Classification of MALDI-MS imaging data of tissue microarrays using canonical correlation analysis based variable selection

First published: 9 May 2016Applying MALDI-MS imaging to tissue microarrays (TMAs) provides access to proteomics data from large cohorts of patients in a cost- and time-efficient way, and opens the potential for applying this technology in clinical diagnosis. The complexity of these TMA data—high-dimensional low sample size—provides challenges for the statistical analysis, as classical methods typically require a nonsingular covariance matrix that cannot be satisfied if the dimension is greater than the sample size. We use TMAs to collect data from endometrial primary carcinomas from 43 patients. Each patient has a lymph node metastasis (LNM) status of positive or negative, which we predict on the basis of the MALDI-MS imaging TMA data. We propose a variable selection approach based on canonical correlation analysis that explicitly uses the LNM information. We apply LDA to the selected variables only. Our method misclassifies 2.3–20.9% of patients by leave-one-out cross-validation and strongly outperforms LDA after reduction of the original data with principle component analysis.Lyron Winderbaum, Inge Koch, Parul Mittal and Peter Hoffman

Feature extraction for proteomics imaging mass spectrometry data

Imaging mass spectrometry (IMS) has transformed proteomics by providing an avenue for collecting spatially distributed molecular data. Mass spectrometry data acquired with matrix assisted laser desorption ionization (MALDI) IMS consist of tens of thousands of spectra, measured at regular grid points across the surface of a tissue section. Unlike the more standard liquid chromatography mass spectrometry, MALDI-IMS preserves the spatial information inherent in the tissue. Motivated by the need to differentiate cell populations and tissue types in MALDI-IMS data accurately and efficiently, we propose an integrated cluster and feature extraction approach for such data. We work with the derived binary data representing presence/absence of ions, as this is the essential information in the data. Our approach takes advantage of the spatial structure of the data in a noise removal and initial dimension reduction step and applies k -means clustering with the cosine distance to the high-dimensional binary data. The combined smoothing-clustering yields spatially localized clusters that clearly show the correspondence with cancer and various noncancerous tissue types. Feature extraction of the high-dimensional binary data is accomplished with our difference in proportions of occurrence (DIPPS) approach which ranks the variables and selects a set of variables in a data-driven manner. We summarize the best variables in a single image that has a natural interpretation. Application of our method to data from patients with ovarian cancer shows good separation of tissue types and close agreement of our results with tissue types identified by pathologists.Lyron J. Winderbaum, Inge Koch, Ove J. R. Gustafsson, Stephan Meding and Peter Hoffman

Proteomic responses to gold(III)-toxicity in the bacterium Cupriavidus metallidurans CH34

Accepted 11th October 2016The metal-resistant β-proteobacterium Cupriavidus metallidurans drives gold (Au) biomineralisation and the (trans)formation of Au nuggets largely via unknown biochemical processes, ultimately leading to the reductive precipitation of mobile, toxic Au(i/iii)-complexes. In this study proteomic responses of C. metallidurans CH34 to mobile, toxic Au(iii)-chloride are investigated. Cells were grown in the presence of 10 and 50 μM Au(iii)-chloride, 50 μM Cu(ii)-chloride and without additional metals. Differentially expressed proteins were detected by difference gel electrophoresis and identified by liquid chromatography coupled mass spectrometry. Proteins that were more abundant in the presence of Au(iii)-chloride are involved in a range of important cellular functions, e.g., metabolic activities, transcriptional regulation, efflux and metal transport. To identify Au-binding proteins, protein extracts were separated by native 2D gel electrophoresis and Au in protein spots was detected by laser absorption inductively coupled plasma mass spectrometry. A chaperon protein commonly understood to bind copper (Cu), CupC, was identified and shown to bind Au. This indicates that it forms part of a multi-metal detoxification system and suggests that similar/shared detoxification pathways for Au and Cu exist. Overall, this means that C. metallidurans CH34 is able to mollify the toxic effects of cytoplasmic Au(iii) by sequestering this Au-species. This effect may in the future be used to develop CupC-based biosensing capabilities for the in-field detection of Au in exploration samples.Carla M. Zammit, Florian Weiland, Joël Brugger, Benjamin Wade, Lyron Juan Winderbaum, Dietrich H. Nies, Gordon Southam, Peter Hoffmann and Frank Reit

MALDI mass spectrometry imaging reveals decreased CK5 levels in vulvar squamous cell carcinomas compared to the precursor lesion differentiated vulvar intraepithelial neoplasia

