Interactive Visual Exploration of 3D Mass Spectrometry Imaging Data Using Hierarchical Stochastic Neighbor Embedding Reveals Spatiomolecular Structures at Full Data Resolution

Markedly Enhanced Analysis of Mass Spectrometry Images Using Weakly Supervised Machine Learning

Supervised and unsupervised machine learning algorithms are routinely applied to time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging data and, more broadly, to mass spectrometry imaging (MSI). These algorithms have accelerated large-scale, single-pixel analysis, classification, and regression. However, there is relatively little research on methods suited for so-called weakly supervised problems, where ground-truth class labels exist at the image level, but not at the individual pixel level. Unsupervised learning methods are usually applied to these problems. However, these methods cannot make use of available labels. Here a novel method specifically designed for weakly supervised MSI data is presented. A dual-stream multiple instance learning (MIL) approach is adapted from computational pathology that reveals the spatial-spectral characteristics distinguishing different classes of MSI images. The method uses an information entropy-regularized attention mechanism to identify characteristic class pixels that are then used to extract characteristic mass spectra. This work provides a proof-of-concept exemplification using printed ink samples imaged by ToF-SIMS. A second application-oriented study is also presented, focusing on the analysis of a mixed powder sample type. Results demonstrate the potential of the MIL method for broader application in MSI, with implications for understanding subtle spatial-spectral characteristics in various applications and contexts.

Application of clustering strategy for automatic segmentation of tissue regions in mass spectrometry imaging

RATIONALE

Mass spectrometry imaging (MSI) has been widely used in biomedical research fields. Each pixel in MSI consists of a mass spectrum that reflects the molecule feature of the tissue spot. Because MSI contains high-dimensional datasets, it is highly desired to develop computational methods for data mining and constructing tissue segmentation maps.

METHODS

To visualize different tissue regions based on mass spectrum features and improve the efficiency in processing enormous data, we proposed a computational strategy that consists of four procedures including preprocessing, data reduction, clustering, and quantitative validation.

RESULTS

In this study, we examined the combination of t-distributed stochastic neighbor embedding (t-SNE) and hierarchical clustering (HC) for MSI data analysis. Using publicly available MSI datasets, one dataset of mouse urinary bladder, and one dataset of human colorectal cancer, we demonstrated that the generated tissue segmentation maps from this combination were superior to other data reduction and clustering algorithms. Using the staining image as a reference, we assessed the performance of clustering algorithms with external and internal clustering validation measures, including purity, adjusted Rand index (ARI), Davies-Bouldin index (DBI), and spatial aggregation index (SAI). The result indicated that SAI delivered excellent performance for automatic segmentation of tissue regions in MSI.

CONCLUSIONS

We used a clustering algorithm to construct tissue automatic segmentation in MSI datasets. The performance was evaluated by comparing it with the stained image and calculating clustering validation indexes. The results indicated that SAI is important for automatic tissue segmentation in MSI, different from traditional clustering validation measures. Compared to the reports that used internal clustering validation measures such as DBI, our method offers more effective evaluation of clustering results for MSI segmentation. We envision that the proposed automatic image segmentation strategy can facilitate deep learning in molecular feature extraction and biomarker discovery for the biomedical applications of MSI.

The journey towards clinical adoption of MALDI-MS-based imaging proteomics: from current challenges to future expectations

Among the spatial omics techniques available, mass spectrometry imaging (MSI) represents one of the most promising owing to its capability to map the distribution of hundreds of peptides and proteins, as well as other classes of biomolecules, within a complex sample background in a multiplexed and relatively high-throughput manner. In particular, matrix-assisted laser desorption/ionisation (MALDI-MSI) has come to the fore and established itself as the most widely used technique in clinical research. However, the march of this technique towards clinical utility has been hindered by issues related to method reproducibility, appropriate biocomputational tools, and data storage. Notwithstanding these challenges, significant progress has been achieved in recent years regarding multiple facets of the technology and has rendered it more suitable for a possible clinical role. As such, there is now more robust and extensive evidence to suggest that the technology has the potential to support clinical decision-making processes under appropriate circumstances. In this review, we will discuss some of the recent developments that have facilitated this progress and outline some of the more promising clinical proteomics applications which have been developed with a clear goal towards implementation in mind.

