Computational systems immunology core

摘要 该项目的三个研究项目利用单细胞基因组策略， 确定系统性狼疮靶组织中细胞-细胞相互作用的互补成像策略 红斑狼疮（SLE）和类风湿性关节炎（RA）。这些项目将用于深入了解 B和T淋巴细胞的发育、功能、定位和相互作用（项目1），单核细胞（项目2）， 和成纤维细胞（项目3）。理解这些细胞类型的关键是有效地解释复杂的高- 维度数据集。计算系统免疫学（CSI）核心将支持分析和 三个项目的计算需求。这个核心将建立在我们的经验与加速 医学合作伙伴关系（AMP）RA/SLE计划，其中高维单细胞数据集的分析现在已经 让我们第一次看到了免疫和基质细胞群的组成。CSI将1）雇用 计算策略，以分析和整合多个单细胞RNA-seq数据集， 群体2）使用轨迹分析来定义细胞之间的过渡状态。3)定义克隆种群 和4）定义细胞-细胞相互作用和转录之间的关系 states. CSI具有独特的优势，可以分析和整合RA和SLE数据， 在风湿性疾病、系统生物学和成像分析方面的专业知识。

Abstract The three research projects of this program project grant utilize single cell genomic strategies and complementary imaging strategies to define cell-cell interactions in target tissues in systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA). These projects will be used to develop insight into the development, function, localization, and interactions of B and T lymphocytes (Project 1), monocytes (Project 2), and fibroblasts (Project 3). Key to understanding these cell-types is the effective interpretation of complex high- dimensional data sets. The Computational Systems Immunology (CSI) core will support the analytical and computational needs for the three projects. This core will build from our experience with the Accelerating Medical Partnerships (AMP) RA/SLE program where analysis of high dimensional single cell data sets has now given us the first look at the constituent immune and stromal cellular populations. CSI will 1) employ computational strategies to analyze and integrate multiple single cell RNA-seq datasets to define key cellular populations 2) use trajectory analysis to define transitional states between cells. 3) Define clonal populations with repertoire sequencing and 4) Define the relationship between cell-cell interactions and transcriptional states. The CSI is uniquely positioned to analyze and integrate RA and SLE data with its long-standing expertise in rheumatic diseases, systems biology, and imaging analysis.