Dataset Information

MS1 Peptide Ion Intensity Chromatograms in MS2 (SWATH) Data Independent Acquisitions

ABSTRACT: Quantitative analysis of discovery-based proteomic workflows now relies on high-throughput large-scale methods for identification and quantification of proteins and post-translational modifications. Advancements in label-free quantitative techniques, using either data-dependent or data-independent mass spectrometric acquisitions, have coincided with improved instrumentation featuring greater precision, increased mass accuracy, and faster scan speeds. We recently reported on a new quantitative method called MS1 Filtering (Schilling et al. (2012) Mol. Cell. Proteomics 11, 202-214) for processing data-independent MS1 ion intensity chromatograms from peptide analytes using the Skyline software platform. In contrast, data-independent acquisitions from MS2 scans, or SWATH, can quantify all fragment ion intensities when reference spectra are available. As each SWATH acquisition cycle typically contains an MS1 scan, these two independent label-free quantitative approaches can be acquired in a single experiment. Here, we have expanded the capability of Skyline to extract both MS1 and MS2 ion intensity chromatograms from a single SWATH data-independent acquisition (DIA) in an Integrated Dual Scan Analysis (IDSA) approach. The performance of both MS1 and MS2 data was examined in simple and complex samples using standard concentration curves. Cases of interferences in MS1 and MS2 ion intensity data were assessed, as were the differentiation and quantitation of phosphopeptide isomers in MS2 scan data. In addition, we demonstrated an approach for optimization of SWATH m/z window sizes to reduce interferences using MS1 scans as a guide. Finally, a correlation analysis was performed on both MS1 and MS2 ion intensity data obtained from SWATH acquisitions on a complex mixture using a linear model that automatically removes signals containing interferences. This work demonstrates the practical advantages of properly acquiring and processing MS1 precursor data in addition to MS2 fragment ion intensity data in a DIA (SWATH), and provides an approach to simultaneously obtain independent measurements of relative peptide abundance from a single experiment.

INSTRUMENT(S): TripleTOF 5600

ORGANISM(S): Mus Musculus (ncbitaxon:10090)

SUBMITTER: Bradford W. Gibson

PROVIDER: MSV000079092 | MassIVE | Wed Apr 01 18:20:00 BST 2015

SECONDARY ACCESSION(S): PXD002008

REPOSITORIES: MassIVE

ACCESS DATA

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Project description:Data independent acquisition-mass spectrometry (DIA-MS) coupled with liquid chromatography is a promising approach for rapid, automatic sampling of MS/MS data in untargeted metabolomics. However, wide isolation windows in DIA-MS generate MS/MS spectra containing a mixed population of fragment ions together with their precursor ions. This precursor-fragment ion map in a comprehensive MS/MS spectral library is crucial for relative quantification of fragment ions uniquely representative of each precursor ion. However, existing reference libraries are not sufficient for this purpose since the fragmentation patterns of small molecules can vary in different instrument setups. Here we developed a bioinformatics workflow called MetaboDIA to build customized MS/MS spectral libraries using a user's own data dependent acquisition (DDA) data and to perform MS/MS-based quantification with DIA data, thus complementing conventional MS1-based quantification. MetaboDIA also allows users to build a spectral library directly from DIA data in studies of a large sample size. Using a marine algae data set, we show that quantification of fragment ions extracted with a customized MS/MS library can provide as reliable quantitative data as the direct quantification of precursor ions based on MS1 data. To test its applicability in complex samples, we applied MetaboDIA to a clinical serum metabolomics data set, where we built a DDA-based spectral library containing consensus spectra for 1829 compounds. We performed fragment ion quantification using DIA data using this library, yielding sensitive differential expression analysis. </br></br> Serum metabolome of 40 age-related macular degeneration patients and 20 control samples was analyzed using untargeted mass spectrometry. We used data dependent acquisition data to build a MS/MS spectral assay library for more than 1,000 compounds and performed targeted extraction of MS2 ion chromatograms from data independent acquisition analysis.

