TMT data from ovarian cancer cells
Ontology highlight
ABSTRACT:
INSTRUMENT(S): Orbitrap Eclipse
ORGANISM(S): Homo Sapiens (human)
TISSUE(S): Cell Culture
DISEASE(S): Malignant Neoplasm Of Ovary
SUBMITTER: 
Cristina Chiva
LAB HEAD: Eduard Sabidó
PROVIDER: PXD041002 | Pride | 2023-05-04
REPOSITORIES: pride
Dataset's files
Items per page: 5 1 - 5 of 39 |
Similar Datasets
Project description:In the young field of single-cell proteomics (scMS), there is a great need for improved global proteome characterization, both in terms of proteins quantified per cell and quantitative performance thereof. The recently introduced real-time search (RTS) on the Orbitrap Eclipse Tribrid mass spectrometer in combination with SPS-MS3 acquisition has been shown to be beneficial for the measurement of samples that are multiplexed using isobaric tags. Multiplexed single-cell proteomics requires high ion injection times and high-resolution spectra to quantify the single-cell signal, however the carrier channel facilitates peptide identification and thus offers the opportunity for fast on-the-fly precursor filtering before committing to the time intensive quantification scan. Here, we compared classical MS2 acquisition against RTS-SPS-MS3, both using the Orbitrap Eclipse Tribrid MS with the FAIMS Pro ion mobility interface and we present a new acquisition strategy termed RETICLE (RTS Enhanced Quant of Single Cell Spectra) that makes use of fast real-time searched linear ion trap scans to preselect MS1 peptide precursors for quantitative MS2 Orbitrap acquisition. Here we show that classical MS2 acquisition is outperformed by both RTS-SPS-MS3 through increased quantitative accuracy at similar proteome coverage, and RETICLE through higher proteome coverage, with the latter enabling the quantification of over 1000 proteins per cell at a MS2 injection time of 750ms using a 2h gradient.
2022-03-14 | PXD029320 | Pride
Project description:Stable isotope labelling of peptides using isobaric reagents such as iTRAQ and TMT enables the multiplexed analysis of proteomes with deep quantitative coverage. Isobaric tagging demonstrates high precision but imperfect accuracy due to ratio underestimation caused by co-fragmentation of ions with mass-to-charge ratios within the isolation window of the targeted precursors. Prompted by empirical observations of isobaric-labelled peptide MS2 spectra, we argue that although a very narrow isolation window will result in severe loss of backbone fragment ions, rendering the spectra unsuitable for peptide identification, the reporter ion signals will remain intense enough to generate quantitative information for a significant portion of the spectra. Based on this assumption we have designed a Dual Isolation Width Acquisition (DIWA) method, in which each precursor is first fragmented with HCD using a standard isolation width for peptide identification and preliminary quantification followed by a concomitant MS2 HCD fragmentation using a much narrower isolation width for the acquisition of quantification-only spectra with reduced interference. We leverage the quantitative values obtained by the “narrow” scans to build linear regression models and apply these to decompress the fold-changes measured at the “standard” scans. Here, we evaluate the DIWA method using a two species TMT-6plex model and discuss the potential of this approach.
2019-01-07 | PXD010571 | Pride
Project description:We propose the isotopically labeled Ac-IP (acetyl-isoleucine-proline) tag for multiplexed quantification of peptides at the MS1 level in the data-independent acquisition (DIA) mode. Differentially labeled peptides have distinct precursor ions carrying the quantitative information but identical MS2 spectra. The isotopically labeled Ac-Ile part leaves as a neutral loss upon collision-induced dissociation (CID) while fragmentation of the peptide backbone generates information for peptide identification.
