Literature DB >> 19819144

A single molecule view of gene expression.

Daniel R Larson1, Robert H Singer, Daniel Zenklusen.   

Abstract

Analyzing the expression of single genes in single cells appears minimalistic in comparison to gene expression studies based on more global approaches. However, stimulated by advances in imaging technologies, single-cell studies have become an essential tool in understanding the rules that govern gene expression. This quantitative view of single-cell gene expression is based on counting mRNAs in single cells, monitoring transcription in real time, and visualizing single proteins. Parallel advances in mathematical models based on stochastic, discrete descriptions of biochemical processes have provided crucial insights into the underlying cellular mechanisms that control expression. The view that has emerged is rooted in a probabilistic understanding of cellular processes that quantitatively explains both the mean and the variation observed in gene-expression patterns among single cells. Thus, the close coupling between imaging and mathematical theory has established single-cell analysis as an essential branch of systems biology.

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Year:  2009        PMID: 19819144      PMCID: PMC2783999          DOI: 10.1016/j.tcb.2009.08.008

Source DB:  PubMed          Journal:  Trends Cell Biol        ISSN: 0962-8924            Impact factor:   20.808


  55 in total

1.  The glucocorticoid receptor: rapid exchange with regulatory sites in living cells.

Authors:  J G McNally; W G Müller; D Walker; R Wolford; G L Hager
Journal:  Science       Date:  2000-02-18       Impact factor: 47.728

2.  Stochasticity in transcriptional regulation: origins, consequences, and mathematical representations.

Authors:  T B Kepler; T C Elston
Journal:  Biophys J       Date:  2001-12       Impact factor: 4.033

3.  Analysis of gene expression in single live neurons.

Authors:  J Eberwine; H Yeh; K Miyashiro; Y Cao; S Nair; R Finnell; M Zettel; P Coleman
Journal:  Proc Natl Acad Sci U S A       Date:  1992-04-01       Impact factor: 11.205

4.  Transcriptional control of noise in gene expression.

Authors:  Alvaro Sánchez; Jané Kondev
Journal:  Proc Natl Acad Sci U S A       Date:  2008-03-19       Impact factor: 11.205

5.  Effects of protein maturation on the noise in gene expression.

Authors:  Guang Qiang Dong; David R McMillen
Journal:  Phys Rev E Stat Nonlin Soft Matter Phys       Date:  2008-02-13

6.  A stochastic model for gene induction.

Authors:  M S Ko
Journal:  J Theor Biol       Date:  1991-11-21       Impact factor: 2.691

7.  Visualization of single RNA transcripts in situ.

Authors:  A M Femino; F S Fay; K Fogarty; R H Singer
Journal:  Science       Date:  1998-04-24       Impact factor: 47.728

8.  Stochastic mechanisms in gene expression.

Authors:  H H McAdams; A Arkin
Journal:  Proc Natl Acad Sci U S A       Date:  1997-02-04       Impact factor: 11.205

9.  Rapid changes in Drosophila transcription after an instantaneous heat shock.

Authors:  T O'Brien; J T Lis
Journal:  Mol Cell Biol       Date:  1993-06       Impact factor: 4.272

10.  Noise minimization in eukaryotic gene expression.

Authors:  Hunter B Fraser; Aaron E Hirsh; Guri Giaever; Jochen Kumm; Michael B Eisen
Journal:  PLoS Biol       Date:  2004-04-27       Impact factor: 8.029

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  76 in total

Review 1.  Transcription goes digital.

Authors:  Timothée Lionnet; Robert H Singer
Journal:  EMBO Rep       Date:  2012-04-02       Impact factor: 8.807

2.  Multiscale stochastic modelling of gene expression.

Authors:  Pavol Bokes; John R King; Andrew T A Wood; Matthew Loose
Journal:  J Math Biol       Date:  2011-10-07       Impact factor: 2.259

3.  Memories of lost enhancers.

Authors:  Ranjan Sen; Rudolf Grosschedl
Journal:  Genes Dev       Date:  2010-05-15       Impact factor: 11.361

4.  The Igκ gene enhancers, E3' and Ed, are essential for triggering transcription.

Authors:  Xiaorong Zhou; Yougui Xiang; William T Garrard
Journal:  J Immunol       Date:  2010-11-12       Impact factor: 5.422

Review 5.  Random monoallelic expression of autosomal genes: stochastic transcription and allele-level regulation.

Authors:  Björn Reinius; Rickard Sandberg
Journal:  Nat Rev Genet       Date:  2015-10-07       Impact factor: 53.242

6.  Dichotomous noise models of gene switches.

Authors:  Davit A Potoyan; Peter G Wolynes
Journal:  J Chem Phys       Date:  2015-11-21       Impact factor: 3.488

7.  A nucleoporin, Nup60p, affects the nuclear and cytoplasmic localization of ASH1 mRNA in S. cerevisiae.

Authors:  Erin A Powrie; Daniel Zenklusen; Robert H Singer
Journal:  RNA       Date:  2010-10-29       Impact factor: 4.942

Review 8.  Microtubule-dependent mRNA transport in fungi.

Authors:  Kathi Zarnack; Michael Feldbrügge
Journal:  Eukaryot Cell       Date:  2010-05-14

9.  LlamaTags: A Versatile Tool to Image Transcription Factor Dynamics in Live Embryos.

Authors:  Jacques P Bothma; Matthew R Norstad; Simon Alamos; Hernan G Garcia
Journal:  Cell       Date:  2018-05-10       Impact factor: 41.582

10.  MicroRNA binding to the HIV-1 Gag protein inhibits Gag assembly and virus production.

Authors:  Antony K Chen; Prabuddha Sengupta; Kayoko Waki; Schuyler B Van Engelenburg; Takahiro Ochiya; Sherimay D Ablan; Eric O Freed; Jennifer Lippincott-Schwartz
Journal:  Proc Natl Acad Sci U S A       Date:  2014-06-17       Impact factor: 11.205

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