The epigenetic landscape is driven by thousands of properties influencing transcription and defining cell state and function. This landscape is established and maintained by a complex configuration of dozens of epigenetic marks and properties that continually influence one another. When the balance is disrupted the result can be the programmed transition to a new state, such as in development, or the progression into a disease state.
We aim to apply systems-based logic to understand the most basic circuitry of a cell and how the configuration of that circuitry locks it in place in its functional role, or drives its differentiation. It has long been established that the smallest functional unit of an organism or tissue is the cell; however the majority of research is carried out at the tissue level by pooling thousands to millions of cells and assuming homogeneity. This strategy results in the obfuscation of latent cell subtypes, for which dozens may be present in any individual population, as has been revealed by new advances in single cell transcriptional profiling. We have developed a platform for assessing multiple epigenomic properties genome-wide at the single cell level in high throughput. This platform does not require specialized equipment, is inexpensive, and is highly robust and versatile. We plan to continue the development of this platform to profile active regulatory elements and genome structural variation in single cells, and to adapt the technology to interrogate DNA methylation, chromatin organization, and other epigenetic properties. In addition to our aims of distributing these tools to broadly benefit the life sciences community, we will apply these methods to catalogue epigenomic cell subtypes, with a focus on populations that exhibit strong evidence for the presence of latent subpopulations, and to chart the precise ordering of events during an epigenomic reprograming cascade. These efforts will not only be of broad use to the life sciences community, but allow us to ascribe causal relationships between factors that may ultimately impart phenotypic consequences such as the initiation of a disease state.
Cell type specification and the progression of a cell into a disease state is driven by underlying epigenetic machinery that dictates transcription and ultimately cell function. In order to understand this epigenetic landscape, we must employ technologies that provide information at the single cell level to accurately characterize latent cell subtypes and pinpoint the precise perturbations that result in a reconfigured epigenome. To accomplish this goal, we have developed a platform to assess multiple epigenetic properties in single cells in a cost-effective, high throughput manner and aim to deploy these technologies to expand our understanding of cell state identity.
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