Evaluation of HCD- and CID-type fragmentation within their respective detection platforms for murine phosphoproteomics.
ABSTRACT Protein phosphorylation modulates a myriad of biological functions, and its regulation is vital for proper cellular activity. Mass spectrometry is the enabling tool for phosphopeptide analysis, where recent instrumentation advances in both speed and sensitivity in linear ion trap and orbitrap technologies may yield more comprehensive phosphoproteomic analyses in less time. Protein phosphorylation analysis by MS relies on structural information derived through controlled peptide fragmentation. Compared with traditional, ion-trap-based collision-induced dissociation (CID), a more recent type of fragmentation termed HCD (higher energy collisional dissociation) provides beam type CID tandem MS with detection of fragment ions at high resolution in the orbitrap mass analyzer. Here we compared HCD to traditional CID for large-scale phosphorylation analyses of murine brain under three separate experimental conditions. These included a same-precursor analysis where CID and HCD scans were performed back-to-back, separate analyses of a phosphotyrosine peptide immunoprecipitation experiment, and separate whole phosphoproteome analyses. HCD generally provided higher search engine scores with more peptides identified, thus out-performing CID for back-to-back experiments for most metrics tested. However, for phosphotyrosine IPs and in a full phosphoproteome study of mouse brain, the greater acquisition speed of CID-only analyses provided larger data sets. We reconciled our results with those in direct contradiction from Nagaraj N, D'Souza RCJ et al. (J. Proteome Res. 9:6786, 2010). We conclude, for large-scale phosphoproteomics, CID fragmentation with rapid detection in the ion trap still produced substantially richer data sets, but the back-to-back experiments demonstrated the promise of HCD and orbitrap detection for the future.
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ABSTRACT: Progress in the fields of protein separation and identification technologies has accelerated research into biofluids proteomics for protein biomarker discovery. Urine has become an ideal and rich source of biomarkers in clinical proteomics. Here we performed a proteomic analysis of urine samples from pregnant and non-pregnant patients using gel electrophoresis and high-resolution mass spectrometry. Furthermore, we also apply a non-prefractionation quantitative phosphoproteomic approach using mTRAQ labeling to evaluate the expression of specific phosphoproteins during pregnancy comparison with non-pregnancy. In total, 2579 proteins (10429 unique peptides) were identified, including 1408 from the urine of pregnant volunteers and 1985 from the urine of non-pregnant volunteers. One thousand and twenty-three proteins were not reported in previous studies at the proteome level and were unique to our study. Furthermore, we obtained 237 phosphopeptides, representing 105 phosphoproteins. Among these phosphoproteins, 16 of them were found to be significantly differentially expressed, of which 14 were up-regulated and two were down-regulated in urine samples from women just before vaginal delivery. Taken together, these results offer a comprehensive urinary proteomic profile of healthy women during before and after vaginal delivery and novel information on the phosphoproteins that are differentially regulated during the maintenance of normal pregnancy. Our results may provide a better understanding of the mechanisms of pregnancy maintenance, potentially leading to the development of biomarker-based sensitive assays for understanding pregnancy.BMC Genomics 11/2013; 14(1):777. · 4.40 Impact Factor
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ABSTRACT: The proposed protocol presents a comprehensive approach for large-scale qualitative and quantitative analysis of glycated proteins (GP) in complex biological samples including biological fluids and cell lysates such as plasma and red blood cells. The method, named glycation isotopic labeling (GIL), is based on the differential labeling of proteins with isotopic [(13)C6]-glucose, which supports quantitation of the resulting glycated peptides after enzymatic digestion with endoproteinase Glu-C. The key principle of the GIL approach is the detection of doublet signals for each glycated peptide in MS precursor scanning (glycated peptide with in vivo [(12)C6]- and in vitro [(13)C6]-glucose). The mass shift of the doublet signals is +6, +3 or +2 Da depending on the peptide charge state and the number of glycation sites. The intensity ratio between doublet signals generates quantitative information of glycated proteins that can be related to the glycemic state of the studied samples. Tandem mass spectrometry with high-energy collisional dissociation (HCD-MS2) and data-dependent methods with collision-induced dissociation (CID-MS3 neutral loss scan) are used for qualitative analysis.Journal of Proteome Research 01/2014; · 5.06 Impact Factor
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ABSTRACT: Phosphorylation site assignment of high throughput tandem mass spectrometry (LC-MS/MS) data is one of the most common and critical aspects of phosphoproteomics. Correctly assigning phosphorylated residues helps us understand their biological significance. The design of common search algorithms (such as Sequest, Mascot etc.) do not incorporate site assignment; therefore additional algorithms are essential to assign phosphorylation sites for mass spectrometry data. The main contribution of this study is the design and implementation of a linear time and space dynamic programming strategy for phosphorylation site assignment referred to as PhosSA. The proposed algorithm uses summation of peak intensities associated with theoretical spectra as an objective function. Quality control of the assigned sites is achieved using a post-processing redundancy criteria that indicates the signal-to-noise ratio properties of the fragmented spectra. The quality assessment of the algorithm was determined using experimentally generated data sets using synthetic peptides for which phosphorylation sites were known. We report that PhosSA was able to achieve a high degree of accuracy and sensitivity with all the experimentally generated mass spectrometry data sets. The implemented algorithm is shown to be extremely fast and scalable with increasing number of spectra (we report up to 0.5 million spectra/hour on a moderate workstation). The algorithm is designed to accept results from both Sequest and Mascot search engines. An executable is freely available at http://helixweb.nih.gov/ESBL/PhosSA/ for academic research purposes.Proteome Science 11/2013; 11(Suppl 1):S14. · 2.42 Impact Factor