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Large-scale inference of protein tissue origin in gram-positive sepsis plasma using quantitative targeted proteomics.

Malmström, Erik LU ; Kilsgård, Ola; Hauri, Simon LU ; Smeds, Emanuel LU ; Herwald, Heiko LU ; Malmström, Lars LU and Malmström, Johan LU (2016) In Nature Communications 7.
Abstract
The plasma proteome is highly dynamic and variable, composed of proteins derived from surrounding tissues and cells. To investigate the complex processes that control the composition of the plasma proteome, we developed a mass spectrometry-based proteomics strategy to infer the origin of proteins detected in murine plasma. The strategy relies on the construction of a comprehensive protein tissue atlas from cells and highly vascularized organs using shotgun mass spectrometry. The protein tissue atlas was transformed to a spectral library for highly reproducible quantification of tissue-specific proteins directly in plasma using SWATH-like data-independent mass spectrometry analysis. We show that the method can determine drastic changes of... (More)
The plasma proteome is highly dynamic and variable, composed of proteins derived from surrounding tissues and cells. To investigate the complex processes that control the composition of the plasma proteome, we developed a mass spectrometry-based proteomics strategy to infer the origin of proteins detected in murine plasma. The strategy relies on the construction of a comprehensive protein tissue atlas from cells and highly vascularized organs using shotgun mass spectrometry. The protein tissue atlas was transformed to a spectral library for highly reproducible quantification of tissue-specific proteins directly in plasma using SWATH-like data-independent mass spectrometry analysis. We show that the method can determine drastic changes of tissue-specific protein profiles in blood plasma from mouse animal models with sepsis. The strategy can be extended to several other species advancing our understanding of the complex processes that contribute to the plasma proteome dynamics. (Less)
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author
organization
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type
Contribution to journal
publication status
published
subject
in
Nature Communications
volume
7
publisher
Nature Publishing Group
external identifiers
  • pmid:26732734
  • wos:000369020500001
  • scopus:84954468136
ISSN
2041-1723
DOI
10.1038/ncomms10261
language
English
LU publication?
yes
id
9c38478b-542e-47ed-9272-112c16c0823a (old id 8593053)
alternative location
http://www.ncbi.nlm.nih.gov/pubmed/26732734?dopt=Abstract
date added to LUP
2016-02-01 21:35:01
date last changed
2017-11-19 03:50:16
@article{9c38478b-542e-47ed-9272-112c16c0823a,
  abstract     = {The plasma proteome is highly dynamic and variable, composed of proteins derived from surrounding tissues and cells. To investigate the complex processes that control the composition of the plasma proteome, we developed a mass spectrometry-based proteomics strategy to infer the origin of proteins detected in murine plasma. The strategy relies on the construction of a comprehensive protein tissue atlas from cells and highly vascularized organs using shotgun mass spectrometry. The protein tissue atlas was transformed to a spectral library for highly reproducible quantification of tissue-specific proteins directly in plasma using SWATH-like data-independent mass spectrometry analysis. We show that the method can determine drastic changes of tissue-specific protein profiles in blood plasma from mouse animal models with sepsis. The strategy can be extended to several other species advancing our understanding of the complex processes that contribute to the plasma proteome dynamics.},
  articleno    = {10261},
  author       = {Malmström, Erik and Kilsgård, Ola and Hauri, Simon and Smeds, Emanuel and Herwald, Heiko and Malmström, Lars and Malmström, Johan},
  issn         = {2041-1723},
  language     = {eng},
  publisher    = {Nature Publishing Group},
  series       = {Nature Communications},
  title        = {Large-scale inference of protein tissue origin in gram-positive sepsis plasma using quantitative targeted proteomics.},
  url          = {http://dx.doi.org/10.1038/ncomms10261},
  volume       = {7},
  year         = {2016},
}