Computational Analysis of CD8 T Cells Using Single Cell Sequencing

项目摘要 自身免疫是一种复杂的疾病，影响着超过2000万美国人。揭开多步骤过程的面纱 导致自身免疫并最终有效治疗疾病的能力需要深入研究 了解自身反应性淋巴细胞及其逃避耐受的机制， 促进自身组织的破坏虽然已经积累了大量的定量数据， 感染后CD8 T细胞反应的动力学，关于幼稚CD8 T细胞如何 分化为各种效应途径，也没有关于全球CD8 T细胞基因表达的变化， 自身免疫性疾病我们的建议旨在将联合收割机的实验、计算和数学 通过分析CD8 T细胞来了解自身免疫性疾病的发生和发展的方法。 使用自身免疫性疾病的良好表征，易于处理的实验模型，我们将测试，完善和 验证先前发表的CD4 T细胞分化数学模型，以开发CD8 T细胞分化模型。 在自身免疫性疾病过程中的分化和失调。目标1将定量定义 具有多种疾病表现的自发性自身免疫模型中的基因表达动力学。在目标2中 我们将把这种评估扩展到几种自身免疫模型， 疾病的结果，系统地确定基因和途径，免疫异常的关键 个体疾病和多种疾病的基础疾病的发展。我们将进一步比较 将这些数据与已发表的患者数据相比较，以关注临床相关基因。在目标3中，我们将联合收割机 目的1和2的目的，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基因签名驱动 CD8 T细胞命运选择和触发自身免疫。

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.