Dataset Information

Quantitative analysis of murine T-cells

ABSTRACT: 1D-LC-MS/MS analysis of OGE-prefractionated and TMT labeled mouse samples consiting of 2 unstimulated and 2 stimulated T-cell samples. The acquired raw-files were converted to the mascot generic file (mgf) format using the msconvert tool (part of ProteoWizard, version 3.0.4624 (2013-6-3)). Using the MASCOT algorithm (Matrix Science, Version 2.4.0), the mgf files were searched against a decoy database containing normal and reverse sequences of the predicted SwissProt entries of mus musculus (www.ebi.ac.uk, release date 16/05/2012) and commonly observed contaminants (in total 33,832 sequences) generated using the SequenceReverser tool from the MaxQuant software (Version 1.0.13.13). The precursor ion tolerance was set to 10 ppm and fragment ion tolerance was set to 0.01 Da. The search criteria were set as follows: full tryptic specificity was required (cleavage after lysine or arginine residues unless followed by proline), 2 missed cleavages were allowed, carbamidomethylation (C), TMT6plex (K and peptide n-terminus) were set as fixed modification and oxidation (M) as a variable modification. Next, the database search results were imported to the Scaffold Q+ software (version 4.1.1, Proteome Software Inc., Portland, OR) and the protein false identification rate was set to 1% based on the number of decoy hits. Specifically, peptide identifications were accepted if they could be established at greater than 94.0% probability to achieve an FDR less than 1.0% by the scaffold local FDR algorithm. Protein identifications were accepted if they could be established at greater than 6.0% probability to achieve an FDR less than 1.0% and contained at least 1 identified peptide. Protein probabilities were assigned by the Protein Prophet program (Nesvizhskii, et al, Anal. Chem. 2003; 75(17):4646-58). Proteins that contained similar peptides and could not be differentiated based on MS/MS analysis alone were grouped to satisfy the principles of parsimony. Proteins sharing significant peptide evidence were grouped into clusters. For quantification, acquired reporter ion intensities in the experiment were globally normalized across all acquisition runs. Individual quantitative samples were normalized within each acquisition run. Intensities for each peptide identification were normalized within the assigned protein. The reference channels were normalized to produce a 1:1 fold change. All normalization calculations were performed using medians to multiplicatively normalize data. A list of identified and quantified proteins is available in the xls file.

OTHER RELATED OMICS DATASETS IN: PRJNA238137

INSTRUMENT(S): LTQ Orbitrap

ORGANISM(S): Mus Musculus (mouse)

TISSUE(S): Blood Cell, Primary Cell

SUBMITTER: Alexander Schmidt

LAB HEAD: Alexander Schmidt

PROVIDER: PXD000543 | Pride | 2014-12-02

REPOSITORIES: Pride

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Publications

Global 3' UTR shortening has a limited effect on protein abundance in proliferating T cells.

Gruber Andreas R AR Martin Georges G Müller Philipp P Schmidt Alexander A Gruber Andreas J AJ Gumienny Rafal R Mittal Nitish N Jayachandran Rajesh R Pieters Jean J Keller Walter W van Nimwegen Erik E Zavolan Mihaela M

Nature communications 20141121

Alternative polyadenylation is a cellular mechanism that generates mRNA isoforms differing in their 3' untranslated regions (3' UTRs). Changes in polyadenylation site usage have been described upon induction of proliferation in resting cells, but the underlying mechanism and functional significance of this phenomenon remain largely unknown. To understand the functional consequences of shortened 3' UTR isoforms in a physiological setting, we used 3' end sequencing and quantitative mass spectromet ...[more]

