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PMID: 17608399 Published · ppublish English Journal Article

Analysis of the mouse liver proteome using advanced mass spectrometry.

Journal of proteome research ·Vol. 6 ·No. 8 ·2007-08-00 ·Pages 2963-72

Shi R, Kumar C, Zougman A, Zhang Y, Podtelejnikov A, Cox J, Wiśniewski JR, Mann M

Abstract

We report a large-scale analysis of mouse liver tissue comprising a novel fractionation approach and high-accuracy mass spectrometry techniques. Two fractions enriched for soluble and membrane proteins from 20 mg of frozen tissue were separated by one-dimensional electrophoresis followed by LC-MS/MS on the hybrid linear ion trap (LTQ)-Orbitrap mass spectrometer. Confident identification of 2210 proteins relied on at least two peptides. We combined this proteome with our previously reported organellar map (Foster et al. Cell 2006, 125, 187-199) to generate a very high confidence mouse liver proteome of 3244 proteins. The identified proteins represent the liver proteome with no discernible bias due to protein physicochemical properties, subcellular distribution, or biological function. Forty-seven percent of identified proteins were annotated as membrane-bound, and for 35.3%, transmembrane domains were predicted. For potential application in toxicology or clinical studies, we demonstrate that it is possible to consistently identify more than 1000 proteins in a single run.

MeSH Terms
Animals Chromatography, High Pressure Liquid/methods Liver Extracts/analysis Mice Proteome/metabolism Tandem Mass Spectrometry/methods
Chemicals
Liver Extracts Proteome
Authors & Affiliations
8 authors, click to expand affiliations / ORCID
Shi Rong
Department of Proteomics and Signal Transduction, Max-Planck-Institute for Biochemistry, Am Klopferspitz 18, 82152 Martinsried, Germany.
Kumar Chanchal
Zougman Alexandre
Zhang Yanling
Podtelejnikov Alexandre
Cox Jürgen
Wiśniewski Jacek R
Mann Matthias
Article Info
Journal
Journal of proteome research
Abbr.
J Proteome Res
ISSN
1535-3893
Published
2007-08-00
Epub
2007-00-03
Pages
2963-72
Language
English
Region
United States
NLM ID
101128775
Subset
IM
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