Quantitative analyses of embryonic stem cell fate responses to cytokine supplementation: Mechanisms of LIF-mediated regulation

The therapeutic use of stem cells is limited by our inability to control and predict their behavior. Applying engineering analyses to experimental results offers the means for developing the kind of quantitative appreciation of stem cell fate mechanisms that is needed for their eventual clinical use. In this study, a novel experimental and computational framework was developed to predictably link murine embryonic stem (ES) cell responses to interleukin-6 (IL-6)-type cytokine stimuli. It was hypothesized that thresholds in levels of appropriate ligand-receptor interactions modulate individual stem cell proliferation, differentiation, and survival responses, thus dynamically contributing to population composition. Using two cytokines that exhibited quantitative differences in ligand-receptor interactions, a direct correlation between the number of ligand-receptor signaling complexes formed and individual ES cell fate was demonstrated. To investigate whether signaling complex numbers were also responsible for selectively promoting the survival and/or proliferation of undifferentiated cells, cell responses were kinetically tracked using a transgenic ES cell line (that expressed green fluorescent protein (GFP) under control of the Oct4 promoter; Oct4 is a transcription factor critical to the establishment and maintenance of ES cells). Sub-population specific analyses showed that undifferentiated cells had a ligand dose-dependent survival and proliferation advantage over their immediately differentiated counterparts. The findings attributed changes in population output to the net outcome of competitive responses between undifferentiated and differentiated cells and showed that strategic alteration of culture conditions (e.g., adjusting relative concentrations of exogenous cytokines/growth factors) can allow one sub-population to predictably dominate the system. Kinetic measurements of sub-fraction specific responses were used to develop a comprehensive computational model of stem cell fate determination that predicted cell outputs over multiple cell divisions. Model simulations identified parameters (e.g., Oct4 protein half-life, cell growth and death rates, sensitivity to extrinsic regulation, etc.) that distinctly impacted the growth of stem vs. differentiated cells, and therefore provided clues to differentially control the two sub-populations. Together, these integrated experimental and computational analyses provided novel insights on the effects of exogenous parameters on stem cell fate regulation. These findings have important applications in the emerging field of stem cell-derived products and therapeutics.