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Matrix Effect on Chemical Isotope Labeling and Its Implication in Metabolomic Sample Preparation for Quantitative Metabolomics

ABSTRACT: Matrix effect from various constituents in biological samples can reduce the accuracy of quantitative metabolomics. Differential chemical isotope labeling liquid chromatography mass spectrometry (CIL LC-MS) can overcome the matrix effect on MS detection based on measuring the intensity ratios of metabolite peak pairs detected in a mixture of a light-isotope labeled sample and a heavy-isotope labeled reference sample. However, the chemical labeling process itself may encounter matrix effect which can influence the overall quantitative results. In this work, we report the effects of salts and buffers commonly present in metabolomic samples on dansylation labeling. It is shown that high concentrations of NaCl and phosphate buffer (>50 mM) or PBS can reduce or enhance the labeling efficiencies of metabolites. By maintaining similar matrix contents in an individual sample vs. a reference sample, relative quantification of metabolites can be performed without compromising the metabolomic profiling results. For samples containing varying amounts of high salts such as urine, we demonstrate that the matrix effect can be largely overcome by diluting the original sample before dansylation labeling (e.g., 4-fold dilution for urine).

INSTRUMENT(S): apex-ultra hybrid Qq-FTMS (Bruker)

SUBMITTER: Wei Han

PROVIDER: MTBLS194 | MetaboLights | 2015-07-01

REPOSITORIES: MetaboLights

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Project description:Untargeted approaches and thus biological interpretation of metabolomics results are still hampered by the reliable assignment of the global metabolome as well as classification and (putative) identification of metabolites. In this work we present an liquid chromatography-mass spectrometry (LC-MS)-based stable isotope assisted approach that combines global metabolome and tracer based isotope labeling for improved characterization of (unknown) metabolites and their classification into tracer derived submetabolomes. To this end, wheat plants were cultivated in a customized growth chamber, which was kept at 400 ± 50 ppm 13CO2 to produce highly enriched uniformly 13C-labeled sample material. Additionally, native plants were grown in the greenhouse and treated with either 13C9-labeled phenylalanine (Phe) or 13C11-labeled tryptophan (Trp) to study their metabolism and biochemical pathways. After sample preparation, liquid chromatography-high resolution mass spectrometry (LC-HRMS) analysis and automated data evaluation, the results of the global metabolome- and tracer-labeling approaches were combined. A total of 1,729 plant metabolites were detected out of which 122 respective 58 metabolites account for the Phe- and Trp-derived submetabolomes. Besides m/z and retention time, also the total number of carbon atoms as well as those of the incorporated tracer moieties were obtained for the detected metabolite ions. With this information at hand characterization of unknown compounds was improved as the additional knowledge from the tracer approaches considerably reduced the number of plausible sum formulas and structures of the detected metabolites. Finally, the number of putative structure formulas was further reduced by isotope-assisted annotation tandem mass spectrometry (MS/MS) derived product ion spectra of the detected metabolites. A major innovation of this paper is the classification of the metabolites into submetabolomes which turned out to be valuable information for effective filtering of database hits based on characteristic structural subparts. This allows the generation of a final list of true plant metabolites, which can be characterized at different levels of specificity.

			Action	DRS
	MTBLS194	Other
	FILES	Other
	a_MTBLS194_cil_matrix_effect_metabolite_profiling_mass_spectrometry.txt	Txt
	i_Investigation.txt	Txt
	s_MTBLS194.txt	Txt

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Matrix Effect on Chemical Isotope Labeling and Its Implication in Metabolomic Sample Preparation for Quantitative Metabolomics

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