Project description:Previously, we published a dataset of human blood plasma and serum samples of 10 healthy males and 10 healthy females, fractionated on a set of sorbents (cation exchange Toyopearl CM-650M, CM Bio-Gel A, SP Sephadex C-25 and anion exchange QAE Sephadex A-25) and analyzed by LC-MS/MS individually and pooled in equal amounts (Supplementary Table S1, Sheet 1) [33]. Blood is a complex tissue and can theoretically contain products of all processes occurring in different parts of the body. To identify potential sources of “alien” (non-human) plasma peptides, we performed a de novo analysis of mass spectrometry data. De novo analysis is a less efficient method of identification than search against databases, since the accuracy and resolution of modern mass spectrometers such as QTOF or Orbitrap remains not high enough. However, we use de novo analysis to identify organisms that include the most abundant components of the complex peptide mixture. This procedure allows us to develop a hypothesis and then perform a standard search against a database of proteins of identified organisms. The de novo identification was carried out using PEAKS Studio 8.0 (Bioinformatics Solutions Inc., Canada) and mass spectrometry driven BLAST analysis against the RefSeq non-redundant database NCBInr (https://www.ncbi.nlm.nih.gov/protein/). Mass spectra identified as fragments of proteins from different organisms were assigned to a taxonomic level at which these different organisms converged (Supplementary Table S2). 33. Arapidi, G. et al. Peptidomics dataset: Blood plasma and serum samples of healthy donors fractionated on a set of chromatography sorbents. Data Brief 18, 1204–1211 (2018).
Project description:We have develop a proteogenomics-based approach for identification of human MHC class I-associated peptides, including those deriving from polymorphisms, mutations and non-canonical reading frames
Project description:We performed LC-MSMS analysis using both CID and ETD for the identification of endogenous peptides. Endogenous peptides were extracted from mouse AtT 20 cells by acidified methanol method and all large molecules including proteins were removed by centrifugation. The supernatant containing endogenous peptides was freeze-dried. For LC-MSMS analysis, extracted peptides were resuspended and injected to Ultimate 3000 HPLC system and analysed on LTQ Orbitrap XL mass spectrometer. A 60 min gradient from 2% acetonitrile to 50% acetonitrile, both containing 0.1% formic acid was used to separate peptides on C18 column.The LTQ-Orbitrap mass spectrometer was operated in data-dependent mode, automatically switching between MS and MS/MS acquisition for the three most abundant peaks in a given MS spectrum. A chosen precursor ion was first fragmented by CID and ETD. Data processing: The raw data files were processed with Proteome Discoverer 1.3. The CID and ETD spectra were then written to Mascot generic files. OMSSA (version 2.1.9) was used and b- and y- ions were selected for CID data, and c-, y- and z- ions were used for ETD. The spectra were searched by setting the parent ion mass accuracy to +/- 0.02 Da. For fragment ions, the mass tolerance was set to +/- 0.4 Da. For the genome-wide peptide search, the mouse genomic sequence (NCBI build 37.61) was directly translated in its 6 reading frames, and used for spectral searching. No enzymatic cleavage was taken into account during the database searches. No variable PTMs were included.
