Cue, signal, response analysis of murine embryonic stem cell differentiation : multivariable analysis of cytokine and ECM effects on commitment to differentiation
Author(s)Prudhomme, Wendy A. (Wendy Adele), 1975-
Massachusetts Institute of Technology. Dept. of Chemical Engineering.
Douglas A. Lauffenburger.
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The highly complex nature of developmental cell fate decisions is exemplified by murine embryonic stem cell (ES cell) differentiation, for which almost two decades of study has identified a small set of putative individual molecular cues which are difficult to generalize broadly or discern determinative signals from. Here we offer a combined experimental/computational investigation aimed at elucidating in a systematic manner important synergies between multiple extracellular cues, especially from cytokines and matrix proteins together, and consequent critical intracellular signals within a multi-pathway network governing mouse ES cell differentiation. A factorial design was used to evaluate responses of ES cells to combinations of leukemia inhibitory factor (LIF) and fibroblast growth factor-4 (FGF4) as soluble signals along with fibronectin (Fn) and laminin (Ln) as adhesion substrates. Factorial analysis revealed significant and unexpected cytokine/matrix synergistic effects. These results showed that the outcome elicited by a specific factor depends on the context in which that factor is presented to the cell. For example, LIF is known to completely inhibit the differentiation of ES cells. Its ability to prevent differentiation is unaffected by the presence of FGF4 or Fn. However, when LIF is present with both FGF4 and Fn, increased differentiation is observed. In order to understand underlying mechanistic regulatory causes for these kinds of complicated synergies, we developed a mathematical model capable of decomposing differentiation and proliferation cell fates from overall cell population dynamics. This decomposition showed that the various cue combinations governed one or another, or sometimes multiple, of these cell fates.(cont.) Using this model as a framework for interpreting population-averaged differentiation data allowed us to follow sub-population dynamics in mixtures of differentiating cells and was critical for correctly interpreting subsequent signaling data. Finally, we applied partial least square analysis to proteomic data for 31 different intracellular kinases, determining network signals critically correlative with the cell fate decisions. Two significant predictions arising from this analysis were confirmed independently: (a) inhibition of translocation of PKCepsilon results in a decrease in the growth rate of differentiated cells; and, (b) inhibition of Rafl also leads to a decrease in the growth rate of differentiated cells. We believe that our quantitative, systems-oriented approach bears promise for further advances in understanding regulation of complex cell fate processes in development.
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, February 2004.Includes bibliographical references (p. 118-127).
DepartmentMassachusetts Institute of Technology. Dept. of Chemical Engineering.; Massachusetts Institute of Technology. Department of Chemical Engineering
Massachusetts Institute of Technology