Metabolic heterogeneity of cancer contributes significantly to its poor treatment outcomes and prognosis. As a result, studies continue to focus on identifying new biomarkers and metabolic vulnerabilities, both of which depend on the understanding of altered metabolism in cancer. In the recent decades, the rise of mass spectrometry imaging (MSI) enables the in situ detection of large numbers of small molecules in tissues. Therefore, researchers look to using MSI-mediated spatial metabolomics to further study the altered metabolites in cancer patients. In this review, we examined the two most commonly used spatial metabolomics techniques, MALDI-MSI and DESI-MSI, and some recent highlights of their applications in cancer studies. We also described AFADESI-MSI as a recent variation from the DESI-MSI and compare it with the two major techniques. Specifically, we discussed spatial metabolomics results in four types of heterogeneous malignancies, including breast cancer, esophageal cancer, glioblastoma and lung cancer. Multiple studies have effectively classified cancer tissue subtypes using altered metabolites information. In addition, distribution trends of key metabolites such as fatty acids, high-energy phosphate compounds, and antioxidants were identified. Therefore, while the visualization of finer distribution details requires further improvement of MSI techniques, past studies have suggested spatial metabolomics to be a promising direction to study the complexity of cancer pathophysiology.

/pdf/comparing-desi-msi-and-maldi-msi-mediated-spatial-b8cv6ps9.pdf

Comparing DESI-MSI and MALDI-MSI Mediated Spatial Metabolomics and Their Applications in Cancer Studies

Resistance to drug treatment is a critical barrier in cancer therapy. There is an unmet need to explore cancer hallmarks that can be targeted to overcome this resistance for therapeutic gain. Over time, metabolic reprogramming has been recognised as one hallmark that can be used to prevent therapeutic resistance. With the advent of metabolomics, targeting metabolic alterations in cancer cells and host patients represents an emerging therapeutic strategy for overcoming cancer drug resistance. Driven by technological and methodological advances in mass spectrometry imaging, spatial metabolomics involves the profiling of all the metabolites (metabolomics) so that the spatial information is captured bona fide within the sample. Spatial metabolomics offers an opportunity to demonstrate the drug-resistant tumor profile with metabolic heterogeneity, and also poses a data-mining challenge to reveal meaningful insights from high-dimensional spatial information. In this review, we discuss the latest progress, with the focus on currently available bulk, single-cell and spatial metabolomics technologies and their successful applications in pre-clinical and translational studies on cancer drug resistance. We provide a summary of metabolic mechanisms underlying cancer drug resistance from different aspects; these include the Warburg effect, altered amino acid/lipid/drug metabolism, generation of drug-resistant cancer stem cells, and immunosuppressive metabolism. Furthermore, we propose solutions describing how to overcome cancer drug resistance; these include early detection during cancer initiation, monitoring of clinical drug response, novel anticancer drug and target metabolism, immunotherapy, and the emergence of spatial metabolomics. We conclude by describing the perspectives on how spatial omics approaches (integrating spatial metabolomics) could be further developed to improve the management of drug resistance in cancer patients.

/pdf/when-cancer-drug-resistance-meets-metabolomics-bulk-single-2npwo677.pdf

When cancer drug resistance meets metabolomics (bulk, single-cell and/or spatial): Progress, potential, and perspective

Molecular network strategy in multi-omics and mass spectrometry imaging.

The lipid composition of the brain is well regulated during development, and the specific temporospatial distribution of various lipid species is essential for the development of optimal neural functions. Dietary lipids are the main source of brain lipids and thus contribute to the brain lipidome. Human milk is the only source of a dietary lipids for exclusively breastfed infant. Notably, it contains milk fat globule membrane (MFGM) enriched in polar lipids (PL). While early life is a key for early brain development, the interplay between dietary intake of polar lipids and spatial dynamics of lipid distribution during brain development is poorly understood. Here, we carried out an exploratory study to assess the early postnatal temporal profiling of brain lipidome between postnatal day (PND) 7 and PND 50 using matrix-assisted laser desorption ionization as a mass spectrometry imaging (MALDI-MSI) in an in vivo preclinical model. We also assessed the effect of chronic supplementation with PL extracted from alpha-lactalbumin-enriched whey protein concentrate (WPC) containing 10% lipids, including major lipid classes found in the brain (37% phospholipids and 15% sphingomyelin). MALDI-MSI of the spatial and temporal accretion of lipid species during brain development showed that the brain lipidome is changing heterogeneously along time during brain development. In addition, increases in 400+ PL supplement-dependent lipids were observed. PL supplementation had significant spatial and temporal effect on specific fatty esters, glycerophosphocholines, glycerophosphoethanolamines, and phosphosphingolipids. Interestingly, the average levels of these lipids per brain area tended to be constant in various brain structures across the age groups, paralleling the general brain growth. In contrast, other lipids, such as cytidine diphosphate diacylglycerol, diacylglycerophosphates, phosphocholines, specific ether-phosphoethanolamines, phosphosphingolipids, glycerophosphoinositols, and glycerophosphoserines showed clear age-dependent changes uncoupled from the general brain growth. These results suggest that the dietary PL supplementation may preferentially provide the building blocks for the general brain growth during development. Our findings add to the understanding of brain-nutrient relations, their temporospatial dynamics, and potential impact on neurodevelopment.

