rMSIproc: an R package for mass spectrometry imaging data processing

Mass spectrometry imaging (MSI) is a technique that provides comprehensive molecular information with high spatial resolution from tissue. Today, there is a strong push toward sharing data sets through public repositories in many research fields where MSI is commonly applied; yet, there is no standardized protocol for analyzing these data sets in a reproducible manner. Shifts in the mass-to-charge ratio ( m / z ) of molecular peaks present a major obstacle that can make it impossible to distinguish one compound from another. Here, we present a label-free m / z alignment approach that is compatible with multiple instrument types and makes no assumptions on the sample’s molecular composition. Our approach, MSIWarp ( ), finds an m / z recalibration function by maximizing a similarity score that considers both the intensity and m / z position of peaks matched between two spectra. MSIWarp requires only centroid spectra to find the recalibration function and is thereby readily applicable to almost any MSI data set. To deal with particularly misaligned or peak-sparse spectra, we provide an option to detect and exclude spurious peak matches with a tailored random sample consensus (RANSAC) procedure. We evaluate our approach with four publicly available data sets from both time-of-flight (TOF) and Orbitrap instruments and demonstrate up to 88% improvement in m / z alignment.

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“…Our goal is to make it possible to interface MSIWarp with existing MSI analysis packages such as MALDIquant, 27 Cardinal, 28 and rMSIproc. 29…”

Section: ■ Results and Discussionmentioning

confidence: 99%

“…The source code of MSIWarp along with Python bindings are available at . Our goal is to make it possible to interface MSIWarp with existing MSI analysis packages such as MALDIquant, Cardinal, and rMSIproc …”

Section: Resultsmentioning

confidence: 99%

MSIWarp: A General Approach to Mass Alignment in Mass Spectrometry Imaging

et al. 2020

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“…The goal of this study was to develop, optimize and validate a new algorithm to annotate signals attributed to the LDI promoting material in MSI. The developed algorithm is packaged and released as rMSIcleanup, an open-source R package freely available for the scientific community and fully integrated with rMSIproc [20], a stand-alone package for the visualization, pre-processing and analysis of MSI datasets.…”

Section: Discussionmentioning

confidence: 99%

“…The raw spectral data of each MSI dataset was exported to the imzML data format [19] in profile mode. The software rMSIproc [20] was used to process the data and generate a peak matrix in centroid mode. The default processing parameters were used.…”

Section: Msi Data Processingmentioning

confidence: 99%

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

et al. 2020

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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.

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“…Raw data were exported as an imzML file for preprocessing and peak picking using the SCiLS Lab Software (v2016b, Bruker Daltonics, Bremen, Germany). In the R environment (www.r-project.org accessed 15 July 2021), data processing was conducted using the rMSIproc package [17]. Spectra were aligned using a maximum shift of 50 ppm, alignment iterations = 3, and alignment oversampling = 2.…”

Section: Maldi-msi Data Processingmentioning

confidence: 99%

Proteomic Analysis Identifies FNDC1, A1BG, and Antigen Processing Proteins Associated with Tumor Heterogeneity and Malignancy in a Canine Model of Breast Cancer

Cordeiro

Mulder

Zeijl

et al. 2021

Cancers

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New insights into the underlying biological processes of breast cancer are needed for the development of improved markers and treatments. The complex nature of mammary cancer in dogs makes it a great model to study cancer biology since they present a high degree of tumor heterogeneity. In search of disease-state biomarkers candidates, we applied proteomic mass spectrometry imaging in order to simultaneously detect histopathological and molecular alterations whilst preserving morphological integrity, comparing peptide expression between intratumor populations in distinct levels of differentiation. Peptides assigned to FNDC1, A1BG, and double-matching keratins 18 and 19 presented a higher intensity in poorly differentiated regions. In contrast, we observed a lower intensity of peptides matching calnexin, PDIA3, and HSPA5 in poorly differentiated cells, which enriched for protein folding in the endoplasmic reticulum and antigen processing, assembly, and loading of class I MHC. Over-representation of collagen metabolism, coagulation cascade, extracellular matrix components, cadherin-binding and cell adhesion pathways also distinguished cell populations. Finally, an independent validation showed FNDC1, A1BG, PDIA3, HSPA5, and calnexin as significant prognostic markers for human breast cancer patients. Thus, through a spatially correlated characterization of spontaneous carcinomas, we described key proteins which can be further validated as potential prognostic biomarkers.

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rMSIproc: an R package for mass spectrometry imaging data processing

Cited by 27 publications

References 9 publications

MSIWarp: A General Approach to Mass Alignment in Mass Spectrometry Imaging

MSIWarp: A General Approach to Mass Alignment in Mass Spectrometry Imaging

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

Proteomic Analysis Identifies FNDC1, A1BG, and Antigen Processing Proteins Associated with Tumor Heterogeneity and Malignancy in a Canine Model of Breast Cancer

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