"Lung MAP" Atlas Research Center

描述(由申请者提供)：“The LongMap”RFA任务研究中心在一个联合体中工作，以建立一个开放获取的分子资源，用于研究发育后期的人和小鼠肺。我们的研究计划将重点放在生成和分析详细的NexGen基因表达数据，从小管球囊晚期到肺泡期的人和小鼠肺。肺由不同的实质细胞和造血细胞组成，其丰度、分化和功能因发育、区域和物种而异。不同细胞类型之间的时间和空间相互作用协调着肺的形成和功能。虽然许多调节肺形成的基因和网络在脊椎动物物种中是共享的，但小鼠和人类的肺在生理、结构和区域特定的细胞类型以及基因表达模式上存在差异。为了有效地将数据从老鼠模型转换到人类，在细胞水平上了解它们的分子相似和不同是至关重要的。我们将为人和小鼠的肺实质建立一个详细的细胞特异性RNA数据库。单细胞RNA-SEQ将利用激光捕获显微解剖(LCM)、流式细胞仪(FACS)、RNA分析和分子成像的数据进行解释，以创建人和小鼠肺的表达图谱。计算和“系统生物学”方法将整合这些数据，以创建易于访问的数据库，这些数据库将协同与肺发育、疾病和功能相关的研究。这一应用将集中于分析来自正常肺实质的细胞(例如，导气管和肺泡上皮细胞；血管内皮细胞，包括静脉、动脉、淋巴管和毛细血管细胞；间质和 外膜细胞，包括成纤维细胞、周细胞、肌成纤维细胞和软骨；以及神经上皮细胞和神经元细胞)。目的1：细胞特异性NexGen RNA分析以建立小鼠肺中基因表达的“图谱”。单细胞分离、FACS纯化、肺实质细胞LCM和RNA-Seq将创建小鼠从胚胎15.5天到出生后28天的基因表达的时空图谱。目的2：细胞特异性NexGen RNA分析建立人胎肺基因表达图谱。单细胞分离、LCM和FACS分析以及随后的RNA-Seq将创建从怀孕22周到出生后肺泡期的人类肺形成过程中基因表达的时空模式图。目的3：对NexGen RNA进行生物信息学分析，以确定小鼠和人肺形成过程中的基因表达。来自小鼠和人类肺部研究的RNA序列数据的生物信息学和计算生物学将被分析，使用定义的解剖本体进行管理，并被整合，以提供精确的RNA剪接形式、在肺形成过程中表达的microRNAs和其他非编码RNAs的基因表达模式的灵敏和定量的测量。(摘要结束)

DESCRIPTION (provided by applicant): "The LungMap" RFA tasks Research Centers to work in a consortium to build an open access molecular resource for the study of human and mouse lung during later stages of development. Our research plan will focus to the generation and analysis of detailed NexGen gene expression data for both human and mouse lung from the late canalicular-saccular through the alveolar periods of development. The lung is comprised of diverse parenchymal- and hematopoietic derived cells, whose abundance, differentiation, and functions vary developmentally, regionally, and among species. Temporal and spatial interactions among diverse cell types orchestrate formation and function of the lung. While many of the genes and networks regulating lung formation are shared among vertebrate species, the physiology, structure and regional specific cell types, and gene expression patterns vary between murine and human lung. To effectively translate data from mouse models to humans, it is critical to understand their molecular similarities and differences at the cellular level. We will generate a detailed cell specific RNA database for human and mouse lung parenchyma. Single cell RNA-seq will be interpreted with data derived from laser capture microdissection (LCM), FACS, RNA analyses, and molecular imaging to create an expression map of human and mouse lung. Computational and "systems biology" approaches will integrate these data to create readily accessible databases that will synergize research related to pulmonary development, disease, and function. This application will focus to the analysis of cells from normal lung parenchyma (e.g., conducing airway and alveolar epithelial cells; vascular endothelial cells, including venous, arterial, lymphatic, and capillary cells; stromal and adventitial cells, including fibroblasts, pericytes, myofibroblasts, and cartilage; and neuroepithelial and neuronal cells). Aim 1: Cell Specific NexGen RNA Analyses to Create an "Atlas" of Gene Expression In the Murine Lung. Single cell isolation, FACS purification, and LCM of lung parenchymal cells, and RNA-Seq will create a temporal-spatial map of gene expression in the mouse from E15.5 to postnatal day 28. Aim 2: Cell Specific NexGen RNA Analysis to Create an Atlas of Gene Expression In Human Fetal Lung. Single cell isolation, LCM, and FACS analysis followed by RNA-Seq will create a map of the temporal-spatial patterns of the gene expression during human lung formation from 22 weeks of gestation through the postnatal-alveolar period. Aim 3: Bioinformatics Analysis of NexGen RNAs to Define Gene Expression During Murine and Human Lung Formation. Bioinformatics and computational biology of RNA sequence data from the murine and human lung studies will be analyzed, curated using defined anatomic ontologies, and integrated to provide a sensitive and quantitative measure of gene expression patterns of the precise RNA splice forms, microRNAs and other noncoding RNAs expressed during lung formation. (End of Abstract)