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

Abstract

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. STEM CELLS 2015;33:2712-2725 SIGNIFICANCE STATEMENTAs cells specialize to carry out specific tasks during the development of embryos, the collection of proteins that they express (the proteome) changes in a defined and coordinated manner. In this work we map these changes during the targeted differentiation of embryonic stem cells into one of the first specialized cell types that appears in mammalian development. This roadmap of proteome change will be an important reference to better understand how changes in the building blocks of cells drive the change of a cell's function, and will be useful to validate future efforts in which immature stem cells are being differentiated into cell types for regenerative approaches.

FGF/MAPK signaling sets the switching threshold of a bistable circuit controlling cell fate decisions in ES cells

“…Consistent with previous studies (Fujikura et al, 2002;Mulvey et al, 2015;Shimosato et al, 2007), we found that transient expression of doxycycline-inducible GATA4-FLAG instead of GATA6-FLAG led to the same expression pattern of endogenous GATA factors, but doubled the proportion of differentiating cells ( Fig. 1 D -F, supplementary material Fig.…”

“…To model in culture the transition from Nanog;GATA6 co-expression to mutually exclusive expression of Epi and PrE markers that characterizes the Epi-versus-PrE fate decision (Plusa et al, 2008), we used a doxycycline-inducible system to transiently express GATA6-FLAG in ES cells (Beard et al, 2006;Mulvey et al, 2015;Wamaitha et al, 2015) (Fig. 1A).…”

Dynamic post-transcriptional regulation during embryonic stem cell differentiation

“…1b). Moreover, the XEN-like cells measured here were similar to embryo derived XEN cells in their proteome (Mulvey et al, 2015) (r = 0.65, Supplementary Fig. 1c).…”

“…4b). This might be related to the fact that XEN cells have to produce high levels of extracellular matrix proteins (Mulvey et al, 2015), like laminin (Lamb1) or collagen (Col4a2) . Consequently, these proteins must be synthesized in a timely manner to ensure the proper function of the XEN cells.…”

“…L2FC: log 2 fold-change. (C) Protein expression in XEN cells relative to ESCs as measured in this study compared with in vivo derived XEN cells measured by Mulvey et al (2015). Pluripotency- and XEN-marker gene sets were defined using a support vector machine learning algorithm.…”