Dataset Information

MetaboKit: a comprehensive data extraction tool for untargeted metabolomics (Mouse liver assays)

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.

Mouse liver assays are reported in the current study MTBLS1266.

Standard mixture assay is reported in MTBLS1311.

INSTRUMENT(S): Liquid Chromatography MS - positive, Liquid Chromatography MS - negative

SUBMITTER: guoshou teo

PROVIDER: MTBLS1266 | MetaboLights | 2021-11-02

REPOSITORIES: MetaboLights

ACCESS DATA

Dataset's files

Source:

			Action	DRS
	MTBLS1266	Other
	FILES	Other
	a_MTBLS1266_LC-MS_negative_metabolite_profiling.txt	Txt
	a_MTBLS1266_LC-MS_positive_metabolite_profiling.txt	Txt
	i_Investigation.txt	Txt

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