LICAR: An Application for Isotopic Correction of Targeted Lipidomic Data Acquired with Class-Based Chromatographic Separations Using Multiple Reaction Monitoring.

Abstract

Lipidomics is developing as an important area in biomedical and clinical research. Reliable quantification of lipid species is required for clinical translation of lipidomic studies. Hydrophilic interaction chromatography (HILIC), normal-phase liquid chromatography (NPLC), and supercritical fluid chromatography (SFC) are commonly used techniques in lipidomics and provide class-based separation of lipids. While co-elution of lipid species and their internal standards is an advantage for accurate quantification, it leads to isotopic overlap between species of the same lipid class. In shotgun lipidomics, isotopic correction is typically done based on elemental formulas of precursor ions. In multiple reaction monitoring (MRM) analyses, however, this approach should not be used, as the overall contribution of heavy isotopes to the MRM transitions' intensities depends on their location in the molecule with respect to the fragmentation pattern. We present an algorithm, provided in the R programming language, for isotopic correction in class-based separation using MRM, extracting relevant structural information from MRM transitions to apply adequate isotopic correction factors. Using standards, we show that our algorithm accurately estimates the isotopic contribution of isotopologues to MRM transitions' measured intensities. Using human plasma as an example, we demonstrate the necessity of adequate isotopic correction for accurate quantitation of lipids measured by MRM with class-based chromatographic separation. We show that over a third of the measured phosphatidylcholine species had their intensity corrected by more than 10%. This isotopic correction algorithm and R-implemented application enable a more accurate quantification of lipids in class-based separation-MRM, a prerequisite for successful translation of lipidomic applications.