PMID: 25413384

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Project description:Two datasets: StREM1.3: methyl-beta cyclodextrin-dependent distribution of StREM1.3 to detergent resistant and detergent soluble membrane fractions. SLAH3: localization of SLAH3 to detergent resistant and detergent soluble fractions dependent on co-expression with CPK21 and ABI1. Data analysis: Protein identiﬁcation and ion intensity quantitation was carried out by MaxQuant version 1.3.0.5. Spectra were matched against the Arabidopsis proteome (TAIR10, 35,386 entries) using Andromeda. Thereby, carbamidomethylation of cysteine was set as a ﬁxed modiﬁcation; oxidation of methionine as well as phosphorylation of serine, threonine, and tyrosine was set as variable modiﬁcations. Because label-free quantitation based on ion intensities was used, multiplicity was set to 1. Mass tolerance for the database search was set to 20 ppm on full scans and 0.5 Da for fragment ions. Retention time matching between runs was chosen within a time window of 2 min. Peptide false discovery rate (FDR), protein FDR was set to 0.01, and site FDR was set to 0.05. Contaminants and reverse hits identiﬁed by MaxQuant were excluded from further analys Protein identiﬁcation and ion intensity quantitation was carried out by MaxQuant version 1.3.0.5. Spectra were matched against the Arabidopsis proteome (TAIR10, 35,386 entries) using Andromeda. Thereby, carbamidomethylation of cysteine was set as a ﬁxed modiﬁcation; oxidation of methionine as well as phosphorylation of serine, threonine, and tyrosine was set as variable modiﬁcations. Because label-free quantitation based on ion intensities was used, multiplicity was set to 1. Mass tolerance for the database search was set to 20 ppm on full scans and 0.5 Da for fragment ions. Retention time matching between runs was chosen within a time window of 2 min. Peptide false discovery rate (FDR), protein FDR was set to 0.01, and site FDR was set to 0.05. Contaminants and reverse hits identiﬁed by MaxQuant were excluded from further analysis. PXD000211 is also assoicated with the same study/paper.

Project description:Background: Although human gingival fibroblasts (hGF) and human periodontal ligament fibroblasts (hPDLF) exhibit numerous phenotypic similarities, it has been suggested that the secretory and behavioral differences, which exist between these cell types, are a result of the membrane protein composition of these cells. Methods: Four matched pairs of hGF and hPDLF were cultured. Prior to confluence, membrane bound and associated proteins from cells of the 4th passage were extracted. The processed protein samples were identified by digestion with trypsin and sequenced using capillary-liquid chromatography tandem mass spectrometry on an Thermo Scientific LTQ-Orbitrap XL mass spectrometer. Scaffold by Proteome Software was used to quantitate and validate protein identifications derived from MS/MS sequencing results. Results: Four hundred fifty proteins were common to both hGF and hPDLF. Of the proteins identified, 214 were known membrane bound or associated proteins and 165 proteins were known nuclear associated proteins. Twenty-seven proteins, identified from the 450 proteins, common to both hGF and hPDLF, were detected in statistically significant greater quantities in either hGF or hPDLF. More specifically, 13 proteins were detected in significantly greater quantities in hGF, while 14 proteins were detected in significantly greater quantities in hPDLF. Conclusions: Distinct differences in the cellular protein catalog may reflect the dynamic role and high energy requirements of hGF in extracellular matrix remodeling and response to inflammatory challenge as well as the role of hPDGF in monitoring mechanical stress and maintaining tissue homeostasis during regeneration and remineralization. Method Details: Sequence information from the MS/MS data was processed by converting the .raw files into a merged file (.mgf) using an in-house program, RAW2MZXML_n_MGF_batch (merge.pl, a Perl script). Isotope distributions for the precursor ions of the MS/MS spectra were deconvoluted to obtain the charge states and monoisotopic m/z values of the precursor ions during the data conversion. The resulting mgf files were searched using Mascot Daemon by Matrix Science version 2.3.2 (Boston, MA) and the database searched against the full SwissProt database version 2012_06 (536,489 sequences; 190,389,898 residues) or NCBI database version 20120515 (18,099,548 sequences; 6,208,559,787 residues The mass accuracy of the precursor ions were set to 20ppm, accidental pick of 13C peaks was also included into the search. The fragment mass tolerance was set to 0.5 Da. Considered variable modifications were oxidation (Met), deamidation (N and Q) and carbamidomethylation (Cys). Four missed cleavages for the enzyme were permitted. A decoy database was also searched to determine the false discovery rate (FDR) and peptides were filtered according to the FDR. The significance threshold was set at p less than 0.05 and bold red peptides is required for valid peptide identification. Proteins with a Mascot score of 50 or higher with a minimum of two unique peptides from one protein having a -b or -y ion sequence tag of five residues or better were accepted. Any modifications or low score peptide/protein identifications were manually checked for validation. Spectral Counting: Label Free Quantitation was performed using the spectral counting approach. Scaffold (version Scaffold_3.4.9, Proteome Software Inc., Portland, OR) was used to validate MS/MS based peptide and protein identifications. Peptide identifications were accepted if they could be established at greater than 95.0% probability as specified by the Peptide Prophet algorithm (Keller, A et al Anal. Chem. 2002;74(20):5383-92). Protein identifications were accepted if they could be established at greater than 95.0% probability and contained at least 1 identified peptides. Protein probabilities were assigned by the Protein Prophet algorithm (Nesvizhskii, AI Anal Chem. 2003 Sep 1;75(17):4646-58). Proteins that contained similar peptides and could not be differentiated based on MS/MS analysis alone were grouped to satisfy the principles of parsimony.

