Computational Analysis of CD8 T Cells Using Single Cell Sequencing

使用单细胞测序对 CD8 T 细胞进行计算分析

• 批准号: 9981905
• 负责人: Katrina K Hoyer
• 金额: $ 11.06万
• 依托单位: UNIVERSITY OF CALIFORNIA, MERCED
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-03-09 至 2021-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9981905
• 关键词: Address; Adoptive Transfer; Affect; American; Atypical lymphocyte; Autoimmune Diseases; Autoimmune Process; Autoimmune hemolytic anemia; Autoimmunity; Automobile Driving; Biological; CD4 Positive T Lymphocytes; CD8-Positive T-Lymphocytes; CD8B1 gene; Chronic; Complex; Computer Analysis; Data; Data Set; Defect; Development; Disease; Disease Outcome; Disease Progression; Disease model; Evaluation; Event; Exhibits; Experimental Models; Gene Deletion; Gene Expression; Genes; Goals; Immune; Immune Targeting; Immune response; Individual; Infection; Inflammation; Interleukin-2; Kinetics; Knockout Mice; Knowledge; Lupus; Maps; Mathematics; Modeling; Mus; Mutation; Nature; Onset of illness; Pancreatitis; Pancytopenia; Pathogenesis; Pathology; Pathway interactions; Patients; Pattern; Process; Publishing; Regulatory T-Lymphocyte; T cell differentiation; T cell response; T-Lymphocyte Subsets; Testing; Time; Tissues; Validation; base; clinically relevant; effector T cell; genetic signature; in vivo; innovation; mathematical methods; mathematical model; mouse model; prevent; reaction rate; single cell sequencing; stoichiometry; systemic autoimmunity; transcriptome sequencing

PROJECT SUMMARY Autoimmunity is a complex disorder affecting over 20 million Americans. Unveiling the multi-step process leading to autoimmunity and ultimately the ability to effectively treat disease requires an in-depth understanding of the self-reactive lymphocytes and the mechanisms by which they evade tolerance and promote destruction of self-tissue. Although there has been a large accumulation of quantitative data on the dynamics of CD8 T cell responses following infection, much less is known about how naive CD8 T cells differentiate into various effector pathways, nor about global CD8 T cell gene expression changes during autoimmune disease. Our proposal seeks to combine experimental, computational and mathematical approaches to understand the initiation and development of autoimmune disease by analysis of CD8 T cells. Using well-characterized, tractable experimental models of autoimmune disease, we will test, refine and validate previously published CD4 T cell differentiation mathematical models to develop a model of CD8 T cell differentiation and dysregulation during the autoimmune disease process. Aim 1 will quantitatively define the gene expression kinetics in spontaneous autoimmune models with multiple disease manifestations. In Aim 2 we will expand this evaluation to several autoimmune models with some overlapping and distinct autoimmune disease outcomes to systematically define the genes and pathways that underlie immune abnormalities critical to the development of individual diseases and those that underlie multiple diseases. We will further compare these data to published patient data to focus on clinically relevant genes. In Aim 3 we will combine the results of Aim 1 and 2 to mathematically model CD8 T cell gene expression kinetics, and validate this model using mouse in vivo disease studies. This mathematical model will enable us to predict the gene signatures driving CD8 T cell fate choices and triggering autoimmunity.

项目总结 自身免疫是一种复杂的疾病，影响着2000多万美国人。揭开多步骤流程的面纱 导致自身免疫并最终有效治疗疾病的能力需要深入的 了解自身反应性淋巴细胞及其逃避耐受和免疫抑制的机制 促进自我组织的破坏。尽管已经有大量的量化数据积累在 感染后CD8T细胞反应的动态，关于幼稚的CD8T细胞是如何产生的，我们知之甚少 分化为不同的效应通路，也不关于全球CD8 T细胞基因表达的变化 自身免疫性疾病。我们的建议寻求将实验、计算和数学相结合 通过分析CD8T细胞了解自身免疫性疾病发生发展的途径 使用具有良好特征的、易于处理的自身免疫性疾病的实验模型，我们将测试、改进和 验证先前发表的CD8T细胞分化数学模型以建立CD8T细胞模型 自身免疫性疾病过程中的分化和失调。目标1将定量地定义 具有多种疾病表现的自发自身免疫模型中的基因表达动力学。在AIM 2 我们将把这一评估扩展到几个具有重叠和不同的自身免疫的自身免疫模型 系统地定义免疫异常背后的基因和途径对疾病结局至关重要 与个别疾病和导致多种疾病的疾病的发展有关。我们将进一步比较 这些数据将公布的患者数据集中在临床相关基因上。在目标3中，我们将结合结果 目的1和2的目的是对CD8 T细胞基因表达动力学进行数学建模，并使用 小鼠体内疾病研究。这个数学模型将使我们能够预测驱动基因签名 CD8T细胞命运选择和触发自身免疫。