Defining the epigenetic landscape at single cell resolution

项目总结 表观遗传格局是由数千种影响转录和定义细胞状态的特性驱动的 和功能。这一景观是由数十个表观遗传的复杂构型建立和维持的 不断相互影响的标志和特性。当平衡被打破时，结果可能是 向新状态的程序化转变，如在发育过程中，或向疾病状态的进展。我们 目的应用基于系统的逻辑来理解单元的最基本电路以及如何配置 该电路将其锁定在其功能角色中，或驱动其分化。很早以前就已经确定了 生物体或组织的最小功能单位是细胞；然而，大多数研究都是进行的 在组织水平上，通过汇集数千到数百万个细胞并假定同质性。这一战略导致了 潜在细胞亚型的混淆，在任何个体中都可能存在数十个亚型，就像 单细胞转录图谱的新进展揭示了这一点。我们已经开发了一个平台 在高通量的单细胞水平上评估全基因组的多个表观基因组特性。这个平台 不需要专门的设备，价格低廉，非常坚固和多功能。我们计划继续 这一平台的开发用于分析单个基因组中的活性调控元件和基因组结构变异 细胞，并采用这项技术来询问DNA甲基化、染色质组织和其他 表观遗传特性。除了我们分发这些工具以广泛造福生命科学的目标之外 社区，我们将应用这些方法来编目表观基因组细胞亚型，重点是群体 这显示了潜在亚群存在的强有力证据，并绘制了 表观基因组重编程级联过程中的事件。这些努力不仅对生活有广泛的用处 科学界，但允许我们归因于因素之间的因果关系，最终可能会 表型后果，如疾病状态的启动。

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.