Dataset Information

Proteome analysis by charge state-selective separation of peptides: a multidimensional approach

ABSTRACT: Multidimensional peptide fractionation is widely used in proteomics to reduce the complexity of peptide mixtures prior to mass spectrometric analysis. Here, we describe the sequential use of strong cation exchange and reversed phase liquid chromatography in both basic and acidic pH buffers for separating tryptic peptides from complex mixtures of proteins. Strong cation exchange exclusively separates peptide by their charge state into neutral, singly and multi-charged species. To further reduce complexity, each peptide group was separated by reversed phase liquid chromatography at basic pH and the resultant fractions were analyzed by LC-MS/MS. This workflow was applied to a soluble protein lysate from mouse embryonic fibroblast cells were more than 5000 proteins from 30470 peptides were identified. The high selectivity displayed during the SCX step (95% to 100%) and the overlaps between proteins identified from the SCX-separated peptide groups, result in additional assets of the procedure that significantly increase the confidence of protein identifications. Raw data files were processed with Mascot Distiller (2.2), the mgf files were searched with Mascot Daemon (2.4), all data was stored in ms_lims Fixed modifications: cysteine alkylation Variable modifications: methionine oxidation, protein N-terminus acetylation, pyroglutamate formation of N-terminal glutamine, cyclic carbamate formation of N-terminal threonine or serine Enzyme: trypsin/P with 2 missed cleavages allowed Precursor mass tolerance: 10 ppm Peptide fragment mass tolerance: 0.5 Da Database: Swiss Prot mouse (version 56.4) The xml files of RH0 - RH1 - RH2 CID were created with pride converter 2.5.5 because this is still able to link to ms_lims. The old converter can not deal with ETD spectra, so the xml file of RH2 ETD was created with the new pride converter 2.0.3. But there is no link possible with ms_lims here, the dat files were used for xml file creation. Because of this, there's a small difference between the number of proteins and peptides in the xml file and the ones reported in the manuscript. Therefor, an extra excel file is submitted that contains all of the ETD identifications that were used in the manuscript.

INSTRUMENT(S): LTQ Orbitrap, instrument model

ORGANISM(S): Mus Musculus (mouse)

SUBMITTER: Pieter-Jan De Bock

PROVIDER: PXD000210 | Pride | 2013-11-27

REPOSITORIES: Pride

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Publications

SCX charge state selective separation of tryptic peptides combined with 2D-RP-HPLC allows for detailed proteome mapping.

Betancourt Lázaro H LH De Bock Pieter-Jan PJ Staes An A Timmerman Evy E Perez-Riverol Yasset Y Sanchez Aniel A Besada Vladimir V Gonzalez Luis Javier LJ Vandekerckhove Joël J Gevaert Kris K

Journal of proteomics 20130711

Multidimensional peptide fractionation is widely used in proteomics to reduce the complexity of peptide mixtures prior to mass spectrometric analysis. Here, we describe the sequential use of strong cation exchange and reversed phase liquid chromatography in both basic and acidic pH buffers for separating tryptic peptides from complex mixtures of proteins. Strong cation exchange exclusively separates peptide by their charge state into neutral, singly and multi-charged species. To further reduce c ...[more]