Project description:Isolated murine kidney glomeruli were lysed in buffer containing 8M urea, and proteins were reduced and alkylated. Proteins were digested with trypsin. After reverse-phase chomatrography, peptides were fractionated using strong cation exchange chromatography. Phosphopeptides were enriched using IMAC (FeNTA) columns. Peptides were analyzed by LC-MS/MS on a Q Exactive mass spectrometer or an LTQ Orbitrap XL mass spectrometer. Metadata for Orbitrap samples (MaxQuant Output files include „Orbi“) 1. General features – 1.1 Global descriptors a. Responsible person or role: Markus Rinschen. markus.rinschen@uk-koeln.de; tobias.lamkemeyer@uni-koeln.de b. Instrument manufacturer, model: LTQ-Orbitrap XL, Thermo Scientific c. customization 2. Ion sources – ESI fed by nLC 2 (Proxeon) 3. Post source component 3.1. Analyser: MS1 survey scan in an Orbitrap and MS2 analysed in Linear Trap. 3.2. Activation/Dissociation: CID 4. Spectrum and peak list generation and annotation 4.1. Data acquisition: MaxQuant v. 1.3.05 Top one method with a cycle of one full MS1 scan in the Orbitrap, followed by the fragmentation with dynamic exclusion window of 60 seconds and followed by the acquisition of 5 product ion scans generated in the LTQ analyser, and detected in the LTQ. Unselected fragmentation. For further details, see .raw files. URL of file: Filename 4.2. Data analysis: MaxQuant v 1.3.05 Parameters used in the generation of peak lists or processed spectra: see annotation in “parameters” file. The mouse uniprot reference database “complete proteome” obtained on January 2nd, 2013 was used. Metadata for QExactive samples (Max Quant output files include „QE“) 1. General features – 1.1 Global descriptors a. Responsible person or role: Markus Rinschen. markus.rinschen@uk-koeln.de; Marcus Krüger marcus.krueger@mpi-bn.mpg.de b. Instrument manufacturer, model: Q Exactive Thermo Scientific c. customization 2. Ion sources – ESI fed by nLC 1000 (Proxeon) 3. Post source component 3.1. Analyser: MS1 survey scan in an Orbitrap and MS2 analysed in Orbitrap/HCD cell 3.2. Activation/Dissociation: HCD 4. Spectrum and peak list generation and annotation 4.1. Data acquisition: MaxQuant v. 1.3.05 Top one method with a cycle of one full MS1 scan in the Orbitrap, followed by the fragmentation with dynamic exclusion window of 20 seconds in the HCD cell and followed by the acquisition of 10 product ion scans generated in the LTQ analyser, and detected in the LTQ. Unselected fragmentation. For further details, see .raw files. URL of file: Filename 4.2. Data analysis: MaxQuant v 1.3.05 Parameters used in the generation of peak lists or processed spectra: see annotation in “parameters” file. The mouse uniprot reference database “complete proteome” obtained on January 2nd, 2013 was used.

Project description:Use of Information Dependent Acquisition mass spectra and Sequential Window Acquisition of all Theoretical fragment-ion mass spectra for fruit juices metabolomics and authentication. LC-MS based untargeted metabolomics are the main untargeted methods used for juice metabolomics to solve the authentication problem faced in fruit juice industry. Objectives To evaluate the performances of different untargeted metabolomics methods on fruit juices metabolomics and authentication, orange and apple fruit juices were selected for this study. Methods IDA-MS and SWATH-MS based on UHPLC-QTOF were used for the metabolomics and authenticity determination of apple and orange juices, including the lab-made samples of oranges (Citrus sinensis Osb.) from Jiangxi Province, apples (Malus domestica Borkh) from Shandong Province, and different brands of commercial orange and apple juice samples from markets. Results IDA-MS and SWATH-MS could both acquire numerous MS1 features and MS2 information of juice components, while SWATH-MS excels at the acquisition rate of MS2. Distinctive separation between authentic orange juice and not authentic orange juice could be seen from principal component analysis and hierarchical clustering analysis based on both IDA-MS and SWATH-MS. After analysis of variance, fold change analysis and orthogonal projection to latent structures discriminant mode, 53 and 46 potential markers were defined by IDA-MS and SWATH-MS (with 77.4% and 100% MS2 acquisition rate) separately. Subsequently, these potential markers were putatively annotated using general chemical databases with 6 more annotated by SWATH-MS. Furthermore, 7 of the potential markers, l-asparagine, umbelliferone, glucosamine, phlorin, epicatechin, phytosphingosine and chlorogenic acid, were identified with standards. For the consideration of model simplicity, two determined makers (umbelliferone and chlorogenic acid) were selected to construct the DD-SIMCA model in commercial samples because of their good correlation with apple adulteration proportion, and the sensitivity and specificity of the model were 100% and 95%. Conclusion SWATH-MS excels at the MS2 acquisition of juice components and potential markers. This study provides an overall performance comparison between IDA-MS and SWATH-MS, and guidance for the method selection on fruit juice metabolomics and juice authenticity determination. Two of the potential markers determined, umbelliferone and chlorogenic acid, could be used as apple juice indicators in orange juice.

