Date of Completion

12-14-2015

Embargo Period

12-14-2015

Keywords

Stem Cells, Pluripotency, Gene Regulatory Network, Single Cell Analysis, iPSC, Mathematical Modeling

Major Advisor

Craig Nelson

Associate Advisor

Barbara Mellone

Associate Advisor

David Goldhamer

Associate Advisor

Charles Giardina

Associate Advisor

Michael O'Neill

Field of Study

Molecular and Cell Biology

Degree

Doctor of Philosophy

Open Access

Open Access

Abstract

In recent years, the fields of regenerative medicine and developmental biology have been revolutionized by the ability to reprogram adult somatic cells back to a pluripotent state, producing induced pluripotent stem cells (iPSCs) through the induction of the reprogramming factors OCT4, SOX2, KLF4 and c-MYC (OSKM). The promise of this technique has been somewhat limited by the low efficiency and long duration of the process which produces heterogeneous mixtures of cells in culture, many of which fail to fully reprogram over a 3-4 week period. Research in recent years has focused on the underlying mechanisms of reprogramming and identifying rate-limiting steps in the process. Most studies to date utilize bulk measurements of cells undergoing reprogramming however, these techniques cannot measure the changes occurring in rare cells that will become iPSCs and are inherently biased towards unsuccessful reprogramming events. Here we apply single cell technologies to measure the dynamics of mRNA and protein expression and develop mathematical models to precisely describe these behaviors. We find that productively reprogramming cells activate genes in an ordered, probabilistic fashion but do so independently of one another, lacking hallmarks of gene regulatory network activity. Some genes, despite their expression as mRNAs, are not immediately translated into protein, identifying post-transcriptional mechanisms as a potential rate limiting step. In contrast, cells moving along an alternate trajectory away from fibroblast but not towards iPSC, fail to activate pluripotency genes and do not express the full complement of OSKM. This is due to premature inactivation of the individual factors, causing cells to drop off the productive trajectory and fail to reprogram. Performing these analyses under two different delivery methods of OSKM and in two cell types reveals that while the timing of gene activation varies between conditions, the probabilistic order of gene activation is conserved, suggesting a common reprogramming trajectory. Taken together these findings represent the first descriptions of mRNA and protein expression dynamics in single, human cells undergoing reprogramming. We also provide a robust mathematical framework for identifying rate-limiting steps in the process and dissecting the mechanism of action of treatments known to enhance the efficiency of reprogramming.

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