Graph Learning of Cell-cell Communications in Spatial Transcriptomics

空间转录组学中细胞间通信的图学习

• 批准号: 10672669
• 负责人: Zuoheng Wang
• 金额: $ 12.56万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-06 至 2026-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10672669
• 关键词: Affect; Air; Air Pollutants; Air Pollution; Airway Disease; Allergens; Asthma; Automobile Driving; Blood Circulation; Cell Communication; Cells; Code; Complex; Connecticut; Data; Data Analyses; Data Set; Databases; Development; Disease; Disease Management; ENG gene; Environmental Exposure; Environmental Risk Factor; Exposure to; GSTM3 gene; Gene Expression; Genes; Genetic; Goals; Grant; Humidity; Individual; Knowledge; Lead; Link; Machine Learning; Measurement; Molds; Molecular; Nature; Network-based; Occupational Exposure; Online Systems; Parents; Patients; Persons; Phenotype; Rain; Reporting; Research; Research Design; Research Methodology; Research Project Grants; Research Project Summaries; Risk; Severities; Severity of illness; Signal Transduction; Smoking; Sputum; Supervision; Symptoms; Temperature; United States Environmental Protection Agency; Variant; Visit; allergic response; asthmatic; asthmatic patient; atmospheric conditions; base; cell type; chronic inflammatory disease; climate change; climate data; clinical phenotype; data centers; design; disease phenotype; disorder control; extreme weather; gene environment interaction; genome wide association study; genome-wide; graph learning; improved; intercellular communication; pet animal; prevent; pulmonary function; severe weather; single-cell RNA sequencing; therapeutic development; tool; transcriptomics; weather stations

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.

项目概要 研究项目：研究表明极端天气与疾病严重程度的变化有关 然而，大气条件影响导致哮喘的分子机制。 人们对哮喘的严重程度和活动性知之甚少，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变化不断增加，至关重要的是 了解这些基因与环境对哮喘的相互作用，以便更好地控制哮喘症状。 此前曾报道环境相互作用是哮喘风险的重要决定因素，但 这些关系的确切性质以及与这些相互作用相关的分子信号仍不清楚。 基因-环境相互作用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都没有考虑到大气的变化。 分析中的条件，在相互作用的分子机制上留下了知识空白 气候变化和基因及其对哮喘严重程度和活动表型的贡献填补了这一知识。 差距，我们将探讨从最近的天文台收集的环境因素与 哮喘患者全基因组细胞类型特异性基因表达水平及其对哮喘的贡献 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。 哮喘患者的循环和气道变化与大气条件的波动有关 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，2) 评估 大气条件波动引起的细胞间通讯扰动。 研究设计和方法：母体 R01 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 可以识别与大气条件波动相关的变化。目标 2 和 中开发的工具。 母公司 R01 拨款的 3 部分将用于在每位患者体内构建细胞间通信网络并检测 使用去卷积计算这些网络中与大气条件波动相关的扰动 数据。已确定的大气条件相关变化对哮喘表型的贡献。 将评估其批量表达数据、scRNA-seq 数据和临床表型。 哮喘已在 Chupp 博士的实验室中生成并存储在构建的在线 YCAAD 数据库中 由 Rajeevan 博士维护的来自最近气象站的每日大气状况数据。 Rajeevan 博士将根据邮政编码下载并组织与每位患者相关的信息。 所有数据的预处理以及使用母 R01 拨款中开发的工具对数据的分析将是 严西廷博士和王作恒博士指导和监督。