PMID: 23851314

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Project description:ABSTRACT FOR BOTH PLASMA AND PLATELET-LYSATE In this article we describe the involvement of exchange factor activated by cAMP 1 (Epac1) in hemostasis and platelet activation. We have used plasma and platelet-lysate from EPAC1 knockout mice and wild-type control mice in label-free proteomics with an Orbitrap Velos Pro. Blood was obtained from wild-type and Epac1-/- mice euthanized with CO2. Approximately 1000 ul was drawn from the left ventricle into a 2 ml syringe, containing 100 ul ACD and 200 ul modified Tyrode`s buffer. The blood was centrifuged at 200g for 5 minutes at room temperature, and the resulting platelet-rich plasma (PRP) centrifuged at 700g for another 10 minutes in the presence of 10 ul ACD. The resulting platelet-poor plasma (PPP) was transferred to Eppendorf tubes and the pelleted platelets resuspended in modified Tyrode`s buffer and adjusted to 2.5 x 108 platelets/ml. Platelets were allowed to rest at room temperature before experimentation. For proteomics analysis, platelets were further purified by size-exclusion through Sepharose CL-2B gel (Pharmacia Biotec, Sweden) as previously described by Jensen et al. in Blood 2004; 104. Plasma was crude or depleted for Albumin prior to trypsination and LCMS with quantification using Progenesis LCMS. The platelet-lysates were fractioned on SDS-PAGE prior to trypsination and LC-MS analysis with MaxQuant quantification. Label-free protein quantification for plasma The software Progenesis LC-MS® Ver 2.7 (Nonlinear Dynamics Ltd, Newcastle, UK) was used for label-free quantification and comparison of LC-MS proteomics data based on the volume, m/z and retention time of the MS1 features (peptides). In Progenesis, the LC-MS runs were automatically aligned, and only features with charges between +2 to +7 and containing associated MSMS spectra were accepted for export as an mgf file for identification. The mgf file was search against the human SwissProt Mus musculus database (version September 2012) using SearchGUI Ver 1.8.9. The search criteria were: trypsin as the protease with no miss-cleavages accepted, fixed carbamidomethylation on cystein, variable oxidation on methionine, precursor mass tolerance of 10 ppm, fragment mass tolerance of 0.7 and OMSSA as the search engine. The search result and associated spectra were combined and assigned to proteins in Peptide Shaker Ver 0.17.3 (http://peptideshaker.googlecode.com) at 1% FDR. The results were exported from PeptideShaker as validated PSMs in a Phenyx format, and imported back into Progenesis. The protein abundances reported from Progenesis were based on the sum of the normalized abundance of the unique identified petides. The proteomics raw files and PRIDE XML identification files were uploaded to PRIDE using ProteomeXchange ver 1.0.4, and the projects are public available. Two PRIDE XML files were generated using PeptideShaker, one for crude plasma and one for albumin-depleted plasma.

Project description:This dataset contains the raw data from the JASMS paper with the same title and DOI: 10.1007/s13361-012-0544-2. We compared the results of shotgun proteomic experiments of a trypsin digest of SUM52 cell lysate using 2D-LC fractionated (strong cation exchange and reversed phase of 6 SCX fractions) with FAIMS based fractionation. The FAIMS fractionation was performed using two different methods, an internal stepping method where the compensation voltage (CV) is cycled through 6 different voltages during the LC run (the three most abundant ions are fragmented at each CV. To match with the other methods this was run in six replicates. The other method was termed external stepping, for this method individual LC runs were performed at the six CV’s used earlier (-25 V to -50 V in 5 V steps). The three most abundant ions were again fragmented. For all three of these methods data was collect using electron transfer dissociation (ETD) and collision induced dissociation (CID) giving a total of 6 datasets with 6 raw files in each. SUM52 breast cancer carcinoma cells were cultured in HPMi-1640 formulation, supplemented with 2 mM L-glutamine, 1% Pen-Strep and 10% PBS at 37 oC in a 5% CO2 atmosphere. Lysis buffer contained Triton X-100 (0.5%), NaCl (0.15 M), PhosphoStop phosphatase inhibitor tablet (Roche) and Mini-complete protease inhibitor (Roche). The lysed cells were digested with trypsin at a ratio of 50:1 protein to enzyme. Part of the digest was fractionated with strong cation exchange using a Polysulfoethyl A column. All fractions and crude digest were desalted prior to mass spectrometry analysis. Approximately 1.66 µg of digest was loaded per LC-MS/MS run onto a 150mm Acclaim PepMap100 C18 column and separated over a 30 minute gradient from 3.2% acetonitrile (0.1% formic acid) to 44% acetonitrile (0.1% formic acid). The raw files were searched using proteome discoverer 1.2 against the human IPI database V3.81 supplemented with known contaminants and the reverse sequences. The files were searched with both sequest and mascot (v2.2.0). The MSF files were manually filtered using mascot significance scores and XCORR vs change state to give a 1% FDR, for filter setting see the linked paper. Included in the downloadable files is a xls file which documents the raw files listed and the names they had for the original searches, the names have been changed for clarity of the dataset.

