Single-Cell Transcriptomic Analysis to Identify Drivers of Pulmonary Fibrosis

项目摘要 K08奖的这一提议是出于两个总体目标：1）支持科学和专业 候选人的发展，博士保罗Reyfman，实现他的职业目标，成功地作为一名医生 科学家和独立研究者，具有系统生物学方法的专门知识， 慢性肺部疾病，以及2）研究单细胞转录组学分析是否可以提供新的见解 肺纤维化的病理学肺纤维化是一种致命的进行性疾病， 诊断方法仍然不精确，缺乏有效的治疗方法。与他的导师，博士。 斯科特布丁格和路易斯阿马拉尔，候选人已经制定了一个全面的培训计划，将确保博士。 Reyfman在发展假设、设计和完成 实验，分析数据，并将发现传达给科学界。作为必要组分 在本培训计划中，候选人将采用系统生物学方法来开发分析工具 从肺纤维化患者产生的单细胞转录组数据集。 单细胞转录组学分析越来越多地用于研究人类疾病的病理生物学， 作为开发新的疾病生物标志物的基础。开发和验证用于快速分析 越来越多的单细胞转录组数据是一个挑战，因为改进技术， 来自健康和患病组织的数据。特别是，尚不清楚单细胞RNA的分析是否 测序（scRNA-Seq）和单核RNA测序（snRNA-Seq）数据可用于定量 准确的细胞组成的肺，并确定基因表达之间的差异健康和 肺纤维化我们的初步数据表明，肺的scRNA-Seq鉴定了促纤维化基因表达， 在肺纤维化患者中，个体之间是异质性的。我们还发现scRNA-Seq 对某些组成肺细胞群的采样不足。因此，我们设计了这个提案来测试 假设来自肺纤维化患者的肺样品的单细胞转录组学分析可以 用于识别疾病的内在型。在具体目标1中，候选人将确定snRNA-Seq是否能够 肺纤维化期间肺的细胞组成的定量。在具体目标2中， 将开发工具，用于使用从SSc-ILD患者和正常人中获得的标本进行scRNA-Seq 控制，以获得对疾病病理学的新见解。在这个奖项的过程中，候选人将获得 新的技术，包括从冷冻保存的肺组织中产生snRNA-Seq，肺体视学，RNA原位 互补基因组数据集的分析，以及临床表型的整合 基因组分析的信息。完成拟议的工作将提供一个严格的培训计划， 博士Reyfman和将提供见解，可能导致改善治疗肺纤维化患者。

PROJECT SUMMARY This proposal for a K08 Award is motivated by two overarching goals: 1) to support the scientific and professional development of the candidate, Dr. Paul Reyfman, towards achieving his career goal of succeeding as a physician scientist and independent investigator with expertise in systems biology approaches applied to the study of chronic lung disease, and 2) to investigate whether single-cell transcriptomic profiling can provide novel insights into the pathobiology of pulmonary fibrosis. Pulmonary fibrosis is a deadly and progressive condition for which diagnostic approaches remain imprecise and effective therapies are lacking. Together with his mentors, Drs. Scott Budinger and Luís Amaral, the candidate has developed a comprehensive training plan that will ensure Dr. Reyfman acquires new knowledge and proficiencies in developing hypotheses, designing and completing experiments, analyzing data, and communicating findings to the scientific community. As an essential component of this training plan, the candidate will employ systems biology approaches to developing tools for analysis of single-cell transcriptomic datasets generated from patients with pulmonary fibrosis. Single-cell transcriptomic profiling is increasingly used to investigate the pathobiology of disease in humans and as a basis for developing novel biomarkers of disease. Developing and validating tools for analyzing the rapidly growing quantity of single-cell transcriptomic data is as much of a challenge as refining techniques for generating data from healthy and diseased tissues. In particular, it is not known whether analysis of single-cell RNA sequencing (scRNA-Seq) and single-nucleus RNA sequencing (snRNA-Seq) data can be used to quantify accurately the cellular composition of the lung and to identify gene expression differences between health and pulmonary fibrosis. Our preliminary data suggest that scRNA-Seq of lung identifies profibrotic gene expression in patients with pulmonary fibrosis that is heterogeneous between individuals. We also found that scRNA-Seq undersampled certain constituent lung cellular populations. Accordingly, we designed this proposal to test the hypothesis that single-cell transcriptomic analysis of lung samples from patients with pulmonary fibrosis can be used to identify disease endotypes. In Specific Aim 1, the candidate will determine whether snRNA-Seq enables quantification of the cellular composition of the lung during pulmonary fibrosis. In Specific Aim 2, the candidate will develop tools for using scRNA-Seq performed on specimens obtained from patients with SSc-ILD and normal controls to gain novel insights into disease pathobiology. Over the course of this award, the candidate will gain new skills including in generating snRNA-Seq from cryopreserved lung tissue, in lung stereology, in RNA in situ hybridization, in analysis of complementary genomic datasets, and in incorporation of clinical phenotypic information into genomic analyses. Accomplishing the proposed work will provide a rigorous training program for Dr. Reyfman and will provide insights that could lead to improved therapies for patients with pulmonary fibrosis.