Literature DB >> 23996796

Using variability in gene expression as a tool for studying gene regulation.

Olivia Padovan-Merhar1, Arjun Raj.   

Abstract

With the advent of quantitative tools for measuring gene expression in single cells, researchers have made the discovery that in many contexts, messenger RNA and protein levels can vary widely from cell to cell, often because of inherently stochastic events associated with gene expression. The study of this cellular individuality has become a field of study in its own right, characterized by a blend of technological development, theoretical analysis, and, more recently, applications to biological phenomena. In this review, we focus on the use of the variability inherent to gene expression as a tool to understand gene regulation. We discuss the use of variability as a natural systems-level perturbation, its use in quantitatively characterizing the biological processes underlying transcription, and its application to the discovery of new gene regulatory interactions. We believe that use of variability can provide new biological insights into different aspects of transcriptional control and can provide a powerful complementary approach to that of existing techniques.
Copyright © 2013 Wiley Periodicals, Inc.

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Year:  2013        PMID: 23996796      PMCID: PMC4561544          DOI: 10.1002/wsbm.1243

Source DB:  PubMed          Journal:  Wiley Interdiscip Rev Syst Biol Med        ISSN: 1939-005X


  59 in total

1.  Dynamic proteomics of individual cancer cells in response to a drug.

Authors:  A A Cohen; N Geva-Zatorsky; E Eden; M Frenkel-Morgenstern; I Issaeva; A Sigal; R Milo; C Cohen-Saidon; Y Liron; Z Kam; L Cohen; T Danon; N Perzov; U Alon
Journal:  Science       Date:  2008-11-20       Impact factor: 47.728

Review 2.  Imaging intracellular RNA distribution and dynamics in living cells.

Authors:  Sanjay Tyagi
Journal:  Nat Methods       Date:  2009-05       Impact factor: 28.547

Review 3.  Nature, nurture, or chance: stochastic gene expression and its consequences.

Authors:  Arjun Raj; Alexander van Oudenaarden
Journal:  Cell       Date:  2008-10-17       Impact factor: 41.582

4.  Chromatin signature reveals over a thousand highly conserved large non-coding RNAs in mammals.

Authors:  Mitchell Guttman; Ido Amit; Manuel Garber; Courtney French; Michael F Lin; David Feldser; Maite Huarte; Or Zuk; Bryce W Carey; John P Cassady; Moran N Cabili; Rudolf Jaenisch; Tarjei S Mikkelsen; Tyler Jacks; Nir Hacohen; Bradley E Bernstein; Manolis Kellis; Aviv Regev; John L Rinn; Eric S Lander
Journal:  Nature       Date:  2009-02-01       Impact factor: 49.962

5.  A stochastic single-molecule event triggers phenotype switching of a bacterial cell.

Authors:  Paul J Choi; Long Cai; Kirsten Frieda; X Sunney Xie
Journal:  Science       Date:  2008-10-17       Impact factor: 47.728

6.  Transcriptome-wide noise controls lineage choice in mammalian progenitor cells.

Authors:  Hannah H Chang; Martin Hemberg; Mauricio Barahona; Donald E Ingber; Sui Huang
Journal:  Nature       Date:  2008-05-22       Impact factor: 49.962

7.  mRNA-Seq whole-transcriptome analysis of a single cell.

Authors:  Fuchou Tang; Catalin Barbacioru; Yangzhou Wang; Ellen Nordman; Clarence Lee; Nanlan Xu; Xiaohui Wang; John Bodeau; Brian B Tuch; Asim Siddiqui; Kaiqin Lao; M Azim Surani
Journal:  Nat Methods       Date:  2009-04-06       Impact factor: 28.547

8.  Single-RNA counting reveals alternative modes of gene expression in yeast.

Authors:  Daniel Zenklusen; Daniel R Larson; Robert H Singer
Journal:  Nat Struct Mol Biol       Date:  2008-11-16       Impact factor: 15.369

Review 9.  MicroRNAs: target recognition and regulatory functions.

Authors:  David P Bartel
Journal:  Cell       Date:  2009-01-23       Impact factor: 41.582

10.  Quantitative characteristics of gene regulation by small RNA.

Authors:  Erel Levine; Zhongge Zhang; Thomas Kuhlman; Terence Hwa
Journal:  PLoS Biol       Date:  2007-09       Impact factor: 8.029

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  12 in total

1.  Decoding the complex genetic causes of heart diseases using systems biology.

Authors:  Djordje Djordjevic; Vinita Deshpande; Tomasz Szczesnik; Andrian Yang; David T Humphreys; Eleni Giannoulatou; Joshua W K Ho
Journal:  Biophys Rev       Date:  2014-12-10

Review 2.  Single-cell genome-wide studies give new insight into nongenetic cell-to-cell variability in animals.

Authors:  Arkadiy K Golov; Sergey V Razin; Alexey A Gavrilov
Journal:  Histochem Cell Biol       Date:  2016-07-13       Impact factor: 4.304

3.  RNA imaging. Spatially resolved, highly multiplexed RNA profiling in single cells.

Authors:  Kok Hao Chen; Alistair N Boettiger; Jeffrey R Moffitt; Siyuan Wang; Xiaowei Zhuang
Journal:  Science       Date:  2015-04-09       Impact factor: 47.728

4.  On comparing heterogeneity across biomarkers.

Authors:  Robert J Steininger; Satwik Rajaram; Luc Girard; John D Minna; Lani F Wu; Steven J Altschuler
Journal:  Cytometry A       Date:  2014-11-25       Impact factor: 4.355

5.  A UNIFIED STATISTICAL FRAMEWORK FOR SINGLE CELL AND BULK RNA SEQUENCING DATA.

Authors:  Lingxue Zhu; Jing Lei; Bernie Devlin; Kathryn Roeder
Journal:  Ann Appl Stat       Date:  2018-03-09       Impact factor: 2.083

Review 6.  Genetics and immunity in the era of single-cell genomics.

Authors:  Felipe A Vieira Braga; Sarah A Teichmann; Xi Chen
Journal:  Hum Mol Genet       Date:  2016-07-12       Impact factor: 6.150

7.  RNA Imaging with Multiplexed Error-Robust Fluorescence In Situ Hybridization (MERFISH).

Authors:  J R Moffitt; X Zhuang
Journal:  Methods Enzymol       Date:  2016-04-27       Impact factor: 1.600

Review 8.  Single-cell transcriptome sequencing: recent advances and remaining challenges.

Authors:  Serena Liu; Cole Trapnell
Journal:  F1000Res       Date:  2016-02-17

9.  Paf1c defects challenge the robustness of flower meristem termination in Arabidopsis thaliana.

Authors:  Kateryna Fal; Matthieu Cortes; Mengying Liu; Sam Collaudin; Pradeep Das; Olivier Hamant; Christophe Trehin
Journal:  Development       Date:  2019-10-25       Impact factor: 6.868

10.  Dormancy-to-death transition in yeast spores occurs due to gradual loss of gene-expressing ability.

Authors:  Théo Maire; Tim Allertz; Max A Betjes; Hyun Youk
Journal:  Mol Syst Biol       Date:  2020-11       Impact factor: 11.429

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