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Quantitative analysis of SILAC data sets using spectral counting
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| Zeitschriftentitel: | PROTEOMICS |
|---|---|
| Personen und Körperschaften: | , , |
| In: | PROTEOMICS, 10, 2010, 7, S. 1408-1415 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
Wiley
|
| Schlagwörter: |
| author_facet |
Parker, Sarah J. Halligan, Brian D. Greene, Andrew S. Parker, Sarah J. Halligan, Brian D. Greene, Andrew S. |
|---|---|
| author |
Parker, Sarah J. Halligan, Brian D. Greene, Andrew S. |
| spellingShingle |
Parker, Sarah J. Halligan, Brian D. Greene, Andrew S. PROTEOMICS Quantitative analysis of SILAC data sets using spectral counting Molecular Biology Biochemistry |
| author_sort |
parker, sarah j. |
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Parker, Sarah J. Halligan, Brian D. Greene, Andrew S. 1615-9853 1615-9861 Wiley Molecular Biology Biochemistry http://dx.doi.org/10.1002/pmic.200900684 <jats:title>Abstract</jats:title><jats:p>We report a new quantitative proteomics approach that combines the best aspects of stable isotope labeling of amino acids in cell culture (SILAC) labeling and spectral counting. The SILAC peptide count ratio analysis (SPeCtRA, <jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="http://proteomics.mcw.edu/visualize">http://proteomics.mcw.edu/visualize</jats:ext-link>) method relies on MS<jats:sup>2</jats:sup> spectra rather than ion chromatograms for quantitation and therefore does not require the use of high mass accuracy mass spectrometers. The inclusion of a stable isotope label allows the samples to be combined before sample preparation and analysis, thus avoiding many of the sources of variability that can plague spectral counting. To validate the SPeCtRA method, we have analyzed samples constructed with known ratios of protein abundance. Finally, we used SPeCtRA to compare endothelial cell protein abundances between high (20 mM) and low (11 mM) glucose culture conditions. Our results demonstrate that SPeCtRA is a protein quantification technique that is accurate and sensitive as well as easy to automate and apply to high‐throughput analysis of complex biological samples.</jats:p> Quantitative analysis of SILAC data sets using spectral counting PROTEOMICS |
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Quantitative analysis of SILAC data sets using spectral counting |
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Quantitative analysis of SILAC data sets using spectral counting |
| title_full |
Quantitative analysis of SILAC data sets using spectral counting |
| title_fullStr |
Quantitative analysis of SILAC data sets using spectral counting |
| title_full_unstemmed |
Quantitative analysis of SILAC data sets using spectral counting |
| title_short |
Quantitative analysis of SILAC data sets using spectral counting |
| title_sort |
quantitative analysis of silac data sets using spectral counting |
| topic |
Molecular Biology Biochemistry |
| url |
http://dx.doi.org/10.1002/pmic.200900684 |
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2010 |
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1408-1415 |
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<jats:title>Abstract</jats:title><jats:p>We report a new quantitative proteomics approach that combines the best aspects of stable isotope labeling of amino acids in cell culture (SILAC) labeling and spectral counting. The SILAC peptide count ratio analysis (SPeCtRA, <jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="http://proteomics.mcw.edu/visualize">http://proteomics.mcw.edu/visualize</jats:ext-link>) method relies on MS<jats:sup>2</jats:sup> spectra rather than ion chromatograms for quantitation and therefore does not require the use of high mass accuracy mass spectrometers. The inclusion of a stable isotope label allows the samples to be combined before sample preparation and analysis, thus avoiding many of the sources of variability that can plague spectral counting. To validate the SPeCtRA method, we have analyzed samples constructed with known ratios of protein abundance. Finally, we used SPeCtRA to compare endothelial cell protein abundances between high (20 mM) and low (11 mM) glucose culture conditions. Our results demonstrate that SPeCtRA is a protein quantification technique that is accurate and sensitive as well as easy to automate and apply to high‐throughput analysis of complex biological samples.</jats:p> |
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| author | Parker, Sarah J., Halligan, Brian D., Greene, Andrew S. |
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| description | <jats:title>Abstract</jats:title><jats:p>We report a new quantitative proteomics approach that combines the best aspects of stable isotope labeling of amino acids in cell culture (SILAC) labeling and spectral counting. The SILAC peptide count ratio analysis (SPeCtRA, <jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="http://proteomics.mcw.edu/visualize">http://proteomics.mcw.edu/visualize</jats:ext-link>) method relies on MS<jats:sup>2</jats:sup> spectra rather than ion chromatograms for quantitation and therefore does not require the use of high mass accuracy mass spectrometers. The inclusion of a stable isotope label allows the samples to be combined before sample preparation and analysis, thus avoiding many of the sources of variability that can plague spectral counting. To validate the SPeCtRA method, we have analyzed samples constructed with known ratios of protein abundance. Finally, we used SPeCtRA to compare endothelial cell protein abundances between high (20 mM) and low (11 mM) glucose culture conditions. Our results demonstrate that SPeCtRA is a protein quantification technique that is accurate and sensitive as well as easy to automate and apply to high‐throughput analysis of complex biological samples.</jats:p> |
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| spelling | Parker, Sarah J. Halligan, Brian D. Greene, Andrew S. 1615-9853 1615-9861 Wiley Molecular Biology Biochemistry http://dx.doi.org/10.1002/pmic.200900684 <jats:title>Abstract</jats:title><jats:p>We report a new quantitative proteomics approach that combines the best aspects of stable isotope labeling of amino acids in cell culture (SILAC) labeling and spectral counting. The SILAC peptide count ratio analysis (SPeCtRA, <jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="http://proteomics.mcw.edu/visualize">http://proteomics.mcw.edu/visualize</jats:ext-link>) method relies on MS<jats:sup>2</jats:sup> spectra rather than ion chromatograms for quantitation and therefore does not require the use of high mass accuracy mass spectrometers. The inclusion of a stable isotope label allows the samples to be combined before sample preparation and analysis, thus avoiding many of the sources of variability that can plague spectral counting. To validate the SPeCtRA method, we have analyzed samples constructed with known ratios of protein abundance. Finally, we used SPeCtRA to compare endothelial cell protein abundances between high (20 mM) and low (11 mM) glucose culture conditions. Our results demonstrate that SPeCtRA is a protein quantification technique that is accurate and sensitive as well as easy to automate and apply to high‐throughput analysis of complex biological samples.</jats:p> Quantitative analysis of SILAC data sets using spectral counting PROTEOMICS |
| spellingShingle | Parker, Sarah J., Halligan, Brian D., Greene, Andrew S., PROTEOMICS, Quantitative analysis of SILAC data sets using spectral counting, Molecular Biology, Biochemistry |
| title | Quantitative analysis of SILAC data sets using spectral counting |
| title_full | Quantitative analysis of SILAC data sets using spectral counting |
| title_fullStr | Quantitative analysis of SILAC data sets using spectral counting |
| title_full_unstemmed | Quantitative analysis of SILAC data sets using spectral counting |
| title_short | Quantitative analysis of SILAC data sets using spectral counting |
| title_sort | quantitative analysis of silac data sets using spectral counting |
| title_unstemmed | Quantitative analysis of SILAC data sets using spectral counting |
| topic | Molecular Biology, Biochemistry |
| url | http://dx.doi.org/10.1002/pmic.200900684 |