Core E Data Management Core

核心E项目总结 高通量免疫表型和基因组技术的最新进展使分析 宿主对疫苗接种和感染的免疫反应在组织和细胞上都是前所未有的详细 水平。Viva数据管理和分析核心(DMAC，核心E)的目标将是 为基因表达提供全面的数据管理和计算分析能力， 疫苗接种和感染后PBMC和血浆中产生的抗体、细胞因子和趋化因子谱 用不同疫苗处理的人扁桃体组织培养物(HC)的个体和 非典型肺炎冠状病毒2(SARS-CoV-2；项目1)、流感病毒(IV；项目2)或 登革热病毒(DENV；项目3)。在目标1中，该核心将执行高吞吐量的计算分析 组织水平转录、抗体、细胞因子和趋化因子图谱为所有个体和 免疫表型核心(核心C)和基因组核心在每个项目中的组织培养 (核心D)。在目标2中，我们将使用大量PBMC和血浆图谱的分析结果--近距离 与每个项目团队进行协调-帮助为选择样本提供信息，以便在 利用最新的单细胞CITEseq和空间转录组学技术进行细胞水平的基因组学研究 核心。我们还将负责使用已建立的数据分析结果单元格数据集 显示细胞组成和表达的变化如何在细胞水平上驱动变化的管道 组织水平。在目标3中，我们将进一步结合所有三个项目的组织和细胞水平的数据 对针对每种病原体接种疫苗的宿主反应进行综合分析，以确定共同的和疫苗- 这些回应的具体组成部分。这种整合将通过集中的跨项目数据来促进 由该核心提供的管理设施，以及由免疫系统协调的数据生成 表型核心和基因组核心使用相同的平台和技术来分析 所有项目的样本。最后，我们将直接或通过重债穷国协调机制与ImmPort对接 中心，以确保所有数据、分析结果和分析代码及时公开可用 举止。总而言之，我们希望我们的多尺度计算方法能够实现全面的 疫苗接种、宿主免疫反应和疫苗结果之间关系的表征。

CORE E PROJECT SUMMARY Recent advances in high-throughput immune phenotyping and genomic technologies have enabled profiling of host immune responses to vaccination and infection at unprecedented detail, at both the tissue and cellular level. The goal of the VIVA DATA MANAGEMENT AND ANALYSIS CORE (DMAC, CORE E) will be to provide comprehensive data management and computational analysis capabilities for the gene expression, antibody, cytokine and chemokine profiles generated for PBMCs and plasma from vaccinated and infected individuals, as well as from human tonsillar histocultures (HC) treated with different vaccines against Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2; Project 1), influenza virus (IV; Project 2) or Dengue virus (DENV; Project 3). In Aim 1, this core will perform computational analysis of high-throughput tissue-level transcriptional, antibody, cytokine and chemokine profiles generated for all individuals and histocultures in each project by the IMMUNE PHENOTYPING CORE (CORE C) and the GENOMICS CORE (CORE D). In Aim 2, we will use the results from the analyses of bulk PBMC and plasma profiles – in close coordination with each project team – to help inform the selection of samples for detailed profiling at the cellular level using the latest single-cell CITEseq and spatial transcriptomics technologies by the GENOMICS CORE. We will also be responsible for analyzing the resulting single-cell datasets using well-established pipelines to show how changes in cell composition and expression at the cellular level drive changes at the tissue level. In Aim 3 we will further combine data at the tissue and cellular level across all three projects for an integrated analysis of host responses to vaccination against each pathogen to identify common and vaccine- specific components of these responses. This integration will be facilitated by the centralized cross-project data management facilities provided by this core, as well as coordinated data generation by the IMMUNE PHENOTYPING CORE and GENOMICS CORE that use the same platforms and technologies to profile samples across all projects. Finally, we will interface with ImmPort, directly or through the HIPC Coordinating Center, to ensure that all data, analysis results, and analysis code is made publically available in a timely manner. Altogether we expect that our multi-scale computational approaches will enable a comprehensive characterization of the relationship between vaccination, host immune responses and vaccine outcomes.