390109 Metabolomic Analysis of Neural Differentiation in Mouse Embryonic Stem Cells

Wednesday, November 19, 2014: 9:24 AM
214 (Hilton Atlanta)
Amy Su, Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA and Mark P. Styczynski, School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA

Pluripotent stems cells are able to differentiate into any cell type of the three germ layers, and thus hold much promise for clinical therapy. Therefore, improvements in the fledgling field of stem cell biomanufacturing, or the production of stem cells in vitro on a commercial scale, are of critical importance to realizing stem cells’ medical potential. However, despite extensive research, differentiation to desired cell types in vitro is non-trivial to assess and control. Recently, the metabolic remodeling that occurs during differentiation has been gathering considerable attention as not only an indicator of differentiation, but also as a potential means to regulate it. The systems-level dynamics of metabolism in differentiation are still largely unexplored, but will be vital to a deeper understanding of differentiation that would inform applications of metabolic control.

To achieve this goal, we have characterized the metabolic changes during differentiation of mouse embryonic stem cells using untargeted gas chromatography-mass spectrometry metabolomics, which provides semi-quantitative data for a wide range of highly diverse metabolites. As proof of principle, we first measured metabolic profiles of both intracellular and extracellular samples of mouse embryonic stem cells during differentiation down a neural lineage.  While the progress of differentiation was initially verified by previously-established protein and gene expression markers using RT-PCR and flow cytometry, we were able to detect distinct intracellular metabolic changes even at very early times during the process of differentiation. The impacts of medium renewal on metabolism were investigated, as were the impacts of culture format on the measured metabolite profiles. Samples over the course of differentiation clustered into a number of distinct groups based on their sampling time and the degree of differentiation of the cell population. Thus, monitoring metabolic change over the time course of differentiation was shown to offer both another way to describe the transient cellular identities found in heterogeneously differentiating populations and insight into how this transition might be better controlled.

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See more of this Session: Proteomics & Metabolomic Approaches to Systems Biology
See more of this Group/Topical: Topical Conference: Systems Biology