Analysis and Interpretation of Imaging Mass Spectrometry Data by Clustering Mass-to-Charge Images According to Their Spatial Similarity

Seeing is Believing: Developing Multimodal Metabolic Insights at the Molecular Level

This outlook explores how two different molecular imaging approaches might be combined to gain insight into dynamic, subcellular metabolic processes. Specifically, we discuss how matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) and stimulated Raman scattering (SRS) microscopy, which have significantly pushed the boundaries of imaging metabolic and metabolomic analyses in their own right, could be combined to create comprehensive molecular images. We first briefly summarize the recent advances for each technique. We then explore how one might overcome the inherent limitations of each individual method, by envisioning orthogonal and interchangeable workflows. Additionally, we delve into the potential benefits of adopting a complementary approach that combines both MSI and SRS spectro-microscopy for informing on specific chemical structures through functional-group-specific targets. Ultimately, by integrating the strengths of both imaging modalities, researchers can achieve a more comprehensive understanding of biological and chemical systems, enabling precise metabolic investigations. This synergistic approach holds substantial promise to expand our toolkit for studying metabolites in complex environments.

DeepION: A Deep Learning-Based Low-Dimensional Representation Model of Ion Images for Mass Spectrometry Imaging

Mass spectrometry imaging (MSI) is a high-throughput imaging technique capable of the qualitative and quantitative in situ detection of thousands of ions in biological samples. Ion image representation is a technique that produces a low-dimensional vector embedded with significant spectral and spatial information on an ion image, which further facilitates the distance-based similarity measurement for the identification of colocalized ions. However, given the low signal-to-noise ratios inherent in MSI data coupled with the scarcity of annotated data sets, achieving an effective ion image representation for each ion image remains a challenge. In this study, we propose DeepION, a novel deep learning-based method designed specifically for ion image representation, which is applied to the identification of colocalized ions and isotope ions. In DeepION, contrastive learning is introduced to ensure that the model can generate the ion image representation in a self-supervised manner without manual annotation. Since data augmentation is a crucial step in contrastive learning, a unique data augmentation strategy is designed by considering the characteristics of MSI data, such as the Poisson distribution of ion abundance and a random pattern of missing values, to generate plentiful ion image pairs for DeepION model training. Experimental results of rat brain tissue MSI show that DeepION outperforms other methods for both colocalized ion and isotope ion identification, demonstrating the effectiveness of ion image representation. The proposed model could serve as a crucial tool in the biomarker discovery and drug development of the MSI technique.

Neurotrauma investigation through spatial omics guided by mass spectrometry imaging: Target identification and clinical applications

Traumatic brain injury (TBI) represents one of the major public health concerns worldwide due to the increase in TBI incidence as a result of injuries from daily life accidents such as sports and motor vehicle transportation as well as military-related practices. This type of central nervous system trauma is known to predispose patients to several neurological disorders such as Parkinson's disease, Alzheimer's disease, chronic trauamatic encephalopathy, and age-related Dementia. Recently, several proteomic and lipidomic platforms have been applied on different TBI studies to investigate TBI-related mechanisms that have broadened our understanding of its distinct neuropathological complications. In this study, we provide an updated comprehensive overview of the current knowledge and novel perspectives of the spatially resolved microproteomics and microlipidomics approaches guided by mass spectrometry imaging used in TBI studies and its applications in the neurotrauma field. In this regard, we will discuss the use of the spatially resolved microproteomics and assess the different microproteomic sampling methods such as laser capture microdissection, parafilm assisted microdissection, and liquid microjunction extraction as accurate and precise techniques in the field of neuroproteomics. Additionally, we will highlight lipid profiling applications and their prospective potentials in characterizing molecular processes involved in the field of TBI. Specifically, we will discuss the phospholipid metabolism acting as a precursor for proinflammatory molecules such as eicosanoids. Finally, we will survey the current state of spatial neuroproteomics and microproteomics applications and present the various studies highlighting their findings in these fields.

Statistical Analysis of Multiplex Immunofluorescence and Immunohistochemistry Imaging Data

Advances in multiplexed single-cell immunofluorescence (mIF) and multiplex immunohistochemistry (mIHC) imaging technologies have enabled the analysis of cell-to-cell spatial relationships that promise to revolutionize our understanding of tissue-based diseases and autoimmune disorders. Multiplex images are collected as multichannel TIFF files; then denoised, segmented to identify cells and nuclei, normalized across slides with protein markers to correct for batch effects, and phenotyped; and then tissue composition and spatial context at the cellular level are analyzed. This chapter discusses methods and software infrastructure for image processing and statistical analysis of mIF/mIHC data.