Published: 8 July 2016Vulvar cancer is the fourth most common gynecological cancer worldwide. However, limited studies have been completed on the molecular characterization of vulvar squamous cell carcinoma resulting in a poor understanding of the disease initiation and progression. Analysis and early detection of the precursor lesion of HPV-independent vulvar squamous cell carcinoma (VSCC), differentiated vulvar intraepithelial neoplasia (dVIN), is of great importance given dVIN lesions have a high level of malignant potential. Here we present an examination of adjacent normal vulvar epithelium, dVIN, and VSCC from six patients by peptide Matrix-assisted laser desorption/ionization Mass Spectrometry Imaging (MALDI-MSI). The results reveal the differential expression of multiple peptides from the protein cytokeratin 5 (CK5) across the three vulvar tissue types. The difference observed in the relative abundance of CK5 by MALDI-MSI between the healthy epithelium, dVIN, and VSCC was further analyzed by immunohistochemistry (IHC) in tissue from eight VSCC patients. A decrease in CK5 immunostaining was observed in the VSCC compared to the healthy epithelium and dVIN. These results provide an insight into the molecular fingerprint of the vulvar intraepithelial neoplasia that appears to be more closely related to the healthy epithelium than the VSCC.Chao Zhang, Georgia Arentz, Lyron Winderbaum, Noor A. Lokman, Manuela Klingler-Hoffmann, Parul Mittal, Christopher Carter, Martin K. Oehler and Peter Hoffman

Data visualization in yield component analysis: an expert study

Even though data visualization is a common analytical tool in numerous disciplines, it has rarely been used in agricultural sciences, particularly in agronomy. In this paper, we discuss a study on employing data visualization to analyze a multiplicative model. This model is often used by agronomists, for example in the so-called yield component analysis. The multiplicative model in agronomy is normally analyzed by statistical or related methods. In practice, unfortunately, usefulness of these methods is limited since they help to answer only a few questions, not allowing for a complex view of the phenomena studied. We believe that data visualization could be used for such complex analysis and presentation of the multiplicative model. To that end, we conducted an expert survey. It showed that visualization methods could indeed be useful for analysis and presentation of the multiplicative model

Multivariate analysis of an LA-ICP-MS trace element dataset for pyrite

Application of multivariate statistics to trace element datasets is reviewed using 164 multi-element LA-ICP-MS spot analyses of pyrite from the Moonlight epithermal gold prospect, Queensland, Australia. Multivariate analysis of variance (MANOVA) is used to demonstrate that classification of pyrite on morphological and other non-numeric factors is geochemically valid. Parallel coordinate plots and correlation cluster analysis using Spearman’s coefficients are used to discover unexpected elemental relationships without making assumptions a priori. Finally, principal component analysis and factor analysis are used to demonstrate the presence of sub-classes of pyrite. Corroborated with geological data, statistical analysis provides evidence for successive generations of hydrothermal fluids, each introducing specific metals, and for partial or complete replacement of different minerals. The data permit reinterpretation of Moonlight as a telescoped system where epithermal-Au (± base metals) is superposed onto early porphyry-Mo mineralization.Lyron Winderbaum, Cristiana L. Ciobanu, Nigel J. Cook, Matthew Paul, Andrew Metcalfe, Sarah Gilber

Applications of mass spectrometry imaging to cancer

Chapter two -from Applications of Mass Spectrometry Imaging to Cancer Edited by Richard R. Drake and Liam A. McDonnell. ISBN: 9780128052495Pathologists play an essential role in the diagnosis and prognosis of benign and cancerous tumors. Clinicians provide tissue samples, for example, from a biopsy, which are then processed and thin sections are placed onto glass slides, followed by staining of the tissue with visible dyes. Upon processing and microscopic examination, a pathology report is provided, which relies on the pathologist's interpretation of the phenotypical presentation of the tissue. Targeted analysis of single proteins provide further insight and together with clinical data these results influence clinical decision making. Recent developments in mass spectrometry facilitate the collection of molecular information about such tissue specimens. These relatively new techniques generate label-free mass spectra across tissue sections providing nonbiased, nontargeted molecular information. At each pixel with spatial coordinates (x/y) a mass spectrum is acquired. The acquired mass spectrums can be visualized as intensity maps displaying the distribution of single m/z values of interest. Based on the sample preparation, proteins, peptides, lipids, small molecules, or glycans can be analyzed. The generated intensity maps/images allow new insights into tumor tissues. The technique has the ability to detect and characterize tumor cells and their environment in a spatial context and combined with histological staining, can be used to aid pathologists and clinicians in the diagnosis and management of cancer. Moreover, such data may help classify patients to aid therapy decisions and predict outcomes. The novel complementary mass spectrometry-based methods described in this chapter will contribute to the transformation of pathology services around the world.G. Arentz, P. Mittal, C. Zhang, Y.-Y. Ho, M. Briggs, L. Winderbaum, M.K. Hoffmann, P. Hoffman