Comparative, single-cell analysis of COVID-19 and other respiratory diseases

COVID-19 和其他呼吸道疾病的比较单细胞分析

基本信息

批准号：
10361031
负责人：
Alexander Minchev Tsankov
金额：
$ 12.68万
依托单位：
ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-03-01 至 2024-02-29
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10361031
关键词：
2019-nCoV ACE2 Accounting Affect Age Ageusia Alveolus Anatomy Atlases Biology COVID-19 COVID-19 long hauler COVID-19 patient COVID-19 survivors COVID-19 treatment Categories Cell Communication Cells Censuses Chest Pain Clinical Clinical Data Communication Communities Complex Computer Analysis Coughing Data Data Set Disease Dropout Dyspnea Epithelial Epithelial Cells Event Functional disorder Future Gender Gene Expression Genes Homeostasis Human Immune Individual International Laboratories Ligands Location Long COVID Lower Respiratory System Lung Lung diseases Measures Messenger RNA Methods Morbidity - disease rate Nose Organ Pathway interactions Patients Pharmaceutical Preparations Positioning Attribute Protocols documentation Public Health Publishing Pulmonary Fibrosis Pulmonology RNA RNA Splicing Recovery Research Resolution Resources Respiratory Disease Sampling Severity of illness Smoking History Smoking Status Survivors Symptoms TMPRSS2 gene Tissues Variant Visualization Work analysis pipeline cell type clinical effect cohort comorbidity comparative computer framework data integration experience global health high dimensionality individualized medicine insight medical complication mortality multidimensional data new therapeutic target novel novel coronavirus pandemic disease peripheral blood personalized medicine post SARS-CoV-2 infection programs receptor regenerative respiratory severe COVID-19 single cell analysis single cell technology single-cell RNA sequencing targeted treatment tissue processing tool treatment strategy

2019-nCoV ACE2 Accounting Affect Age Ageusia Alveolus Anatomy Atlases Biology COVID-19 COVID-19 long hauler COVID-19 patient COVID-19 survivors COVID-19 treatment Categories Cell Communication Cells Censuses Chest Pain Clinical Clinical Data Communication Communities Complex Computer Analysis Coughing Data Data Set Disease Dropout Dyspnea Epithelial Epithelial Cells Event Functional disorder Future Gender Gene Expression Genes Homeostasis Human Immune Individual International Laboratories Ligands Location Long COVID Lower Respiratory System Lung Lung diseases Measures Messenger RNA Methods Morbidity - disease rate Nose Organ Pathway interactions Patients Pharmaceutical Preparations Positioning Attribute Protocols documentation Public Health Publishing Pulmonary Fibrosis Pulmonology RNA RNA Splicing Recovery Research Resolution Resources Respiratory Disease Sampling Severity of illness Smoking History Smoking Status Survivors Symptoms TMPRSS2 gene Tissues Variant Visualization Work analysis pipeline cell type clinical effect cohort comorbidity comparative computer framework data integration experience global health high dimensionality individualized medicine insight medical complication mortality multidimensional data new therapeutic target novel novel coronavirus pandemic disease peripheral blood personalized medicine post SARS-CoV-2 infection programs receptor regenerative respiratory severe COVID-19 single cell analysis single cell technology single-cell RNA sequencing targeted treatment tissue processing tool treatment strategy

