The broad, long-term goals of this project are 1) to advance embryonic stem cell biology with regard to self-renewal, pluripotency, and cell-type specific differentiation programs, and 2) to develop computational approaches for the study of these and similar complex biological systems and processes. Potential impacts of this project include the generation of knowledge that may lead to improved control of in vitro manipulation of ES cells for important applications such as tissue engineering, cellular transplantation and pharmacological testing. Cross-disciplinary training of students and research fellows in this project will contribute to the education of a new generation of multi-skilled scientists who are equally adept in cell biological and computational analyses. Specifically, we propose to use high-density oligonucleotide arrays to obtain global transcriptional profiles during in vitro development of ES to EB. The issue of whether the embryoid body contains significant residual ES cells will be examined. To study the genetic program of ES cell differentiation, antibodies will be developed to tag cell surface markers for the primary germ layers. ES cell lines will be engineered to harbor reporter genes such as a Green Fluorescent Protein under the control germ layer-specific promoters. Fluorescence Activated Cell Sorting will be used to purify ectodermal, mesoderm and endodermal progenies of these ES cell lines during the time course of their development into embryoid bodies, and global transcriptional profiles will be obtained on these purified samples. Furthermore, the activities of developmentally important growth factors such as TGF-beta and bFGF, or key regulators such as Oct-3/4 and Stat3 will be manipulated to study their effect on the global transcriptional profiles. Statistical and computational analyses will be used to identify the difference in transcriptional state of ES cells and early embryoid body cells that are responsible for their different potential for self-renewal. We will develop novel approaches to the analysis of the time course expression profile data generated in these experiments. These include nonlinear multivariate analysis and methods for the study of invariance relations among genes. Furthermore, we will develop advanced methods and software for cis-regulatory sequence analysis in order to discover novel regulatory sequence motifs. We will also develop methods that can exploit existing knowledge on transcription factors binding sites in order to study the impact of their combinatorial patterns on stem cell differentiation.
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