Computational Modeling of Lineage Decisions using Single-cell Data

使用单细胞数据进行谱系决策的计算建模

• 批准号: 10705651
• 负责人: Manu N Setty
• 金额: $ 42.62万
• 依托单位: FRED HUTCHINSON CANCER CENTER
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-17 至 2027-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10705651
• 关键词: ATAC-seq; Algorithms; Biological Process; Biology; Cell Communication; Cells; Chromatin; Complex; Computer Models; Data; Data Set; Disease; Enhancers; Event; Genetic Transcription; Heterochromatin; Homeostasis; Individual; Life; Measurement; Modeling; Natural regeneration; Organism; Phenotype; Process; Research; Role; Shapes; Signal Transduction; Technology; Therapeutic; Tissues; Transcriptional Regulation; blastomere structure; cell type; cellular engineering; data-driven model; embryo cell; in vivo; insight; multimodality; novel; single-cell RNA sequencing; spatiotemporal; stem cells; transcriptome sequencing; transmission process; wound healing

Project Summary Cellular differentiation is a fundamental biological process through which complex multi-cellular organisms develop from single-cell embryos and maintain tissue homeostasis throughout life. Cells integrate signals from the microenvironment and transmit them to downstream transcriptional regulators, which execute the expression and chromatin changes to define phenotypic state transitions in differentiation trajectories. Elucidating the principles of how cells choose their fate, and the path they take to get there, is a major challenge in the field. Single-cell (sc) RNA sequencing technologies are revolutionizing our understanding of the cellular spatio- temporal trajectories that shape differentiation. The emergence of additional high throughput, multimodal technologies such as paired RNA&ATAC-seq, scCUT&Tag and spatial technologies provide unprecedented opportunities to extract mechanistic insights into the lineage decisions that underly differentiation trajectories. This proposal aims to exploit this enormous potential by developing sophisticated new algorithms that integrate single-cell measurements to model and interpret complex biology. Through analysis of multiple single-cell RNA- seq datasets, we demonstrate that phenotypic asymmetries are a pervasive feature of lineage decisions. We will develop algorithms to unravel the mechanisms that drive lineage decisions and the underlying asymmetries in three broad research directions. We will investigate the role of: (i) enhancer priming and transcriptional regulation, (ii) open and heterochromatin dynamics, and (iii) cell communication in shaping differentiation trajectories. Our studies will lead to novel insights surrounding cell-autonomous and non-autonomous mechanisms engaged by cells as they navigate the phenotypic landscape. Successful completion of this research will provide a robust mechanistic basis to delineate normal differentiation events, decipher dysregulation of these mechanisms in disease, understand repurposing of differentiation mechanisms in wound healing and regeneration, and reconstruct differentiation processes in vivo and ex vivo to unlock the therapeutic potential of cell engineering.

项目摘要 细胞分化是一个基本的生物学过程，通过它复杂的多细胞生物体 从单细胞胚胎发育，并在整个生命过程中保持组织的稳态。细胞整合来自 微环境，并将它们传递给下游转录调节因子，后者执行表达 和染色质变化来定义分化轨迹中的表型状态转变。阐明 细胞如何选择自己的命运，以及它们到达那里的路径，是该领域的一个主要挑战。 单细胞（sc）RNA测序技术正在彻底改变我们对细胞空间的理解， 形成差异的时间轨迹。额外的高通量、多模式 配对RNA和ATAC-seq、scCUT和Tag以及空间技术等技术提供了前所未有的 机会，以提取机制的见解血统的决定，根本分化的轨迹。 该提案旨在通过开发复杂的新算法来利用这一巨大潜力， 单细胞测量来模拟和解释复杂的生物学。通过分析多个单细胞RNA- seq数据集，我们证明了表型不对称是血统决策的一个普遍特征。我们将 开发算法来解开驱动血统决策和潜在不对称的机制， 三大研究方向。我们将研究的作用：（i）增强子引发和转录 调控，（ii）开放和异染色质动力学，以及（iii）形成分化的细胞通讯 轨迹我们的研究将导致围绕细胞自主和非自主的新见解 细胞在浏览表型景观时所参与的机制。成功完成本 研究将提供一个强大的机制基础，描绘正常分化事件，破译 疾病中这些机制的失调，了解伤口中分化机制的再利用 愈合和再生，并重建体内和离体分化过程，以解锁治疗 细胞工程的潜力。