Lineage-based inference of cell state transition dynamics in development and disease

基于谱系的发育和疾病中细胞状态转变动力学的推断

• 批准号: 9533037
• 负责人: Sahand Hormoz
• 金额: $ 24.9万
• 依托单位: DANA-FARBER CANCER INST
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-15 至 2020-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9533037
• 关键词: Affect; Area; Award; B cell differentiation; Back; Biology; Cell Cycle; Cell Lineage; Cells; Cellular biology; Clustered Regularly Interspaced Short Palindromic Repeats; Complex; DNA; Dependence; Development; Developmental Biology; Disease; Embryo; Engineering; Ensure; Epiblast; Event; Exhibits; Faculty; Fibroblasts; Gene Expression; Gene Expression Profile; Generations; Genes; Genome; Health; Human; Hybrids; In Situ; Individual; Interdisciplinary Study; Lead; Learning; Length; Maintenance; Malignant Neoplasms; Mammalian Cell; Mathematics; Measurement; Measures; Mediating; Mentors; Methodology; Methods; Microscopy; Molecular; Molecular Profiling; Mus; Mutagenesis; Mutation; Natural regeneration; Neurodevelopmental Disorder; Neurons; Non-Insulin-Dependent Diabetes Mellitus; Pathology; Patients; Phase; Phenotype; Physiological; Positioning Attribute; Predisposition; Process; Protocols documentation; RNA; Recording of previous events; Research; Signal Transduction; Site; Somatic Cell; Stem cells; System; Techniques; Time; Tissues; Totipotent; Training; Trees; Validation; Williams Syndrome; Work; base; blastomere structure; cancer cell; cell motility; computer framework; developmental disease; differentiated B cell; embryonic stem cell; experimental study; in situ imaging; in vivo; induced pluripotent stem cell; laboratory experience; molecular imaging; neoplastic cell; neurodevelopment; neurogenesis; novel; novel strategies; pluripotency; progenitor; programs; public health relevance; relating to nervous system; single molecule; synthetic biology; theories; time use; tumor

 DESCRIPTION (provided by applicant): Living cells transition among many physiologically distinct states during development, for tissue maintenance, and in disease. Dysregulation of transition rates between cell states can lead to pathologies, such as developmental disorders and cancer. In these systems and others, we would like to know which transitions can occur between the cellular states, in what sequence, and at what rates. Single-cell methods have expanded the ability to identify distinct cellular states in many systems. State of the art single-cell techniques can measure the expression levels of thousands of genes, but destroy cells in the process, and provide only static snapshots. No existing method can be used to infer the complex dynamics of state transitions in situ and without perturbations. To overcome this problem, we recently showed that combining the lineage history of a group of related cells with single-cell measurements of their gene expression profiles can be used to infer the dynamic transition rates between the cellular states. Here, I propose a comprehensive interdisciplinary research program to: 1) Use this approach to infer the rates of stochastic and reversible cell state transitions between the distinct pluripotent states in mouse embryonic (ES) cells, and identify the sequence of transitions involved in reprogramming of somatic cells into stem cells. 2) Extend the framework to in vivo systems by developing a synthetic platform for individual cells to autonomously record their lineage history within their DNA. 3) Apply the inference framework and the lineage-recording platform to study the dynamics of neural differentiation and neurodevelopmental diseases. My extensive background in theoretical/computational quantitative biology and training in experimental cell biology puts me in a unique position to accomplish the objectives of this proposal, which requires a seamless integration between computational and experimental approaches. I will use the mentoring phase of the K99 to facilitate my transition from theory to experimental biology and complete my laboratory training in mammalian cell biology and synthetic biology. In addition, I will work closely with collaborator to learn emerging techniques in multiplexed single-molecule imaging, the protocols for reprogramming, and methodology for induced neurogenesis. Together, the research program proposed here and the training during the mentored phase of the award will ensure that I will be well equipped to start an independent research lab and bring a novel approach to fundamental questions in diverse areas of developmental biology.

 描述(由申请人提供)：活细胞在发育、组织维持和疾病过程中在许多不同的生理状态之间转换。细胞状态之间转移率的失调可能会导致病理，如发育障碍和癌症。在这些系统和其他系统中，我们想知道细胞状态之间的哪些转换可以发生，以什么顺序，以什么速率。单细胞方法扩展了在许多系统中识别不同细胞状态的能力。最先进的单细胞技术可以测量数千个基因的表达水平，但在这个过程中会破坏细胞，并且只提供静态快照。没有一种现有的方法可以用来在原位和无扰动下推断复杂的状态转变动力学。为了克服这个问题，我们最近展示了将一组相关细胞的谱系历史与其基因表达谱的单细胞测量相结合，可以用来推断细胞状态之间的动态转移率。在这里，我提出了一个综合性的跨学科研究计划：1)使用这种方法来推断小鼠胚胎(ES)细胞中不同多潜能状态之间的随机和可逆细胞状态转变的比率，并确定体细胞重新编程为干细胞所涉及的转变序列。2)通过开发一个合成平台，让单个细胞在其DNA中自主记录它们的谱系历史，将该框架扩展到体内系统。3)应用推理框架和谱系记录平台研究神经分化和神经发育性疾病的动力学。我在理论/计算数量生物学方面的广泛背景和在实验细胞生物学方面的培训使我处于一个独特的位置来实现这项提议的目标，这需要计算方法和实验方法之间的无缝结合。我将利用K99的指导阶段，促进我从理论到实验生物学的过渡，并完成我在哺乳动物细胞生物学和合成生物学方面的实验室培训。此外，我将与合作者密切合作，学习多路复用单分子成像方面的新兴技术，重新编程的方案，以及诱导神经发生的方法学。总之，这里提出的研究计划和奖项指导阶段的培训将确保我将充分准备好启动一个独立的研究实验室，并为发育生物学不同领域的基本问题带来一种新的方法。