用于病毒抑制剂研究的单细胞分析。

Q3 Biochemistry, Genetics and Molecular Biology
Enzymes Pub Date : 2021-01-01 Epub Date: 2021-08-23 DOI:10.1016/bs.enz.2021.07.004
Mohamad S Sotoudegan, Jamie J Arnold, Craig E Cameron
{"title":"用于病毒抑制剂研究的单细胞分析。","authors":"Mohamad S Sotoudegan,&nbsp;Jamie J Arnold,&nbsp;Craig E Cameron","doi":"10.1016/bs.enz.2021.07.004","DOIUrl":null,"url":null,"abstract":"<p><p>Stochastic outcomes of viral infections are attributed in large part to multiple layers of intrinsic and extrinsic heterogeneity that exist within a population of cells and viruses. Traditional methods in virology often lack the ability to demonstrate cell-to-cell variability in response to the invasion of viruses, and to decipher the sources of heterogeneities that are reflected in the variable infection dynamics. To overcome this challenge, the field of single-cell virology emerged less than a decade ago, enabling researchers to reveal the behavior of single, isolated, infected cells that has been masked in population-based assays. The use of microfluidics in single-cell virology, in particular, has resulted in the development of high-throughput devices that are capable of capturing, isolating, and monitoring single infected cells over the duration of an infection. Results from the studies of viral infection dynamics presented in this chapter indicate how single-cell data provide a more accurate prediction of the start time, replication rate, duration, and yield of infection when compared to population-based data. Additionally, single-cell analysis reveals striking differences between genetically distinct viruses that are almost indistinguishable in population methods. Importantly, both the efficacy and distinct mechanisms of action of antiviral compounds can be elucidated by using single-cell analysis.</p>","PeriodicalId":39097,"journal":{"name":"Enzymes","volume":" ","pages":"195-213"},"PeriodicalIF":0.0000,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Single-cell analysis for the study of viral inhibitors.\",\"authors\":\"Mohamad S Sotoudegan,&nbsp;Jamie J Arnold,&nbsp;Craig E Cameron\",\"doi\":\"10.1016/bs.enz.2021.07.004\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Stochastic outcomes of viral infections are attributed in large part to multiple layers of intrinsic and extrinsic heterogeneity that exist within a population of cells and viruses. Traditional methods in virology often lack the ability to demonstrate cell-to-cell variability in response to the invasion of viruses, and to decipher the sources of heterogeneities that are reflected in the variable infection dynamics. To overcome this challenge, the field of single-cell virology emerged less than a decade ago, enabling researchers to reveal the behavior of single, isolated, infected cells that has been masked in population-based assays. The use of microfluidics in single-cell virology, in particular, has resulted in the development of high-throughput devices that are capable of capturing, isolating, and monitoring single infected cells over the duration of an infection. Results from the studies of viral infection dynamics presented in this chapter indicate how single-cell data provide a more accurate prediction of the start time, replication rate, duration, and yield of infection when compared to population-based data. Additionally, single-cell analysis reveals striking differences between genetically distinct viruses that are almost indistinguishable in population methods. Importantly, both the efficacy and distinct mechanisms of action of antiviral compounds can be elucidated by using single-cell analysis.</p>\",\"PeriodicalId\":39097,\"journal\":{\"name\":\"Enzymes\",\"volume\":\" \",\"pages\":\"195-213\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Enzymes\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1016/bs.enz.2021.07.004\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2021/8/23 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q3\",\"JCRName\":\"Biochemistry, Genetics and Molecular Biology\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Enzymes","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1016/bs.enz.2021.07.004","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2021/8/23 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"Biochemistry, Genetics and Molecular Biology","Score":null,"Total":0}
引用次数: 0

摘要

病毒感染的随机结果在很大程度上归因于存在于细胞和病毒群体中的多层内在和外在异质性。病毒学中的传统方法往往缺乏证明细胞间对病毒入侵反应的可变性的能力,也无法破译反映在可变感染动力学中的异质性的来源。为了克服这一挑战,不到十年前出现了单细胞病毒学领域,使研究人员能够揭示单个、分离的、受感染的细胞的行为,这些细胞在基于群体的分析中被掩盖了。特别是,微流体技术在单细胞病毒学中的应用,导致了高通量设备的发展,这些设备能够在感染期间捕获、分离和监测单个感染细胞。本章中介绍的病毒感染动力学研究结果表明,与基于群体的数据相比,单细胞数据如何提供更准确的开始时间、复制速率、持续时间和感染产量的预测。此外,单细胞分析揭示了基因上不同的病毒之间的显著差异,这些病毒在种群方法中几乎无法区分。重要的是,抗病毒化合物的功效和独特的作用机制都可以通过单细胞分析来阐明。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Single-cell analysis for the study of viral inhibitors.

Stochastic outcomes of viral infections are attributed in large part to multiple layers of intrinsic and extrinsic heterogeneity that exist within a population of cells and viruses. Traditional methods in virology often lack the ability to demonstrate cell-to-cell variability in response to the invasion of viruses, and to decipher the sources of heterogeneities that are reflected in the variable infection dynamics. To overcome this challenge, the field of single-cell virology emerged less than a decade ago, enabling researchers to reveal the behavior of single, isolated, infected cells that has been masked in population-based assays. The use of microfluidics in single-cell virology, in particular, has resulted in the development of high-throughput devices that are capable of capturing, isolating, and monitoring single infected cells over the duration of an infection. Results from the studies of viral infection dynamics presented in this chapter indicate how single-cell data provide a more accurate prediction of the start time, replication rate, duration, and yield of infection when compared to population-based data. Additionally, single-cell analysis reveals striking differences between genetically distinct viruses that are almost indistinguishable in population methods. Importantly, both the efficacy and distinct mechanisms of action of antiviral compounds can be elucidated by using single-cell analysis.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Enzymes
Enzymes Biochemistry, Genetics and Molecular Biology-Biotechnology
CiteScore
4.30
自引率
0.00%
发文量
10
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信