SWATH Tandem Mass Spectrometry Workflow for Quantification of Mass Isotopologue Distribution of Intracellular Metabolites and Fragments Labeled with Isotopic ^sup 13^C Carbon

Accurate quantification of mass isotopologue distribution (MID) of metabolites is a prerequisite for 13C-metabolic flux analysis. Currently used mass spectrometric (MS) techniques based on multiple reaction monitoring (MRM) place limitations on the number of MIDs that can be analyzed in a single run...

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Bibliographic Details
Published in:Analytical chemistry (Washington) 2018-06, Vol.90 (11), p.6486
Main Authors: Jaiswal, Damini, Prasannan, Charulata B, Hendry, John I, Wangikar, Pramod P
Format: Article
Language:English
Subjects:
Carbon 13
Chemistry
Cyanobacteria
Data acquisition systems
Fluxes
Fragmentation
Fragments
Mass spectrometry
Metabolites
Precursors
Random errors
Workflow
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Summary:Accurate quantification of mass isotopologue distribution (MID) of metabolites is a prerequisite for 13C-metabolic flux analysis. Currently used mass spectrometric (MS) techniques based on multiple reaction monitoring (MRM) place limitations on the number of MIDs that can be analyzed in a single run. Moreover, the deconvolution step results in amplification of error. Here, we demonstrate that SWATH MS/MS, a data independent acquisition (DIA) technique allows quantification of a large number of precursor and product MIDs in a single run. SWATH sequentially fragments all precursor ions in stacked mass isolation windows. Co-fragmentation of all precursor isotopologues in a single SWATH window yields higher sensitivity enabling quantification of MIDs of fragments with low abundance and lower systematic and random errors. We quantify the MIDs of 53 precursor and product ions corresponding to 19 intracellular metabolites from a dynamic 13C-labeling of a model cyanobacterium, Synechococcus sp. PCC 7002. The use of product MIDs resulted in an improved precision of many measured fluxes compared to when only precursor MIDs were used for flux analysis. The approach is truly untargeted and allows additional metabolites to be quantified from the same data.
ISSN:0003-2700
1520-6882