Tandem Mass Tags for Metabolomics

Lead Research Organisation: Swansea University
Department Name: Institute of Life Science Medical School


Metabolomics can be thought of as the quantitative profiling of all small molecules (Mr <1000) in a biological system. Compared to proteomics, the extra challenge is the diversity of chemical structure and physical properties of metabolites (cf. peptides). To identify and quantify the widest range of metabolites from a biological system LC-MS is the optimum technology. However, LC-MS, like all analytical methods, is biased. The bias of LC-MS is towards acidic or basic molecules and against neutral species. Additionally, due to the wide structural variation of metabolites, absolute quantification must be performed on a compound by compound basis utilising isotope-labelled internal standards, and can only be achieved on 'one sample at a time' basis. Currently there are no multiplexed platforms for quantitative metabolomics. One way to meet the challenge of high-throughput quantitative metabolomics is by application of derivatisation chemistry. Derivatisation can be used to provide enhanced sensitivity, and by the incorporation of isotopic labels, quantitative information. In this proposal we plan to utilise the concept of tandem mass tags (TMT) with LC-MS/MS to provide absolute and relative quantification of groups of metabolites. TMT were first introduced to the field of proteomics by Proteome Sciences as isotope-coded amine-reactive tags which could be used for the absolute and relative quantification of peptides. In this proposal we seek to expand their application to metabolomics. We will initially concentrate our attention on a sub-field of metabolomics, i.e. lipidomics, where we will target specific groups of metabolites for derivatisation. For example, many endogenous steroids posses a carbonyl (oxo) group, which will readily react with a hydrazine reagent to form a hydrazone. Proteome Sciences have modified their panel of TMT reagents to contain a reactive hydrazine group e.g. C7H14N*CH2*CONHCH2CH2CONHNH2, to be applicable to oxo group derivatisation. Pairs of TMT reagents are used which differ by virtue of the location of the 13C (*C) label, but both add 239 Da to the derivatised molecule. In addition the tertiary amine group is easily protonated and enhances MS sensitivity. Any molecule containing an oxo group will be derivatised and pairs of differentially isotope-labelled samples can be mixed and analysed simultaneously by LC-MS/MS. The MS/MS spectra will contain information relating to the structure of the parent molecule and also give isotope-coded reporter ion(s). The relative abundance of differentially isotope-coded reported ions will reflect relative abundance in the two samples. This will then allows the relative quantification of all oxosteroids between samples. Quantification can be put on an absolute basis by using a third TMT reagent to label known amounts of synthetic standards which are then mixed with the original samples. This allows absolute quantification in the absence of expensive, or unavailable, isotope labelled authentic standards. Not only steroids are applicable to oxo group derivatisation but also fatty aldehydes and ketones. In addition to the TMT hydrazine tags, Proteome Sciences have primary amine-reactive tags suitable for derivatisation of phosphoethanolamines and phosphatidylserines, and TMT acid tags, where the terminal group is a carboxylic acid which can react with alcohol groups in a carbodiimide catalysed reaction, further increasing the scope for TMT tags in metabolomics. The key element of this project will be optimisation of derivatisation reactions to allow high yields with minimal side reactions for a range of metabolites with the most effective use of reagents. The developed platform will provide a powerful tool to quantitatively profile the metabolite content of biological fluids, tissues and cells. An immediate application of this technology will be in biomarker discovery related to neurodegenerative diseases. This multiplexed approach will greatly improve throughput.


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