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Analysis of isotopic labeling in peptide fragments by tandem mass spectrometry

Allen, Doug K., Evans, Bradley S., Libourel, Igor G.L.
Plos One 2014 v.9 no.3 pp. 1-11
Glycine max, amino acids, biomass, biosynthesis, byproducts, carbon, dissociation, embryo (plant), energy, glucose, isotope labeling, mass spectrometry, peptides, phenotype, proteins, quantitative analysis, soybeans, spectrometers, stable isotopes
The cellular phenotype is the consequence of dynamic metabolic events that occur in a spacially dependent fashion. This spatial and temporal complexity presents challenges for investigating primary metabolism and improved methods to probe biochemical events such as amino acid biosynthesis may be needed to address these questions effectively. Isotopic labeling can provide insights to cellular phenomena and the recording of enriched amino acids due to metabolic events that are specific to location and time are recorded in the protein pool. Therefore proteins are an important readout for metabolism that can be assessed with modern day mass spectrometers. We examined the naturally abundant levels of isotopes in MS2 spectra that were obtained from tandem mass spectrometry under higher energy collision dissociation (HCD) and collision induced dissociation (CID) fragmentation, and compared the effect of energy levels on the fragmentation products. Developing soybean embryos that served as a source of biological material were cultured with [U-13C6]-glucose and proteins from unlabeled and 13C enriched biomass were used to assess spectrometer performance. Incomplete CID fragmentation resulted in MS1 spectra with a disproportionate amount of remaining heavier isotopes. HCD and CID-based fragmentation resulted in MS2 peptides that could be quantified precisely, but lower abundances gave more variable results and a deviation from simulated distributions was evident under all conditions. Although MS2 methods have the potential to provide information on the labeling of amino acids from peptides that are central to metabolism, their application to highly sensitive quantitative methods such as metabolic flux analysis may not be ready.