Emerging Computational Methods in Mass Spectrometry Imaging

Mass spectrometry imaging (MSI) is a powerful analytical technique that generates maps of hundreds of molecules in biological samples with high sensitivity and molecular specificity. Advanced MSI platforms with capability of high-spatial resolution and high-throughput acquisition generate vast amount of data, which necessitates the development of computational tools for MSI data analysis. In addition, computation-driven MSI experiments have recently emerged as enabling technologies for further improving the MSI capabilities with little or no hardware modification. This review provides a critical summary of computational methods and resources developed for MSI data analysis and interpretation along with computational approaches for improving throughput and molecular coverage in MSI experiments. This review is focused on the recently developed artificial intelligence methods and provides an outlook for a future paradigm shift in MSI with transformative computational methods.

Two-Dimensional and Three-Dimensional Time-of-Flight Secondary Ion Mass Spectrometry Image Feature Extraction Using a Spatially Aware Convolutional Autoencoder

Feature extraction algorithms are an important class of unsupervised methods used to reduce data dimensionality. They have been applied extensively for time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging─commonly, matrix factorization (MF) techniques such as principal component analysis have been used. A limitation of MF is the assumption of linearity, which is generally not accurate for ToF-SIMS data. Recently, nonlinear autoencoders have been shown to outperform MF techniques for ToF-SIMS image feature extraction. However, another limitation of most feature extraction methods (including autoencoders) that is particularly important for hyperspectral data is that they do not consider spatial information. To address this limitation, we describe the application of the convolutional autoencoder (CNNAE) to hyperspectral ToF-SIMS imaging data. The CNNAE is an artificial neural network developed specifically for hyperspectral data that uses convolutional layers for image encoding, thereby explicitly incorporating pixel neighborhood information. We compared the performance of the CNNAE with other common feature extraction algorithms for two biological ToF-SIMS imaging data sets. We investigated the extracted features and used the dimensionality-reduced data to train additional ML algorithms. By converting two-dimensional convolutional layers to three-dimensional (3D), we also showed how the CNNAE can be extended to 3D ToF-SIMS images. In general, the CNNAE produced features with significantly higher contrast and autocorrelation than other techniques. Furthermore, histologically recognizable features in the data were more accurately represented. The extension of the CNNAE to 3D data also provided an important proof of principle for the analysis of more complex 3D data sets.

Applications of multivariate analysis and unsupervised machine learning to ToF-SIMS images of organic, bioorganic, and biological systems

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging offers a powerful, label-free method for exploring organic, bioorganic, and biological systems. The technique is capable of very high spatial resolution, while also producing an enormous amount of information about the chemical and molecular composition of a surface. However, this information is inherently complex, making interpretation and analysis of the vast amount of data produced by a single ToF-SIMS experiment a considerable challenge. Much research over the past few decades has focused on the application and development of multivariate analysis (MVA) and machine learning (ML) techniques that find meaningful patterns and relationships in these datasets. Here, we review the unsupervised algorithms—that is, algorithms that do not require ground truth labels—that have been applied to ToF-SIMS images, as well as other algorithms and approaches that have been used in the broader family of mass spectrometry imaging (MSI) techniques. We first give a nontechnical overview of several commonly used classes of unsupervised algorithms, such as matrix factorization, clustering, and nonlinear dimensionality reduction. We then review the application of unsupervised algorithms to various organic, bioorganic, and biological systems including cells and tissues, organic films, residues and coatings, and spatially structured systems such as polymer microarrays. We then cover several novel algorithms employed for other MSI techniques that have received little attention from ToF-SIMS imaging researchers. We conclude with a brief outline of potential future directions for the application of MVA and ML algorithms to ToF-SIMS images.

Self-supervised clustering of mass spectrometry imaging data using contrastive learning

Mass spectrometry imaging (MSI) is widely used for the label-free molecular mapping of biological samples. The identification of co-localized molecules in MSI data is crucial to the understanding of biochemical pathways. One of key challenges in molecular colocalization is that complex MSI data are too large for manual annotation but too small for training deep neural networks. Herein, we introduce a self-supervised clustering approach based on contrastive learning, which shows an excellent performance in clustering of MSI data. We train a deep convolutional neural network (CNN) using MSI data from a single experiment without manual annotations to effectively learn high-level spatial features from ion images and classify them based on molecular colocalizations. We demonstrate that contrastive learning generates ion image representations that form well-resolved clusters. Subsequent self-labeling is used to fine-tune both the CNN encoder and linear classifier based on confidently classified ion images. This new approach enables autonomous and high-throughput identification of co-localized species in MSI data, which will dramatically expand the application of spatial lipidomics, metabolomics, and proteomics in biological research.

