Validation of the Regions of Interest Multivariate Curve Resolution (ROIMCR) procedure for untargeted LC-MS lipidomic analysis

Abstract

Untargeted liquid chromatography coupled to mass spectrometry (LC-MS) analysis generates massive amounts of information-rich mass data which presents storage and processing challenges. In this work, the validation of a recently proposed procedure for LC-MS data compression and processing is presented, using as example the analysis of lipid mixtures. This method consists of a preliminary selection of the Regions of Interest of the LC-MS data (MSROI) coupled to their throughout chemometric analysis by the Multivariate Curve Resolution Alternating Least Squares method (MCR-ALS). The proposed data selection procedure is based on the search of the most significant mass traces regions with high mass densities. This allows for a drastic reduction of the MS data size and of the computer storage requirements, without any significant loss neither of spectral resolution nor of accuracy on m/z measures. The combination of the MSROI data compression and MCR-ALS data analysis procedures in the new ROIMCR procedure has the main advantage of not requiring neither the chromatographic peak alignment nor the chromatographic peak shape modelling used in many other procedures as a pre-treatment step of the data analysis. The proposed ROIMCR procedure is tested in the analysis of the LC-MS experimental data coming from different lipid mixtures and of a melanoma cell line culture sample with satisfactory results. The proposed strategy is shown to be a general, fast, reliable and easy to use method for general untargeted LC-MS metabolic and lipidomic data analysis type of studies.

Application of the area correlation constraint in the MCR-ALS quantitative analysis of complex mixture samples

“…Secondly, different volumes of the synthetic lipid mixtures were spiked to cell culture samples [40] at the concentrations of 2.5,5,10 and 2 mg L -1 , with three replicates at each concentration level. In these cell culture samples, a large number of unknown cell culture sample constituents can be present and they are possible interferences in the determination of the lipids.…”

“…Acenaphthene is 0.98, which means that they are extremely similar and their estimation will be more difficult, especially in the case of MCR-ALS with only non-negativity constraints. In the LC-MS analysis of the lipid mixtures, a total number of 512 m/z values were selected after the application of the regions of interest using the ROIMCR procedure described in [40,41]. The dimensions of the analyzed LC-MS data matrices for each calibration sample were 638 rows (elution times) × 512 columns (m/z values), and the augmented data matrix had the dimensions of 7656 × 512, for the 12 calibration data matrices.…”

Mutations of OsPLDa1 Increase Lysophospholipid Content and Enhance Cooking and Eating Quality in Rice

“…From the total number of differentially expressed metabolites, six lipid species (total acyl carbon: total acyl double-bond) of LPLs, including LPE (14:0), LPE (16:0), LPE (18:1), LPC (14:0), LPC (16:0) and LPC (18:1), were observed in the wild-type and both ospldα1 mutants (Table S1). The concentration of the lipid species was calculated by peak height intensity [19], and the results indicated that the detected LPC (18:1), LPC (16:0), LPE (18:1), LPE (16:0) and LPE (14:0) were elevated in both mutants by 11%-32%, respectively. In contrast, we observed a 7% decrease in the LPC (14:0) concentration in both mutants compared to their wild-type parents ( Figure 1).…”

“…Plants 2020, 9, 390 3 of 10 S1). The concentration of the lipid species was calculated by peak height intensity [19], and the results indicated that the detected LPC (18:1), LPC (16:0), LPE (18:1), LPE (16:0) and LPE (14:0) were elevated in both mutants by 11%-32%, respectively. In contrast, we observed a 7% decrease in the LPC (14:0) concentration in both mutants compared to their wild-type parents ( Figure 1).…”

Qualitative analysis of phospholipids and their oxidised derivatives – used techniques and examples of their applications related to lipidomic research and food analysis

“…Recently, the regions of interest multivariate curve resolution (ROIMCR) procedure, which enables compressing and processing LC-MS data, was employed in lipid standards analysis, including PLs. This approach consists of a preliminary selection of the regions of interest of the LC-MS data followed by their throughout chemometric analysis [159]. The bioinformatic analysis includes also the interpretation of observed lipid changes in the biochemical context, which is known as a pathway analysis.…”