Using the Chemical Noise Background in MALDI Mass Spectrometry Imaging for Mass Alignment and Calibration.

Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) is an established tool for the investigation of formalin fixed paraffin embedded (FFPE) tissue samples and shows a high potential for applications in clinical research and histopathological diagnosis. The applicability and accuracy of this method, however, heavily depends on the quality of the acquired data, and in particular mass misalignment in axial time-of-flight (TOF) MSI continues to be a serious issue. We present a mass alignment and recalibration method that is specifically designed to operate on MALDI peptide imaging data. The proposed method exploits statistical properties of the characteristic chemical noise background observed in peptide imaging experiments. By comparing these properties to a theoretical peptide mass model, the effective mass shift of each spectrum is estimated and corrected. The method was evaluated on a cohort of 31 FFPE tissue samples, pursuing a statistical validation approach to estimate both the reduction of relative misalignment, as well as the increase in absolute mass accuracy. Our results suggest that a relative mass precision of approximately 5 ppm and an absolute accuracy of approximately 20 ppm are achievable using our method.

Targeted Feature Extraction in MALDI Mass Spectrometry Imaging to Discriminate Proteomic Profiles of Breast and Ovarian Cancer.

PURPOSE: To develop a mass spectrometry imaging (MSI) based workflow for extracting m/z values related to putative protein biomarkers and using these for reliable tumor classification. EXPERIMENTAL DESIGN: Given a list of putative breast and ovarian cancer biomarker proteins, a set of related m/z values are extracted from heterogeneous MSI datasets derived from formalin-fixed paraffin-embedded tissue material. Based on these features, a linear discriminant analysis classification model is trained to discriminate the two tumor types. RESULTS: It is shown that the discriminative power of classification models based on the extracted features is increased compared to the automatic training approach, especially when classifiers are applied to spectral data acquired under different conditions (instrument, preparation, laboratory). CONCLUSIONS AND CLINICAL RELEVANCE: Robust classification models not confounded by technical variation between MSI measurements are obtained. This supports the assumption that the classification of the respective tumor types is based on biological rather than technical differences, and that the selected features are related to the proteomic profiles of the tumor types under consideration.

Supervised non-negative matrix factorization methods for MALDI imaging applications.

MOTIVATION: Non-negative matrix factorization (NMF) is a common tool for obtaining low-rank approximations of non-negative data matrices and has been widely used in machine learning, e.g. for supporting feature extraction in high-dimensional classification tasks. In its classical form, NMF is an unsupervised method, i.e. the class labels of the training data are not used when computing the NMF. However, incorporating the classification labels into the NMF algorithms allows to specifically guide them toward the extraction of data patterns relevant for discriminating the respective classes. This approach is particularly suited for the analysis of mass spectrometry imaging (MSI) data in clinical applications, such as tumor typing and classification, which are among the most challenging tasks in pathology. Thus, we investigate algorithms for extracting tumor-specific spectral patterns from MSI data by NMF methods. RESULTS: In this article, we incorporate a priori class labels into the NMF cost functional by adding appropriate supervised penalty terms. Numerical experiments on a MALDI imaging dataset confirm that the novel supervised NMF methods lead to significantly better classification accuracy and stability as compared with other standard approaches. AVAILABILITY AND IMPLEMENTATON: https://gitlab.informatik.uni-bremen.de/digipath/Supervised_NMF_Methods_for_MALDI.git. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A new classification method for MALDI imaging mass spectrometry data acquired on formalin-fixed paraffin-embedded tissue samples.

Matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) shows a high potential for applications in histopathological diagnosis, and in particular for supporting tumor typing and subtyping. The development of such applications requires the extraction of spectral fingerprints that are relevant for the given tissue and the identification of biomarkers associated with these spectral patterns. We propose a novel data analysis method based on the extraction of characteristic spectral patterns (CSPs) that allow automated generation of classification models for spectral data. Formalin-fixed paraffin embedded (FFPE) tissue samples from N=445 patients assembled on 12 tissue microarrays were analyzed. The method was applied to discriminate primary lung and pancreatic cancer, as well as adenocarcinoma and squamous cell carcinoma of the lung. A classification accuracy of 100% and 82.8%, resp., could be achieved on core level, assessed by cross-validation. The method outperformed the more conventional classification method based on the extraction of individual m/z values in the first application, while achieving a comparable accuracy in the second. LC-MS/MS peptide identification demonstrated that the spectral features present in selected CSPs correspond to peptides relevant for the respective classification. This article is part of a Special Issue entitled: MALDI Imaging, edited by Dr. Corinna Henkel and Prof. Peter Hoffmann.

An approach to optimize sample preparation for MALDI imaging MS of FFPE sections using fractional factorial design of experiments.

A standardized workflow for matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI imaging MS) is a prerequisite for the routine use of this promising technology in clinical applications. We present an approach to develop standard operating procedures for MALDI imaging MS sample preparation of formalin-fixed and paraffin-embedded (FFPE) tissue sections based on a novel quantitative measure of dataset quality. To cover many parts of the complex workflow and simultaneously test several parameters, experiments were planned according to a fractional factorial design of experiments (DoE). The effect of ten different experiment parameters was investigated in two distinct DoE sets, each consisting of eight experiments. FFPE rat brain sections were used as standard material because of low biological variance. The mean peak intensity and a recently proposed spatial complexity measure were calculated for a list of 26 predefined peptides obtained by in silico digestion of five different proteins and served as quality criteria. A five-way analysis of variance (ANOVA) was applied on the final scores to retrieve a ranking of experiment parameters with increasing impact on data variance. Graphical abstract MALDI imaging experiments were planned according to fractional factorial design of experiments for the parameters under study. Selected peptide images were evaluated by the chosen quality metric (structure and intensity for a given peak list), and the calculated values were used as an input for the ANOVA. The parameters with the highest impact on the quality were deduced and SOPs recommended.