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Population structure of myeloid cells in healthy and diseased human lungs.

Population structure of myeloid cells in healthy and diseased human lungs.
健康和患病人肺中骨髓细胞的种群结构。
批准号:
347286815
负责人:
Professorin Dr. Christine Falk
金额:
$0.0万
依托单位:
依托单位国家:
德国
项目类别:
Research Grants
财政年份:
2017
资助国家:
德国
项目状态:
已结题
起止时间:
2016-12-31 至 2020-12-31

项目摘要

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中文摘要
翻译
肺是直接暴露于环境中的病原体和颗粒的三个器官之一。毫不奇怪,肺需要一个局部免疫系统,以便对这些病原体做出快速有效的反应,同时在任何感染或非感染的情况下确保器官的完整性和功能。在稳态条件下,肺中有特化免疫细胞,包括肺泡巨噬细胞(AM)、常规和浆细胞样树突状细胞(dc)、肥大细胞、先天淋巴样细胞(ILCs)、不变自然杀伤T细胞(iNKT)、NK细胞、记忆T细胞和调节性T细胞(Treg)。我们对免疫细胞的理解已经大大加快了经典表型和细胞表面标记定义免疫细胞群的功能分析。然而,目前用于区分亚群的表型技术也产生了相互矛盾的结果。虽然这是向前迈出的重要一步,但即使是评估40多个参数的CyTof等技术,也不能以完全公正的方式识别免疫细胞的亚群结构。特别是对于像人肺这样的组织中的骨髓细胞室,用经典的方法来明确地描述、理解和研究现有的细胞异质性和可塑性变得越来越困难,不仅在稳态中,而且在病理条件下。我们假设,如果我们能够以前所未有的分辨率(几百个基因/每个细胞)评估人类肺部免疫系统的细胞区室,特别是髓细胞区室,我们可以对人类肺部真正的免疫细胞群结构和肺部主要疾病的病理生理有更高的了解。我们进一步假设,需要这样的高分辨率分析来更好地了解肺免疫系统的功能。随着单细胞水平测序技术的出现(例如scRNA-seq),开辟了一条全新的途径,我们现在可以应用这些技术来描述感兴趣的器官的真实种群结构。由于scRNA-seq等技术从未应用于人类健康肺组织中的免疫细胞,并且由于我们有幸结合了我们在肺移植生物学、肺免疫生物学、巨噬细胞生物学、scRNA-seq、转录组学和生物信息学方面的专业知识,我们建议使用scRNA-seq来发现人肺髓系细胞室的真实种群结构,并确定新的亚群结构对肺部疾病和移植的临床影响。
英文摘要
The lung is one of three organs that is directly exposed to pathogens and particles from the environment. Not surprisingly, the lung requires a local immune system that allows for fast and effective responses against such pathogens, yet secures organ integrity and function during any insult, infectious or non-infectious. Under homeostatic conditions, the lung harbors specialized immune cells including alveolar macrophages (AM), conventional and plasmacytoid dendritic cells (DCs), mast cells, innate lymphoid cells (ILCs), invariant natural killer T (iNKT) cells, NK cells, memory T cells, and regulatory T cells (Treg). Our understanding of immune cells has been greatly accelerated by classical phenotyping and functional analysis of cell surface marker defined immune cell populations. However, current phenotyping technologies used to distinguish subpopulations have also generated contradictory results. While a significant step forward, even technologies such as CyTof assessing 40 and more parameters are not designed to identify a subpopulation structure of immune cells in an entirely unbiased fashion. Particularly for the myeloid cell compartment in tissues such as the human lung, it is becoming increasingly difficult using classical approaches to unequivocally describe, understand and study the existing cellular heterogeneity and plasticity, not only in homeostasis, but particularly under pathological conditions.We hypothesize that we can gain a higher understanding of the true immune cell population structure of the human lung and the pathophysiology of the major diseases of the lung if we can assess the cellular compartments of the human lungs immune system, particularly the myeloid cell compartment at an unprecedented level of resolution (several hundred genes/per cell). We further hypothesize that such a high-resolution analysis is required to better understand the functionality of pulmonary immune system.With the advent of sequencing technologies on the single cell level (e.g. scRNA-seq), a completely new avenue has been opened and we are now in the position to apply such technologies to describe the true population structure of an organ of interest. Since technologies such as scRNA-seq have never been applied to immune cells in human healthy lung tissue and since we are in the fortunate position to combine our expertise in lung transplant biology, lung immune biology, macrophage biology, scRNA-seq, transcriptomics and bioinformatics, we propose to use scRNA-seq to discover the true population structure in the myeloid cell compartment of the human lung and to define the clinical impact of the new subpopulation structure for lung diseases and transplantation.
期刊论文(5)
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科研奖励(0)
会议论文
DOI: 10.1183/23120541.00915-2020
发表时间: 2021-07-01
期刊: ERJ OPEN RESEARCH
影响因子: 4.6
作者: [Fujii, Wataru, Kapellos, Theodore S., Schultze, Joachim L.]
通讯作者: Schultze, Joachim L.
DOI: 10.1016/j.cell.2020.04.035
发表时间: 2020-05-28
期刊: CELL
影响因子: 64.5
作者: [Ziegler, Carly G. K., Allon, Samuel J., Ordovas-Montanes, Jose]
通讯作者: Ordovas-Montanes, Jose
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