Multiple statistical analysis techniques corroborate intratumor heterogeneity in imaging mass spectrometry datasets of myxofibrosarcoma

MALDI mass spectrometry can generate profiles that contain hundreds of biomolecular ions directly from tissue. Spatially-correlated analysis, MALDI imaging MS, can simultaneously reveal how each of these biomolecular ions varies in clinical tissue samples. The use of statistical data analysis tools to identify regions containing correlated mass spectrometry profiles is referred to as imaging MS-based molecular histology because of its ability to annotate tissues solely on the basis of the imaging MS data. Several reports have indicated that imaging MS-based molecular histology may be able to complement established histological and histochemical techniques by distinguishing between pathologies with overlapping/identical morphologies and revealing biomolecular intratumor heterogeneity. A data analysis pipeline that identifies regions of imaging MS datasets with correlated mass spectrometry profiles could lead to the development of novel methods for improved diagnosis (differentiating subgroups within distinct histological groups) and annotating the spatio-chemical makeup of tumors. Here it is demonstrated that highlighting the regions within imaging MS datasets whose mass spectrometry profiles were found to be correlated by five independent multivariate methods provides a consistently accurate summary of the spatio-chemical heterogeneity. The corroboration provided by using multiple multivariate methods, efficiently applied in an automated routine, provides assurance that the identified regions are indeed characterized by distinct mass spectrometry profiles, a crucial requirement for its development as a complementary histological tool. When simultaneously applied to imaging MS datasets from multiple patient samples of intermediate-grade myxofibrosarcoma, a heterogeneous soft tissue sarcoma, nodules with mass spectrometry profiles found to be distinct by five different multivariate methods were detected within morphologically identical regions of all patient tissue samples. To aid the further development of imaging MS based molecular histology as a complementary histological tool the Matlab code of the agreement analysis, instructions and a reduced dataset are included as supporting information.     

Quality assessment and interference detection in targeted mass spectrometry data using machine learning

Advances in the field of targeted proteomics and mass spectrometry have significantly improved assay sensitivity and multiplexing capacity. The high-throughput nature of targeted proteomics experiments has increased the rate of data production, which requires development of novel analytical tools to keep up with data processing demand. Currently, development and validation of targeted mass spectrometry assays require manual inspection of chromatographic peaks from large datasets to ensure quality, a process that is time consuming, prone to inter- and intra-operator variability and limits the efficiency of data interpretation from targeted proteomics analyses. To address this challenge, we have developed TargetedMSQC, an R package that facilitates quality control and verification of chromatographic peaks from targeted proteomics datasets. This tool calculates metrics to quantify several quality aspects of a chromatographic peak, e.g. symmetry, jaggedness and modality, co-elution and shape similarity of monitored transitions in a peak group, as well as the consistency of transitions’ ratios between endogenous analytes and isotopically labeled internal standards and consistency of retention time across multiple runs. The algorithm takes advantage of supervised machine learning to identify peaks with interference or poor chromatography based on a set of peaks that have been annotated by an expert analyst. Using TargetedMSQC to analyze targeted proteomics data reduces the time spent on manual inspection of peaks and improves both speed and accuracy of interference detection. Additionally, by allowing the analysts to customize the tool for application on different datasets, TargetedMSQC gives the users the flexibility to define the acceptable quality for specific datasets. Furthermore, automated and quantitative assessment of peak quality offers a more objective and systematic framework for high throughput analysis of targeted mass spectrometry assay datasets and is a step towards more robust and faster assay implementation.     

Data mining in proteomic mass spectrometry

Data mining application to proteomic data from mass spectrometry has gained much interest in recent years. Advances made in proteomics and mass spectrometry have resulted in considerable amount of data that cannot be easily visualized or interpreted. Mass spectral proteomic datasets are typically high dimensional but with small sample size. Consequently, advanced artificial intelligence and machine learning algorithms are increasingly being used for knowledge discovery from such datasets. Their overall goal is to extract useful information that leads to the identification of protein biomarker candidates. Such biomarkers could potentially have diagnostic value as tools for early detection, diagnosis, and prognosis of many diseases. The purpose of this review is to focus on the current trends in mining mass spectral proteomic data. Special emphasis is placed on the critical steps involved in the analysis of surface-enhanced laser desorption/ionization mass spectrometry proteomic data. Examples are drawn from previously published studies and relevant data mining terminology and techniques are exlained.     

