GNPS - Metabolomics of Vibrio diabolicus grown in different culture media
Ontology highlight
ABSTRACT:
INSTRUMENT(S): Xevo G2-XS QTof
ORGANISM(S): Vibrio Diabolicus (ncbitaxon:50719)
SUBMITTER: 
Emilie Mishell Granda Moreira
PROVIDER: MSV000094810 | GNPS | Mon May 20 15:49:00 BST 2024
REPOSITORIES: GNPS
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Project description:Metabolomics of Vibrio diabolicus grown in different culture media. Data dependent and independent acquisition (DDA and DIA) was used in positive and negative ionization mode.
2024-05-20 | MSV000094810 | MassIVE
Project description:Full-scan, data-dependent acquisition (DDA), and data-independent acquisition (DIA) are the three common data acquisition modes in high resolution mass spectrometry-based untargeted metabolomics. It is an important yet underrated research topic on which acquisition mode is more suitable for a given untargeted metabolomics application. In this work, we compared the three data acquisition techniques using a standard mixture of 134 endogenous metabolites and a human urine sample. Both hydrophilic interaction and reversed-phase liquid chromatographic separation along with positive and negative ionization modes were tested. Both the standard mixture and urine samples generated consistent results. Full-scan mode is able to capture the largest number of metabolic features, followed by DIA and DDA (53.7% and 64.8% respective features fewer on average in urine than full-scan). Comparing the MS2 spectra in DIA and DDA, spectra quality is higher in DDA with average dot product score 83.1% higher than DIA in Urine(H), and the number of MS2 spectra (spectra quantity) is larger in DIA (on average 97.8% more than DDA in urine). Moreover, a comparison of relative standard deviation distribution between modes shows consistency in the quantitative precision, with the exception of DDA showing a minor disadvantage (on average 19.8% and 26.8% fewer features in urine with RSD < 5% than full-scan and DIA). In terms of data preprocessing convenience, full-scan and DDA data can be processed by well-established software. In contrast, several bioinformatic issues remain to be addressed in processing DIA data and the development of more effective computational programs is highly demanded.
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