MSI.R scripts reveal volatile and semi-volatile features in low-temperature plasma mass spectrometry imaging (LTP-MSI) of chilli (Capsicum annuum)

Abstract

In cartography, the combination of colour and contour lines is used to express a three-dimensional landscape on a two-dimensional map. We transferred this concept to the analysis of mass spectrometry imaging (MSI) data and developed a collection of R scripts for the efficient evaluation of .imzML archives in a four-step strategy: (1) calculation of the density distribution of mass-to-charge ratio (m/z) signals in the .imzML file and assembling of a pseudo-master spectrum with peak list, (2) automated generation of mass images for a defined scan range and subsequent visual inspection, (3) visualisation of individual ion distributions and export of relevant .mzML spectra and (4) creation of overlay graphics of ion images and photographies. The use of a Hue-Chroma-Luminance (HCL) colour model in MSI graphics takes into account the human perception for colours and supports the correct evaluation of signal intensities. Further, readers with colour blindness are supported. Contour maps promote the visual recognition of patterns in MSI data, which is particularly useful for noisy data sets. We demonstrate the scalability of MSI.R scripts by running them on different systems: on a personal computer, on Amazon Web Services (AWS) instances and on an institutional cluster. By implementing a parallel computing strategy, the execution speed for .imzML data scanning with image generation could be improved by more than an order of magnitude. Applying our MSI.R scripts ( http://www.bioprocess.org/MSI.R ) to low-temperature plasma (LTP)-MSI data shows the localisation of volatile and semi-volatile compounds in the cross-cut of a chilli (Capsicum annuum) fruit. The subsequent identification of compounds by gas and liquid chromatography coupled to mass spectrometry (GC-MS, LC-MS) proves that LTP-MSI enables the direct measurement of volatile organic compound (VOC) distributions from biological tissues.

Unsupervised machine learning for exploratory data analysis in imaging mass spectrometry

“…Many of these packages implement several of the techniques and algorithms that have been discussed in this review. Datacube Explorer enables fast and easy visualization and basic analysis of large IMS datasets, without requiring programming by the user (Smith et al, ; Klinkert et al, ). BioMap is a biomedical image analysis software package by Rausch and Stoeckli, available at http://ms-imaging.org/wp/biomap/. Cardinal is an extensive R‐based statistical analysis package for IMS data (Bemis et al, ). msIQuant is a software package for IMS that enables fast access, visualization, and analysis for large datasets (Källback et al, ). MSI.R is a data exploration tool for IMS data, enabling the use of contour lines to visualize ion intensities (Gamboa‐Becerra et al, ). rMSI is an R‐based IMS data handling and analysis tool (Ràfols et al, ), available at https://github.com/prafols/rMSI. MALDIquant is an R‐based package for the analysis of mass spectrometry data in general, which allows analysis of IMS data as well (Gibb and Strimmer, ). OpenMSI is a web‐based visualization, analysis, and management platform for IMS data (Rübel et al, ; Fischer et al, ). MSiReader is an open‐source MATLAB‐based tool for analysis and visualization of IMS data (Robichaud et al, ). SpectralAnalysis is an open‐source MATLAB‐based IMS data analysis toolbox, allowing multivariate analysis of large IMS datasets (Race et al, ). BASIS is an open‐source bioinformatics platform for processing large‐scale mass spectrometry imaging datasets, developed by Veselkov et al (). OmniSpect is an open‐source MATLAB‐based toolbox, developed for the visualization and analysis of IMS data (Parry et al, ). MCR‐ALS GUI is a MATLAB‐based toolbox that provides MCR‐ALS analysis for various data, and includes a module for the analysis of spectral imaging data (Jaumot et al, ; Jaumot, de Juan, & Tauler, ). NESAC/BIO MVA Toolbox is a MATLAB‐based toolbox developed by Dan Graham for the multivariate analysis of TOF‐SIMS imaging data, available at http://www.nb.engr.washington.edu/mvsa/nbtoolbox.…”

“…MSI.R is a data exploration tool for IMS data, enabling the use of contour lines to visualize ion intensities (Gamboa‐Becerra et al, ).…”

Review and perspectives on the applications of mass spectrometry imaging under ambient conditions

“…Ambient MS imaging has matured to such an extent that biologically relevant issues can be solved in a multitude of natural product applications. Recently, research has been focused on the developmental stages of plant and seed growth, the onset and production of plant metabolites, ecological significance and function, and pathogenic and pesticide control strategies …”

“…In a following study, LTP‐MS with the aid of MSI.R. scripts localized volatile and semi‐volatile compounds more rapidly in cross cuts of chili fruit ( C. annuum ) . LEMS‐MS imaging of fresh pink and red New Guinea Impatiens flower petal and leaf was visualized by mapping sugars, anthrocyanins and other plant metabolites using a non‐resonant, femtosecond laser with improved resolution (a spot size of 60 μm × 70 μm) .…”

“…Ambient MS imaging has matured to such an extent that biologically relevant issues can be solved in a multitude of natural product applications. Recently, research has been focused on the 113 Tata et al, 106 and Angolini et al, 110 respectively] developmental stages of plant and seed growth, 117,118 the onset and production of plant metabolites, [119][120][121][122][123] ecological significance and function, 118,124 and pathogenic and pesticide control strategies. 119,125 In the direct analysis of plant tissues, the tissues are mounted directly onto the target plate without further manipulation.…”