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

单细胞多组学利用细胞状态转变的景观

• 批准号: 10712491
• 负责人: Guangyu Wang
• 金额: $ 40.37万
• 依托单位: METHODIST HOSPITAL RESEARCH INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2028-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10712491
• 关键词: Address; Alzheimer' s Disease; Atlases; Biological; Cell Differentiation process; Cell Fate Control; Cells; Computing Methodologies; Data Set; Development; Disease; Foundations; Goals; Health; Heart failure; Hematopoiesis; Human; Knowledge; Learning; Multiomic Data; Non-Insulin-Dependent Diabetes Mellitus; Obesity; Organ; Organogenesis; Pathogenesis; Population; Regenerative Medicine; Regulation; Research; Role; Statistical Models; Study models; Tissues; cardiogenesis; comparative; epigenetic regulation; human disease; insight; mathematical methods; multiple omics; neurogenesis; novel; novel strategies; novel therapeutic intervention; organ growth; tissue regeneration; transcriptome; transdifferentiation

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.

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节。细胞命运的转变经常发生在器官 发育、组织再生和发病机制。细胞命运转变的失调可导致 异常发育或疾病，如2型糖尿病、肥胖、心力衰竭和阿尔茨海默病。 定量解码细胞命运如何变化可以提供对器官发生的新的机制洞察， 组织再生，并帮助确定治疗人类疾病的新策略。但我们的 细胞命运转变及其调节的知识只是冰山一角，这是由于长期研究的不切实际性。 细胞转录组的术语跟踪。在过去的十年中，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， 人类细胞图谱（Human Cell Atlas，HCA）这些地图集提供了一个机会， 细胞动力学和调控机制。计算方法的缺乏是一个主要的问题。 知识差距的理解细胞动力学和调节杠杆那些大的参考 地图集为了解决这一知识缺口，我们提出了一个新的概念，“基于参考的细胞动力学 推理”，这是一种新的策略，通过自动注释新数据集中的细胞状态转换， 从适当的参考中学习，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，我们将进行三个平行但互补的研究 方向：1）开发用于生成细胞动力学图谱的第一个计算方法和工具包， 基于适当的参考图谱分析细胞状态转换; 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）产生第一个动态 细胞分化的参考景观，例如心脏发生、造血和神经发生，以及细胞内 容纳着转分化的景观。这个项目将建立在我们最近的研究基础上， 发展计算方法，从单细胞转录组中揭示细胞状态转换， 同质和异质细胞群体以及研究表观遗传作用的研究 调控细胞命运的转变。拟议的研究将产生先进的计算工具包， 广泛适用的动态参考地图集，有望揭示深刻的机制控制 健康和疾病中的细胞状态转变。从长远来看，建立细胞动力学的能力参考 景观将打开一个新的视野，通过比较分析了解细胞命运的多样性 并增强再生医学。