Project description:This dataset derives from experiments testing quantitation using label-free methods. We used the Sigma-Aldrich UPS1 and UPS2 protein standard sets, containing proteins of 6-83 kD; the UPS1 set includes 49 human proteins at a single molar concentration, while the UPS2 set includes six groups of eight human proteins spanning a six orders of magnitude in concentration. In both cases, dilutions of UPS1 or UPS2 proteins were made into an E. coli extract, which allows these experiments to mimic detection of low abundance proteins in a complex protein mixture. The UPS1/E. coli and UPS2/E. coli mixtures were run on three mass spectrometers, an Orbitrap Velos Elite and two ion-trap instruments (Velos and LTQ). Proteins were quantified using MS1 peak volume (Orbitrap) or MS2 intensities and spectral counts (Orbitrap, Velos, LTQ). The analyzed results show that the ion-trap instruments perform nearly as well for label-free quantitation as does the Orbitrap. In addition, the \"Top 3\" method for quantitation—measuring only the three most abundant peptides—performed no better than the \"iBAQ\" quantitation method. Data analysis: MS1 data were analyzed using MaxQuant. MaxQuant version 1.2.2.5 software was used for protein identification and quantitation. Using the search engine Andromeda, mass spectrometry data were searched against the database containing UPS and E. coli proteins. MaxQuant reports summed intensity for each protein, as well as its iBAQ value. In the iBAQ algorithm, the intensities of the precursor peptides that map to each protein are summed together and divided by the number of theoretically observable peptides, which is considered to be all tryptic peptides between 6 and 30 amino acids in length. This operation converts a measure that is expected to be proportional to mass (intensity) into one that is proportional to molar amount (iBAQ). To determine relative molar abundances for each E. coli and human protein using MS1 data, two methods were used. In the first, we determined each proteins relative iBAQ (riBAQ), a normalized measure of molar abundance. We removed from the analysis all contaminant proteins that entered our sample-preparation workflow, for example keratins and trypsin, and then divided each remaining proteins iBAQ value by the sum of all non-contaminant iBAQ values. We also measured the average intensity of the three (or fewer, if fewer peptides were detected) peptides with the highest intensity, Top3, for each protein detected9. Peptides were chosen separately for each experiment, so the same peptides were not necessarily used across the four experiments. We generated a normalized \"top three\" abundance factor by dividing the average intensity for the three most abundant peptides of an individual protein and by the sum of all Top3 values in an experiment. Peptide intensities were summed for all charge states and variable modifications. For MS2 label-free analysis with MS2 spectra, MS2 data were searched against the database described above using SEQUEST. Setting to 1% the peptide false-discovery rate, estimated using a decoy (reversed) database, proteins were identified using the PAW pipeline. Normalized molar intensity (im) was calculated by dividing i, the summed intensity for an individual protein, by its molecular mass; the i/mr value for each protein was divided by the sum of all i/mr values. Normalized molar counts (cm) were calculated similarly, except the summed spectral counts for a protein (c) was used instead.

Dataset Information

MS1 Peptide Ion Intensity Chromatograms in MS2 (SWATH) Data Independent Acquisitions

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets