rMSIcleanup: an open-source tool for matrix-related peak annotation in mass spectrometry imaging and its application to silver-assisted laser desorption/ionization

Abstract

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.

An Organ-Specific Metabolite Annotation Approach for Ambient Mass Spectrometry Imaging Reveals Spatial Metabolic Alterations of a Whole Mouse Body

“…To date, the available metabolite annotation tools for MSI are limited. A few tools are specialized for annotation, including METASPACE, , MassPix, rMSIannotation, rMSIcleanup, and others. , Some comprehensive tools nearly cover the entire workflow for MSI data analysis and include annotation pipeline, such as LipostarMSI and MSiReader . For these tools, the principle of annotation generally uses isotopic patterns of metabolite ions in MS spectra, including isotopic ion intensity ratios and/or spatial localization of isotopic ion images.…”

What are we imaging? Software tools and experimental strategies for annotation and identification of small molecules in mass spectrometry imaging

“…4.3.12 | rMSIcleanup rMSIcleanup (Baquer et al, 2020) is an R package that annotates matrix-related signals in MSI data sets. It annotates them by computing all the theoretical isotopic patterns related to the matrix clusters and matching them to the spectra using cluster spectral similarity and intra-cluster morphological similarity.…”

“…There is no consensus on the relationship between different scores or how to unify them. Typically, when multiple scores are available, each score is scaled to fit a range of 0–1 and the product of all scores is taken as a single metric (Baquer et al, 2020; Palmer et al, 2016; Sementé et al, 2021; Tortorella et al, 2020).…”

“…It is estimated that monoisotopic endogenous peaks only represent 5% of the MS signals in an MSI experiment (Wang et al, 2019). This is particularly challenging in metabolomics since matrix signals and in‐source fragments are densely concentrated in the low mass range (Baquer et al, 2020; Janda et al, 2021). The vast amount of MS signals leaves research groups using MSI around the world struggling with the question: “What are we detecting in MSI experiments?”…”

Effects of Silane Monolayers on Lysophosphatidylcholine (LysoPC) Detection by Desorption Ionization on Silicon Mass Spectrometry (DIOS-MS) in Solution and Plasma

“…Although no consensus has yet been found as to the exact mechanism involved, it is well-established that the substrates need strong absorbance in the UV region, low heat capacity, and low thermal conductivity to allow analyte desorption and ionization . Several types of nanostructured substrate, such as gold, , silver, , and alumina, ,, have been used in SALDI-MS due to their optical properties. Carbon-based nanostructures , and silicon − have also been reported.…”