Dataset Information

MetaboKit: a comprehensive data extraction tool for untargeted metabolomics (Standard mixture assay)

ABSTRACT:

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.

Standard mixture assay is reported in the current study MTBLS1311.

Mouse liver assays are reported in MTBLS1266.

INSTRUMENT(S): Liquid Chromatography MS - positive - hilic

SUBMITTER: guoshou teo

PROVIDER: MTBLS1311 | MetaboLights | 2021-11-05

REPOSITORIES: MetaboLights

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Publications

MetaboKit: a comprehensive data extraction tool for untargeted metabolomics.

Narayanaswamy Pradeep P Teo Guoshou G Ow Jin Rong JR Lau Adam A Kaldis Philipp P Tate Stephen S Choi Hyungwon H

Molecular omics 20201001 5

PMID: 32519713

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Project description: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. Mouse liver assays are reported in the current study <a href='https://www.ebi.ac.uk/metabolights/MTBLS1266' rel='noopener noreferrer' target='_blank'>MTBLS1266</a>.Standard mixture assay is reported in <a href='https://www.ebi.ac.uk/metabolights/MTBLS1311' rel='noopener noreferrer' target='_blank'>MTBLS1311</a>.

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. 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: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.

Dataset Information

MetaboKit: a comprehensive data extraction tool for untargeted metabolomics (Standard mixture assay)

Publications

MetaboKit: a comprehensive data extraction tool for untargeted metabolomics.

Similar Datasets

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