Dataset Information

Proteomics analysis of human pericardial fluid

ABSTRACT: Pericardial fluid (PF) is considered as biochemical window of heart. Knowledge of the entire protein content, the proteome of human PF, would give insights into heart and cardiovascular diseases. To date, there have been limited attempt to perform an in-depth analysis of the PF proteome. In this study, a SDS-PAGE-LC-MS/MS platform was utilized to explore abundant protein-depleted PF samples and showed great coverage of low-abundance proteins. In total, 1,007 non-redundant proteins were identified with at least two peptides, generating the first human PF proteome. Data processing: The mass spectra were searched using MaxQuant (v1.2.2.5) against the human subset of the Uniprot database (2012/05/16), which contains 87,187 entries. The search parameters were set as follows: peptide (parent ion) tolerance of 20 ppm, fragment ion tolerance of 0.5 Da, two missed cleavages were allowed, fixed modification of carbamidomethylation on Cys (+57 Da), and a differential modification of oxidation on Met (+16 Da). The false discovery rates (FDR) for both peptides and proteins were evaluated by searching against the combined database with the reversed amino acid sequence, and calculated by MaxQuant. To get high-confidence peptide and protein identifications, the cutoffs used were 1% for peptides and 1% for proteins. Absolute protein amounts were calculated as the sum of all peptide peak intensities divided by the number of theoretically observable tryptic peptides (intensity based absolute quantification, or iBAQ).

INSTRUMENT(S): LTQ Orbitrap Velos

ORGANISM(S): Homo Sapiens (human)

SUBMITTER: fei xiang

PROVIDER: PXD000194 | Pride | 2013-07-09

REPOSITORIES: Pride

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Proteomics analysis of human pericardial fluid.

Xiang Fei F Guo Xuejiang X Chen Wen W Wang Jing J Zhou Tao T Huang Fuhua F Cao Changchun C Chen Xin X

Proteomics 20130805 17

Pericardial fluid (PF) is considered as a biochemical window of heart. To date, there have been limited attempts to perform an in-depth analysis of the PF proteome. In this study, an SDS-PAGE-LC-MS/MS platform was utilized to explore depleted PF, which showed great coverage of low-abundant proteins. In total, 1007 nonredundant proteins were identified with at least two peptides. This is the first comprehensive analysis of human PF proteome and provides a foundation for further application of PF ...[more]

PMID: 23797974

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Project description:Protein identification was carried out by searching against a joint Human + Yeast Swissprot database (Uniprot release 57.3 May 2009, 26885 entries), in order to increase statistical power of peptide identification, which was supplemented with porcine trypsin, and using SEQUEST algorithm (Bioworks 3.2 package, Thermo Finnigan). For SILAC, we used variable (methionine oxidation, lysine and arginine modification of +6 Da) and fixed modifications (cysteine carboxamidomethylation). Precursor mass tolerance was set to 2Da, fragment mass tolerance to 1.2Da and up to two missed cleavage sites for trypsin were allowed. The same collections of MS/MS spectra were also searched against inverted databases constructed from the same target databases. SEQUEST results were analyzed using the probability ratio method 22b, taking into account isoelectric points of peptides to improve peptide identification 9b. False discovery rates (FDR) of peptide identifications were calculated from the search results against the inverted databases using the refined method 9b. The combination of stable isotope labeling (SIL) with mass spectrometry (MS) allows comparison of the abundance of thousands of proteins in complex mixtures. However, interpretation of the large datasets generated by these techniques remains a challenge since appropriate statistical standards are lacking. Here we present the first generally applicable model that accurately explains the behavior of data obtained using current SIL approaches, including 18O, iTRAQ and SILAC labeling, and different MS instruments. The model decomposes the total technical variance into the spectral, peptide and protein variance components, and its general validity was demonstrated by confronting 48 experimental distributions against 18 different null hypotheses. In addition to its general applicability, the performance of the algorithm was similar or better than that of other existing methods. The model also provides a general framework to integrate quantitative information, allowing a comparative analysis of the results obtained from different SIL experiments. The model was applied to the global analysis of protein alterations induced by low H2O2 concentrations in yeast, demonstrating the increased statistical power that may be achieved by rigorous data integration. Our results highlight the importance of establishing an adequate and validated statistical framework for the analysis of high-throughput data. Except for TOF/TOF MS/MS files, protein identification was carried out by searching against a joint Human+YeastSwissprot database (Uniprot release 57.3 May 2009, 26885 entries), in order to increase statistical power of peptide identification, which was supplemented with porcine trypsin, and using SEQUEST algorithm (Bioworks 3.2 package, Thermo Finnigan). For 18O-labeled samples variable (methionine oxidation, lysine and arginine modification of +4 Da) and fixed modifications (cysteine carboxamidomethylation) were used. For iTRAQ, we allowed variable (methionine oxidation) and fixed modifications (cysteine carboxamidomethylation, lysine and N- terminal modification of +144.1020 Da). For SILAC, we used variable (methionine oxidation, lysine and arginine modification of +6 Da) and fixed modifications (cysteine carboxamidomethylation). Precursor mass tolerance was set to 2Da, fragment mass tolerance to 1.2Da and up to two missed cleavage sites for trypsin were allowed. The same collections of MS/MS spectra were also searched against inverted databases constructed from the same target databases. SEQUEST results were analyzed using the probability ratio method 22b, taking into account isoelectric points of peptides to improve peptide identification 9b. False discovery rates (FDR) of peptide identifications were calculated from the search results against the inverted databases using the refined method 9b. TOF/TOF MS/MS spectra were converted to MGF files by using the 4000 Series Explorer V.3.5.1 internal processor and the following parameters: mass range from precursor -20 Da to 60 Da, peak density of 50 peaks per 200 Da with S/N >1 and minimum area >1 with a maximum number of 100 peaks per precursor. These files were analyzed with the Mascot V2.2.06 search engine using the database and search conditions indicated above.

Dataset Information

Proteomics analysis of human pericardial fluid

Publications

Proteomics analysis of human pericardial fluid.

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