Automatic Registration of the Mass Spectrometry Imaging Data of Sagittal Brain Slices to the Reference Atlas

The registration of the mass spectrometry imaging (MSI) data with mouse brain tissue slices from the atlases could perform automatic anatomical interpretation, and the comparison of MSI data in particular brain regions from different mice could be accelerated. However, the current registration of MSI data with mouse brain tissue slices is mainly focused on the coronal. Although the sagittal plane is able to provide more information about brain regions on a single histological slice than the coronal, it is difficult to directly register the complete sagittal brain slices of a mouse as a result of the more significant individualized differences and more positional shifts of brain regions. Herein, by adding the auxiliary line on the two brain regions of central canal (CC) and cerebral peduncle (CP), the registration accuracy of the MSI data with sagittal brain slices has been improved (∼2-5-folds for different brain regions). Moreover, the histological sections with different degrees deformation and different dyeing effects have been used to verify that this pipeline has a certain universality. Our method facilitates the rapid comparison of sagittal plane MSI data from different animals and accelerates the application in the discovery of disease markers.

Spatially aware clustering of ion images in mass spectrometry imaging data using deep learning

Computational analysis is crucial to capitalize on the wealth of spatio-molecular information generated by mass spectrometry imaging (MSI) experiments. Currently, the spatial information available in MSI data is often under-utilized, due to the challenges of in-depth spatial pattern extraction. The advent of deep learning has greatly facilitated such complex spatial analysis. In this work, we use a pre-trained neural network to extract high-level features from ion images in MSI data, and test whether this improves downstream data analysis. The resulting neural network interpretation of ion images, coined neural ion images, is used to cluster ion images based on spatial expressions. We evaluate the impact of neural ion images on two ion image clustering pipelines, namely DBSCAN clustering, combined with UMAP-based dimensionality reduction, and k-means clustering. In both pipelines, we compare regular and neural ion images from two different MSI datasets. All tested pipelines could extract underlying spatial patterns, but the neural network-based pipelines provided better assignment of ion images, with more fine-grained clusters, and greater consistency in the spatial structures assigned to individual clusters. Additionally, we introduce the relative isotope ratio metric to quantitatively evaluate clustering quality. The resulting scores show that isotopical m/z values are more often clustered together in the neural network-based pipeline, indicating improved clustering outcomes. The usefulness of neural ion images extends beyond clustering towards a generic framework to incorporate spatial information into any MSI-focused machine learning pipeline, both supervised and unsupervised.

Spatial Segmentation of Mass Spectrometry Imaging Data by Combining Multivariate Clustering and Univariate Thresholding

Spatial segmentation partitions mass spectrometry imaging (MSI) data into distinct regions, providing a concise visualization of the vast amount of data and identifying regions of interest (ROIs) for downstream statistical analysis. Unsupervised approaches are particularly attractive, as they may be used to discover the underlying subpopulations present in the high-dimensional MSI data without prior knowledge of the properties of the sample. Herein, we introduce an unsupervised spatial segmentation approach, which combines multivariate clustering and univariate thresholding to generate comprehensive spatial segmentation maps of the MSI data. This approach combines matrix factorization and manifold learning to enable high-quality image segmentation without an extensive hyperparameter search. In parallel, some ion images inadequately represented in the multivariate analysis were treated using univariate thresholding to generate complementary spatial segments. The final spatial segmentation map was assembled from segment candidates that were generated using both techniques. We demonstrate the performance and robustness of this approach for two MSI data sets of mouse uterine and kidney tissue sections that were acquired with different spatial resolutions. The resulting segmentation maps are easy to interpret and project onto the known anatomical regions of the tissue.

ColocML: machine learning quantifies co-localization between mass spectrometry images

Motivation

Imaging mass spectrometry (imaging MS) is a prominent technique for capturing distributions of molecules in tissue sections. Various computational methods for imaging MS rely on quantifying spatial correlations between ion images, referred to as co-localization. However, no comprehensive evaluation of co-localization measures has ever been performed; this leads to arbitrary choices and hinders method development.