Imaging mass spectrometry: principle and application

Imaging mass spectrometry (IMS) is two-dimensional mass spectrometry to visualize the spatial distribution of biomolecules, which does not need either separation or purification of target molecules, and enables us to monitor not only the identification of unknown molecules but also the localization of numerous molecules simultaneously. Among the ionization techniques, matrix assisted laser desorption/ionization (MALDI) is one of the most generally used for IMS, which allows the analysis of numerous biomolecules ranging over wide molecular weights. Proper selection and preparation of matrix is essential for successful imaging using IMS. Tandem mass spectrometry, which is referred to MSⁿ, enables the structural analysis of a molecule detected by the first step of IMS. Applications of IMS were initially developed for studying proteins or peptides. At present, however, targets of IMS research have expanded to the imaging of small endogenous metabolites such as lipids, exogenous drug pharmacokinetics, exploring new disease markers, and other new scientific fields. We hope that this new technology will open a new era for biophysics.     

Imaging mass spectrometry in microbiology

Imaging mass spectrometry tools allow the two-dimensional visualization of the distribution of trace metals, metabolites, surface lipids, peptides and proteins directly from biological samples without the need for chemical tagging or antibodies, and are becoming increasingly useful for microbiology applications. These tools, comprising different imaging mass spectrometry techniques, are ushering in an exciting new era of discovery by enabling the generation of chemical hypotheses based on the spatial mapping of atoms and molecules that can correlate to or transcend observed phenotypes. In this Innovation article, we explore the wide range of imaging mass spectrometry techniques that is available to microbiologists and describe the unique applications of these tools to microbiology with respect to the types of samples to be investigated.     

Tissue profiling by mass spectrometry: a review of methodology and applications

Matrix-assisted laser desorption ionization mass spectrometry (MALDI MS) has become a valuable tool to address a broad range of questions in many areas of biomedical research. One such application allows spectra to be obtained directly from intact tissues, termed "profiling" (low resolution) and "imaging" (high resolution). In light of the fact that MALDI tissue profiling allows over a thousand peptides and proteins to be rapidly detected from a variety of tissues, its application to disease processes is of special interest. For example, protein profiles from tumors may allow accurate prediction of tumor behavior, diagnosis, and prognosis and uncover etiologies underlying idiopathic diseases. MALDI MS, in conjunction with laser capture microdissection, is able to produce protein expression profiles from a relatively small number of cells from specific regions of heterogeneous tissue architectures. Imaging mass spectrometry enables the investigator to assess the spatial distribution of proteins, drugs, and their metabolites in intact tissues. This article provides an overview of several tissue profiling and imaging applications performed by MALDI MS, including sample preparation, matrix selection and application, histological staining prior to MALDI analysis, tissue profiling, imaging, and data analysis. Several applications represent direct translation of this technology to clinically relevant problems.     

MALDI imaging mass spectrometry of human tissue: method challenges and clinical perspectives

The molecular complexity of biological tissue and the spatial and temporal variation in the biological processes involved in human disease requires new technologies and new approaches to provide insight into disease processes. Imaging mass spectrometry is an effective tool that provides molecular images of tissues in the molecular discovery process. The analysis of human tissue presents special challenges and limitations because the heterogeneity among human tissues and diseases is much greater than that observed in animal models, and discoveries made in animal tissues might not translate well to their human counterparts. In this article, we briefly review the challenges of imaging human tissue using mass spectrometry and suggest approaches to address these issues.     

Localization of water-soluble carbohydrates in wheat stems using imaging matrix-assisted laser desorption ionization mass spectrometry

The pool of endogenous water-soluble oligosaccharides found in the stems of wheat (Triticum aestivum) is being investigated as a potential indicator of grain yield. Techniques such as liquid chromatography with mass spectrometry (LC-MS) can profile these analytes but provide no spatial information regarding their distribution in the wheat stem. The imaging matrix-assisted laser desorption ionization (MALDI) mass spectrometry technique has not been utilized for the analysis of oligosaccharides in plant systems previously. Imaging MALDI mass spectrometry was used to analyse cross and longitudinal sections from the stems of Triticum aestivum. A range of oligosaccharides up to Hex(11) were observed. Water-soluble oligosaccharides were ionized as potassiated molecules, and found to be located in the stem pith that is retained predominantly around the inner stem wall. Imaging MALDI analyses provided spatial information on endogenous oligosaccharides present in wheat stems. The technique was found to offer comparable sensitivities for oligosaccharide detection to those of our established LC-MS method, and has potential for broad application in studying the in situ localization of other compound types in plant material.     

