<i>Cardinal</i>: an R package for statistical analysis of mass spectrometry-based imaging experiments

Bemis, Kyle D.; Harry, April; Eberlin, Lívia S.; Ferreira, Christina R.; Ven, Stephanie M. W. Y. van de; Mallick, Parag; Stolowitz, Mark L.; Vitek, Olga

doi:10.1093/bioinformatics/btv146

Cited by 232 publications

(243 citation statements)

References 7 publications

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“…Concomitantly, an effort should be made to assure the most appropriate statistical planning approaches and tools are used for interpreting and classifying mass spectra. For example, although many studies have relied on PCA for grouping spectra by similarity and demonstrating discrimination, more sophisticated statistical approaches for classification and feature selection may yield better results with similar computational requirements (44, 68, 69). Another critical factor that has been underexplored is the sensitivity of ambient ionization MS approaches in detecting cancer cells at a low tumor cell concentration, a common scenario in the evaluation of samples at the margins of the tumor.…”

Section: Perspectives and Conclusionmentioning

confidence: 99%

Ambient Ionization Mass Spectrometry for Cancer Diagnosis and Surgical Margin Evaluation

2016

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BACKGROUND There is a clinical need for new technologies that would enable rapid disease diagnosis based on diagnostic molecular signatures. Ambient ionization mass spectrometry has revolutionized the means by which molecular information can be obtained from tissue samples in real time and with minimal sample pretreatment. New developments in ambient ionization techniques applied to clinical research suggest that ambient ionization mass spectrometry will soon become a routine medical tool for tissue diagnosis. CONTENT This review summarizes the main developments in ambient ionization techniques applied to tissue analysis, with focus on desorption electrospray ionization mass spectrometry, probe electrospray ionization, touch spray, and rapid evaporative ionization mass spectrometry. We describe their applications to human cancer research and surgical margin evaluation, highlighting integrated approaches tested for ex vivo and in vivo human cancer tissue analysis. We also discuss the challenges for clinical implementation of these tools and offer perspectives on the future of the field. SUMMARY A variety of studies have showcased the value of ambient ionization mass spectrometry for rapid and accurate cancer diagnosis. Small molecules have been identified as potential diagnostic biomarkers, including metabolites, fatty acids, and glycerophospholipids. Statistical analysis allows tissue discrimination with high accuracy rates (>95%) being common. This young field has challenges to overcome before it is ready to be broadly accepted as a medical tool for cancer diagnosis. Growing research in new, integrated ambient ionization mass spectrometry technologies and the ongoing improvements in the existing tools make this field very promising for future translation into the clinic.

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Section: Perspectives and Conclusionmentioning

confidence: 99%

Ambient Ionization Mass Spectrometry for Cancer Diagnosis and Surgical Margin Evaluation

2016

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“…Recently, Cardinal, a package for the statistical analysis of MSI data using the free statistical programming language R (http://www.r-project.org/), was published, showing the great potential of open source solutions [19]. Therefore, we aimed to develop a scalable strategy to efficiently analyse .imzML MSI data files with R. In particular, we were interested to enable the visual screening for metabolic features in biological tissues.…”

Section: Introductionmentioning

confidence: 99%

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

Gamboa-Becerra¹,

Ramı́rez-Chávez²,

Molina‐Torres³

et al. 2015

Anal Bioanal Chem

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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.

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“…Spectral smoothing using the Savitzky-Golasky algorithm, Total Ion Current (TIC) normalization, and peak picking using a signal-to-noise ratio of 3.0 were performed using the MALDIquant package in the R environment51. Generated peak intensity data were segmented using spatially aware K-means (r = 1, k = 7)52, as implemented in the Cardinal MSI R package53. Partial Least Squares–Discriminant Analysis (PLS-DA) and ROC analysis were performed in the R environment using the mixOmics54 and ROCR55 packages, respectively.…”

Section: Methodsmentioning

confidence: 99%

Assessment of pathological response to therapy using lipid mass spectrometry imaging

Patterson

Alabdulkarim

Lazaris

et al. 2016

Sci Rep

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In many cancers, the establishment of a patient’s future treatment regime often relies on histopathological assessment of tumor tissue specimens in order to determine the extent of the ‘pathological response’ to a given therapy. However, histopathological assessment of pathological response remains subjective. Here we use MALDI mass spectrometry imaging to generate lipid signatures from colorectal cancer liver metastasis specimens resected from patients preoperatively treated with chemotherapy. Using these signatures we obtained a unique pathological response score that correlates with prognosis. In addition, we identify single lipid moieties that are overexpressed in different histopathological features of the tumor, which have potential as new biomarkers for assessing response to therapy. These data show that computational methods, focusing on the lipidome, can be used to determine prognostic markers for response to chemotherapy and may potentially improve risk assessment and patient care.

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Cardinal: an R package for statistical analysis of mass spectrometry-based imaging experiments

Cited by 232 publications

References 7 publications

Ambient Ionization Mass Spectrometry for Cancer Diagnosis and Surgical Margin Evaluation

Ambient Ionization Mass Spectrometry for Cancer Diagnosis and Surgical Margin Evaluation

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

Assessment of pathological response to therapy using lipid mass spectrometry imaging

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