Project description:Primary Open-Angle Glaucoma (POAG) and Pseudoexfoliation Glaucoma (PEXG) are forms of glaucoma with distinct etiologies, but they share a common type of optic neurodegeneration that leads to irreversible loss of retinal ganglion cells, visual field and blindness if left untreated. The main objective in the present study was to explore whether differential-proteomic analysis of blood serum from patients with POAG or PEXG could lead to the identification of candidate biomarkers of glaucoma and provide clues to pathogenic mechanisms. A total of 202 blood samples from medically treated patients with POAG (n=73), PEXG (n=59), and cataracts (control, n=70) were collected and analyzed following a proteomic workflow that involved two phases: discovery and validation. In the discovery phase, differential-proteomics (2D-DIGE) methodology led to the identification of a panel of 35 proteins that were altered (i.e., over- or under-expressed) in serum samples of glaucoma patients. Functional pathway analysis suggested a potential involvement of these proteins in immunological and inflammatory pathways. In the validation phase, ELISA assays of the top-17-ranked proteins confirmed that most of them were over-expressed in the glaucoma groups. Stepwise discriminant analysis of the ELISA data resulted in a six-protein panel (APOA4, TF, C3, APOL1, IGHG2, C4A) that was used to generate multivariate predictive models. These models achieved a diagnostic efficiency of 81% (correct assignment), specificity of 83%, and sensitivity of 92%. Specifically, 100% efficacy was reached in the discrimination of the POAG group from control, and 86% efficacy in the discrimination of POAG from PEXG. Overall, these data demonstrate the discovery of a panel of serum biomarkers that could be used in large-scale multiplexed screenings for the diagnosis of POAG and PEXG patients. Furthermore, these biomarkers may have a role in the pathogenesis, and association to immune-inflammatory processes with glaucoma.
Project description:We performed nanoLC-MS/MS analysis of an in vitro generated, trypsin-digested brominated human serum albumin standard, spiked into a complex trypsin-digested proteomic background, in an LTQ-Orbitrap instrument. We found that brominated peptides spiked in at a 1-10% ratio (mass:mass) were easily identified by manual inspection when higher-energy collisional dissociation (HCD) and collision induced dissociation (CID) were employed as the dissociation mode; however, confident assignment of brominated peptides from protein database searches required a novel approach. By addition of a custom modification, corresponding to the substitution of a single bromine with 81Br rather than 79Br for dibromotyrosine (79Br81BrY), the number of validated assignments for peptides containing dibromotyrosine increased significantly when analyzing both high resolution and low resolution MS/MS data.
Project description:Seeking to identify HLA class I peptides that originate from vaccinia virus proteins to understand the mechanism of immune protection. Note that vaccinia-infected B cells will still continue to present (primarily) a wide variety of peptides originating from endogenous proteins; this data set contains evidence for more than 5000 such peptides. The objective and challenge is to detect and identify the peptides that originate from the pathogen (vaccinia virus) in the presence (background) of this large number of endogensous 'self' peptides. Keywords: Peptide search results from multiple injections of multiple strong cation exchange fractions combined into one set of results.
Project description:The purpose of this study was to generate a basis for the decision of what protein quantities are reliable and find a way for accurate and precise protein quantification. To investigate this we have used thousands of peptide measurements to estimate variance and bias for quantification by iTRAQ (isobaric tags for relative and absolute quantification) mass spectrometry in complex human samples. A549 cell lysate was mixed in the proportions 2:2:1:1:2:2:1:1, fractionated by high resolution isoelectric focusing and liquid chromatography and analyzed by three mass spectrometry platforms; LTQ Orbitrap Velos, 4800 MALDI-TOF/TOF and 6530 Q-TOF. We have investigated how variance and bias in the iTRAQ reporter ions data are affected by common experimental variables such as sample amount, sample fractionation, fragmentation energy, and instrument platform. Based on this, we have suggested a concept for experimental design and a methodology for protein quantification. By using duplicate samples in each run, each experiment is validated based on its internal experimental variation. The duplicates are used for calculating peptide weights, unique to the experiment, which is used in the protein quantification. By weighting the peptides depending on reporter ion intensity, we can decrease the relative error in quantification at the protein level and assign a total weight to each protein that reflects the protein quantitation