Results

We present ColocML, a machine learning approach addressing this gap. With the help of 42 imaging MS experts from nine laboratories, we created a gold standard of 2210 pairs of ion images ranked by their co-localization. We evaluated existing co-localization measures and developed novel measures using term frequency–inverse document frequency and deep neural networks. The semi-supervised deep learning Pi model and the cosine score applied after median thresholding performed the best (Spearman 0.797 and 0.794 with expert rankings, respectively). We illustrate these measures by inferring co-localization properties of 10 273 molecules from 3685 public METASPACE datasets.

Availability and implementation

https://github.com/metaspace2020/coloc.

Supplementary information

Supplementary data are available at Bioinformatics online.

The metamorphosis of analytical chemistry

Defining analytical chemistry as the measurement of isolated compositional features in a selected study object ignores the unique perspective that analytical chemists bring to twenty-first century science and society. In this feature article, we will discuss some of the existing preconceptions and misinterpretations of analytical chemistry that occur at present and will tackle them from the more up-to-date perspective of science in the Big Data Era. This will place their influence in context while simultaneously enlarging the scope of the discipline analytical chemistry to its well-deserved prevalent position in present-day science and technology. Graphical abstract.

Unsupervised machine learning for exploratory data analysis in imaging mass spectrometry

Imaging mass spectrometry (IMS) is a rapidly advancing molecular imaging modality that can map the spatial distribution of molecules with high chemical specificity. IMS does not require prior tagging of molecular targets and is able to measure a large number of ions concurrently in a single experiment. While this makes it particularly suited for exploratory analysis, the large amount and high-dimensional nature of data generated by IMS techniques make automated computational analysis indispensable. Research into computational methods for IMS data has touched upon different aspects, including spectral preprocessing, data formats, dimensionality reduction, spatial registration, sample classification, differential analysis between IMS experiments, and data-driven fusion methods to extract patterns corroborated by both IMS and other imaging modalities. In this work, we review unsupervised machine learning methods for exploratory analysis of IMS data, with particular focus on (a) factorization, (b) clustering, and (c) manifold learning. To provide a view across the various IMS modalities, we have attempted to include examples from a range of approaches including matrix assisted laser desorption/ionization, desorption electrospray ionization, and secondary ion mass spectrometry-based IMS. This review aims to be an entry point for both (i) analytical chemists and mass spectrometry experts who want to explore computational techniques; and (ii) computer scientists and data mining specialists who want to enter the IMS field. © 2019 The Authors. Mass Spectrometry Reviews published by Wiley Periodicals, Inc. Mass SpecRev 00:1-47, 2019.

Spatial Metabolomics and Imaging Mass Spectrometry in the Age of Artificial Intelligence

Spatial metabolomics is an emerging field of omics research that has enabled localizing metabolites, lipids, and drugs in tissue sections, a feat considered impossible just two decades ago. Spatial metabolomics and its enabling technology-imaging mass spectrometry-generate big hyper-spectral imaging data that have motivated the development of tailored computational methods at the intersection of computational metabolomics and image analysis. Experimental and computational developments have recently opened doors to applications of spatial metabolomics in life sciences and biomedicine. At the same time, these advances have coincided with a rapid evolution in machine learning, deep learning, and artificial intelligence, which are transforming our everyday life and promise to revolutionize biology and healthcare. Here, we introduce spatial metabolomics through the eyes of a computational scientist, review the outstanding challenges, provide a look into the future, and discuss opportunities granted by the ongoing convergence of human and artificial intelligence.

Developing a Drug Screening Platform: MALDI-Mass Spectrometry Imaging of Paper-Based Cultures

Many potential chemotherapeutics fail to reach patients. One of the key reasons is that compounds are tested during the drug discovery stage in two-dimensional (2D) cell cultures, which are often unable to accurately model in vivo outcomes. Three-dimensional (3D) in vitro tumor models are more predictive of chemotherapeutic effectiveness than 2D cultures, and thus, their implementation during the drug screening stage has the potential to more accurately evaluate compounds earlier, saving both time and money. Paper-based cultures (PBCs) are an emerging 3D culture platform in which cells suspended in Matrigel are seeded into paper scaffolds and cultured to generate a tissue-like environment. In this study, we demonstrate the potential of matrix-assisted laser desorption/ionization-mass spectrometry imaging with PBCs (MALDI-MSI-PBC) as a drug screening platform. This method discriminated regions of the PBCs with and without cells and/or drugs, indicating that coupling PBCs with MALDI-MSI has the potential to develop rapid, large-scale, and parallel mass spectrometric drug screens.

