Project description:Advancing Negative Ion Mode Proteomics. The main objective of the project is the exploration of the unconvetional negative ion mode for proteomics studies. In this work, we thoroughly studied the best chromatographic conditions for negative ion mode proteomics before testing different enzymatic digestion. The final goal is to establish the best working conditions in the negative polarity for negative ion mode. The method also refrains from any fragmentation events, which are unpredictable in negative ion mode.
Project description:Samples from mice infected and then treated with vehicle, carnitine or benznidazole in the chronic stage of infection. Tissue samples extracted with 50% methanol followed by 3:1 dichloromethane:methanol. C8 chromatography with negative mode data acquisition
Project description:Maldi imaging with NEDC matrix of a rat brain tissue section. Image was acquired with 50 um resolution. Ion mobility seperation enabled. Negative ion mode.
Project description:Cardiolipin from bovine heart authentic standard analysed in negative mode C18-ESI-HRMS with water and acetonitrile bot 0.05% ammonium hydroxide
Project description:Metabolism of the 4-hydroxyphenylpyruvate dioxygenase-inhibiting herbicide mesotrione in Amaranthus palmeri and Amaranthus tuberculatus populations (LCMS negative mode)
Project description:<p>Large-scale metabolite annotation is a challenge in liquid chromatogram-mass spectrometry (LC-MS)-based untargeted metabolomics. Here, we develop a metabolic reaction network (MRN)-based recursive algorithm (MetDNA) that expands metabolite annotations without the need for a comprehensive standard spectral library. MetDNA is based on the rationale that seed metabolites and their reaction-paired neighbors tend to share structural similarities resulting in similar MS2 spectra. MetDNA characterizes initial seed metabolites using a small library of MS2 spectra, and utilizes their experimental MS2 spectra as surrogate spectra to annotate their reaction-paired neighbor metabolites, which subsequently serve as the basis for recursive analysis. Using different LC-MS platforms, data acquisition methods, and biological samples, we showcase the utility and versatility of MetDNA and demonstrate that about 2000 metabolites can cumulatively be annotated from one experiment. Our results demonstrate that MetDNA substantially expands metabolite annotation, enabling quantitative assessment of metabolic pathways and facilitating integrative multi-omics analysis.</p><p><br></p><p><strong>Aging mouse liver positive mode</strong> is reported in <a href='https://www.ebi.ac.uk/metabolights/MTBLS601' rel='noopener noreferrer' target='_blank'><strong>MTBLS601</strong></a>.</p><p><strong>Aging mouse liver negative mode</strong> is reported in <a href='https://www.ebi.ac.uk/metabolights/MTBLS606' rel='noopener noreferrer' target='_blank'><strong>MTBLS606</strong></a>.</p><p><strong>Aging fruit fly positive mode</strong> is reported in <a href='https://www.ebi.ac.uk/metabolights/MTBLS612' rel='noopener noreferrer' target='_blank'><strong>MTBLS612</strong></a>.</p><p><strong>Aging fruit fly negative mode</strong> is reported in <a href='https://www.ebi.ac.uk/metabolights/MTBLS615' rel='noopener noreferrer' target='_blank'><strong>MTBLS615</strong></a>.</p>
Project description:<p>Large-scale metabolite annotation is a challenge in liquid chromatogram-mass spectrometry (LC-MS)-based untargeted metabolomics. Here, we develop a metabolic reaction network (MRN)-based recursive algorithm (MetDNA) that expands metabolite annotations without the need for a comprehensive standard spectral library. MetDNA is based on the rationale that seed metabolites and their reaction-paired neighbors tend to share structural similarities resulting in similar MS2 spectra. MetDNA characterizes initial seed metabolites using a small library of MS2 spectra, and utilizes their experimental MS2 spectra as surrogate spectra to annotate their reaction-paired neighbor metabolites, which subsequently serve as the basis for recursive analysis. Using different LC-MS platforms, data acquisition methods, and biological samples, we showcase the utility and versatility of MetDNA and demonstrate that about 2000 metabolites can cumulatively be annotated from one experiment. Our results demonstrate that MetDNA substantially expands metabolite annotation, enabling quantitative assessment of metabolic pathways and facilitating integrative multi-omics analysis.</p><p><br></p><p><strong>Aging mouse liver positive mode</strong> is reported in <a href='https://www.ebi.ac.uk/metabolights/MTBLS601' rel='noopener noreferrer' target='_blank'><strong>MTBLS601</strong></a>.</p><p><strong>Aging mouse liver negative mode</strong> is reported in <a href='https://www.ebi.ac.uk/metabolights/MTBLS606' rel='noopener noreferrer' target='_blank'><strong>MTBLS606</strong></a>.</p><p><strong>Aging fruit fly positive mode</strong> is reported in <a href='https://www.ebi.ac.uk/metabolights/MTBLS612' rel='noopener noreferrer' target='_blank'><strong>MTBLS612</strong></a>.</p><p><strong>Aging fruit fly negative mode</strong> is reported in <a href='https://www.ebi.ac.uk/metabolights/MTBLS615' rel='noopener noreferrer' target='_blank'><strong>MTBLS615</strong></a>.</p>