Suppr超能文献

单细胞组学技术在胃肠道和疾病研究中的应用,从单细胞身份到患者特征。

Use of Single-Cell -Omic Technologies to Study the Gastrointestinal Tract and Diseases, From Single Cell Identities to Patient Features.

机构信息

Epithelial Biology Center and Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, Tennessee.

Chemical and Physical Biology Program, Vanderbilt University, Nashville, Tennessee.

出版信息

Gastroenterology. 2020 Aug;159(2):453-466.e1. doi: 10.1053/j.gastro.2020.04.073. Epub 2020 May 14.

Abstract

Single cells are the building blocks of tissue systems that determine organ phenotypes, behaviors, and functions. Understanding the differences between cell types and their activities might provide us with insights into normal tissue physiology, development of disease, and new therapeutic strategies. Although -omic level single-cell technologies are a relatively recent development that have been used only in research settings, these approaches might eventually be used in the clinic. We review the prospects of applying single-cell genome, transcriptome, epigenome, proteome, and metabolome analyses to gastroenterology and hepatology research. Combining data from multi-omic platforms coupled to rapid technological development could lead to new diagnostic, prognostic, and therapeutic approaches.

摘要

单细胞是决定组织表型、行为和功能的系统的构建块。了解细胞类型之间的差异及其活动可能为我们提供对正常组织生理学、疾病发展和新治疗策略的深入了解。尽管 -omic 水平的单细胞技术是相对较新的发展,仅在研究环境中使用,但这些方法最终可能会在临床上使用。我们综述了将单细胞基因组、转录组、表观基因组、蛋白质组和代谢组分析应用于胃肠病学和肝病学研究的前景。将多组学平台的数据与快速技术发展相结合,可能会带来新的诊断、预后和治疗方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/89b3/7484006/ee91c4326d17/nihms-1614655-f0001.jpg

相似文献

4
Defining Cell Identity with Single-Cell Omics.单细胞组学定义细胞身份
Proteomics. 2018 Sep;18(18):e1700312. doi: 10.1002/pmic.201700312. Epub 2018 May 28.
9
An overview of technologies for MS-based proteomics-centric multi-omics.基于 MS 的蛋白质组学中心型多组学技术概述。
Expert Rev Proteomics. 2022 Mar;19(3):165-181. doi: 10.1080/14789450.2022.2070476. Epub 2022 May 2.
10
Multi-omics study for interpretation of genome-wide association study.多组学研究用于解释全基因组关联研究。
J Hum Genet. 2021 Jan;66(1):3-10. doi: 10.1038/s10038-020-00842-5. Epub 2020 Sep 18.

引用本文的文献

本文引用的文献

9
Exploring single-cell data with deep multitasking neural networks.用深度多任务神经网络探索单细胞数据。
Nat Methods. 2019 Nov;16(11):1139-1145. doi: 10.1038/s41592-019-0576-7. Epub 2019 Oct 7.

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