Defining the epigenetic landscape at single cell resolution

项目摘要 表观遗传景观是由成千上万的影响转录和定义细胞状态的属性驱动的 和功能这种景观是由几十个表观遗传的复杂配置建立和维持的。 它们之间的关系和性质是相互影响的。当平衡被打破时，结果可能是 向新状态的程序性转变，如在发育中，或进展为疾病状态。我们 旨在应用基于系统的逻辑来理解细胞的最基本电路以及细胞的配置如何 该电路将其锁定在其功能角色中，或者驱动其分化。长期以来， 生物体或组织的最小功能单位是细胞;然而，大多数研究都是在 在组织水平上，通过汇集数千到数百万个细胞并假设同质性。这一战略导致 潜伏细胞亚型的混淆，其中几十个可能存在于任何个体群体中， 单细胞转录谱的新进展揭示了这一点。我们开发了一个平台， 以高通量在单细胞水平上全基因组评估多种表观基因组特性。这个平台 不需要专门的设备，价格便宜，并且非常坚固和通用。我们计划继续 该平台的发展，以分析活性调控元件和基因组结构变异的单一 细胞，并采用该技术来询问DNA甲基化，染色质组织和其他 表观遗传特性除了我们的目标是分发这些工具，使生命科学广泛受益之外， 社区，我们将应用这些方法对表观基因组细胞亚型进行分类，重点关注人群 显示出潜在亚群存在的强有力证据，并绘制出 在表观基因组重编程级联过程中发生的事件。这些努力不仅将对生活有广泛的用途， 科学界，但允许我们归因于因素之间的因果关系，可能最终赋予 表型后果，如疾病状态的开始。

PROJECT SUMMARY 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.