| Literature DB >> 25588721 |
Liqing Gu1, Adam R Evans, Renã A S Robinson.
Abstract
Cysteine-selective proteomics approaches simplify complex protein mixtures and improve the chance of detecting low abundant proteins. It is possible that cysteinyl-peptide/protein enrichment methods could be coupled to isotopic labeling and isobaric tagging methods for quantitative proteomics analyses in as few as two or up to 10 samples, respectively. Here we present two novel cysteine-selective proteomics approaches: cysteine-selective dimethyl labeling (cysDML) and cysteine-selective combined precursor isotopic labeling and isobaric tagging (cPILOT). CysDML is a duplex precursor quantification technique that couples cysteinyl-peptide enrichment with on-resin stable-isotope dimethyl labeling. Cysteine-selective cPILOT is a novel 12-plex workflow based on cysteinyl-peptide enrichment, on-resin stable-isotope dimethyl labeling, and iodoTMT tagging on cysteine residues. To demonstrate the broad applicability of the approaches, we applied cysDML and cPILOT methods to liver tissues from an Alzheimer's disease (AD) mouse model and wild-type (WT) controls. From the cysDML experiments, an average of 850 proteins were identified and 594 were quantified, whereas from the cPILOT experiment, 330 and 151 proteins were identified and quantified, respectively. Overall, 2259 unique total proteins were detected from both cysDML and cPILOT experiments. There is tremendous overlap in the proteins identified and quantified between both experiments, and many proteins have AD/WT fold-change values that are within ~20% error. A total of 65 statistically significant proteins are differentially expressed in the liver proteome of AD mice relative to WT. The performance of cysDML and cPILOT are demonstrated and advantages and limitations of using multiple duplex experiments versus a single 12-plex experiment are highlighted.Entities:
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Year: 2015 PMID: 25588721 DOI: 10.1007/s13361-014-1059-9
Source DB: PubMed Journal: J Am Soc Mass Spectrom ISSN: 1044-0305 Impact factor: 3.109