LAUSR

Abstract Liquid chromatography-mass spectrometry (LC-MS) is, due to its high sensitivity and selectivity, currently the method of choice in (bio)analytical studies involving the (comprehensive) profiling of metabolites in body fluids. However, as closely related isomers are often hard to distinguish on the basis of LC-MS(MS) and identification is often dependent on the availability of reference standards, the identification of the chemical structures of detected mass spectral features remains the primary limitation. Infrared ion spectroscopy (IRIS) aids identification of MS-detected ions by providing an infrared (IR) spectrum containing structural information for a detected MS-feature. Moreover, IR spectra can be routinely and reliably predicted for many types of molecular structures using quantum-chemical calculations, potentially avoiding the need for reference standards. In this work, we demonstrate a workflow for reference-free metabolite identification that combines experiments based on high-pressure liquid chromatography (HPLC), MS and IRIS with quantum-chemical calculations that efficiently generate IR spectra and give the potential to enable reference-standard free metabolite identification. Additionally, a scoring procedure is employed which shows the potential for automated structure assignment of unknowns. Via a simple, illustrative example where we identify lysine in the plasma of a hyperlysinemia patient, we show that this approach allows the efficient assignment of a database-derived molecular structure to an unknown.