Multivariate proteomic analysis of murine embryonic stem cell self-renewal versus differentiation signaling

A number of extracellular stimuli, including soluble cytokines and insoluble matrix factors, are known to influence murine embryonic stem cell self-renewal and differentiation behavioral responses via intracellular signaling pathways, but their net effects in combination are difficult to understand. To gain insight concerning key intracellular signals governing these behavioral responses, we employ a multivariate systems analysis of proteomic data generated from combinatorial stimulation of mouse embryonic stem cells by fibronectin, laminin, leukemia-inhibitory factor, and fibroblast growth factor 4. Phosphorylation states of 31 intracellular signaling network components were obtained across 16 different stimulus conditions at three time points by quantitative Western blotting, and partial-least-squares modeling was used to determine which components were most strongly correlated with cell proliferation and differentiation rate constants obtained from flow cytometry measurements of Oct-4 expression levels. This data-driven, multivariate (16 conditions x 31 components x 3 time points = approximately 1,500 values) proteomic approach identified a set of signaling network components most critically associated (positively or negatively) with differentiation (Stat3, Raf1, MEK, and ERK), proliferation of undifferentiated cells (MEK and ERK), and proliferation of differentiated cells (PKB alpha, Stat3, Src, and PKC epsilon). These predictions were found to be consistent with previous in vivo literature, along with direct in vitro test here by a peptide inhibitor of PKC epsilon. Our results demonstrate how a computational systems biology approach can elucidate key sets of intracellular signaling protein activities that combine to govern cell phenotypic responses to extracellular cues.