"Lung MAP" Atlas Research Center

描述（由申请人提供）：“The LungMap”RFA 要求研究中心与一个联盟合作，建立一个开放获取的分子资源，用于研究人类和小鼠肺的后期发展阶段。我们的研究计划将侧重于生成和分析人类和小鼠肺从小管-囊状晚期到肺泡发育时期的详细 NexGen 基因表达数据。肺由多种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。虽然调节肺形成的许多基因和网络在脊椎动物物种之间是共享的，但小鼠和人类肺的生理学、结构和区域特定细胞类型以及基因表达模式有所不同。为了有效地将小鼠模型的数据转化为人类，了解它们在细胞水平上的分子相似性和差异至关重要。我们将为人和小鼠肺实质生成详细的细胞特异性 RNA 数据库。单细胞 RNA-seq 将使用来自激光捕获显微切割 (LCM)、FACS、RNA 分析和分子成像的数据进行解释，以创建人和小鼠肺部的表达图谱。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，从而协同与肺部发育、疾病和功能相关的研究。该应用将重点分析来自正常肺实质的细胞（例如，传导气道和肺泡上皮细胞；血管内皮细胞，包括静脉、动脉、淋巴和毛细血管细胞；基质和肺组织）。 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）。目标 1：细胞特异性 NexGen RNA 分析以创建小鼠肺中基因表达的“图谱”。单细胞分离、FACS 纯化、肺实质细胞的 LCM 以及 RNA-Seq 将创建小鼠从 E15.5 到出生后第 28 天的基因表达时空图谱。目标 2：细胞特异性 NexGen RNA 分析，创建人胎肺基因表达图谱。单细胞分离、LCM 和 FACS 分析以及 RNA 测序将创建从妊娠 22 周到出生后肺泡期人类肺形成过程中基因表达的时空模式图。目标 3：NexGen RNA 的生物信息学分析，以定义小鼠和人类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，使用定义的解剖学本体进行整理，并进行整合，以提供精确的 RNA 剪接形式、microRNA 和在肺形成过程中表达的其他非编码 RNA 的基因表达模式的灵敏和定量测量。 （摘要完）

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)