Optimization of Mass Spectrometry Imaging for Drug Metabolism and Distribution Studies in the Zebrafish Larvae Model: A Case Study with the Opioid Antagonist Naloxone

Zebrafish (ZF; Danio rerio) larvae have emerged as a promising in vivo model in drug metabolism studies. Here, we set out to ready this model for integrated mass spectrometry imaging (MSI) to comprehensively study the spatial distribution of drugs and their metabolites inside ZF larvae. In our pilot study with the overall goal to improve MSI protocols for ZF larvae, we investigated the metabolism of the opioid antagonist naloxone. We confirmed that the metabolic modification of naloxone is in high accordance with metabolites detected in HepaRG cells, human biosamples, and other in vivo models. In particular, all three major human metabolites were detected at high abundance in the ZF larvae model. Next, the in vivo distribution of naloxone was investigated in three body sections of ZF larvae using LC-HRMS/MS showing that the opioid antagonist is mainly present in the head and body sections, as suspected from published human pharmacological data. Having optimized sample preparation procedures for MSI (i.e., embedding layer composition, cryosectioning, and matrix composition and spraying), we were able to record MS images of naloxone and its metabolites in ZF larvae, providing highly informative distributional images. In conclusion, we demonstrate that all major ADMET (absorption, distribution, metabolism, excretion, and toxicity) parameters, as part of in vivo pharmacokinetic studies, can be assessed in a simple and cost-effective ZF larvae model. Our established protocols for ZF larvae using naloxone are broadly applicable, particularly for MSI sample preparation, to various types of compounds, and they will help to predict and understand human metabolism and pharmacokinetics.

Visinity: Visual Spatial Neighborhood Analysis for Multiplexed Tissue Imaging Data

New highly-multiplexed imaging technologies have enabled the study of tissues in unprecedented detail. These methods are increasingly being applied to understand how cancer cells and immune response change during tumor development, progression, and metastasis, as well as following treatment. Yet, existing analysis approaches focus on investigating small tissue samples on a per-cell basis, not taking into account the spatial proximity of cells, which indicates cell-cell interaction and specific biological processes in the larger cancer microenvironment. We present Visinity, a scalable visual analytics system to analyze cell interaction patterns across cohorts of whole-slide multiplexed tissue images. Our approach is based on a fast regional neighborhood computation, leveraging unsupervised learning to quantify, compare, and group cells by their surrounding cellular neighborhood. These neighborhoods can be visually analyzed in an exploratory and confirmatory workflow. Users can explore spatial patterns present across tissues through a scalable image viewer and coordinated views highlighting the neighborhood composition and spatial arrangements of cells. To verify or refine existing hypotheses, users can query for specific patterns to determine their presence and statistical significance. Findings can be interactively annotated, ranked, and compared in the form of small multiples. In two case studies with biomedical experts, we demonstrate that Visinity can identify common biological processes within a human tonsil and uncover novel white-blood cell networks and immune-tumor interactions.

DiviK: divisive intelligent K-means for hands-free unsupervised clustering in big biological data

BACKGROUND

Investigating molecular heterogeneity provides insights into tumour origin and metabolomics. The increasing amount of data gathered makes manual analyses infeasible-therefore, automated unsupervised learning approaches are utilised for discovering tissue heterogeneity. However, automated analyses require experience setting the algorithms' hyperparameters and expert knowledge about the analysed biological processes. Moreover, feature engineering is needed to obtain valuable results because of the numerous features measured.

RESULTS

We propose DiviK: a scalable stepwise algorithm with local data-driven feature space adaptation for segmenting high-dimensional datasets. The algorithm is compared to the optional solutions (regular k-means, spatial and spectral approaches) combined with different feature engineering techniques (None, PCA, EXIMS, UMAP, Neural Ions). Three quality indices: Dice Index, Rand Index and EXIMS score, focusing on the overall composition of the clustering, coverage of the tumour region and spatial cluster consistency, are used to assess the quality of unsupervised analyses. Algorithms were validated on mass spectrometry imaging (MSI) datasets-2D human cancer tissue samples and 3D mouse kidney images. DiviK algorithm performed the best among the four clustering algorithms compared (overall quality score 1.24, 0.58 and 162 for d(0, 0, 0), d(1, 1, 1) and the sum of ranks, respectively), with spectral clustering being mostly second. Feature engineering techniques impact the overall clustering results less than the algorithms themselves (partial [Formula: see text] effect size: 0.141 versus 0.345, Kendall's concordance index: 0.424 versus 0.138 for d(0, 0, 0)).

