Suppr超能文献

SCovid:单细胞图谱揭示 COVID-19 在 10 个人体组织中的分子特征

SCovid: single-cell atlases for exposing molecular characteristics of COVID-19 across 10 human tissues.

机构信息

College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China.

Faculty of Computing, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China.

出版信息

Nucleic Acids Res. 2022 Jan 7;50(D1):D867-D874. doi: 10.1093/nar/gkab881.

Abstract

SCovid (http://bio-annotation.cn/scovid) aims at providing a comprehensive resource of single-cell data for exposing molecular characteristics of coronavirus disease 2019 (COVID-19) across 10 human tissues. COVID-19, an epidemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been found to be accompanied with multiple-organ failure since its first report in Dec 2019. To reveal tissue-specific molecular characteristics, researches regarding to COVID-19 have been carried out widely, especially at single-cell resolution. However, these researches are still relatively independent and scattered, limiting the comprehensive understanding of the impact of virus on diverse tissues. To this end, we developed a single-cell atlas of COVID-19. Firstly we collected 21 single-cell datasets of COVID-19 across 10 human tissues paired with control datasets. Then we constructed a pipeline for the analysis of these datasets to reveal molecular characteristics of COVID-19 based on manually annotated cell types. The current version of SCovid documents 1 042 227 single cells of 21 single-cell datasets across 10 human tissues, 11 713 stably expressed genes and 3778 significant differentially expressed genes (DEGs). SCovid provides a user-friendly interface for browsing, searching, visualizing and downloading all detailed information.

摘要

SCovid(http://bio-annotation.cn/scovid)旨在提供一个全面的单细胞数据资源,以揭示 2019 年冠状病毒病(COVID-19)在 10 个人体组织中的分子特征。COVID-19 是由严重急性呼吸系统综合征冠状病毒 2(SARS-CoV-2)引起的传染病,自 2019 年 12 月首次报告以来,已被发现伴有多器官衰竭。为了揭示组织特异性的分子特征,针对 COVID-19 的研究已经广泛开展,尤其是在单细胞分辨率水平。然而,这些研究仍然相对独立和分散,限制了对病毒对不同组织影响的全面理解。为此,我们开发了 COVID-19 的单细胞图谱。首先,我们收集了 COVID-19 在 10 个人体组织中的 21 个单细胞数据集,并与对照数据集配对。然后,我们构建了一个分析这些数据集的流程,以基于手动注释的细胞类型揭示 COVID-19 的分子特征。当前版本的 SCovid 记录了 21 个人体组织的 21 个单细胞数据集的 1 042 227 个单细胞、11 713 个稳定表达的基因和 3778 个显著差异表达的基因(DEGs)。SCovid 提供了一个用户友好的界面,用于浏览、搜索、可视化和下载所有详细信息。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6074/8728244/e8a04ae9c92a/gkab881fig1.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验