Multi-scale analysis of single cell sequencing data to dissect the complexity of influenza infections

单细胞测序数据的多尺度分析以剖析流感感染的复杂性

• 批准号: 10057816
• 负责人: CHRISTIAN FORST
• 金额: $ 25.43万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-10 至 2022-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10057816
• 关键词: Address; Affect; Animals; Antibodies; Antibody-mediated protection; Antiviral Agents; Applied Research; Basic Science; Biological; Biological Factors; Cell Communication; Cells; Cellular Immunity; Cessation of life; Child; Collection; Communicable Diseases; Communities; Data; Data Set; Databases; Diagnostic; Disease; Economic Burden; Epitopes; Expression Profiling; Generations; Genetic; Genetic Predisposition to Disease; Genomics; Goals; Hospitalization; Human; Immune; Immune response; Immune system; Immunologics; In Vitro; Individual; Infection; Influenza; Investments; Lead; Life; Maps; Mediating; Modeling; Molecular; Morbidity - disease rate; National Institute of Allergy and Infectious Disease; Native-Born; Obesity; Outcome; Pathway Analysis; Pathway interactions; Patients; Physiological; Population; Pregnant Women; Prevention; Process; Proteomics; Public Domains; Recovery; Recurrence; Research; Resolution; Resources; Risk; Sample Size; Sampling; Severities; Severity of illness; Signal Pathway; System; Therapeutic; Validation; Viral; Viral Proteins; Virulent; Virus; Virus Diseases; Work; base; bioinformatics resource; cohort; cross reactivity; cross-species transmission; health economics; high dimensionality; high risk population; improved; in silico; influenza epidemic; influenza virus strain; influenzavirus; mortality; multi-scale modeling; multiple omics; network models; personalized medicine; programs; resilience; response; scaffold; sex; single cell analysis; single cell sequencing; single-cell RNA sequencing; targeted treatment; therapeutic target; tool; transcriptomics

Project Summary Influenza virus infection is a recurrent health and economic burden. It cycles between the human population and the animal reservoir, causing millions of hospitalizations and thousands of deaths each year, especially in high-risk groups, such as young children, pregnant women, obese, individuals with compromised immune system and indigenous populations. Disease morbidity and mortality increase when a new influenza strain reasserts or jumps the host, and becomes capable of infecting humans. In this case, there is no (or minimal) pre-existing antibody-mediated immunity to the new viral strain at the population level, leading to millions of infections and a rapid global spread of the virus. In the absence of antibodies, the severity of the disease can be ameliorated by broadly cross-reactive cellular immunity. But, the precise mechanism of how immune cells mediate recovery in some individuals, but not others, is far from clear. However, a diverse and rich collection of datasets are available in the public domain that have already addressed specific aspects of these concerns. Expression profiles from human cohorts and animal studies in GEO/SRA, immunological profiles in ImmPort or influenza strain data and interaction with immune epitopes in the Influenza Research Database (IRD), a Bioinformatics Resource Center (BRC) of NIAID, are examples of such resources. In particular, high-resolution single-cell RNA-seq data enables us to study relevant processes during influenza infection in great detail. The combination of multiple previously collected datasets, in particular across biological scales, single cell and bulk data, is a central goal in this research. The overarching hypothesis that guides our proposed work is that diversity in influenza virus strains, genetic immune epitopes and in the responding immune cell population contributes to the diverse outcome after influenza infection. In detail we will address the questions about determinants of influenza infections, and key processes that impede any replication, on the one hand, or contribute to a weak immune response, on the other hand. Sex as biological factor will be addressed whenever appropriate data with sufficient sample-size is available. We will further develop an approach to increase the resolution of bulk data by guidance of single cell data. For this purpose, we will not only develop multi-scale models of high resolution and detail but also develop the appropriate tools to facilitate and enable such precision modeling.

项目摘要 流感病毒感染是反复出现的健康和经济负担。它在人类之间循环 人口和动物宿主，造成数百万人住院和数千人死亡 每年，特别是在高危群体，如幼儿、孕妇、肥胖者、 免疫系统受损的个人和土著人口。疾病发病率和 当一种新的流感毒株再次侵袭或跳过宿主，并变得能够 感染人类。在这种情况下，没有(或最低限度)预先存在的抗体介导的对 在人口层面出现新的病毒株，导致数百万人感染，并在全球迅速传播 病毒。在没有抗体的情况下，疾病的严重性可以通过广泛的 交叉反应的细胞免疫。但是，免疫细胞如何调节恢复的确切机制 在一些人身上，但在另一些人身上，还远不清楚。然而，多样化和丰富的集合 公共领域中提供的数据集已经解决了这些问题的特定方面 担忧。来自人类队列和动物研究的表达谱在GEO/SRA，免疫学 Immport或流感病毒株数据的特征及其与流感免疫表位的相互作用 NIAID的生物信息学资源中心(BRC)--研究数据库(IRD)就是这样的例子 资源。特别是，高分辨率的单细胞rna-seq数据使我们能够研究相关的 非常详细地介绍了流感感染期间的过程。以前收集的多个数据的组合 数据集，特别是跨生物规模、单细胞和批量数据的数据集，是这方面的中心目标 研究。指导我们拟议工作的首要假设是流感的多样性 病毒株、遗传免疫表位和在应答的免疫细胞群中起作用 流感感染后的不同结果。详细地说，我们将解决以下问题 流感感染的决定因素，以及阻碍任何复制的关键过程，一方面， 从另一方面来说，也不会导致免疫反应减弱。作为生物因素的性将是 只要有足够样本大小的适当数据就进行寻址。我们将进一步发展 一种通过单元格数据引导提高海量数据分辨率的方法。为此， 我们不仅将开发高分辨率和细节的多比例模型，而且还将开发 适当的工具，以促进和启用这种精确的建模。