CONCLUSIONS

DiviK could be the default choice in the exploration of MSI data. Thanks to its unique, GMM-based local optimisation of the feature space and deglomerative schema, DiviK results do not strongly depend on the feature engineering technique applied and can reveal the hidden structure in a tissue sample. Additionally, DiviK shows high scalability, and it can process at once the big omics data with more than 1.5 mln instances and a few thousand features. Finally, due to its simplicity, DiviK is easily generalisable to an even more flexible framework. Therefore, it is helpful for other -omics data (as single cell spatial transcriptomic) or tabular data in general (including medical images after appropriate embedding). A generic implementation is freely available under Apache 2.0 license at https://github.com/gmrukwa/divik .

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.

Evaluation and comparison of unsupervised methods for the extraction of spatial patterns from mass spectrometry imaging data (MSI)

For the extraction of spatially important regions from mass spectrometry imaging (MSI) data, different clustering methods have been proposed. These clustering methods are based on certain assumptions and use different criteria to assign pixels into different classes. For high-dimensional MSI data, the curse of dimensionality also limits the performance of clustering methods which are usually overcome by pre-processing the data using dimension reduction techniques. In summary, the extraction of spatial patterns from MSI data can be done using different unsupervised methods, but the robust evaluation of clustering results is what is still missing. In this study, we have performed multiple simulations on synthetic and real MSI data to validate the performance of unsupervised methods. The synthetic data were simulated mimicking important spatial and statistical properties of real MSI data. Our simulation results confirmed that K-means clustering with correlation distance and Gaussian Mixture Modeling clustering methods give optimal performance in most of the scenarios. The clustering methods give efficient results together with dimension reduction techniques. From all the dimension techniques considered here, the best results were obtained with the minimum noise fraction (MNF) transform. The results were confirmed on both synthetic and real MSI data. However, for successful implementation of MNF transform the MSI data requires to be of limited dimensions.

MALDI-MSI Towards Multimodal Imaging: Challenges and Perspectives

Multimodal imaging is a powerful strategy for combining information from multiple images. It involves several fields in the acquisition, processing and interpretation of images. As multimodal imaging is a vast subject area with various combinations of imaging techniques, it has been extensively reviewed. Here we focus on Matrix-assisted Laser Desorption Ionization Mass Spectrometry Imaging (MALDI-MSI) coupling other imaging modalities in multimodal approaches. While MALDI-MS images convey a substantial amount of chemical information, they are not readily informative about the morphological nature of the tissue. By providing a supplementary modality, MALDI-MS images can be more informative and better reflect the nature of the tissue. In this mini review, we emphasize the analytical and computational strategies to address multimodal MALDI-MSI.

ADVANCES IN HIGH-RESOLUTION MALDI MASS SPECTROMETRY FOR NEUROBIOLOGY

Research in the field of neurobiology and neurochemistry has seen a rapid expansion in the last several years due to advances in technologies and instrumentation, facilitating the detection of biomolecules critical to the complex signaling of neurons. Part of this growth has been due to the development and implementation of high-resolution Fourier transform (FT) mass spectrometry (MS), as is offered by FT ion cyclotron resonance (FTICR) and Orbitrap mass analyzers, which improves the accuracy of measurements and helps resolve the complex biological mixtures often analyzed in the nervous system. The coupling of matrix-assisted laser desorption/ionization (MALDI) with high-resolution MS has drastically expanded the information that can be obtained with these complex samples. This review discusses notable technical developments in MALDI-FTICR and MALDI-Orbitrap platforms and their applications toward molecules in the nervous system, including sequence elucidation and profiling with de novo sequencing, analysis of post-translational modifications, in situ analysis, key advances in sample preparation and handling, quantitation, and imaging. Notable novel applications are also discussed to highlight key developments critical to advancing our understanding of neurobiology and providing insight into the exciting future of this field. © 2020 John Wiley & Sons Ltd. Mass Spec Rev.

Fingerprints of Element Concentrations in Infective Endocarditis Obtained by Mass Spectrometric Imaging and t-Distributed Stochastic Neighbor Embedding

Staphylococcus aureus-induced infective endocarditis (IE) is a life-threatening disease. Differences in virulence between distinct S. aureus strains, which are partly based on the molecular mechanisms during bacterial adhesion, are not fully understood. Yet, distinct molecular or elemental patterns, occurring during specific steps in the adhesion process, may help to identify novel targets for accelerated diagnosis or improved treatment. Here, we use laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) of post-mortem tissue slices of an established mouse model of IE to obtain fingerprints of element distributions in infected aortic valve tissue. Three S. aureus strains with different virulence due to deficiency in distinct adhesion molecules (fibronectin-binding protein A and staphylococcal protein A) were used to assess strain-specific patterns. Data analysis was performed by t-distributed stochastic neighbor embedding (t-SNE) of mass spectrometry imaging data, using manual reference tissue classification in histological specimens. This procedure allowed for obtaining distinct element patterns in infected tissue for all three bacterial strains and for comparing those to patterns observed in healthy mice or after sterile inflammation of the valve. In tissue from infected mice, increased concentrations of calcium, zinc, and magnesium were observed compared to noninfected mice. Between S. aureus strains, pronounced variations were observed for manganese. The presented approach is sensitive for detection of S. aureus infection. For strain-specific tissue characterization, however, further improvements such as establishing a database with elemental fingerprints may be required.