/pdf/early-life-to-adult-brain-lipidome-dynamic-a-temporospatial-1nx0k5k7.pdf

Early Life to Adult Brain Lipidome Dynamic: A Temporospatial Study Investigating Dietary Polar Lipid Supplementation Efficacy

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.

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

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). 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). 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.

/pdf/detection-and-visualization-of-communities-in-mass-2k433smy74.pdf

Detection and visualization of communities in mass spectrometry imaging data.

Mass Spectrometry Imaging (MSI) is an established and still evolving technique for the spatial analysis of molecular co-location in biological samples. Nowadays, MSI is expanding into new domains such as clinical pathology. In order to increase the value of MSI data, software for visual analysis is required that is intuitive and technique independent. Here, we present QUIMBI (QUIck exploration tool for Multivariate BioImages) a new tool for the visual analysis of MSI data. QUIMBI is an interactive visual exploration tool that provides the user with a convenient and straightforward visual exploration of morphological and spectral features of MSI data. To improve the overall quality of MSI data by reducing non-tissue specific signals and to ensure optimal compatibility with QUIMBI, the tool is combined with the new pre-processing tool ProViM (Processing for Visualization and multivariate analysis of MSI Data), presented in this work. The features of the proposed visual analysis approach for MSI data analysis are demonstrated with two use cases. The results show that the use of ProViM and QUIMBI not only provides a new fast and intuitive visual analysis, but also allows the detection of new co-location patterns in MSI data that are difficult to find with other methods.

/pdf/fast-visual-exploration-of-mass-spectrometry-images-with-olj915nrr0.pdf

Fast visual exploration of mass spectrometry images with interactive dynamic spectral similarity pseudocoloring.

The detection of groups of molecules that co-localize with histopathological patterns or sub-structures is an important step to combine the rich high-dimensional content of mass spectrometry imaging (MSI) with classic histopathological staining. Here we present the evolution of GRINE to COBI-GRINE, an interactive web tool that maps MSI data onto a graph structure to detect communities of laterally similar distributed molecules and co-visualizes the communities with Hematoxylin and Eosin (HE) stained images. Thereby the tool enables biologists and pathologists to examine the MSI image graph in a target-oriented manner and links molecular co-localization to pathology. Another feature is the manual optimization of cluster results with the assist of graph statistics in order to improve the community results. As the graphs can become very complex, those statistics provide good heuristics to support and accelerate the detection of sub-clusters and misclusterings. This kind of edited cluster optimization allows the integration of expert background knowledge into the clustering result and a more precise analysis of links between molecular co-localization and pathology.

pdf/cobi-grine-a-tool-for-visualization-and-advanced-evaluation-4imsdzef4t.pdf

COBI-GRINE: A Tool for Visualization and Advanced Evaluation of Communities in Mass Channel Similarity Graphs

The computational analysis of Mass Spectrometry Imaging (MSI) data aims at the identification of interesting mass co-localizations and the visualization of their lateral distribution in the sample, usually a tissue cross section. But as the morphological structure of tissues and the different kinds of mass co-localization naturally show a huge diversity, the selection and tuning of the computational method is a time-consuming effort. In this work we address the special problem of computationally grouping mass channel images according to their similarities in their lateral distribution patterns. Such an analysis is driven by the idea, that groups of molecules that feature a similar distribution pattern may have a functional relation. But the selection of the similarity function and other parameters is often done by a time-consuming and unsatsifactory trial and error. We propose a new flexible workflow scheme called SoRC (sum of ranked cluster indices) for automating this tuning step and making it much more efficient. We test SoRC using three different data sets acquired from the lab for three different kinds of samples (barley seed, mouse bladder tissue, human PXE skin). We show, that SORC can be applied to score and visualize the results obtained with the applied methods in short time without too much effort. In our application example, the SoRC results for the three data sets reveal that a) some well-known similarity functions are suited to achieve good results for all three data sets and b) for the MSI data featuring a higher degree of irregularity improved results can be achieved by applying non-standard similarity functions. The SoRC scores computed with our approach indicate that an automated testing and scoring of different methods for mass channel image grouping can improve the final outcome of a study by finally selecting the methods of the highest scores.

SoRC - Evaluation of Computational Molecular Co-Localization Analysis in Mass Spectrometry Images.

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Detection and visualization of communities in mass spectrometry imaging data.

Fast visual exploration of mass spectrometry images with interactive dynamic spectral similarity pseudocoloring.

COBI-GRINE: A Tool for Visualization and Advanced Evaluation of Communities in Mass Channel Similarity Graphs

SoRC - Evaluation of Computational Molecular Co-Localization Analysis in Mass Spectrometry Images.