2021-06-07 | PXD022606 | Pride
Project description:Over the past few decades cross-linking mass spectrometry (XLMS) has become a powerful tool for identification of protein-protein interactions and for gaining insight into the structures of proteins in living cells, tissues, and organelles. The development of new crosslinkers, enrichment strategies and data acquisition methods led to the establishment of numerous new software tools specifically for the analysis and interpretation of cross-linking data. We previously published one of these tools called MS Annika, a cross-linking search engine which can accurately identify cross-linked peptides in MS2 spectra from a variety of different MS-cleavable crosslinkers. In this publication we present an updated MS Annika and a new search algorithm that additionally supports processing of data from MS2-MS3-based approaches and identification of peptides from MS3 spectra. In the new MS2-MS3 search algorithm, MS3 spectra are matched to their corresponding precursor doublet peak in the MS2 scan to identify the crosslink modification and the monoisotopic peptide mass. This information is then used to adjust the MS3 spectra for search with MS Amanda, our in-house developed peptide search engine, to identify the cross-linked peptides. Peptides that are identified in the MS2 scan and one or more of the associated product MS3 scans are re-scored with a novel scoring function to reflect the increased confidence. Finally, the detected cross-links are validated by estimating the false discovery rate (FDR) using a target-decoy approach. We evaluated the MS3-search-capabilities of MS Annika on five different datasets covering a variety of experimental approaches and compared it to XlinkX and MaXLinker, two other cross-linking search engines that support MS3 crosslink identification. Three of the datasets were benchmark datasets of synthetic peptides that allow calculation of an experimentally validated FDR, and we show that MS Annika detects up to 4 times more true unique crosslinks than MaXLinker and up to 35% more than XlinkX while simultaneously yielding less false positive hits and therefore a more accurate FDR than the other two search engines. Additionally, for the other two datasets we could show that MS Annika finds between 74% to 2.5 times more crosslinks at 1% estimated FDR and reveals protein-protein interactions that are not detected by either XlinkX or MaXLinker.
2023-08-13 | PXD041955 | Pride
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.
2018-03-26 | MTBLS417 | MetaboLights
Project description:Cross-linking mass spectrometry is a powerful method for the investigation of protein-protein interactions from highly complex samples. XL-MS combined with tandem mass tag labeling holds the promise of large-scale PPI quantification. However, a robust and efficient TMT-based XL-MS quantification method has not yet been established due to the lack of a benchmarking dataset and thorough evaluation of various MS parameters. To tackle these limitations, we generate a two-interactome dataset by spiking-in TMT-labeled cross-linked E. coli lysate into TMT-labeled cross-linked HEK293T lysate using a defined mixing scheme. Using this benchmarking dataset, we assess the efficacy of cross-link identification and accuracy of cross-link quantification using different MS acquisition strategies. For identification, we compare various MS2- and MS3-based XL-MS methods, and optimize stepped HCD energies for TMT-labeled cross-links. We observed a need for notably higher fragmentation energies compared to unlabeled cross-links. For quantification, we assess the quantification accuracy and dispersion of MS2-, MS3- and synchronous precursor selection-MS3-based methods. We show that a stepped HCD-MS2 method with stepped collision energies 36-42-48 provides a vast number of quantifiable cross-links with high quantification accuracy. This widely applicable method paves the way for multiplexed quantitative PPI characterization from complex biological systems.
2022-04-07 | PXD031114 | Pride
Project description:<p>We have developed MetaboKit, a comprehensive software package for compound identification and relative quantification in mass spectrometry-based untargeted metabolomics analysis. In data dependent acquisition (DDA) analysis, MetaboKit constructs a customized spectral library with compound identities from reference spectral libraries, adducts, dimers, in-source fragments (ISF), MS/MS fragmentation spectra, and more importantly the retention time information unique to the chromatography system used in the experiment. Using the customized library, the software performs targeted peak integration for precursor ions in DDA analysis and for precursor and product ions in data independent acquisition (DIA) analysis. With its stringent identification algorithm requiring matches by both MS and MS/MS data, MetaboKit provides identification results with significantly greater specificity than the competing software packages without loss in sensitivity. The proposed MS/MS-based screening of ISFs also reduces the chance of unverifiable identification of ISFs considerably. MetaboKit's quantification module produced peak area values highly correlated with known concentrations in a DIA analysis of the metabolite standards at both MS1 and MS2 levels. Moreover, the analysis of Cdk1Liv-/- mouse livers showed that MetaboKit can identify a wide range of lipid species and their ISFs, and quantitatively reconstitute the well-characterized fatty liver phenotype in these mice. In DIA data, the MS1-level and MS2-level peak area data produced similar fold change estimates in the differential abundance analysis, and the MS2-level peak area data allowed for quantitative comparisons in compounds whose precursor ion chromatogram was too noisy for peak integration.