Imaging Mass Spectrometry Reveals Acyl-Chain- and Region-Specific Sphingolipid Metabolism in the Kidneys of Sphingomyelin Synthase 2-Deficient Mice

Obesity was reported to cause kidney injury by excessive accumulation of sphingolipids such as sphingomyelin and ceramide. Sphingomyelin synthase 2 (SMS2) is an important enzyme for hepatic sphingolipid homeostasis and its dysfunction is considered to result in fatty liver disease. The expression of SMS2 is also high in the kidneys. However, the contribution of SMS2 on renal sphingolipid metabolism remains unclear. Imaging mass spectrometry is a powerful tool to visualize the distribution and provide quantitative data on lipids in tissue sections. Thus, in this study, we analyzed the effects of SMS2 deficiency on the distribution and concentration of sphingomyelins in the liver and kidneys of mice fed with a normal-diet or a high-fat-diet using imaging mass spectrometry and liquid chromatography/electrospray ionization-tandem mass spectrometry. Our study revealed that high-fat-diet increased C18–C22 sphingomyelins, but decreased C24-sphingomyelins, in the liver and kidneys of wild-type mice. By contrast, SMS2 deficiency decreased C18–C24 sphingomyelins in the liver. Although a similar trend was observed in the whole-kidneys, the effects were minor. Interestingly, imaging mass spectrometry revealed that sphingomyelin localization was specific to each acyl-chain length in the kidneys. Further, SMS2 deficiency mainly decreased C22-sphingomyelin in the renal medulla and C24-sphingomyelins in the renal cortex. Thus, imaging mass spectrometry can provide visual assessment of the contribution of SMS2 on acyl-chain- and region-specific sphingomyelin metabolism in the kidneys.     

Combined dynamic arrays for storing and searching semi-ordered tandem mass spectrometry data.

When performing bioinformatics analysis on tandem mass spectrometry data, there is a computational need to efficiently store and sort these semi-ordered datasets. To solve this problem, a new data structure based on dynamic arrays was designed and implemented in an algorithm that parses semi-ordered data made by Mascot, a separate software program that matches peptide tandem mass spectra to protein sequences in a database. By accommodating the special features of these large datasets, the combined dynamic array (CDA) provides efficient searching and insertion operations. The operations on real datasets using this new data structure are hundreds times faster than operations using binary tree and red-black tree structures. The difference becomes more significant when the dataset size grows. This data structure may be useful for improving the speed of other related types of protein assembling software or other types of software that operate on datasets with similar semi-ordered features.     

Missing values in mass spectrometry based metabolomics: an undervalued step in the data processing pipeline

Missing values in mass spectrometry metabolomic datasets occur widely and can originate from a number of sources, including for both technical and biological reasons. Currently, little is known about these data, i.e. about their distributions across datasets, the need (or not) to consider them in the data processing pipeline, and most importantly, the optimal way of assigning them values prior to univariate or multivariate data analysis. Here, we address all of these issues using direct infusion Fourier transform ion cyclotron resonance mass spectrometry data. We have shown that missing data are widespread, accounting for ca. 20% of data and affecting up to 80% of all variables, and that they do not occur randomly but rather as a function of signal intensity and mass-to-charge ratio. We have demonstrated that missing data estimation algorithms have a major effect on the outcome of data analysis when comparing the differences between biological sample groups, including by t test, ANOVA and principal component analysis. Furthermore, results varied significantly across the eight algorithms that we assessed for their ability to impute known, but labelled as missing, entries. Based on all of our findings we identified the k-nearest neighbour imputation method (KNN) as the optimal missing value estimation approach for our direct infusion mass spectrometry datasets. However, we believe the wider significance of this study is that it highlights the importance of missing metabolite levels in the data processing pipeline and offers an approach to identify optimal ways of treating missing data in metabolomics experiments.

