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，受大气条件影响的分子机制有助于哮喘 严重性和活动的理解很少，这阻止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下的可变性增加，至关重要 了解这些基因 - 环境相互作用在哮喘上有更好地控制哮喘症状。基因- 以前已经报道了环境相互作用是哮喘风险的重要决定者，但是 与这些相互作用相关的关系的确切性质和分子信号尚不清楚。 基因环境相互作用研究主要集中于暴露于宠物，霉菌，吸烟，职业 暴露，空气污染和其他过敏原。没有研究考虑大气的变化 分析中的条件，留下知识差距有关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性表型的贡献。填写这一知识 差距，我们将探讨从最近的观察结果和 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 哮喘的严重程度和活动。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 在大气条件下与波动相关的哮喘患者的循环和气道变化 以及它们对哮喘严重程度和活动表型的相互作用的贡献，2）评估 大气条件下波动引起的细胞间通信的扰动。 研究设计和方法：在父级R01的AIM 1中开发的工具将应用于Deconvolve 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 可以确定与大气条件下波动相关的变化。 AIMS 2和 父级R01赠款中的3个将应用于每个患者中的细胞电池通信网络并检测 这些网络中与大气条件波动相关的这些网络使用反应 数据。确定的大气条件与哮喘表型相关的变化的贡献 将评估严重性和活动。大量表达数据，SCRNA-SEQ数据和临床表型 哮喘是在Chupp博士的实验室中产生的，并存储在构建的在线YCAAD数据库中 由Rajeevan博士维护。来自最近气象站的每日大气状况数据 根据他们的邮政编码，与每个患者相关联，将由Rajeevan博士下载和组织。 使用父R01赠款中开发的工具对所有数据进行预处理和数据分析 由博士指导和监督。 XITING YAN和ZUOHENG WANG。