Stem cell differentiation is intimately regulated by the extracellular matrix (ECM) surrounding cells. While substantial research has identified extracellular signals and their intracellular transducers, there remains an incomplete knowledge of these signals regulate the broad genetic networks that cause lineage commitment. One potentially interesting candidate for this regulation is microRNAs (miRNAs), which are known to exert profound influence on gene networks in many contexts. In this proposal, we seek to ask whether miRNAs serve as a networking bridge between ECM-mediated signals and the stem cell lineage commitment. One of the critical bottlenecks in understanding ECM-mediated signals is the low-throughput nature of current state-of- the-art technologies to study mechanobiology in vitro. We have recently developed a novel technology that condenses hundreds of experiments conducted with current technologies into a single experiment. We now propose to combine this technology with high-throughput analysis of microRNA expression (miRNA-seq) and use this platform to identify ECM-sensitive miRNA networks involved in stem cell differentiation. First, we will combine and optimize our high-throughput mechanobiology platform with laser capture microdissection and miRNA-seq to produce a list of miRNA candidates whose expression is correlated with changes in matrix stiffness and/or ligand density. Second, we will perform gain- and loss-of-function studies to confirm that the miRNAs candidates functionally contribute to ECM-mediated stem cell differentiation. If successful, this proposal will develop a very powerful high-throughput technique to study other ECM-mediated phenomenon in other cells. Furthermore, this proposal will develop a deeper understanding of the relationship between ECM- signals and gene-network regulation, which would in turn yield a new set of molecular targets that could be leveraged to facilitate successful stem cell engineering for therapeutic translation.
While it has become clear that biophysical signals encoded in the extracellular matrix can influence stem cell lineage commitment, little is understood of how these signals control specific gene programs needed to execute this commitment. MicroRNAs exert profound control over gene networks and are mechanosensitive in many contexts, suggesting they may serve to as a central signaling mechanism connecting biophysical signals to gene regulation. In this proposal, we develop a high-throughput, single-cell miRNAseq platform to identify key microRNAs that regulate mechanosensitive stem cell differentiation, which we envision will provide novel targets for control of stem cell differentiation for therapeutic applications.
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