A new procedure based on the simultaneous analysis of multiple mass spectrometry images using multivariate curve resolution is presented in this work. Advantages of the application of the proposed approach are shown for three cases of plant studies demonstrating its potential usefulness in metabolomics studies, particularly in lipidomics. In the first dataset, a three stage germination time course process of green bean seeds is presented. The second example is a dose-response study where the stem bases of a non-exposed plant are compared to those of plants exposed to increasing concentrations of the pesticide chlorpyrifos. Finally, the third study is the simultaneous analysis of several sequential transversal and longitudinal cuts of the same green bean plant stem segment. The analysis of these three examples required the comprehensive adaptation of different chemometric methodologies including data compression by selection of the regions of interest (ROI strategy), appropriate data normalization and baseline correction, all of them before MCR-ALS simultaneous image analysis of multiple samples and post processing of the achieved results. MCR-ALS resolved components provided spatial information about the changes in the spatial composition and distribution of the different lipids on the surface of the investigated samples. These results enabled the identification of single lipids and the clustering of those lipids that behaved similarly in the different images simultaneously analyzed. The proposed strategy for MSI analysis represents a step forward in the simultaneous analysis of multiple sets of images providing an improved recovery of both spatial and structural information in environmental and biomedical studies.
Keywords: Chemometrics; Lipidomics; MALDI-TOF; Mass spectrometry imaging; Multiple images; Multiset; Multivariate curve resolution.
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