rMSIcleanup: an open-source tool for matrix-related peak annotation in mass spectrometry imaging and its application to silver-assisted laser desorption/ionization.
Gerard BaquerLluc SementéMaría García-AltaresYoung-Jin LeePierre ChaurandXavier CorreigPere RàfolsPublished in: Journal of cheminformatics (2020)
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.
Keyphrases
- mass spectrometry
- high resolution
- gold nanoparticles
- liquid chromatography
- rna seq
- electronic health record
- optical coherence tomography
- machine learning
- big data
- magnetic resonance imaging
- magnetic resonance
- deep learning
- computed tomography
- single cell
- convolutional neural network
- quantum dots
- cancer therapy
- drug delivery
- ms ms
- tandem mass spectrometry