Literature DB >> 12480334

Gene expression and the myth of the average cell.

Jeffrey M Levsky1, Robert H Singer.   

Abstract

We all know that gene expression occurs within cells, yet we do not think of expression in terms of its fundamental unit -- a single cell. Instead, we understand the expression of genes in terms of a cell population as all of our information comes from samples containing millions of cells. From a complex mixture of cells, we attempt to infer the probable state of an average cell in the population. In truth, what we obtain is an averaged cell, a contrivance for representing biological knowledge beyond the limits of detection. We never know the variation among the members of the population that our methods average into a mean. Recent technological advances allow the precise measurement of single-cell transcriptional states to study this variability more rigorously. How genes are expressed in the population is strikingly different to what we have assumed from extrapolating to an average cell. Does the average cell actually exist? As we discuss, it is becoming increasingly clear that it doesn't.

Mesh:

Year:  2003        PMID: 12480334     DOI: 10.1016/s0962-8924(02)00002-8

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


  95 in total

1.  A new method for choosing the computational cell in stochastic reaction-diffusion systems.

Authors:  Hye-Won Kang; Likun Zheng; Hans G Othmer
Journal:  J Math Biol       Date:  2011-11-10       Impact factor: 2.259

Review 2.  Methods for transcriptional profiling in plants. Be fruitful and replicate.

Authors:  Blake C Meyers; David W Galbraith; Timothy Nelson; Vikas Agrawal
Journal:  Plant Physiol       Date:  2004-06-01       Impact factor: 8.340

Review 3.  Methodological considerations regarding single-cell gene expression profiling for brain injury.

Authors:  Jason E Davis; James H Eberwine; David A Hinkle; Paolo G Marciano; David F Meaney; Tracy K McIntosh
Journal:  Neurochem Res       Date:  2004-06       Impact factor: 3.996

Review 4.  Imaging gene expression in single living cells.

Authors:  Yaron Shav-Tal; Robert H Singer; Xavier Darzacq
Journal:  Nat Rev Mol Cell Biol       Date:  2004-10       Impact factor: 94.444

5.  Locked nucleic acid and flow cytometry-fluorescence in situ hybridization for the detection of bacterial small noncoding RNAs.

Authors:  Kelly L Robertson; Gary J Vora
Journal:  Appl Environ Microbiol       Date:  2011-11-04       Impact factor: 4.792

6.  Single-cell profiling of developing and mature retinal neurons.

Authors:  Jillian J Goetz; Jeffrey M Trimarchi
Journal:  J Vis Exp       Date:  2012-04-19       Impact factor: 1.355

7.  Microfluidic single-cell real-time PCR for comparative analysis of gene expression patterns.

Authors:  Veronica Sanchez-Freire; Antje D Ebert; Tomer Kalisky; Stephen R Quake; Joseph C Wu
Journal:  Nat Protoc       Date:  2012-04-05       Impact factor: 13.491

8.  The effect of the signalling scheme on the robustness of pattern formation in development.

Authors:  Hye-Won Kang; Likun Zheng; Hans G Othmer
Journal:  Interface Focus       Date:  2012-03-21       Impact factor: 3.906

9.  In situ detection and genotyping of individual mRNA molecules.

Authors:  Chatarina Larsson; Ida Grundberg; Ola Söderberg; Mats Nilsson
Journal:  Nat Methods       Date:  2010-04-11       Impact factor: 28.547

Review 10.  Quantitative imaging of protein interactions in the cell nucleus.

Authors:  Ty C Voss; Ignacio A Demarco; Richard N Day
Journal:  Biotechniques       Date:  2005-03       Impact factor: 1.993

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