Integrated analyses of the epigenome to understand the molecular basis of hematopoietic malignancies

项目总结 研究计划：像这样的骨髓恶性肿瘤背后的造血分化过程受损 白血病，但我们仍然缺乏对一系列调控事件的机械性理解，这些事件误导了 分化过程。由于表观基因组调控模式是白血病发展的主要特征， 了解失败的(恶性)造血分化过程中的染色质动力学有助于 定义白血病的分子基础。这种理解的先决条件是一个框架，该框架允许 造血过程中调节元件活性进行性变化的研究 差异化。单细胞切割和标记(scCUT&Tag)技术非常适合于RE活性等研究 通过组蛋白修饰，可以以特定于谱系的方式研究图谱。然而，贫穷 理解特定细胞类型的组蛋白修饰模式使这项任务具有挑战性。要克服 在这个挑战中，我们设计了scCUT&Tag-PRO，它允许同时测量细胞表面蛋白 以及基因表达和染色质可及性的电子整合。我将利用这一新颖的多式联运 研究RE及其活性在造血过程中的渐进性变化的框架。首先，我会定义 一种用于小鼠造血的多模式参考图谱框架。这个框架将使我能够整合 多个组蛋白修饰谱到一个参考上，并比较RE的染色质状态 野生型(WT)和组蛋白甲基转移酶(HMT)功能丧失的小鼠模型(目标1)。第二, 因为HMT通过RE的相互作用网络来调节转录。我将定义一种染色质状态 在发展轨迹上动态链接RE的地图。我将使用这个框架来调查 HMT丢失引起的RE相互作用的变化(目标2)。第三，由于细胞的转录状态出现 从潜在的基因调控网络(GRN)出发，我将把单细胞基因表达数据与组蛋白相结合 修饰轮廓并对其进行扩展以定义GRN的染色质状态感知模型。我会比较WT和 HMT损失实验和定义差分GRN(目标3)。总而言之，这项研究提案试图 开创了多模式单细胞组蛋白修饰综合分析的计算方法 并系统地剖析了调节电路的系统级功能的渐进性变化 用条件性HMT功能丧失的小鼠模型误导造血分化 造血室。我制定了一个为期5年的职业发展计划，以实现我成为 在计算癌症生物学的多学科领域的独立研究员。导师制 委员会还将在我的研究和学术求职方面为我提供指导。考虑到优秀的 培养多名独立科学家的卓越记录，纽约基因组中心为我提供了理想 为实现我的科学职业目标创造了良好的环境。

PROJECT SUMMARY Research Plan: An impaired hematopoietic differentiation process underlies bone marrow malignancies like leukemia, but we still lack the mechanistic understanding of the sequence of regulatory events that misleads the differentiation process. Since epigenomic regulatory patterns are major features of leukemic development, understanding the chromatin dynamics of a failed (malignant) hematopoietic differentiation process can help define the molecular basis of leukemia. A prerequisite to such an understanding is a framework that allows investigation of the progressive changes in the activity of the regulatory elements (RE) during hematopoietic differentiation. Single-cell CUT&Tag (scCUT&Tag) technology is well-suited for such studies as RE activity through histone modification profiles can be investigated in a lineage-specific manner. However, poor understanding of the cell-type-specific histone modification patterns makes the task challenging. To overcome this challenge, we designed scCUT&Tag-pro which allows simultaneous measurement of cell-surface protein and in-silico integration of gene-expression and chromatin accessibility. I will leverage this novel multimodal framework to investigate the RE and progressive changes in their activity during hematopoiesis. First, I will define a multimodal reference mapping framework for mouse hematopoiesis. This framework will allow me to integrate multiple histone modification profiles onto one reference and compare the chromatin states of the RE between a wild type (WT) and mouse model with loss of function in histone methyl transferase (HMT) (Aim 1). Second, since HMTs regulate transcription through the interaction network of RE. I will define a chromatin state aware map that dynamically links REs across developmental trajectories. I will use this framework to investigate the changes in the interaction of REs due to HMT loss (Aim 2). Third, since the transcriptional state of a cell emerges from the underlying gene regulatory network (GRN), I will integrate single-cell gene expression data with histone modification profiles and extend it to define a chromatin state aware model of GRN. I will compare the WT and HMT loss experiments and define the differential GRN (Aim 3). Altogether, this research proposal seeks to pioneer the computational methods for the integrated analyses of multimodal single-cell histone modifications and systematically dissect progressive changes in the system-level function of the regulatory circuits that misleads hematopoietic differentiation using mouse models with conditional HMT loss of function in the hematopoietic compartment. I have developed a 5-year career development plan to meet my goal of becoming an independent investigator in the multi-disciplinary field of computational cancer biology. The mentorship committee will also provide me the guidance in my research and academic job search. Given the excellent the outstanding record of training multiple independent scientists, New York Genome Center provides me an ideal environment to attain my scientific career goals.