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单细胞系统生物学:探究信息流的基本单位

Single-cell systems biology: probing the basic unit of information flow.

作者信息

Patange Simona, Girvan Michelle, Larson Daniel R

机构信息

Laboratory of Receptor Biology and Gene Expression, Center for Cancer Research, National Cancer Institute. Bethesda, MD 20892.

Institute for Physical Science and Technology, University of Maryland, College Park, MD.

出版信息

Curr Opin Syst Biol. 2018 Apr;8:7-15. doi: 10.1016/j.coisb.2017.11.011. Epub 2017 Dec 6.

Abstract

Gene expression varies across cells in a population or a tissue. This heterogeneity has come into sharp focus in recent years through developments in new imaging and sequencing technologies. However, our ability to measure variation has outpaced our ability to interpret it. Much of the variability may arise from random effects occurring in the processes of gene expression (transcription, RNA processing and decay, translation). The molecular basis of these effects is largely unknown. Likewise, a functional role of this variability in growth, differentiation and disease has only been elucidated in a few cases. In this review, we highlight recent experimental and theoretical advances for measuring and analyzing stochastic variation.

摘要

基因表达在群体细胞或组织中存在差异。近年来,通过新的成像和测序技术的发展,这种异质性已成为人们关注的焦点。然而,我们测量变异的能力已经超过了我们解释变异的能力。许多变异性可能源于基因表达过程(转录、RNA加工和降解、翻译)中发生的随机效应。这些效应的分子基础在很大程度上尚不清楚。同样,这种变异性在生长、分化和疾病中的功能作用仅在少数情况下得到阐明。在这篇综述中,我们重点介绍了测量和分析随机变异的最新实验和理论进展。

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本文引用的文献

1
CTCF-Mediated Enhancer-Promoter Interaction Is a Critical Regulator of Cell-to-Cell Variation of Gene Expression.
Mol Cell. 2017 Sep 21;67(6):1049-1058.e6. doi: 10.1016/j.molcel.2017.08.026.
2
Tracing Information Flow from Erk to Target Gene Induction Reveals Mechanisms of Dynamic and Combinatorial Control.
Mol Cell. 2017 Sep 7;67(5):757-769.e5. doi: 10.1016/j.molcel.2017.07.016. Epub 2017 Aug 17.
3
Rare cell variability and drug-induced reprogramming as a mode of cancer drug resistance.
Nature. 2017 Jun 15;546(7658):431-435. doi: 10.1038/nature22794. Epub 2017 Jun 7.
4
Visualizing adenosine-to-inosine RNA editing in single mammalian cells.
Nat Methods. 2017 Aug;14(8):801-804. doi: 10.1038/nmeth.4332. Epub 2017 Jun 12.
5
Deconstructing Olfactory Stem Cell Trajectories at Single-Cell Resolution.
Cell Stem Cell. 2017 Jun 1;20(6):817-830.e8. doi: 10.1016/j.stem.2017.04.003. Epub 2017 May 11.
6
The BET Protein BRD2 Cooperates with CTCF to Enforce Transcriptional and Architectural Boundaries.
Mol Cell. 2017 Apr 6;66(1):102-116.e7. doi: 10.1016/j.molcel.2017.02.027.
7
8
Single-Cell and Single-Molecule Analysis of Gene Expression Regulation.
Annu Rev Genet. 2016 Nov 23;50:267-291. doi: 10.1146/annurev-genet-120215-034854.
9
Estimating intrinsic and extrinsic noise from single-cell gene expression measurements.
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10
Pan-cancer analysis of somatic copy-number alterations implicates IRS4 and IGF2 in enhancer hijacking.
Nat Genet. 2017 Jan;49(1):65-74. doi: 10.1038/ng.3722. Epub 2016 Nov 21.

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