Graph Learning of Cell-cell Communications in Spatial Transcriptomics

项目摘要 研究项目：研究表明，极端天气与疾病严重程度的变化有关 和活动。然而，受大气条件影响导致哮喘的分子机制 严重性和活动性知之甚少，这阻碍了我们设计有效的哮喘治疗方法。 此外，鉴于气候变化和日常大气条件的日益多变， 了解这些基因-环境相互作用对哮喘的影响，以便更好地控制哮喘症状。吉恩 以前曾报道环境相互作用是哮喘风险的重要决定因素，但 与这些相互作用相关的关系和分子信号的确切性质仍不清楚。 基因-环境相互作用的研究主要集中在接触宠物，霉菌，吸烟，职业 暴露，空气污染和其他过敏原。这些研究都没有考虑到大气层的变化， 条件的分析，留下的知识差距之间的相互作用的分子机制 气候变化和基因及其对哮喘严重程度和活动性表型的影响。为了填补这些知识 gap，我们将探讨从最近的观测站收集的环境因素与 哮喘患者的全基因组细胞类型特异性基因表达水平及其对 哮喘严重程度和活动性。为了实现这一目标，我们建议1）评估细胞类型特异性转录组学 与大气条件波动相关的哮喘患者循环和气道变化 以及它们的相互作用对哮喘严重程度和活动性表型的贡献，以及2）评估 由大气条件的波动引起的细胞间通讯的扰动。 研究设计和方法：在母体R 01的目标1中开发的工具将用于对 基于单细胞RNA测序（scRNA-seq）数据的批量表达数据，因此细胞类型特异性转录组学 可以确定与大气条件波动有关的变化。在目标2和 3的父R 01赠款将用于在每个患者中构建细胞间通信网络，并检测 这些网络中的扰动与大气条件的波动有关， 数据确定的大气条件相关变化对哮喘表型的贡献 将评估严重性和活动性。大量表达数据，scRNA-seq数据和临床表型， Chupp博士的实验室已经产生了哮喘，并存储在YCAAD的在线数据库中， 由拉杰万医生负责维护最近气象站的每日大气状况数据 与每个病人的邮政编码相关的数据将由Rajeevan博士下载和组织。 所有数据的预处理和使用母R 01赠款中开发的工具进行的数据分析将 由严希婷博士和王作恒博士指导和监督。

PROJECT SUMMARY Research Project: Studies have shown that extreme weather is associated with changes in disease severity and activity. However, the molecular mechanisms influenced by atmospheric conditions that contribute to asthma severity and activity are poorly understood, which prevents us in designing effective asthma treatments. Furthermore, given climate change and increasing variability in daily atmospheric conditions, it is critical to understand these gene-environment interactions on asthma for better control of asthma symptoms. Gene- environment interactions have been previously reported as important determinants of risk for asthma, but the exact nature of the relationships and the molecular signals associated with these interactions remain unclear. Gene-environment interaction studies have mostly focused on exposure to pets, mold, smoking, occupational exposure, air pollution, and other allergens. None of the studies have considered changes in atmospheric conditions in the analysis, leaving a knowledge gap on the molecular mechanism of the interaction between climate change and gene and its contribution to phenotypes of asthma severity and activity. To fill this knowledge gap, we will explore the relationships between environmental factors collected from the nearest observatory and genome-wide cell type-specific gene expression levels in patients with asthma as well as its contribution to asthma severity and activity. To achieve this goal, we propose to 1) assess cell type-specific transcriptomic changes in the circulation and airway of asthma patients associated with fluctuations in atmospheric conditions and the contribution of their interaction to the phenotypes of asthma severity and activity, and 2) evaluate perturbations in intercellular communication induced by fluctuations in atmospheric conditions. Research design and methods: Tools developed in Aim 1 of the parent R01 will be applied to deconvolve the bulk expression data based on single-cell RNA sequencing (scRNA-seq) data so cell type-specific transcriptomic changes associated with fluctuations in atmospheric conditions can be identified. Tools developed in Aims 2 and 3 of the parent R01 grant will be applied to construct cell-cell communication networks in each patient and detect perturbations in these networks associated with fluctuations of atmospheric conditions using the deconvolved data. The contribution of identified atmospheric condition associated changes to the phenotypes of asthma severity and activity will be evaluated. The bulk expression data, scRNA-seq data and clinical phenotypes of asthma have been generated in Dr. Chupp’s lab and stored in the online YCAAD database that is constructed and maintained by Dr. Rajeevan. The daily atmospheric condition data from the closest weather station associated with each patient based on their zip codes will be downloaded and organized by Dr. Rajeevan. Preprocessing of all the data and the analysis of the data using tools developed in the parent R01 grant will be guided and supervised by Drs. Xiting Yan and Zuoheng Wang.