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

• 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
• 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

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疾病是一种全球性的健康威胁， 传播、发病率和死亡率。据估计，至少25%的严重COVID-19疾病幸存者 出现持续的呼吸系统并发症。这强调了更好地理解细胞如何 类型，再生谱系和细胞间通讯在SARS-CoV-2感染后改变， 受影响组织中的调节机制和相互作用可能是长期COVID的基础， COVID-19长途运输者的并发症。高通量单细胞RNA测序研究进展 （scRNA-seq）使得能够全面表征肺的细胞普查，这导致了 在新的细胞类型和细胞稳态和疾病中的新发现。 然而，我们对细胞类型关系及其更高级组织结构的理解仍然是有限的。 在COVID-19的背景下缺乏。我们的实验室和人类细胞图谱（HCA）肺网络使用 一份来自健康个体的组织概要显示，鼻粘膜上皮细胞的一个独特亚群， 通道、气道、肺泡和肠道共表达ACE 2和TMPRSS 2，与器官特异性疾病一致 COVID-19的介绍。最近，超过一百万个细胞的scRNA-seq数据来自超过一个 数百名COVID-19患者公开，这提供了前所未有的解决方案， 统计功效来研究具有不同肺细胞微环境的个体之间的变化。 COVID-19的介绍。这样的奋进提出了几个计算挑战，由于高- 由于scRNA-seq数据中细胞通讯的分析工具， 仍然需要与协调的多细胞基因表达程序的整合。我们建议整合 高维scRNA-seq数据，并校正实验室之间的批次效应，以建立一个 一百多名COVID-19患者的统一注释的单细胞图谱。这样的地图集将 使我们能够表征细胞组成和细胞间相互作用的相似性和差异， 评估临床相关因素（如性别、年龄、药物和吸烟史）对组织的影响 organization.我们的大型综合对照队列中肺部微环境之间的比较 COVID-19患者将发现通常失调的细胞类型和细胞间相互作用， 共同的治疗靶点。此外，比较失调的细胞类型， 再生谱系和细胞-细胞相互作用，将提供重要的 深入了解如何重新利用现有药物和推荐治疗COVID-19长途运输， 呼吸系统并发症