文章基本信息

标题：rMSIcleanup: an open-source tool for matrix-related peak annotation in mass spectrometry imaging and its application to silver-assisted laser desorption/ionization
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作者：Gerard Baquer ; Lluc Sementé ; María García-Altares 等
期刊名称：Journal of Cheminformatics
印刷版ISSN：1758-2946
电子版ISSN：1758-2946
出版年度：2020
卷号：12
期号：1
页码：1-13
DOI：10.1186/s13321-020-00449-0
出版社：BioMed Central
摘要：Mass spectrometry imaging (MSI) has become a mature, widespread analytical technique to perform non-targeted spatial metabolomics. However, the compounds used to promote desorption and ionization of the analyte during acquisition cause spectral interferences in the low mass range that hinder downstream data processing in metabolomics applications. Thus, it is advisable to annotate and remove matrix-related peaks to reduce the number of redundant and non-biologically-relevant variables in the dataset. We have developed rMSIcleanup, an open-source R package to annotate and remove signals from the matrix, according to the matrix chemical composition and the spatial distribution of its ions. To validate the annotation method, rMSIcleanup was challenged with several images acquired using silver-assisted laser desorption ionization MSI (AgLDI MSI). The algorithm was able to correctly classify m/z signals related to silver clusters. Visual exploration of the data using Principal Component Analysis (PCA) demonstrated that annotation and removal of matrix-related signals improved spectral data post-processing. The results highlight the need for including matrix-related peak annotation tools such as rMSIcleanup in MSI workflows.
关键词：Mass spectrometry imaging ; Spatial metabolomics ; Matrix annotation ; Overlapping-signal detection ; Silver-assisted laser/desorption ionization ; Spectral processing