Tracking clonal dynamics during hematopoiesis: mechanistic insight via modeling and data analysis

项目摘要/摘要： 了解造血、血液的形成和维持以及许多获得性免疫 系统对于了解血液疾病和干细胞/基因治疗等临床方法是必不可少的 和骨髓移植。一个长期目标是理清并量化 造血干细胞(HSC)在血液中产生无数不同的细胞谱系。具体来说，我们希望 评估实验观察和数据，以推断不同可能的干和祖细胞生态位，推断 发展的“树”结构，推断主要的调节机制，并估计时间尺度内 血液病。为了实现这些目标并将观察和数据与机械洞察联系起来，我们必须 第一，实现以下具体目标：(I)发展随机和确定性细胞计数和克隆计数 条形码造血干细胞的模型，(Ii)将时间变异性的机制纳入 模型，(Iii)开发小血样中克隆分布的概率框架，以及(Iv) 根据数据对我们的模型进行统计测试。我们的假设是条形码/标签实验 可以使用“中性”基本模型以最低的近似顺序进行处理，在此基础上可以进行细化 使其包含额外的复杂性，如细胞异质性。这一战略意义重大 因为它允许我们制定一个高维的数学框架，这将是一个普遍的开始 用于分析长期克隆跟踪实验的点，在该实验中，多达数千个克隆谱系 被追踪到了。这项研究在解决高维克隆跟踪数据和推进 简约，但知情的多尺度模型，允许推断造血机制。

PROJECT SUMMARY/ABSTRACT: Understanding hematopoiesis, the formation and maintenance of blood and much of the adaptive immune system is essential for understanding blood diseases and clinical approaches such as stem cell/gene therapy and bone marrow transplantation. A long term goal is to disentangle and quantify the dynamics of how an hematopoietic stem cell (HSC) generates the myriad of different cell lineages in blood. Specifically, we wish to evaluate experimental observations and data to deduce the different possible stem and progenitor niches, infer the developmental “tree” structure, deduce the main regulation mechanisms, and estimate time scales within hematpoiesis. In order to achieve these goals and link observations and data to mechanistic insight, we must first achieve the specific aims of (I) developing both stochastic and deterministic cell count and clone count models for hematopoiesis of barcoded HSCs, (ii) incorporate mechanisms of temporal variability into the models, (iii) develop a probabilistic framework for clonal distributions in small blood samples, and (iv) implement statistical tests of our models against data. Our hypothesis is that barcoding/tagging experiments can be treated at the lowest order of approximation using a “neutral'' base model, upon which refinements can be made to incorporate additional complexities such as cellular heterogeneity. The strategy is significant because it allows us to formulate a high-dimensional mathematical framework that will be a general starting point for analyzing long term clonal tracking experiments in which up to thousands of clone lineages are tracked. The research is innovative for addressing high-dimensional clonal tracking data and advancing parsimonious, yet informed multiscale models that allow inference of hematopoietic mechanisms.