Principal components analysis plot (PCA) of the mouse stem cell dataset.

<p>Proteins are clustered according to their density gradient distributions. Each point on the PCA plot represents one protein. Filled circles are the original protein markers used in classification, hollow circles show new locations as assigned by the SVM TL classifier. The 4 proteins GTR3_MOUSE, SNTB2_MOUSE, PAR6B_MOUSE and ADA17_MOUSE that were found in the SVM TL method and not in an SVM classification with LOPIT only are highlighted.</p

Boxplots, displaying the estimated generalisation performance over 100 test partitions.

<p>Results for the <i>k</i>-NN transfer learning algorithm applied with (i) optimised class-specific weights (combined), (ii) only primary data and (iii) only auxiliary data, for each dataset.</p