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项目摘要

PROJECT SUMMARY COVID-19 disease, caused by the novel coronavirus SARS-CoV-2, is a global health threat due to its rapid spread, morbidity, and mortality. It is estimated that at least 25% of severe COVID-19 disease survivors experience persistent respiratory complications. This underscores the need to better understand how the cell types, regenerative lineages, and cell-cell communication are altered following SARS-CoV-2 infection and what regulatory mechanisms and interactions in affected tissue may underlie Long-COVID, the long-term medical complications in COVID-19 long-haulers. Recent advances in high-throughput single cell RNA-sequencing (scRNA-seq) have enabled comprehensive characterization of the cellular census of the lung, which has led to a remarkable number of novel findings in new cell types and cell states at homeostasis and in disease. However, our understanding of the cell type relationships and their higher order tissue organization is still lacking in the context of COVID-19. Analyses by our lab and the Human Cell Atlas (HCA) lung network using a compendium of tissues from healthy individuals revealed that a distinct subset of epithelial cells in the nasal passages, airways, alveoli, and gut co-express ACE2 and TMPRSS2, consistent with organ-specific disease presentations of COVID-19. More recently, over one million cells of scRNA-seq data from greater than one hundred COVID-19 patients became publicly available, which provides an unprecedented resolution and statistical power to study the variation in the lung cellular microenviroment between individuals with different presentation of COVID-19. Such an endeavor presents several computational challenges due to the high- dimensionality of the data and because analysis tools of cellular communication in scRNA-seq data and integration with coordinated multicellular gene expression programs are still needed. We propose to integrate high-dimensional scRNA-seq data and correct for batch effects between laboratories in order to build a uniformly annotated single-cell atlas of more than one hundred COVID-19 patients. Such an atlas would enable us to characterize the similarities and differences in cellular composition and cell-cell interactions and assess the effects of clinical correlates--such as gender, age, medications, and smoking history--on tissue organization. Comparisons between the lung microenvironment across our large, integrated cohort of control and COVID-19 patients will uncover commonly dysregulated cell types and cell-cell interactions that can inform on shared pathways to target therapeutically. Moreover, comparison of the dysregulated cell types, regenerative lineages, and cell-cell interactions to those observed in other lung diseases will provide important insight on how to repurpose existing drugs and recommended treatments for COVID-19 long haulers with respiratory complications.

项目摘要由新型冠状病毒SARS-COV-2引起的Covid-19疾病是全球健康威胁，因为它的迅速传播，发病率和死亡率。据估计，至少有25％的严重covid-19疾病幸存者经历持续的呼吸并发症。这强调了更好地了解细胞的需求 SARS-COV-2感染后，类型，再生谱系和细胞 - 细胞通信发生了改变以及什么长期医学 19009年长途旅行者的并发症。高通量单细胞RNA测序的最新进展（SCRNA-SEQ）已实现了肺部细胞普查的全面表征，这导致了在稳态和疾病中，新的细胞类型和细胞状态中有大量新发现。但是，我们对细胞类型关系及其高阶组织组织的理解仍在缺乏在Covid-19的背景下。通过我们的实验室和人类细胞图集（HCA）肺网进行分析来自健康个体的组织的汇编表明，鼻腔中上皮细胞的一个独特的子集通道，气道，肺泡和肠道共表达ACE2和TMPRSS2，与器官特异性疾病一致 COVID-19的演讲。最近，超过100万个单元格的scrna-seq数据来自超过一个数百名Covid-19患者公开可用，提供了前所未有的解决方案和研究不同患者之间肺细胞微观病的差异的统计能力 COVID-19的介绍。这样的努力提出了一些计算挑战，这是由于高数据的维度和由于scrna-seq数据中的蜂窝通信的分析工具和仍然需要与协调的多细胞基因表达程序集成。我们建议整合高维SCRNA-seq数据并纠正实验室之间的批处理效应，以构建超过一百个Covid-19患者的均匀注释的单细胞图集。这样的地图集会使我们能够表征细胞组成和细胞 - 细胞相互作用的相似性和差异以及评估临床相关的影响 - 例如性别，年龄，药物和吸烟史 - 在组织组织。在我们大的，整合的控制队列中的肺微环境之间的比较 Covid-19患者将发现常见失调的细胞类型和细胞 - 细胞相互作用，可以告知在共享靶向治疗的途径上。此外，比较失调的细胞类型，再生谱系以及与其他肺部疾病中观察到的谱系相互作用将提供重要的有关如何重新利用现有药物的洞察力，并建议对Covid-19的长途运输者进行推荐治疗呼吸并发症。