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Quantitative proteomics targeting classes of motif-containing peptides using immunoaffinity-based mass spectrometry.

Olsson, Niclas LU ; James, Peter LU ; Borrebaeck, Carl LU and Wingren, Christer LU (2012) In Molecular & Cellular Proteomics 11(8). p.342-354
Abstract
The development of high-performance technology platforms for generating detailed protein expression profiles, or protein atlases, is essential. Recently, we presented a novel platform that we termed global proteome survey (GPS), where we combined the best features of affinity proteomics and mass spectrometry, to probe any proteome in a species independent manner while still using a limited set of antibodies. We used so called context-independent-motif-specific antibodies, directed against short amino acid motifs. This enabled enrichment of motif-containing peptides from a digested proteome, which then were detected and identified by mass spectrometry. In this study, we have demonstrated the quantitative capability, reproducibility,... (More)
The development of high-performance technology platforms for generating detailed protein expression profiles, or protein atlases, is essential. Recently, we presented a novel platform that we termed global proteome survey (GPS), where we combined the best features of affinity proteomics and mass spectrometry, to probe any proteome in a species independent manner while still using a limited set of antibodies. We used so called context-independent-motif-specific antibodies, directed against short amino acid motifs. This enabled enrichment of motif-containing peptides from a digested proteome, which then were detected and identified by mass spectrometry. In this study, we have demonstrated the quantitative capability, reproducibility, sensitivity, and coverage of the GPS technology by targeting SILAC-labeled yeast cultures cultivated in glucose or ethanol. The data showed that a wide range of motif-containing peptides (proteins) could be detected, identified, and quantified in a highly reproducible manner. On average, each of six different motif-specific antibodies was found to target about 75 different motif-containing proteins. Furthermore, peptides originating from proteins spanning in abundance from over a million down to less than 50 copies per cell, could be targeted. It is worth noting that a significant set of peptides previously not reported in the PeptideAtlas database was among the profiled targets. The quantitative data corroborated well with the corresponding data generated after conventional SCX fractionation of the same samples. Finally, several differentially expressed proteins, with both known and unknown functions, many relevant for the central carbon metabolism, could be detected in the glucose- versus ethanol-cultivated yeast. Taken together, the study demonstrated the potential of our immunoaffinity-based mass spectrometry platform for reproducible quantitative proteomics targeting classes of motif-containing peptides. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Molecular & Cellular Proteomics
volume
11
issue
8
pages
342 - 354
publisher
American Society for Biochemistry and Molecular Biology
external identifiers
  • pmid:22543061
  • wos:000308024200010
  • scopus:84864827587
ISSN
1535-9484
DOI
10.1074/mcp.M111.016238
project
CREATE Health
language
English
LU publication?
yes
id
e717018b-a06b-4322-9582-afb2db01ee18 (old id 2609372)
date added to LUP
2012-06-07 12:17:34
date last changed
2017-09-17 03:39:36
@article{e717018b-a06b-4322-9582-afb2db01ee18,
  abstract     = {The development of high-performance technology platforms for generating detailed protein expression profiles, or protein atlases, is essential. Recently, we presented a novel platform that we termed global proteome survey (GPS), where we combined the best features of affinity proteomics and mass spectrometry, to probe any proteome in a species independent manner while still using a limited set of antibodies. We used so called context-independent-motif-specific antibodies, directed against short amino acid motifs. This enabled enrichment of motif-containing peptides from a digested proteome, which then were detected and identified by mass spectrometry. In this study, we have demonstrated the quantitative capability, reproducibility, sensitivity, and coverage of the GPS technology by targeting SILAC-labeled yeast cultures cultivated in glucose or ethanol. The data showed that a wide range of motif-containing peptides (proteins) could be detected, identified, and quantified in a highly reproducible manner. On average, each of six different motif-specific antibodies was found to target about 75 different motif-containing proteins. Furthermore, peptides originating from proteins spanning in abundance from over a million down to less than 50 copies per cell, could be targeted. It is worth noting that a significant set of peptides previously not reported in the PeptideAtlas database was among the profiled targets. The quantitative data corroborated well with the corresponding data generated after conventional SCX fractionation of the same samples. Finally, several differentially expressed proteins, with both known and unknown functions, many relevant for the central carbon metabolism, could be detected in the glucose- versus ethanol-cultivated yeast. Taken together, the study demonstrated the potential of our immunoaffinity-based mass spectrometry platform for reproducible quantitative proteomics targeting classes of motif-containing peptides.},
  author       = {Olsson, Niclas and James, Peter and Borrebaeck, Carl and Wingren, Christer},
  issn         = {1535-9484},
  language     = {eng},
  number       = {8},
  pages        = {342--354},
  publisher    = {American Society for Biochemistry and Molecular Biology},
  series       = {Molecular & Cellular Proteomics},
  title        = {Quantitative proteomics targeting classes of motif-containing peptides using immunoaffinity-based mass spectrometry.},
  url          = {http://dx.doi.org/10.1074/mcp.M111.016238},
  volume       = {11},
  year         = {2012},
}