Quantitative Spatial Analysis of Metabolic Heterogeneity Across in vivo and in vitro Tumor Models

Metabolic preferences of tumor cells vary within a single tumor, contributing to tumor heterogeneity, drug resistance, and patient relapse. However, the relationship between tumor treatment response and metabolically distinct tumor cell populations is not well-understood. Here, a quantitative approach was developed to characterize spatial patterns of metabolic heterogeneity in tumor cell populations within in vivo xenografts and 3D in vitro cultures (i.e., organoids) of head and neck cancer. Label-free images of cell metabolism were acquired using two-photon fluorescence lifetime microscopy of the metabolic co-enzymes NAD(P)H and FAD. Previous studies have shown that NAD(P)H mean fluorescence lifetimes can identify metabolically distinct cells with varying drug response. Thus, density-based clustering of the NAD(P)H mean fluorescence lifetime was used to identify metabolic sub-populations of cells, then assessed in control, cetuximab-, cisplatin-, and combination-treated xenografts 13 days post-treatment and organoids 24 h post-treatment. Proximity analysis of these metabolically distinct cells was designed to quantify differences in spatial patterns between treatment groups and between xenografts and organoids. Multivariate spatial autocorrelation and principal components analyses of all autofluorescence intensity and lifetime variables were developed to further improve separation between cell sub-populations. Spatial principal components analysis and Z-score calculations of autofluorescence and spatial distribution variables also visualized differences between models. This analysis captures spatial distributions of tumor cell sub-populations influenced by treatment conditions and model-specific environments. Overall, this novel spatial analysis could provide new insights into tumor growth, treatment resistance, and more effective drug treatments across a range of microscopic imaging modalities (e.g., immunofluorescence, imaging mass spectrometry).

rMSIKeyIon: An Ion Filtering R Package for Untargeted Analysis of Metabolomic LDI-MS Images

Many MALDI-MS imaging experiments make a case versus control studies of different tissue regions in order to highlight significant compounds affected by the variables of study. This is a challenge because the tissue samples to be compared come from different biological entities, and therefore they exhibit high variability. Moreover, the statistical tests available cannot properly compare ion concentrations in two regions of interest (ROIs) within or between images. The high correlation between the ion concentrations due to the existence of different morphological regions in the tissue means that the common statistical tests used in metabolomics experiments cannot be applied. Another difficulty with the reliability of statistical tests is the elevated number of undetected MS ions in a high percentage of pixels. In this study, we report a procedure for discovering the most important ions in the comparison of a pair of ROIs within or between tissue sections. These ROIs were identified by an unsupervised segmentation process, using the popular k-means algorithm. Our ion filtering algorithm aims to find the up or down-regulated ions between two ROIs by using a combination of three parameters: (a) the percentage of pixels in which a particular ion is not detected, (b) the Mann–Whitney U ion concentration test, and (c) the ion concentration fold-change. The undetected MS signals (null peaks) are discarded from the histogram before the calculation of (b) and (c) parameters. With this methodology, we found the important ions between the different segments of a mouse brain tissue sagittal section and determined some lipid compounds (mainly triacylglycerols and phosphatidylcholines) in the liver of mice exposed to thirdhand smoke.

Detection and visualization of communities in mass spectrometry imaging data

Background

The spatial distribution and colocalization of functionally related metabolites is analysed in order to investigate the spatial (and functional) aspects of molecular networks. We propose to consider community detection for the analysis of m/z-images to group molecules with correlative spatial distribution into communities so they hint at functional networks or pathway activity. To detect communities, we investigate a spectral approach by optimizing the modularity measure. We present an analysis pipeline and an online interactive visualization tool to facilitate explorative analysis of the results. The approach is illustrated with synthetical benchmark data and two real world data sets (barley seed and glioblastoma section).

Results

For the barley sample data set, our approach is able to reproduce the findings of a previous work that identified groups of molecules with distributions that correlate with anatomical structures of the barley seed. The analysis of glioblastoma section data revealed that some molecular compositions are locally focused, indicating the existence of a meaningful separation in at least two areas. This result is in line with the prior histological knowledge. In addition to confirming prior findings, the resulting graph structures revealed new subcommunities of m/z-images (i.e. metabolites) with more detailed distribution patterns. Another result of our work is the development of an interactive webtool called GRINE (Analysis of GRaph mapped Image Data NEtworks).

