Liquid–liquid extraction combined with differential isotope dimethylaminophenacyl labeling for improved metabolomic profiling of organic acids

• An improved method for profiling the carboxylic acid sub-metabolome is reported.
• Liquid–liquid extraction was used for separating the organic acids from the amines.
• 12C/13C-p-dimethylaminophenacyl (DmPA) labeling of the organic acids was carried out on the extract.
• Detection interference by amines and labeling efficiency reduction by water were reduced.
• About 2500 12C/13C-peak pairs or putative metabolites could be detected from 20 μL of human urine.

A large fraction of the known human metabolome belong to organic acids. However, comprehensive profiling of the organic acid sub-metabolome is a major analytical challenge. In this work, we report an improved method for detecting organic acid metabolites. This method is based on the use of liquid–liquid extraction (LLE) to selectively extract the organic acids, followed by using differential isotope p-dimethylaminophenacyl (DmPA) labeling of the acid metabolites. The 12C-/13C-labeled samples are analyzed by liquid chromatography Fourier-transform ion cyclotron resonance mass spectrometry (LC–FTICR–MS). It is shown that this LLE DmPA labeling method offers superior performance over the method of direct DmPA labeling of biofluids such as human urine. LLE of organic acids reduces the interference of amine-containing metabolites that may also react with DmPA. It can also remove water in a biofluid that can reduce the labeling efficiency. Using human urine as an example, it is demonstrated that about 2500 peak pairs or putative metabolites could be detected in a 30-min gradient LC–MS run, which is about 3 times more than that detected in a sample prepared using direct DmPA labeling. About 95% of the 1000 or so matched metabolites to the Human Metabolome Database (HMDB) are organic acids. It is further shown that this method can be used to handle as small as 10 μL of urine. We believe that this method opens the possibility of generating a very comprehensive profile of the organic acid sub-metabolome that will be useful for comparative metabolomics applications for biological studies and disease biomarker discovery.

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