Allergen T cell epitopes during the management of peanut allergic disease

项目摘要 基因表达和系统免疫学(GeSI)核心将通过以下方式为该中心提供支持 实施高级流式细胞术和质量细胞术(CyTOF)，以及散体和单细胞RNA- 测序(RNA-seq)。核心中实现这一功能的工作将围绕三项技术进行组织。 我们将利用先进的多色流式细胞仪和质量细胞仪数据分析技术来 阐明花生变应原反应性CD4+T细胞的部分表型特性。这些研究将 在项目特定的PI的实验室中进行的补体标准流量和质量细胞仪分析 实验。我们将使用rna-seq来阐明大种群的完整转录组表型。 花生过敏原特异性的CD4+T细胞。来自这些实验的知识将推动更好的流程设计 和单个项目中的质量细胞术实验，并将有助于单细胞RNA的分析- 测序(RNA-seq)实验。我们将利用新技术进行单细胞分析 变应原特异性CD4+T细胞的转录组表型和TCR克隆型。这些实验将 有助于了解PA中变应原特异性CD4+T细胞的异质性，并有助于确定 机制(S)参与了成功的OIT。总之，我们希望我们提供的技术将发挥重要作用 在完成组成部分项目方面发挥作用，并推动对PA免疫方面的更好了解。 我们还期待在OIT中确定新的生物标记物、新的靶点和更好的患者选择策略。

Project Summary The Gene expression and Systems Immunology (GeSI) Core will provide support for this center by implementation of advanced analysis of flow and mass cytometry (CyTOF), and bulk and single cell RNA- sequencing (RNA-seq). Work in the Core to achieve this function will be organized around three technologies. We will utilize advanced analysis techniques of multi-color flow cytometry and mass cytometry data to elucidate selected phenotypic properties of peanut allergen-reactive CD4+ T cells. These studies will complement standard flow and mass cytometry analyses performed in the PI's laboratories in project-specific experiments. We will use RNA-seq to elucidate full transcriptome phenotypes of bulk populations of peanut allergen specific CD4+ T cells. Knowledge from these experiments will drive design of better flow and mass cytometry experiments in the individual projects, and will aid in analysis of single cell RNA- sequencing (RNA-seq) experiments. We will utilize novel technologies to perform single cell analysis of transcriptome phenotypes and TCR clonotypes of allergen-specific CD4+ T cells. These experiments will facilitate understanding of heterogeneity of allergen-specific CD4+ T cells in PA and in determining mechanism(s) involved in successful OIT. Together, we expect the technologies we provide to play a major role in completion of the component projects, and in driving improved understanding of immune aspects of PA. We also anticipate identifying new biomarkers, new targets and better strategies for patient selection in OIT.