Evaluation of lipid quantification accuracy using HILIC and RPLC MS on the example of NIST® SRM® 1950 metabolites in human plasma

Abstract

Lipidomics analysis for large-scale studies aiming at the identification and quantification of natural lipidomes is often performed using LC–MS-based data acquisition. However, the choice of suitable LC–MS method for accurate lipid quantification remains a matter of debate. Here, we performed the systematic comparison between two HRAM-MS-based quantification workflows based on HILIC and RPLC MS by quantifying 191 lipids from five lipid classes in human blood plasma using deuterated standards in the “one ISTD-per-lipid class” approach. Lipid quantification was performed considering all necessary isotopic corrections, and obtained correction factors are illustrated. Concentrations of lipids in NIST® SRM® 1950 human blood plasma determined by the two methods were comparable for most of the studied lipid species except for highly unsaturated phosphatidylcholines (PC). A comparison of lipid concentrations to consensus values determined in a previously published multi-laboratory study illustrated possible “overestimation” of concentrations for these highly unsaturated lipids by HILIC MS. We evaluated the influence of lipid loading amounts as well as the difference between quantified lipid and internal standard concentrations on the HILIC MS quantification results. We conclude that both HILIC and RPLC HRAM-MS workflows can be equally used for accurate lysophosphatidylcholine (LPC), lysophosphatidylethanolamine (LPE), phosphatidylcholine (PC), phosphatidylethanolamine (PE), and sphingomyelin (SM) lipid quantification, despite significant differences in the concentration of highly unsaturated PC lipids which need to be addressed by establishing response factors to account for the differences in degree of lipid unsaturation.

Evaluating a targeted multiple reaction monitoring approach to global untargeted lipidomic analyses of human plasma

“…Others have recently reported direct comparisons of NIST human plasma standards on HILIC and RP on the same Q Exactive Plus mass spectrometer. Those studies provide further evidence that HILIC and RP separation can yield similar quantification for several lipid classes such as PE, LPE, and SM; however, overestimation of lipid concentrations for LPCs may occur with HILIC 41 . The RP method offers higher separation power than HILIC, and is the most commonly used method in untargeted lipidomics studies 26,42,43 .…”

“…Those studies provide further evidence that HILIC and RP separation can yield similar quantification for several lipid classes such as PE, LPE, and SM; however, overestimation of lipid concentrations for LPCs may occur with HILIC. 41 The RP method offers higher separation power than HILIC, and is the most commonly used method in untargeted lipidomics studies. 26,42,43 The separation observed using HILIC, having many species in a lipid class co-elute from the column, is particularly helpful for the analysis of phospholipids and sphingomyelins 37 and is better for ensuring that similar ionization and matrix effects occur when deuterated internal standards are used.…”

A combined flow injection/reversed phase chromatography – high resolution mass spectrometry workflow for accurate absolute lipid quantification with13C- internal standards

“…However, the international lipidomics interlaboratory comparison revealed rather broad distribution of measured concentration values for single lipid species52 (DG 34:1: 0-22 µmol L -1 , TG 48:3: 1-10 µmol L -1 , PE 36:2: 2-30 µmol L -1 , PC 36:2: 75-350 µmol L -1 ) making it difficult to validate the different calibration strategies based on these consensus values only. Only certified reference material would allow an actual accuracy assessment 44,53.…”

Plasma Lipidomics Reveals Insights into Anti-Obesity Effect of Chrysanthemum morifolium Ramat Leaves and Its Constituent Luteolin in High-Fat Diet-Induced Dyslipidemic Mice

“…The lipid profiles thus obtained from the ND, HFD, CLE, and LU groups were analyzed by multivariate statistical analysis to identify the extent of differences among the four groups. In the PCA score plot ( Figure 3 a), QC samples (NIST SRM1950 standard blood plasma, [ 40 ]) were tightly clustered, suggesting that the mass spectrometric analysis data has excellent stability and reproducibility. Performance of the constructed principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA) model was assessed by fitness ( R 2 ) and predictability ( Q 2 ), and the performance levels of the good models are higher than 50%.…”