Landscapes for Cell State Transition Leveraging by Single-Cell Multi-Omics

这个项目的总体目标是开发新的数学方法和工具包来连接细胞命运 跨组织和疾病的过渡和表观遗传调控。细胞命运转换经常发生在器官中 发育、组织再生和发病机制。细胞命运转换的失调可导致 发育异常或疾病，如2型糖尿病、肥胖症、心力衰竭和阿尔茨海默病。 定量破译细胞命运的变化可以为器官发生和发展提供新的机制洞察 组织再生，并帮助确定治疗人类疾病的新战略。然而，我们的 关于细胞命运转换及其调控的知识只是冰山一角，因为长时间的 细胞转录本的术语追踪。在过去的十年里，无数包含数百万个细胞的单细胞图谱 在不同的组织、器官、发育阶段和生物条件下，通常由 人类细胞图谱(HCA)等联盟。这些地图集为不偏不倚地研究 细胞动力学及其调控机制。缺乏计算方法是一个主要的问题 这些大型参考文献在理解细胞动力学和调节方面的知识差距 地图集。为了解决这一知识鸿沟，我们提出了一个新的概念--基于参考的细胞动力学 这是一种新的策略，通过以下方式自动标注新数据集中的单元状态转换 借鉴适当的参考，使我们能够轻松地在不同的 组织和疾病状况。在这个项目中，我们将进行三项平行但互补的研究 方向：1)开发第一个计算方法和工具包，用于生成细胞动力学图谱和 基于适当的参考图谱分析细胞状态转变；2)开发新的统计模型 用于从单细胞多组学数据研究细胞命运的表观遗传调控；3)产生第一个动力学 细胞分化的参考图景，如心脏发生、造血和神经发生，以及在 转分化的房屋景观。这个项目将建立在我们最近对 从单细胞转录本中揭示细胞状态转换的计算方法的发展 同质和异质细胞群体及其表观遗传学作用的研究 对细胞命运转换的调控。拟议的研究将产生先进的计算工具包和 广泛适用的动态参考地图集，有望揭示深刻的控制机制 健康和疾病中的细胞状态转换。从长远来看，建立细胞动力学参考的能力 景观将开辟一个新的视野，通过比较分析来理解细胞命运的多样性 跨越组织和疾病，并加强再生医学。

The overall goal of this project is to develop novel mathematic methods and toolkits to connect cell fate transition and epigenetic regulation across tissues and diseases. Cell fate transition often occurs in organ development, tissue regeneration, and pathogenesis. Dysregulation of the cell fate transition can lead to abnormal development or diseases, such as type 2 diabetes, obesity, heart failure, and Alzheimer’s disease. Quantitively decoding how cell fate changes can provide novel mechanistic insight into organogenesis and tissue regeneration, and help identify new strategies for the treatment of human diseases. However, our knowledge of cell fate transition and its regulation is only the tip of the iceberg due to the impracticality of long- term tracing of cell transcriptomes. In the past decade, numerous single-cell atlases containing millions of cells in different tissues, organs, developmental stages, and biological conditions are routinely developed by consortia such as the Human Cell Atlas (HCA). These atlases provide an opportunity for the unbiased study of cellular dynamics and the regulation mechanism. The lack of computational methods presents a major knowledge gap in the understanding of cell dynamics and the regulation leveraging by those large reference atlases. To address this knowledge gap, we proposed a new concept of “reference-based cellular dynamic inference”, which is a novel strategy to automatically annotate the cell state transition in new datasets by learning from the appropriate reference, allowing us to easily perform comparative analysis among different tissues and disease conditions. In this project, we will pursue three parallel but complementary research directions: 1) to develop the first computational methods and toolkits for generating cell dynamics atlases and analyzing cell state transition based on the appropriate reference atlases; 2) to develop novel statistical models for studying epigenetic regulation of cell fate from single-cell multiomics data; 3) to generate the first dynamic reference landscapes of cell differentiation, such as cardiogenesis, hematopoiesis, and neurogenesis, and in- house landscapes of transdifferentiation. This project will be built on the foundation of our recent studies for the development of computational approaches to uncover cell state transition from single-cell transcriptomes in both homogeneous and heterogeneous cell populations and the studies for investigating the role of epigenetic regulation on cell fate transition. The proposed studies will generate advanced computational toolkits and broadly applicable dynamic reference atlases, which are expected to reveal profound mechanisms controlling cell state transition in health and disease. In the long term, the ability to build cell dynamics reference landscapes will open a new horizon to understand the diversity of cell fate through comparative analyses across tissues and diseases and enhance regenerative medicine.