</p><p><br></p><p><strong>Mouse liver assays</strong> are reported in the current study <a href='https://www.ebi.ac.uk/metabolights/MTBLS1266' rel='noopener noreferrer' target='_blank'><strong>MTBLS1266</strong></a>.</p><p><strong>Standard mixture assay</strong> is reported in <a href='https://www.ebi.ac.uk/metabolights/MTBLS1311' rel='noopener noreferrer' target='_blank'><strong>MTBLS1311</strong></a>.</p>
2021-11-02 | MTBLS1266 | MetaboLights
Project description:<p>We have developed MetaboKit, a comprehensive software package for compound identification and relative quantification in mass spectrometry-based untargeted metabolomics analysis. In data dependent acquisition (DDA) analysis, MetaboKit constructs a customized spectral library with compound identities from reference spectral libraries, adducts, dimers, in-source fragments (ISF), MS/MS fragmentation spectra, and more importantly the retention time information unique to the chromatography system used in the experiment. Using the customized library, the software performs targeted peak integration for precursor ions in DDA analysis and for precursor and product ions in data independent acquisition (DIA) analysis. With its stringent identification algorithm requiring matches by both MS and MS/MS data, MetaboKit provides identification results with significantly greater specificity than the competing software packages without loss in sensitivity. The proposed MS/MS-based screening of ISFs also reduces the chance of unverifiable identification of ISFs considerably. MetaboKit's quantification module produced peak area values highly correlated with known concentrations in a DIA analysis of the metabolite standards at both MS1 and MS2 levels. Moreover, the analysis of Cdk1Liv-/- mouse livers showed that MetaboKit can identify a wide range of lipid species and their ISFs, and quantitatively reconstitute the well-characterized fatty liver phenotype in these mice. In DIA data, the MS1-level and MS2-level peak area data produced similar fold change estimates in the differential abundance analysis, and the MS2-level peak area data allowed for quantitative comparisons in compounds whose precursor ion chromatogram was too noisy for peak integration.</p><p><br></p><p><strong>Standard mixture assay</strong> is reported in the current study <a href='https://www.ebi.ac.uk/metabolights/MTBLS1311' rel='noopener noreferrer' target='_blank'><strong>MTBLS1311</strong></a>.</p><p><strong>Mouse liver assays</strong> are reported in <a href='https://www.ebi.ac.uk/metabolights/MTBLS1266' rel='noopener noreferrer' target='_blank'><strong>MTBLS1266</strong></a>.</p>
2021-11-05 | MTBLS1311 | MetaboLights
Project description:Multiplexed proteomics has become a powerful tool for investigating biological systems. Using balancer-peptide conjugates (e.g., TMTproC complementary ions) in the MS2 spectra for quantification circumvents the ratio distortion problem inherent in multiplexed proteomics. However, TMTproC quantification scans require long Orbitrap transients and extended ion injection times to achieve sufficient ion statistics and spectral resolution. Real-Time Search (RTS) algorithms have demonstrated increased speed and sensitivity by selectively informing precursor peak quantification. Here, we combine complementary ion quantification with Real-Time Search (TMTproC-RTS) to enhance sensitivity while maintaining accuracy and precision in quantitative proteomics at the MS2 level. We demonstrate the utility of this method by quantifying protein dynamics during the embryonic development of Drosophila melanogaster (fly), Ciona robusta (sea squirt), and Xenopus laevis (frog). We quantify 7.6k, 8.6k, and 12.7k proteins in each organism, which is an improvement of 12%, 13%, and 14%, respectively, compared to naive TMTproC analysis. For all three organisms, the newly acquired data outperform previously published datasets and provides a diverse, deep, and accurate database of protein dynamics during embryogenesis which will advance the study of evolutionary comparison in early embryogenesis.
2025-01-17 | PXD055647 | Pride
Project description:<p>Chimeric MS/MS spectra contain fragments from multiple precursor ions and therefore hinder compound identification in metabolomics. Historically, deconvolution of these chimeric spectra has been challenging and relied upon specific experimental methods that introduce variation in the ratios of precursor ions between multiple MS/MS scans. DecoID introduces a complementary, method-independent approach where database spectra are computationally mixed to match an experimentally acquired spectrum using LASSO regression. When applied to human plasma, DecoID increased the number of identified metabolites by over 30%.</p>
2021-04-29 | MTBLS2207 | MetaboLights