Conclusions

The proposed method was successfully applied to identify molecular communities of laterally co-localized molecules. For both application examples, the detected communities showed inherent substructures that could easily be investigated with the proposed visualization tool. This shows the potential of this approach as a complementary addition to pixel clustering methods.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2890-6) contains supplementary material, which is available to authorized users.

Colocalization Features for Classification of Tumors Using Desorption Electrospray Ionization Mass Spectrometry Imaging

Supervised modeling of mass spectrometry imaging (MSI) data is a crucial component for the detection of the distinct molecular characteristics of cancerous tissues. Currently, two types of supervised analyses are mainly used on MSI data: pixel-wise segmentation of sample images and whole-sample-based classification. A large number of mass spectra associated with each MSI sample can represent a challenge for designing models that simultaneously preserve the overall molecular content while capturing valuable information contained in the MSI data. Furthermore, intensity-related batch effects can introduce biases in the statistical models. Here we introduce a method based on ion colocalization features that allows the classification of whole tissue specimens using MSI data, which naturally preserves the spatial information associated the with the mass spectra and is less sensitive to possible batch effects. Finally, we propose data visualization strategies for the inspection of the derived networks, which can be used to assess whether the correlation differences are related to coexpression/suppression or disjoint spatial localization patterns and can suggest hypotheses based on the underlying mechanisms associated with the different classes of analyzed samples.

In plaque-mass spectrometry imaging of a bloom-forming alga during viral infection reveals a metabolic shift towards odd-chain fatty acid lipids

Tapping into the metabolic cross-talk between a host and its virus can reveal unique strategies employed during infection. Viral infection is a dynamic process that generates an evolving metabolic landscape. Gaining a continuous view into the infection process is highly challenging and is limited by current metabolomics approaches, which typically measure the average of the entire population at various stages of infection. Here, we took an innovative approach to study the metabolic basis of host-virus interactions between the bloom-forming alga Emiliania huxleyi and its specific virus. We combined a classical method in virology, the plaque assay, with advanced mass spectrometry imaging (MSI), an approach we termed ‘in plaque-MSI’. Taking advantage of the spatial characteristics of the plaque, we mapped the metabolic landscape induced during infection in a high spatiotemporal resolution, unfolding the infection process in a continuous manner. Further unsupervised spatially-aware clustering, combined with known lipid biomarkers, revealed a systematic metabolic shift during infection towards lipids containing the odd-chain fatty acid pentadecanoic acid (C15:0). Applying ‘in plaque-MSI’ might facilitate the discovery of bioactive compounds that mediate the chemical arms race of host-virus interactions in diverse model systems.

Signal preprocessing, multivariate analysis and software tools for MA(LDI)-TOF mass spectrometry imaging for biological applications

Mass spectrometry imaging (MSI) is a label-free analytical technique capable of molecularly characterizing biological samples, including tissues and cell lines. The constant development of analytical instrumentation and strategies over the previous decade makes MSI a key tool in clinical research. Nevertheless, most MSI studies are limited to targeted analysis or the mere visualization of a few molecular species (proteins, peptides, metabolites, or lipids) in a region of interest without fully exploiting the possibilities inherent in the MSI technique, such as tissue classification and segmentation or the identification of relevant biomarkers from an untargeted approach. MSI data processing is challenging due to several factors. The large volume of mass spectra involved in a MSI experiment makes choosing the correct computational strategies critical. Furthermore, pixel to pixel variation inherent in the technique makes choosing the correct preprocessing steps critical. The primary aim of this review was to provide an overview of the data-processing steps and tools that can be applied to an MSI experiment, from preprocessing the raw data to the more advanced strategies for image visualization and segmentation. This review is particularly aimed at researchers performing MSI experiments and who are interested in incorporating new data-processing features, improving their computational strategy, and/or desire access to data-processing tools currently available. © 2016 Wiley Periodicals, Inc. Mass Spec Rev 37:281-306, 2018.

