Regions of Interest Multivariate Curve Resolution Liquid Chromatography with Data-Independent Acquisition Tandem Mass Spectrometry

Abstract

New data-independent acquisition (DIA) modes coupled to chromatographic separations are opening new perspectives in the processing of massive mass spectrometric (MS) data using chemometric methods. In this work, the application of the regions of interest multivariate curve resolution (ROIMCR) method is shown for the simultaneous analysis of MS1 and MS2 DIA raw data obtained by liquid chromatography coupled to quadrupole-timeof-flight MS analysis. The ROIMCR method proposed in this work relies on the intrinsic bilinear structure of the MS1 and MS2 experimental data which allows us for the fast direct resolution of the elution and spectral profiles of all sample constituents giving measurable MS signals, without needing any further data pretreatment such as peak matching, alignment, or modeling. Compound annotation and identification can be achieved directly by the comparison of the ROIMCR-resolved MS1 and MS2 spectra with those from standards or from mass spectral libraries. ROIMCR elution profiles of the resolved components can be used to build calibration curves for the prediction of their concentrations in complex unknown samples. The application of the proposed procedure is shown for the analysis of mixtures of per-and polyfluoroalkyl substances in standard mixtures, spiked hen eggs, and gull egg samples, where these compounds tend to accumulate.

Linking MS1 and MS2 signals in positive and negative modes of LC-HRMS in untargeted metabolomics using the ROIMCR approach

“…Thus, the proposed ROIMCR allows the direct linking and correspondence between the MS signals in the four acquisition modes (MS1( +)/MS2( +)/MS1(-)/MS2(-)), for the same chemical species (see Tables 2 and S5 ), when performing their simultaneous analysis. Linking the spectra of the different amino acids in their four acquisition modes facilitates their coverage, identification, and possible quantitation [ 20 ]. Table S5 (in Supplementary Information) describes the list of MS2 fragment ions obtained in the ROIMCR analysis, confirmed by the literature database, that facilitated the annotation and identification of the amino acids in the standard sample.…”

“…MCR can be performed separately for each chromatographic run using one acquisition and ionization mode or simultaneously using multiple chromatographic runs and acquisition, as described by Pérez-López et al [ 20 ], and ionization positive and negative modes, as shown in the following Eqs. 2 , 3 , 4 , and 5 .…”

“…The steps of the MCR-ALS analysis of the different MSROI data matrices are also given in the Supporting Information and have been described in previous papers [ 17 , 18 , 20 , 22 , 30 ].…”

“…Different computer workflows have been proposed for extracting the significant signal features and their chemical assignation in LC-HRMS datasets [ 12 – 16 ]. The regions of interest multivariate curve resolution (ROIMCR) chemometrics approach is based on the bilinear model intrinsic data structure of the LC–MS data [ 7 , 17 – 20 ] and represents an effective strategy for compressing and resolving data without losing relevant information and maintaining instrumental mass accuracy. Firstly, to reduce the dimensionality of the data matrices, the “regions of interest” (ROI) approach considers some parameters, such as signal intensity threshold, mass error tolerance, and a minimum number of detected occurrences [ 17 , 21 ].…”

“…Within the untargeted metabolomics approach, our research group has recently shown for the first time the possible simultaneous analysis of MS1 and MS2 DIA raw data obtained by LC/Q-TOF by the ROIMCR approach for the identification of per- and poly-fluoroalkyl substances [ 20 ]. This new approach allows an improved linking of MS1 and MS2 DIA spectra of the resolved components by ROIMCR, i.e., linking the MS1 signals of the precursor ions with the MS2 signals of their fragment ions for every chemical compound of the analyzed sample.…”