Comparison of stable‐isotope labeling with amino acids in cell culture and spectral counting for relative quantification of protein expression is a research paper published in Rapid Communications in Mass Spectrometry (2011). On theSindex it has a DataRank of 1.6. It has been cited 35 times, with 28 citing works in its 1-hop citation network.
Protein quantification is one of the principal goals of mass spectrometry (MS)‐based proteomics, and many strategies exist to achieve it. Several approaches involve the incorporation of a stable‐isotope label using either chemical derivatization, enzymatically catalyzed incorporation of 18 O, or metabolic labeling in a cell or tissue culture. These techniques can be cost or time prohibitive or not amenable to the biological system of interest. Label‐free techniques including those utilizing integrated ion abundance and spectral counting offer an alternative to stable‐isotope‐based methodologies. Herein, we present the comparison of stable‐isotope labeling of amino acids in cell culture (SILAC) with spectral counting for the quantification of human embryonic stem cells as they differentiate toward the trophectoderm at three time points. Our spectral counting experimental strategy resulted in the identification of 2641 protein groups across three time points with an average sequence coverage of 30.3%, of which 1837 could be quantified with more than five spectral counts. SILAC quantification was able to identify 1369 protein groups with an average coverage of 24.7%, of which 1027 could be quantified across all time points. Within this context we further explore the capacity of each strategy for proteome coverage, variation in quantification, and the relative sensitivity of each technique to the detection of change in relative protein expression. Copyright © 2011 John Wiley & Sons, Ltd.
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Base Score Contribution
0.538
From this paper's citation signal
Citation Network Contribution
1.1
From 25 citing papers with measurable signal
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 33% comes from its base citations and 67% from the citation network (25 citing papers contributed measurable signal).
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