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Issue 18, 2018
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Absolute two-point quantification of proteins using dimethylated proteotypic peptides

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Absolute targeted proteomics typically employs known amounts of synthetic stable isotopically labeled peptides which are mixed with the analyte and analysed by LC-MS to determine the concentration of proteins. In order to obtain more data, we evaluated the use of two different stable isotopes of the same peptide as spike-in for absolute quantification. For this purpose, peptide labeling by reductive amination was applied, which is a mild reaction for dimethylation of amine groups with very high yield. Three different forms can be generated with e.g., light and heavy labels for spike-in peptides, and medium label for endogenous peptides. The method was studied with peptides of apolipoprotein A-I, apolipoprotein B-100, and leucine-rich alpha-2-glycoprotein without and with serum. In serum, the endogenous protein concentrations were measured across four orders of magnitude by the two-point quantification method. Less than 20% of coefficient of variation (CV) values and strong correlation with R2 of 0.99 across three analytical replicates was observed. Most importantly, the two-point quantification method allows an internal quality control of the spike-in peptide as strong deviations in ratios calculated between the first and second reference indicate a methodical error. Because of the significant lower costs than synthetically stable isotopically labeled peptides, this approach might be particularly interesting for the absolute quantification of multiple proteins.

Graphical abstract: Absolute two-point quantification of proteins using dimethylated proteotypic peptides

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Publication details

The article was received on 12 Jun 2018, accepted on 09 Aug 2018 and first published on 13 Aug 2018

Article type: Paper
DOI: 10.1039/C8AN01081A
Citation: Analyst, 2018,143, 4359-4365

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    Absolute two-point quantification of proteins using dimethylated proteotypic peptides

    T. T. Tran, R. C. Bollineni, C. J. Koehler and B. Thiede, Analyst, 2018, 143, 4359
    DOI: 10.1039/C8AN01081A

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