confidence. We also demonstrate the usability of this methodology in a cancer cell line experiment as well as in a clinical data set of lung cancer tissue samples. In conclusion, we have in this study developed a methodology for improved protein quantification in shotgun proteomics and introduced a way to assess quantification for proteins with few peptides. The experimental design and developed algorithms decreased the relative protein quantification error in the analysis of complex biological samples. Data analysis: LTQ Orbitrap Velos Proteome discoverer 1.1 with Mascot 2.2 (Matrix Science) was used for protein identification. Precursor mass tolerance was set to 10 ppm and for fragments 0.8 Da and 0.015 Da were used for detection in the linear iontrap and the orbitrap, respectively. Oxidized methionine was set as dynamic modification and carbamidomethylation, N-terminal 8plex iTRAQ, and lysyl 8plex iTRAQ as fixed modifications. 4800 MALDI TOF/TOF Peptide identification from the Maldi-TOF/TOF data was carried out using the Paragon algorithm in the ProteinPilot 2.0 software package (Applied Biosystems). Default settings for a 4800 instrument were used (i.e. no manual settings for mass tolerance was given). The following parameters were selected in the analysis method: iTRAQ 8plex peptide labeled as sample type, IAA as alkylating agent of cysteine, trypsin as digesting enzyme, 4800 as instrument, gel based ID and Urea denaturation as special factors, biological modifications as ID focus, and thorough ID as search effort. 6530 QTOF Peptide identification from the QTOF data was carried out using the Spectrum Mill Protein Identification software (Agilent). Data was extracted between MH+ 600 and 4000 Da (Agilent’s definition). Trypsin was used as digesting enzyme, and parent and daughter ion tolerance was set to 25 and 50 ppm, respectively. IAA for cysteine and iTRAQ partial-mix (N-term, K) were set as fixed modifications while oxidized methionine was set as variable modification. Database and peptide cut-off for all searches Searches were performed against the IPI database (build 3.64) limited to human sequences allowing 2 missed cleavages. False discovery rate (FDR) was estimated by searching the data against a database consisting of both forward and reversed sequences and set to < 1 % at the protein level using MAYU. Peptides corresponding to a <1% protein FDR rate was used in the calculations. Peptide and protein identification using Mascot for comparison between instruments Peptide identifications were performed using Mascot Daemon 2.3.2 with Mascot 2.4 for fractions 32 to 36 from IPG-IEF with 400 ug loaded peptides. Carbamidomethylation (CAM) for cysteine was set as fixed modification, oxidized methionine as variable modification and iTRAQ 8plex was set as quantification for all searches. MALDI-TOF/TOF search settings: Parent and daughter ion tolerance was set to 150 ppm and 0.2 Da, respectively. LTQ Orbitrap search settings: Precursor mass tolerance was set to 10 ppm and for fragments 0.8 Da and 0.015 Da were used for data generated in the linear ion trap and the orbitrap, respectively. QTOF search settings: Parent and daughter ion tolerance was set to 25 and 50 ppm, respectively.
Project description:Albuminome from human serum was isolated using ProteaPrep HSA affinity spin columns and Vivapure anti-HSA spin columns. Albuminome from CSF was isolated using ProteaPrep HSA affinity spin columns. Isolated albuminome was trypsin digested and analyzed by LC-MS/MS to determine serum and CSF albuminome proteins. All raw MS/MS data originating from the Orbitrap Elite were batch searched based on albuminome isolation method (ProteaPrep vs. Vivapure). A total of 9 files were acquired for each albuminome analyzed (3 technical reps x 3 MS technical reps). All data were searched using the Sorcerer 2TM-SEQUEST algorithm (Sage-N Research, Milpitas, CA, USA) using default peak extraction parameters. Data were searched against the Human Uniprot database (July 2012) with an Xcorr cutoff of 1.7 using the following criteria: Fixed modification: +57 on C (carbamidomethyl); Variable modification: +16 on M (oxidation); Enzyme: Trypsin with 2 max missed cleavages; Parent Tolerance: 50 ppm; Fragment tolerance: 1.00 Da. Post-search analysis was performed using Scaffold 3 version 3.6.2 (Proteome Software, Inc., Portland, OR, USA) with protein and peptide probability thresholds set to 95% and 90%, respectively, and one peptide required for identification. Protein and peptide false discovery rates were calculated automatically using Scaffold’s probabilistic method and were equal to or less than 0.5 % for all samples using the above thresholds. Data were imported in Protein Center (Proxeon/ThermoFisher) and all data were clustered by indistinguishable proteins to remove protein redundancy within data sets. Only proteins that were observed in all three technical replicates were included in the final protein list, although proteins observed it at least two technical replicates were included in the on-line supplement for reference.