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.

Project description:Flooding is a serious problem for soybean cultivation because it markedly reduces growth. We found that treatment with ABA enhances survival ratio of soybean under flooding stress. To investigate the role of ABA in soybean under flooding stress, proteomic changes were analyzed. Total protein and nuclear protein fractions from root tips of soybean seedlings that treated with flooding or flooding with ABA were analyzed. Protein samples were cleaned up with chloroform/methanol method and then subjected to in-solution digestion with trypsin and lysyl endopeptidase. Resulting peptides were analyzed on a nanospray LTQ XL Orbitrap MS (Thermo Fisher Scientific) operated in data-dependent acquisition mode with the installed Xcalibur software (version 2.0.7, Thermo Fisher Scientific). Using an Ultimate 3,000 nanoLC system (Dionex), peptides in 0.1% formic acid were loaded onto a C18 PepMap trap column (300 µm ID × 5 mm, Dionex). The peptides were eluted from the trap column and their separation and spraying were done using 0.1% formic acid in acetonitrile at a flow rate of 200 nL/min on a C18 Tip column (75 µm 1D × 120 mm, nano HPLC capillary column, NTTC-360/75-3, Nikkyo Technos) with a spray voltage of 1.5 kV. The elution was done with a linear acetonitrile gradient 8-30% in 120 min for gel free proteomics in 0.1% formic acid. Full-scan mass spectra were acquired in the Orbitrap over a mass range of 400-1,500 m/z with a resolution of 30,000. A lock mass function was used to obtain high mass accuracy. The top 10 most intense precursor ions were selected for collision-induced fragmentation in the linear ion trap at normalized collision energy of 35%. Dynamic exclusion was employed within 90 sec to prevent repetitive selection of peptides.Identification of proteins were performed by Mascot search engine (version 2.3.0.2, Matrix Science) using soybean peptide database (55,787 sequences) obtained from the soybean genome database (Phytozome version 8.0)and a common contaminants database (262 sequences). Parameters used in Mascot searches were follows: Carbamidomethylation of cysteine was set as a fixed modification, and oxidation of methionine was set as a variable modification. Trypsin was specified as the proteolytic enzyme and one missed cleavage was allowed. Peptide mass tolerance was set at 5 ppm, fragment mass tolerance was set at 0.5 Da, and peptide charge was set at +2, +3, and +4. Automatic decoy database search was performed in the search. Mascot results were filtered with Mascot percolator to improve accuracy and sensitivity in the peptide identification. False discovery rates for peptide identification of all searches were less than 1.0%. The Mascot results were used for differential analysis using SIEVE software (version 2.0, Thermo Fisher Scientific).

Project description:Flooding injury is one of the abiotic constraints on soybean growth. An experimental system established for evaluating flooding injury in soybean seedlings indicated that the degree of injury is dependent on seedling density in floodwater. To understand the molecular mechanism responsible for the injury, proteomic alterations in soybean seedlings that correlated with severity of stress were analyzed using label-free quantitative proteomics. Two-day-old soybeans, seedlings flooded for 2 days and hypocotyls of seedlings grown for 3 days after flooding were analyzed. The radicles of soybean seedlings were separated to root tips (RT), roots without tip (RwoT) and hypocotyls (Hypo). Protein was extracted from these organs by TCA/Acetone method. Protein samples were cleaned up with chloroform/methanol method and then subjected to in-solution digestion with trypsin. Resulting peptides were analyzed on a nanospray LTQ XL Orbitrap MS (Thermo Fisher Scientific) operated in data-dependent acquisition mode with the installed Xcalibur software (version 2.0.7, Thermo Fisher Scientific). Using an Ultimate 3,000 nanoLC system (Dionex), peptides in 0.1% formic acid were loaded onto a C18 PepMap trap column (300 µm ID × 5 mm, Dionex). The peptides were eluted from the trap column and their separation and spraying were done using 0.1% formic acid in acetonitrile at a flow rate of 200 nL/min on a C18 Tip column (75 µm 1D × 120 mm, nano HPLC capillary column, NTTC-360/75-3, Nikkyo Technos) with a spray voltage of 1.5 kV. The elution was done with a linear acetonitrile gradient 8-30% in 120 min for gel free proteomics in 0.1% formic acid. Full-scan mass spectra were acquired in the Orbitrap over a mass range of 400-1,500 m/z with a resolution of 30,000. A lock mass function was used to obtain high mass accuracy. The top 6 most intense precursor ions were selected for collision-induced fragmentation in the linear ion trap at normalized collision energy of 35%. Dynamic exclusion was employed within 90 sec to prevent repetitive selection of peptides.Identification of proteins were performed by Mascot search engine (version 2.3.0.2, Matrix Science) using soybean peptide database (55,787 sequences) obtained from the soybean genome database (Phytozome version 8.0)and a common contaminants database (262 sequences). Parameters used in Mascot searches were follows: Carbamidomethylation of cysteine was set as a fixed modification, and oxidation of methionine was set as a variable modification. Trypsin was specified as the proteolytic enzyme and one missed cleavage was allowed. Peptide mass tolerance was set at 5 ppm, fragment mass tolerance was set at 0.5 Da, and peptide charge was set at +2, +3, and +4. Automatic decoy database search was performed in the search. Mascot results were filtered with Mascot percolator to improve accuracy and sensitivity in the peptide identification. False discovery rates for peptide identification of all searches were less than 1.0%. The Mascot results were used for differential analysis using SIEVE software (version 1.3, Thermo Fisher Scientific)