     

Missing values in mass spectrometry based metabolomics: an undervalued step in the data processing pipeline

Missing values in mass spectrometry metabolomic datasets occur widely and can originate from a number of sources, including for both technical and biological reasons. Currently, little is known about these data, i.e. about their distributions across datasets, the need (or not) to consider them in the data processing pipeline, and most importantly, the optimal way of assigning them values prior to univariate or multivariate data analysis. Here, we address all of these issues using direct infusion Fourier transform ion cyclotron resonance mass spectrometry data. We have shown that missing data are widespread, accounting for ca. 20% of data and affecting up to 80% of all variables, and that they do not occur randomly but rather as a function of signal intensity and mass-to-charge ratio. We have demonstrated that missing data estimation algorithms have a major effect on the outcome of data analysis when comparing the differences between biological sample groups, including by t test, ANOVA and principal component analysis. Furthermore, results varied significantly across the eight algorithms that we assessed for their ability to impute known, but labelled as missing, entries. Based on all of our findings we identified the k-nearest neighbour imputation method (KNN) as the optimal missing value estimation approach for our direct infusion mass spectrometry datasets. However, we believe the wider significance of this study is that it highlights the importance of missing metabolite levels in the data processing pipeline and offers an approach to identify optimal ways of treating missing data in metabolomics experiments.     

Microscopy Image Browser: A Platform for Segmentation and Analysis of Multidimensional Datasets

Understanding the structure–function relationship of cells and organelles in their natural context requires multidimensional imaging. As techniques for multimodal 3-D imaging have become more accessible, effective processing, visualization, and analysis of large datasets are posing a bottleneck for the workflow. Here, we present a new software package for high-performance segmentation and image processing of multidimensional datasets that improves and facilitates the full utilization and quantitative analysis of acquired data, which is freely available from a dedicated website. The open-source environment enables modification and insertion of new plug-ins to customize the program for specific needs. We provide practical examples of program features used for processing, segmentation and analysis of light and electron microscopy datasets, and detailed tutorials to enable users to rapidly and thoroughly learn how to use the program.     

Graphical Tools for Network Meta-Analysis in STATA

Network meta-analysis synthesizes direct and indirect evidence in a network of trials that compare multiple interventions and has the potential to rank the competing treatments according to the studied outcome. Despite its usefulness network meta-analysis is often criticized for its complexity and for being accessible only to researchers with strong statistical and computational skills. The evaluation of the underlying model assumptions, the statistical technicalities and presentation of the results in a concise and understandable way are all challenging aspects in the network meta-analysis methodology. In this paper we aim to make the methodology accessible to non-statisticians by presenting and explaining a series of graphical tools via worked examples. To this end, we provide a set of STATA routines that can be easily employed to present the evidence base, evaluate the assumptions, fit the network meta-analysis model and interpret its results.     

Systematic characterization of high mass accuracy influence on false discovery and probability scoring in peptide mass fingerprinting

Whereas the bearing of mass measurement error on protein identification is sometimes underestimated, uncertainty in observed peptide masses unavoidably translates to ambiguity in subsequent protein identifications. Although ongoing instrumental advances continue to make high accuracy mass spectrometry (MS) increasingly accessible, many proteomics experiments are still conducted with rather large mass error tolerances. In addition, the ranking schemes of most protein identification algorithms do not include a meaningful incorporation of mass measurement error. This article provides a critical evaluation of mass error tolerance as it pertains to false positive peptide and protein associations resulting from peptide mass fingerprint (PMF) database searching. High accuracy, high resolution PMFs of several model proteins were obtained using matrix-assisted laser desorption/ionization Fourier transform ion cyclotron resonance mass spectrometry (MALDI-FTICR-MS). Varying levels of mass accuracy were simulated by systematically modulating the mass error tolerance of the PMF query and monitoring the effect on figures of merit indicating the PMF quality. Importantly, the benefits of decreased mass error tolerance are not manifest in Mowse scores when operating at tolerances in the low parts-per-million range but become apparent with the consideration of additional metrics that are often overlooked. Furthermore, the outcomes of these experiments support the concept that false discovery is closely tied to mass measurement error in PMF analysis. Clear establishment of this relation demonstrates the need for mass error-aware protein identification routines and argues for a more prominent contribution of high accuracy mass measurement to proteomic science.     