Molecular similarities and differences from human pulmonary fibrosis and corresponding mouse model: MALDI imaging mass spectrometry in comparative medicine

Animal models can reproduce some model-specific aspects of human diseases, but some animal models translate poorly or fail to translate to the corresponding human disease. Here, we develop a strategy to systematically compare human and mouse tissues, and conduct a proof-of-concept experiment to identify molecular similarities and differences using patients with idiopathic pulmonary fibrosis and a bleomycin-induced fibrosis mouse model. Our novel approach employs high-throughput tissue microarrays (TMAs) of humans and mice, high-resolution matrix-assisted laser desorption/ionization-Fourier transform-ion cyclotron resonance-mass spectrometry imaging (MALDI-FT-ICR-MSI) to spatially resolve mass spectra at the level of specific metabolites, and hierarchical clustering and pathway enrichment analysis to identify functionally similar/different molecular patterns and pathways in pathological lesions of humans and mice. We identified a large number of common molecules (n=1366) and fewer exclusive molecules in humans (n=83) and mice (n=54). Among the common molecules, the 'ascorbate and aldarate metabolism' pathway had the highest similarity in human and mouse lesions. This proof-of-concept study demonstrates that our novel strategy employing a reliable and easy-to-perform experimental design accurately identifies pathways and factors that can be directly compared between animal models and human diseases.

Droplet-Based Liquid Extraction for Spatially-Resolved Microproteomics Analysis of Tissue Sections

Obtaining information on protein content while keeping their localization on tissue or organ is of importance in different domains to understand pathophysiological processes. There is increasing interest in studying the microenvironment and heterogeneity of tumors, which currently is difficult with existing proteomics techniques. The advent of new techniques, like MALDI Mass Spectrometry Imaging, made a significant progress in the last decade but is characterized by a number of inherent drawbacks. One of these is the limited identification of proteins. New alternative approaches such as spatially-resolved liquid microextraction have recently been proposed to overcome this limitation. In this chapter, we describe strategies using liquid microjunction to perform extraction of previously digested peptides or of intact proteins from tissue section in a localized manner.

Investigation of 5-(3-Trifluoromethylbenzylidene)thiazolidine-2,4-dione as a Matrix for Analyses of Biogenic Monoamine Transmitters Using MALDI-MS

In order to discover new matrices suitable for the analyses of low molecular-weight compounds using positive-ion mode matrix-assisted laser desorption/ionization (MALDI) time-of-flight mass spectrometry (MS), 5-(3-trifluoromethylbenzylidene)thiazolidine-2,4-dione (3-CF3-BTD) was synthesized, and its effectiveness was compared with that when commercially available α-cyano-4-hydroxycinnamic acid was used. 3-CF3-BTD was sufficiently sensitive to analyze neurotransmitters, i.e., dopamine, serotonin, histamine, and epinephrine, in amounts of several picomoles. Similar to vacuum MALDI experiments, atmospheric-pressure MALDI-MS measurements using 3-CF3-BTD as a matrix also detected dopamine.

DetectTLC: Automated Reaction Mixture Screening Utilizing Quantitative Mass Spectrometry Image Features

Full characterization of complex reaction mixtures is necessary to understand mechanisms, optimize yields, and elucidate secondary reaction pathways. Molecular-level information for species in such mixtures can be readily obtained by coupling mass spectrometry imaging (MSI) with thin layer chromatography (TLC) separations. User-guided investigation of imaging data for mixture components with known m/z values is generally straightforward; however, spot detection for unknowns is highly tedious, and limits the applicability of MSI in conjunction with TLC. To accelerate imaging data mining, we developed DetectTLC, an approach that automatically identifies m/z values exhibiting TLC spot-like regions in MS molecular images. Furthermore, DetectTLC can also spatially match m/z values for spots acquired during alternating high and low collision-energy scans, pairing product ions with precursors to enhance structural identification. As an example, DetectTLC is applied to the identification and structural confirmation of unknown, yet significant, products of abiotic pyrazinone and aminopyrazine nucleoside analog synthesis. Graphical Abstract ᅟ.

Optimized Protocol To Analyze Changes in the Lipidome of Xenografts after Treatment with 2-Hydroxyoleic Acid

Xenografts are a popular model for the study of the action of new antitumor drugs. However, xenografts are highly heterogeneous structures, and therefore it is sometimes difficult to evaluate the effects of the compounds on tumor metabolism. In this context, imaging mass spectrometry (IMS) may yield the required information, due to its inherent characteristics of sensitivity and spatial resolution. To the best of our knowledge, there is still no clear analysis protocol to properly evaluate the changes between samples due to the treatment. Here we present a protocol for the evaluation of the effect of 2-hydroxyoleic acid (2-OHOA), an antitumor compound, on xenografts lipidome based on IMS. Direct treated/control comparison did not show conclusive results. As we will demonstrate, a more sophisticated protocol was required to evaluate these changes including the following: (1) identification of different areas in the xenograft, (2) classification of these areas (necrotic/viable) to compare similar types of tissues, (3) suppression of the effect of the variation of adduct formation between samples, and (4) normalization of the variables using the standard deviation to eliminate the excessive impact of the stronger peaks in the statistical analysis. In this way, the 36 lipid species that experienced the largest changes between treated and control were identified. Furthermore, incorporation of 2-hydroxyoleic acid to a sphinganine base was also confirmed by MS/MS. Comparison of the changes observed here with previous results obtained with different techniques demonstrates the validity of the protocol.

