A “Proteomic Ruler” for Protein Copy Number and Concentration Estimation without Spike-in Standards

Absolute protein quantification using mass spectrometry (MS)-based proteomics delivers protein concentrations or copy numbers per cell. Existing methodologies typically require a combination of isotope-labeled spike-in references, cell counting, and protein concentration measurements. Here we presen...

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Bibliographic Details
Published in:Molecular & cellular proteomics 2014-12, Vol.13 (12), p.3497-3506
Main Authors: Wiśniewski, Jacek R., Hein, Marco Y., Cox, Jürgen, Mann, Matthias
Format: Article
Language:English
Subjects:
Amino Acids - chemistry
Amino Acids - metabolism
Animals
Cell Count
Cell Line, Tumor
DNA - chemistry
Gene Expression
Histones - analysis
Histones - genetics
Histones - metabolism
Humans
Isotope Labeling
Mice
Proteome - analysis
Proteome - genetics
Proteome - metabolism
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Summary:Absolute protein quantification using mass spectrometry (MS)-based proteomics delivers protein concentrations or copy numbers per cell. Existing methodologies typically require a combination of isotope-labeled spike-in references, cell counting, and protein concentration measurements. Here we present a novel method that delivers similar quantitative results directly from deep eukaryotic proteome datasets without any additional experimental steps. We show that the MS signal of histones can be used as a “proteomic ruler” because it is proportional to the amount of DNA in the sample, which in turn depends on the number of cells. As a result, our proteomic ruler approach adds an absolute scale to the MS readout and allows estimation of the copy numbers of individual proteins per cell. We compare our protein quantifications with values derived via the use of stable isotope labeling by amino acids in cell culture and protein epitope signature tags in a method that combines spike-in protein fragment standards with precise isotope label quantification. The proteomic ruler approach yields quantitative readouts that are in remarkably good agreement with results from the precision method. We attribute this surprising result to the fact that the proteomic ruler approach omits error-prone steps such as cell counting or protein concentration measurements. The proteomic ruler approach is readily applicable to any deep eukaryotic proteome dataset—even in retrospective analysis—and we demonstrate its usefulness with a series of mouse organ proteomes.
ISSN:1535-9476
1535-9484
DOI:10.1074/mcp.M113.037309