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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.
Sample Processing Protocol
Cells from each of the six samples (0hr, 16hr, 24hr, 48hr and 72hr of doxycycline time-course, as well as embryo-derived XEN cells) were harvested in 8 M urea, 0.1% SDS, 25 mM TEAB, pH 8.5, with protease inhibitors (Roche, UK) and protein concentration was evaluated with the BCA protein assay (Thermo Fisher). 100 µg of each sample were prepared for labeling with six-plex TMT reagents according to the manufacturer’s instructions (Thermo Fisher). TMT labeling was performed separately for each replicate experiment, and the order of the tags was reversed for one of the replicates. The pooled TMT-labeled sample was fractionated by off-line high pH reversed-phase liquid chromatography, using an Acquity UPLC system with diode array detector (Waters, UK). Peptides were resolved on a 60 minute linear gradient of 5% - 60% Buffer B with a UPLC BEH C18 column (2.1 mm i.d. x 150 mm, 1.7µm particle size, Waters) (Buffer A: 20mM ammonium formate, pH 10; Buffer B: 80% acetonitrile, 20mM ammonium formate, pH 10). Peptide elution was monitored by UV chromatogram in the 200 – 400 nm wavelength range. Forty individual one-minute fractions were collected and subsequently combined into twenty orthogonal fractions, dried and resuspended in 0.1% formic acid immediately before LC-MS/S analysis. The second dimension of chromatographic separation using a nanoAcquity UPLC (Waters) was directly coupled to an LTQ Orbitrap Velos (Thermo Fisher Scientific). Full scans (380 – 1500 m/z) were performed in the Orbitrap with nominal resolution of 30,000. The top 20 most intense monoisotopic ions (above 500 counts) from each full scan were selected for HCD MS/MS analysis with a 1.2 m/z precursor ion selection window using stepped normalized collision energies of 40% and 50% at a nominal resolution of 7,500. Singly charged ions were excluded from MS/MS and a dynamic exclusion window of ± 8 ppm for 300 seconds was applied to any ion sampled twice within a 20 second window. Xcalibur software (version 2.2, Thermo Fisher Scientific) was used for data acquisition.
Data Processing Protocol
Proteome Discoverer (version 220.127.116.119, Thermo Fisher Scientific) was used for data processing. Spectra were searched against a Mascot search engine (version 2.3.02, Matrix Science, UK) with a Swiss-Prot mouse database containing 24,473 entries (downloaded from www.uniprot.org in March 2013) as well as a list of common laboratory contaminants (cRAP repository v1.0, ftp.thegpm.org/fasta/cRAP in March 2013). The MS/MS fragmentation data was collected in centroid mode and a ± 0.0075 Da tolerance window was used for peak integration. The following Mascot search parameters were used: precursor mass tolerance of ± 15 ppm; fragment mass tolerance of ± 0.5 Da; trypsin enzyme was specified with a maximum allowance for two missed cleavages; oxidation (M) and TMT-6plex (Y) were allowed as variable modifications and carbamidomethyl (C), TMT-6plex (K) and TMT-6plex (peptide N-term) were set as fixed modifications. Purity correction values for the reporter ion isotopic distributions and the reporter ion masses of the 6-plex tags (monoisotopic m/z values = 126.12773 Da, 127.12476 Da, 128.13444 Da, 129.13147 Da, 130.14115 Da, 131.13818 Da) were updated in Proteome Discoverer according to the manufacturer’s instructions in the TMT 6-plex kit (Pierce, Thermo Scientific, UK). Percolator (version 1.17) validation was applied using a high confidence FDR threshold of 0.01 based on q-value. Additional Proteome Discoverer analysis parameters were used, including: only highly confident Rank 1 peptides with a minimum Mascot ion score of 20 were accepted; protein grouping was enabled and the strict maximum parsimony principle was applied. Only unique peptides were used for quantification and in order to minimize missing values, only PSMs with full reporter ion series were accepted. A minimum of 2 peptides per protein was required. The data were exported as csv files containing both protein grouping and PSM level information. The data was further filtered as follows to ensure the stringency of the final dataset: each protein groups must contain a minimum of 2 identified peptides, protein groups identified as common contaminants were removed and only protein groups quantified in all three biological replicate experiments were accepted into the final dataset.
Laurent Gatto, Department of Biochemistry, University of Cambridge
Kathryn Lilley, Cambridge Centre for Proteomics Cambridge System Biology Centre Wellcome Trust Stem Cell building University of Cambridge Department of Biochemistry Tennis Court Road Cambridge CB2 1QR ( lab head )
Mulvey CM, Schröter C, Gatto L, Dikicioglu D, Baris Fidaner I, Christoforou A, Deery MJ, Cho LT, Niakan KK, Martinez-Arias A, Lilley KS. Dynamic proteomic profiling of extra-embryonic endoderm differentiation in mouse embryonic stem cells. Stem Cells. 2015 Jun 8 PubMed: 26059426
|#||Accession||Title||Proteins||Peptides||Unique Peptides||Spectra||Identified Spectra||View in Reactome|
|1||46784||e.g. Control sample - Technical replicate #3||9080||90691||15752||555606||50499||
|2||46783||e.g. Control sample - Technical replicate #3||6554||42798||16302||237957||28185||
|3||46781||e.g. Control sample - Technical replicate #3||7055||47722||16664||234771||30527||
|4||46782||e.g. Control sample - Technical replicate #3||7895||66178||17072||389037||40152||