Computational systems immunology core

摘要 这项计划项目资助的三个研究项目利用单细胞基因组策略和 确定系统性红斑狼疮靶组织细胞-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)。这些项目将被用来深入了解 B和T淋巴细胞、单核细胞的发育、功能、定位和相互作用(方案1)， 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 多维数据集。计算系统免疫学(CSI)核心将支持分析和 这三个项目的计算需求。这一核心将建立在我们对加速 医疗伙伴关系(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组成免疫和基质细胞的群体。CSI将1)雇用 分析和集成多个单细胞RNA-SEQ数据集以定义关键细胞的计算策略 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 各州。CSI在分析和集成RA和SLE数据方面具有得天独厚的优势 在风湿病、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.