five

Vernix OzID data

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Research Data Australia2024-12-21 收录
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https://researchdata.edu.au/vernix-ozid/1424456
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This data set accompanies the manuscript "Combining Charge-Switch Derivatisation with Ozone-Induced Dissociation for Facile Fatty Acid Analysis" by Berwyck L.J. Poad, David L. Marshall,Eva Harazim, Rajesh Gupta,Venkateswarra R. Narreddula, Reuben S. E. Young, Todd W. Mitchell, Eva Duchoslav, J. Larry Campbell, James A. Broadbent, Josef Cvačka and Stephen J. Blanksby (In Press). Abstract: The specific positions of carbon-carbon double bond(s) within an unsaturated fatty acid exert a significant effect on the physical and chemical properties of the lipid that ultimately inform its biological function(s). Contemporary liquid-chromatography mass spectrometry (MS) strategies based on electrospray ionisation coupled to tandem MS can easily detect fatty acyl lipids but generally cannot reveal those specific site(s) of unsaturation. Herein, we describe a novel and versatile workflow whereby fatty acids are first converted to fixed charge N-(4-aminomethylphenyl) pyridinium (AMPP) derivatives and subsequently subjected to ozone-induced dissociation (OzID) on a modified triple quadrupole mass spectrometer. The AMPP modification enhances the detection of fatty acids introduced by direct infusion. Fragmentation of the derivatised fatty acids also provides diagnostic fragment ions upon collision-induced dissociation that can be targeted in precursor ion scans to subsequently trigger OzID analyses in an automated data-dependent workflow. It is these OzID analyses that provide unambiguous assignment of carbon-carbon double bond locations in the AMPP-derivatized fatty acids. The performance of this analysis pipeline is assessed in profiling the patterns of unsaturation in fatty acids within the complex biological secretion vernix caseosa. This analysis uncovers significant isomeric diversity within the fatty acid pool of this sample, including a number of hitherto unreported double bond-positional isomers that hint at the activity of potentially new metabolic pathways. Data file types consist of SCIEX Analyst (.wiff) files, and Python (.py) files.
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Queensland University of Technology
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