Reanalysis of ProteomicsDB Using an Accurate,Sensitive, and Scalable False Discovery Rate Estimation Approach for Protein Groups

Estimating false discovery rates (FDRs) of protein identification continues to be an important topic in mass spectrometry–based proteomics, particularly when analyzing very large datasets. One performant method for this purpose is the Picked Protein FDR approach which is based on a target-decoy competition strategy on the protein level that ensures that FDRs scale to large datasets. Here, we present an extension to this method that can also deal with protein groups, that is, proteins that share common peptides such as protein isoforms of the same gene. To obtain well-calibrated FDR estimates that preserve protein identification sensitivity, we introduce two novel ideas. First, the picked group target-decoy and second, the rescued subset grouping strategies. Using entrapment searches and simulated data for validation, we demonstrate that the new Picked Protein Group FDR method produces accurate protein group-level FDR estimates regardless of the size of the data set. The validation analysis also uncovered that applying the commonly used Occam’s razor principle leads to anticonservative FDR estimates for large datasets. This is not the case for the Picked Protein Group FDR method. Reanalysis of deep proteomes of 29 human tissues showed that the new method identified up to 4% more protein groups than MaxQuant. Applying the method to the reanalysis of the entire human section of ProteomicsDB led to the identification of 18,000 protein groups at 1% protein group-level FDR. The analysis also showed that about 1250 genes were represented by ≥2 identified protein groups. To make the method accessible to the proteomics community, we provide a software tool including a graphical user interface that enables merging results from multiple MaxQuant searches into a single list of identified and quantified protein groups.     

On preprocessing and antisymmetry in de novo peptide sequencing: improving efficiency and accuracy

Peptide sequencing plays a fundamental role in proteomics. Tandem mass spectrometry, being sensitive and efficient, is one of the most commonly used techniques in peptide sequencing. Many computational models and algorithms have been developed for peptide sequencing using tandem mass spectrometry. In this paper, we investigate general issues in de novo sequencing, and present results that can be used to improve current de novo sequencing algorithms. We propose a general preprocessing scheme that performs binning, pseudo-peak introduction, and noise removal, and present theoretical and experimental analyses on each of the components. Then, we study the antisymmetry problem and current assumptions related to it, and propose a more realistic way to handle the antisymmetry problem based on analysis of some datasets. We integrate our findings on preprocessing and the antisymmetry problem with some current models for peptide sequencing. Experimental results show that our findings help to improve accuracies for de novo sequencing.     

Combinatorial approaches for mass spectra recalibration

Mass spectrometry has become one of the most popular analysis techniques in Proteomics and Systems Biology. With the creation of larger datasets, the automated recalibration of mass spectra becomes important to ensure that every peak in the sample spectrum is correctly assigned to some peptide and protein. Algorithms for recalibrating mass spectra have to be robust with respect to wrongly assigned peaks, as well as efficient due to the amount of mass spectrometry data. The recalibration of mass spectra leads us to the problem of finding an optimal matching between mass spectra under measurement errors. We have developed two deterministic methods that allow robust computation of such a matching: The first approach uses a computational geometry interpretation of the problem, and tries to find two parallel lines with constant distance that stab a maximal number of points in the plane. The second approach is based on finding a maximal common approximate subsequence, and improves existing algorithms by one order of magnitude exploiting the sequential nature of the matching problem. We compare our results to a computational geometry algorithm using a topological line-sweep.     

New developments in profiling and imaging of proteins from tissue sections by MALDI mass spectrometry

Molecular imaging of tissue by MALDI mass spectrometry is a powerful tool for visualizing the spatial distribution of constituent analytes with high molecular specificity. Although the technique is relatively young, it has already contributed to the understanding of many diverse areas of human health. In recent years, a great many advances in the practice of imaging mass spectrometry have taken place, making the technique more sensitive, robust, and ultimately useful. The purpose of this review is to highlight some of the more recent technological advances that have improved the efficiency of imaging mass spectrometry for clinical applications. Advances in the way MALDI mass spectrometry is integrated with histology, improved methods for automation, and better tools for data analysis are outlined in this review. Refined top-down strategies for the identification and validation of candidate biomarkers found in tissue sections are discussed. A clinical example highlighting the application of these methods to a cohort of clinical samples is described.