Project description:In this project, we aim to pair-wise analyze the genomes, transcriptomes and proteomes of in-bred rats originating from two different genetic backgrounds. These two strains are Brown Norway (BN-Lx) and Spontaneously Hypertensive Rats (SHR). First, we re-sequenced the genomes for both BN and SHR rats, followed by RNA-seq and proteomics of their liver tissues. We then append novel predicted gene models, non-synonymous SNPs and INDELs (derived from genome re-sequencing), as well as transcript variants such as RNA-editing and alternative splicing (derived from RNA-seq) that can diversify existing protein sequences onto the ENSEMBL rat FASTA (Build 68) to build an enhanced database. For proteomics studies, equal amount of liver lysates were digested with trypsin, LysC, GluC, AspN and chymotrypsin and were individually fractionated with strong cationic exchange chromatography. Doubly- and triply-charged fractions were analyzed with an Triple-TOF 5600 with collision-activated dissociation (CAD); while electron-transfer dissociation (ETD) was applied for fractions containing triple charges and above with a LTQ-Orbitrap Velos. Data analysis: Peak List generation: For Wiff files generated from TripleTOF 5600, tandem MS spectra were de-isotoped, charge- deconvoluted and peak lists converted to Mascot generic format (MGF) files using AB Sciex Data Converter (version 1.1). For data generated from the LTQ-Orbitrap Velos, Raw files were converted to MGF files using Proteome Discoverer (version 1.3). The non-fragment filter was used to simplify ETD spectra and the Top N filter for the HCD spectra. Three MGF files were generated (one for HCD, one for ETD IT and one for ETD FT). The files with an orbitrap readout were deisotoped and charge de-convoluted. Database Searching: All MGF files were queried with Mascot search engine (version 2.3) via Proteome Discoverer version 1.3 (PD 1.3, Thermo Fisher) for submission. The spectra were searched against in-house database (NGS_COMBINED). One of the five different enzymes used (Trypsin/P, LysC/P, Chymotrypsin, GluC-DE and AspN_ambic) were selected for each file and up to 9 missed cleavages were allowed. Cysteine carbamidomethylation was set as fixed modification, and oxidation of methionine and acetylation of the N-term as variable modifications. Peptide tolerance was initially set to 50 ppm and the MS/MS tolerance was set to 0.1 Da (for TOF readout), 0.02 Da (orbitrap readout) and 0.5 Da (ion trap readout). All peptide-spectrum matches (PSMs) were evaluated with Percolator for validation. We classified each PSM based on their q value. For proteins identification, we used set a high stringency filter of q = 0 (0% FDR). For peaks lists that do not yield any peptide matches, we exported them with PD 1.3 for further analysis. De novo search with PEAKS: Unassigned peak lists that are exported were re-analyzed with another software suite i.e. PEAKS Studio (version 6.0). The identification workflows is as follows. Peak lists were first filtered with a quality value of 0.65 as suggested by the manufacturer followed by de novo spectra interpretation. In this step, both peptide tolerance and MS/MS tolerance were set according to MASCOT search. To broaden the search space for these unassigned spectra, we additionally set de-amidation of asparagine and glutamine, and pyro-glu from glutamic acid and glutamine as variable modifications, on top of the other modifications indicated above. Maximum allowed variable PTM per peptide was set to 3. Finally de novo interpreted PSMs were submitted to PEAKS DB database matching, this time allowing semi-enzymatic specificity and a maximum cleavages per peptide of 2. Database used was set to NGS_COMBINED. FDR was estimated using decoy-fusion. The genomics and transcriptomics data are already deposited in the respective EBI repositories. Some of these data are derived from an already published manuscript. For the genomics data (from: Genetic basis of transcriptome differences between the founder strains of the rat HXB/BXH recombinant inbred panel by Simonis et al PMID:22541052) DNA data in Sequence Read Archive (SRA): BN-Lx genome: ERP001355 http://www.ebi.ac.uk/ena/data/view/ERP001355, SHR genome: ERP001371, BN reference genome: ERP000510, http://www.ebi.ac.uk/ena/data/view/ERP000510. RNA data in ArrayExpress: BN-Lx and SHR fragment RNA-seq data: E-MTAB-1029 http://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-1029, BN-Lx and SHR paired-end RNA-seq data: to be submitted.

Dataset Information

Proteome analysis by charge state-selective separation of peptides: a multidimensional approach

Publications

SCX charge state selective separation of tryptic peptides combined with 2D-RP-HPLC allows for detailed proteome mapping.

Similar Datasets

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