Project description:Mtb was grown as either an actively growing normal culture or as a hypoxia-induced dormant culture. Whole cell lysates of live bacteria were obtained by mechanical disruption with silica beads. ATP-binding proteins were covalently labeled with desthiobiotin-tagged ATP (ActivX, Thermo Pierce). This chemoproteomic approach profiled the ATP-binding proteins present in either metabolic state. Labeled proteins were enriched via streptavidin affinity chromatography, digested with trypsin and subjected to tandem mass spectrometry (LC-MS/MS) for the identification of proteins containing a desthiobiotin modification of lysine (K +196 Da). Differential abundance of nucleotide binding proteins between the two growth conditions were quantified using label-free spectral counting and normalized spectral abundance factors (NSAF). DATABASE SEARCHING: Tandem mass spectra were extracted, charge state deconvoluted and deisotoped by Xcalibur version 2.2 SP1. All MS/MS samples were analyzed using Mascot (Matrix Science, London, UK; version 2.3.02) and SEQUEST (Thermo Fisher Scientific, San Jose, CA, USA; version v.27, rev. 11). Mascot and SEQUEST were set up to search the MtbReverse041712 database (7992 entries) assuming the digestion enzyme Trypsin. Parameters for both search engines were set to a fragment ion mass tolerance of 1.0 Da and a parent ion tolerance of 2.5 Da. Oxidation of methionine, iodoacetamide derivative of cysteine and the desthiobiotin modification of lysine were specified in Mascot and SEQUEST as variable modifications. PROTEIN IDENTIFICATION AND LABEL-FREE QUANTITATION-- Scaffold (version Scaffold_3.6.1, Proteome Software Inc., Portland, OR) was used to validate MS/MS based peptide and protein identifications. Peptide identifications were accepted if they exceeded specific database search engine thresholds. Mascot identifications required ion scores to be greater than the associated identity scores and 50, 65, 65 and 65 for singly, doubly, triply and quadruply charged peptides. SEQUEST identifications required deltaCn scores of greater than 0.2 and XCorr scores of greater than 1.8, 2.0, 3.0 and 4.0 for singly, doubly, triply and quadruply charged peptides. Proteins identifications were accepted if they contained at least one identified peptide in at least two biological replicates. Peptide spectra meeting the most minimum requirement were manually inspected for quality. Quantification of proteins was performed on normalized spectral abundance factors for each protein (NSAF). BLAST-BASED SEQUENCE DESCRIPTION: The most relevant description for each of the sequences was acquired based on the significant BLAST results. The homologs for the sequences were retrieved using the Blastp algorithm and the nonredundant database of NCBI. The Blast2GO suite was used. GENE ONTOLOGY ANNOTATION: The Pfam domains were mapped to Gene Ontology (GO) terms using the lookup table provided by Pfam2go (http://www.geneontology.org/external2go/pfam2go). An in-house script was written to retrieve GO annotations based on the root term as "molecular function" and their distance from the root term. PFAM DOMAIN-BASED ANNOTATION: The InterPro and Pfam IDs corresponding to the GO term "ATP binding" (GO ID:0005524) were retrieved using the QuickGO (www.ebi.ac.uk/QuickGO/<http://www.ebi.ac.uk/QuickGO/>) and InterPro BioMart web services (http://www.ebi.ac.uk/interpro/biomart/martview). The ATP-binding associated domains were queried against the ATP-binding proteome data sets according to spectral quality (high, medium, low confidence). Their Pfam and InterPro descriptions, were identified using the InterProscan Web service, which was accessed via the Pipeline Pilot (Accelrys) implementation in the sequence analysis collection.

			Action	DRS
	A13-08216.raw	Raw
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	A13-08221.raw	Raw
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