Integrating T cell receptor features with gene expression profiles to define T cell specificity and differentiation

将 T 细胞受体特征与基因表达谱整合以定义 T 细胞特异性和分化

• 批准号: 10433774
• 负责人: Philip Bradley
• 金额: --
• 依托单位: FRED HUTCHINSON CANCER RESEARCH CENTER
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-08 至 2022-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10433774
• 关键词: Algorithms; Antigen Receptors; Atlases; Autoimmune Diseases; COVID-19 pandemic; Catalogs; Cell physiology; Cells; Clinical; Collaborations; Consensus; Data; Data Set; Databases; Development; Disease; Epitopes; Gene Expression; Gene Expression Profile; Goals; Graph; Human; Immune; Immune response; Immunologics; Immunologist; Individual; Infectious Diseases Research; Lead; Link; Literature; Malignant Neoplasms; Maps; Mediating; Meta-Analysis; Metadata; Participant; Phenotype; Population; Process; Research Personnel; Resolution; Resources; Running; Specificity; Standardization; Surface; T-Cell Immunologic Specificity; T-Cell Receptor; T-Lymphocyte; Tissues; Work; analytical method; analytical tool; data cleaning; improved; infectious disease treatment; innovation; insight; interest; multimodality; novel; novel strategies; pathogen; receptor expression; response; secondary analysis; single cell analysis; tool

ABSTRACT The objective of this proposal is to identify linkages between T cell receptor (TCR) sequences and transcriptional profiles across the human T cell landscape. This work is enabled by our recent development of the CoNGA algorithm, a graph theoretic approach that integrates TCR and gene expression (GEX) datasets, and by technological advances that have made it possible to profile both features in parallel at high throughput. Submitted in response to Notice of Special Interest NOT-AI-21-011 ("Secondary Analysis of Existing Datasets for Advancing Immune-mediated and Infectious Disease Research"), our proposal brings together a team of computational biologists and immunologists with a track record of successful collaboration. Our goal is to apply CoNGA on diverse T cell datasets to define the landmark TCR features and their correlated phenotypes in human T cells. In the first Aim, we will identify, acquire, pre-process, and standardize all large, publicly available single-cell datasets that feature linked gene expression and paired TCR sequence information. We will then run the CoNGA pipeline on these individual datasets, correlate the results with available study metadata, and make these results available for download. In the second Aim, we will perform a meta-analysis of the relationship between T cell receptor sequence and T cell transcriptional profile across the entire dataset (1,000+ donors and 1,000,000+ individual T cells). Completion of the work proposed here will lay the groundwork for a comprehensive atlas of the human T cell landscape and provide a valuable dataset for further development of analytical tools and methods. T cell features and sub-populations identified by CoNGA will provide new insight into the individual datasets while also illuminating the global landscape of GEX/TCR covariation.

抽象的 该提案的目的是确定 T 细胞受体 (TCR) 序列和 整个人类 T 细胞景观的转录谱。这项工作是由我们最近开发的 CoNGA 算法，一种集成了 TCR 和基因表达 (GEX) 数据集的图论方法， 技术进步使得以高吞吐量并行分析这两个特征成为可能。 响应特别兴趣通知 NOT-AI-21-011（“现有数据集的二次分析”）而提交 为了促进免疫介导和传染病研究”），我们的提案汇集了一个团队 具有成功合作记录的计算生物学家和免疫学家。我们的目标是申请 CoNGA 在不同的 T 细胞数据集上定义标志性 TCR 特征及其相关表型 人类 T 细胞。在第一个目标中，我们将识别、获取、预处理和标准化所有大型的、公开的 可用的单细胞数据集，具有关联的基因表达和配对的 TCR 序列信息。我们 然后将在这些单独的数据集上运行 CoNGA 管道，将结果与可用的研究相关联 元数据，并使这些结果可供下载。在第二个目标中，我们将进行荟萃分析 整个数据集中 T 细胞受体序列和 T 细胞转录谱之间的关系 （1,000 多个捐赠者和 1,000,000 多个个体 T 细胞）。完成此处建议的工作将奠定 为人类 T 细胞景观的综合图谱奠定了基础，并为进一步研究提供了有价值的数据集 开发分析工具和方法。 CoNGA 识别的 T 细胞特征和亚群将 提供对各个数据集的新见解，同时也阐明 GEX/TCR 的全球景观 共变。