Regions of interest three-way multivariate curve resolution analysis of lipid LC-IMS-MS/MS data

Liquid chromatography-ion mobility-tandem mass spectrometry (LC-IMS-MS/MS) enables multidimensional characterization of complex lipid mixtures. However, the resulting high-dimensional datasets pose significant challenges for reliable component resolution and interpretation. In this work, the Regions-Of-Interest-based Multivariate Curve Resolution strategy (ROIMCR) is proposed for the integrated analysis of LC-IMS-MS/MS data. It explicitly preserves ion mobility information in its native profile form rather than reducing it to a single collision cross section (CCS) value. The proposed approach simultaneously resolves chromatographic elution profiles, ion mobility profiles, and mass spectra profiles (MS1 and MS2). Bilinear or trilinear models are used to enforce chemically consistent solutions across the elution time, m/z, and ion mobility dimensions, yielding interpretable component profiles while maintaining satisfactory model performance.

This study has proven the applicability and chemical interpretability of the trilinear model in resolving complex lipid mixtures using LC-IMS-MS/MS three-way data. Compound annotation and identification were achieved through direct comparison of the ROIMCR-resolved retention time, arrival time, and tandem mass spectra with those of authentic standards or reference data libraries. The applicability of the method was demonstrated using two data examples. The first one is used to validate the proposed approach using a simulated mixture of four analytes. The second example shows the application of the proposed method in the LC-IMS-MS/MS experimental analysis of a complex mixture of lipids. Overall, the proposed three-way MCR framework provides an accurate, interpretable, and scalable solution for high-dimensional LC-IMS-MS/MS data analysis.