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 流感病毒感染是一种反复出现的健康和经济负担。它在人之间循环 人口和动物水库，导致数百万住院和数千人死亡 每年，尤其是在高风险群体中，例如幼儿，孕妇，肥胖， 免疫系统和土著人口受损的人。疾病发病率和 当新的流感菌株重新定位或跳跃宿主并变得能够 感染人类。在这种情况下，没有（或最少）先前存在的抗体介导的对抗体的免疫力 人口水平的新病毒株，导致数百万感染和全球迅速传播 病毒。在没有抗体的情况下，疾病的严重程度可以通过广泛地改善 交叉反应性细胞免疫。但是，免疫细胞如何介导恢复的精确机制 在某些人中，但不是其他人，远非清楚。但是，多元化而丰富的集合 数据集可在公共领域可用，这些数据集已经解决了这些特定方面 关注。人类人群和动物研究中的表达谱，免疫学 Immport或流感菌株数据中的特征以及与流感中的免疫表位相互作用 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 流感感染期间的过程非常详细。多个先前收集的组合 数据集，尤其是在生物学量表，单细胞和批量数据的范围内，是一个核心目标 研究。指导我们拟议的工作的总体假设是流感的多样性 病毒菌株，遗传免疫表位和在反应的免疫细胞中有助于 流感感染后的各种结果。详细说明我们将解决有关的问题 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 另一方面，或导致免疫反应弱。性别为生物因素将是 每当有足够的样品大小的适当数据提供时，请解决。我们将进一步发展 通过指导单细胞数据来增加大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尺度模型，还将开发 适当的工具来促进和启用这种精确建模。