Identification of metabolites from liquid chromatography-coulometric array detection profiling: gas chromatography-mass spectrometry and refractionation provide essential information orthogonal to LC-MS/microNMR.

Liquid chromatography-coulometric array detection (LC-EC) is a sensitive, quantitative, and robust metabolomics profiling tool that complements the commonly used mass spectrometry (MS) and nuclear magnetic resonance (NMR)-based approaches. However, LC-EC provides little structural information. We recently demonstrated a workflow for the structural characterization of metabolites detected by LC-EC profiling combined with LC-electrospray ionization (ESI)-MS and microNMR. This methodology is now extended to include (i) gas chromatography (GC)-electron ionization (EI)-MS analysis to fill structural gaps left by LC-ESI-MS and NMR and (ii) secondary fractionation of LC-collected fractions containing multiple coeluting analytes. GC-EI-MS spectra have more informative fragment ions that are reproducible for database searches. Secondary fractionation provides enhanced metabolite characterization by reducing spectral overlap in NMR and ion suppression in LC-ESI-MS. The need for these additional methods in the analysis of the broad chemical classes and concentration ranges found in plasma is illustrated with discussion of four specific examples: (i) characterization of compounds for which one or more of the detectors is insensitive (e.g., positional isomers in LC-MS, the direct detection of carboxylic groups and sulfonic groups in (1)H NMR, or nonvolatile species in GC-MS), (ii) detection of labile compounds, (iii) resolution of closely eluting and/or coeluting compounds, and (iv) the capability to harness structural similarities common in many biologically related, LC-EC-detectable compounds.

Structural characterization of plasma metabolites detected via LC-electrochemical coulometric array using LC-UV fractionation, MS, and NMR.

Liquid chromatography (LC) separation combined with electrochemical coulometric array detection (EC) is a sensitive, reproducible, and robust technique that can detect hundreds of redox-active metabolites down to the level of femtograms on column, making it ideal for metabolomics profiling. EC detection cannot, however, structurally characterize unknown metabolites that comprise these profiles. Several aspects of LC-EC methods prevent a direct transfer to other structurally informative analytical methods, such as LC-MS and NMR. These include system limits of detection, buffer requirements, and detection mechanisms. To address these limitations, we developed a workflow based on the concentration of plasma, metabolite extraction, and offline LC-UV fractionation. Pooled human plasma was used to provide sufficient material necessary for multiple sample concentrations and platform analyses. Offline parallel LC-EC and LC-MS methods were established that correlated standard metabolites between the LC-EC profiling method and the mass spectrometer. Peak retention times (RT) from the LC-MS and LC-EC system were linearly related (r(2) = 0.99); thus, LC-MS RTs could be directly predicted from the LC-EC signals. Subsequent offline microcoil-NMR analysis of these collected fractions was used to confirm LC-MS characterizations by providing complementary, structural data. This work provides a validated workflow that is transferrable across multiple platforms and provides the unambiguous structural identifications necessary to move primary mathematically driven LC-EC biomarker discovery into biological and clinical utility.