Mass spectrometry based chemical imaging of foods

Chemical imaging based on mass spectrometry is an emerging technology which has opened opportunities for fundamental research in food science.

Deconvolving molecular signatures of interactions between microbial colonies

Motivation: The interactions between microbial colonies through chemical signaling are not well understood. A microbial colony can use different molecules to inhibit or accelerate the growth of other colonies. A better understanding of the molecules involved in these interactions could lead to advancements in health and medicine. Imaging mass spectrometry (IMS) applied to co-cultured microbial communities aims to capture the spatial characteristics of the colonies’ molecular fingerprints. These data are high-dimensional and require computational analysis methods to interpret.

Results: Here, we present a dictionary learning method that deconvolves spectra of different molecules from IMS data. We call this method MOLecular Dictionary Learning (MOLDL). Unlike standard dictionary learning methods which assume Gaussian-distributed data, our method uses the Poisson distribution to capture the count nature of the mass spectrometry data. Also, our method incorporates universally applicable information on common ion types of molecules in MALDI mass spectrometry. This greatly reduces model parameterization and increases deconvolution accuracy by eliminating spurious solutions. Moreover, our method leverages the spatial nature of IMS data by assuming that nearby locations share similar abundances, thus avoiding overfitting to noise. Tests on simulated datasets show that this method has good performance in recovering molecule dictionaries. We also tested our method on real data measured on a microbial community composed of two species. We confirmed through follow-up validation experiments that our method recovered true and complete signatures of molecules. These results indicate that our method can discover molecules in IMS data reliably, and hence can help advance the study of interaction of microbial colonies.

Availability and implementation: The code used in this paper is available at: https://github.com/frizfealer/IMS_project.

Contact:vjojic@cs.unc.edu

Supplementary information:Supplementary data are available at Bioinformatics online.

Mass spectrometry imaging for plant biology: a review

Mass spectrometry imaging (MSI) is a developing technique to measure the spatio-temporal distribution of many biomolecules in tissues. Over the preceding decade, MSI has been adopted by plant biologists and applied in a broad range of areas, including primary metabolism, natural products, plant defense, plant responses to abiotic and biotic stress, plant lipids and the developing field of spatial metabolomics. This review covers recent advances in plant-based MSI, general aspects of instrumentation, analytical approaches, sample preparation and the current trends in respective plant research.

De novo discovery of phenotypic intratumour heterogeneity using imaging mass spectrometry

An essential and so far unresolved factor influencing the evolution of cancer and the clinical management of patients is intratumour clonal and phenotypic heterogeneity. However, the de novo identification of tumour subpopulations is so far both a challenging and an unresolved task. Here we present the first systematic approach for the de novo discovery of clinically detrimental molecular tumour subpopulations. In this proof-of-principle study, spatially resolved, tumour-specific mass spectra were acquired, using matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry, from tissues of 63 gastric carcinoma and 32 breast carcinoma patients. The mass spectra, representing the proteomic heterogeneity within tumour areas, were grouped by a corroborated statistical clustering algorithm in order to obtain segmentation maps of molecularly distinct regions. These regions were presumed to represent different phenotypic tumour subpopulations. This was confirmed by linking the presence of these tumour subpopulations to the patients' clinical data. This revealed several of the detected tumour subpopulations to be associated with a different overall survival of the gastric cancer patients (p = 0.025) and the presence of locoregional metastases in patients with breast cancer (p = 0.036). The procedure presented is generic and opens novel options in cancer research, as it reveals microscopically indistinct tumour subpopulations that have an adverse impact on clinical outcome. This enables their further molecular characterization for deeper insights into the biological processes of cancer, which may finally lead to new targeted therapies.

Mass spectrometry of natural products: current, emerging and future technologies

Although mass spectrometry is a century old technology, we are entering into an exciting time for the analysis of molecular information directly from complex biological systems. In this Highlight, we feature emerging mass spectrometric methods and tools used by the natural product community and give a perspective of future directions where the mass spectrometry field is migrating towards over the next decade.