Advanced applications of mass spectrometry imaging technology in quality control and safety assessments of traditional Chinese medicines

ETHNOPHARMACOLOGICAL RELEVANCE

Traditional Chinese medicines (TCMs) have made great contributions to the prevention and treatment of human diseases in China, and especially in cases of COVID-19. However, due to quality problems, the lack of standards, and the diversity of dosage forms, adverse reactions to TCMs often occur. Moreover, the composition of TCMs makes them extremely challenging to extract and isolate, complicating studies of toxicity mechanisms.

AIM OF THE REVIEW

The aim of this paper is therefore to summarize the advanced applications of mass spectrometry imaging (MSI) technology in the quality control, safety evaluations, and determination of toxicity mechanisms of TCMs.

MATERIALS AND METHODS

Relevant studies from the literature have been collected from scientific databases, such as "PubMed", "Scifinder", "Elsevier", "Google Scholar" using the keywords "MSI", "traditional Chinese medicines", "quality control", "metabolomics", and "mechanism".

RESULTS

MSI is a new analytical imaging technology that can detect and image the metabolic changes of multiple components of TCMs in plants and animals in a high throughput manner. Compared to other chemical analysis methods, such as liquid chromatography-mass spectrometry (LC-MS), this method does not require the complex extraction and separation of TCMs, and is fast, has high sensitivity, is label-free, and can be performed in high-throughput. Combined with chemometrics methods, MSI can be quickly and easily used for quality screening of TCMs. In addition, this technology can be used to further focus on potential biomarkers and explore the therapeutic/toxic mechanisms of TCMs.

CONCLUSIONS

As a new type of analysis method, MSI has unique advantages to metabolic analysis, quality control, and mechanisms of action explorations of TCMs, and contributes to the establishment of quality standards to explore the safety and toxicology of TCMs.

Innovation in drug toxicology: Application of mass spectrometry imaging technology

Mass spectrometry imaging (MSI) is a powerful molecular imaging technology that can obtain qualitative, quantitative, and location information by simultaneously detecting and mapping endogenous or exogenous molecules in biological tissue slices without specific chemical labeling or complex sample pretreatment. This article reviews the progress made in MSI and its application in drug toxicology research, including the tissue distribution of toxic drugs and their metabolites, the target organs (liver, kidney, lung, eye, and central nervous system) of toxic drugs, the discovery of toxicity-associated biomarkers, and explanations of the mechanisms of drug toxicity when MSI is combined with the cutting-edge omics methodologies. The unique advantages and broad prospects of this technology have been fully demonstrated to further promote its wider use in the field of pharmaceutical toxicology.

Peak learning of mass spectrometry imaging data using artificial neural networks

Mass spectrometry imaging (MSI) is an emerging technology that holds potential for improving, biomarker discovery, metabolomics research, pharmaceutical applications and clinical diagnosis. Despite many solutions being developed, the large data size and high dimensional nature of MSI, especially 3D datasets, still pose computational and memory complexities that hinder accurate identification of biologically relevant molecular patterns. Moreover, the subjectivity in the selection of parameters for conventional pre-processing approaches can lead to bias. Therefore, we assess if a probabilistic generative model based on a fully connected variational autoencoder can be used for unsupervised analysis and peak learning of MSI data to uncover hidden structures. The resulting msiPL method learns and visualizes the underlying non-linear spectral manifold, revealing biologically relevant clusters of tissue anatomy in a mouse kidney and tumor heterogeneity in human prostatectomy tissue, colorectal carcinoma, and glioblastoma mouse model, with identification of underlying m/z peaks. The method is applied for the analysis of MSI datasets ranging from 3.3 to 78.9 GB, without prior pre-processing and peak picking, and acquired using different mass spectrometers at different centers.

M2aia-Interactive, fast, and memory-efficient analysis of 2D and 3D multi-modal mass spectrometry imaging data

BACKGROUND

Mass spectrometry imaging (MSI) is a label-free analysis method for resolving bio-molecules or pharmaceuticals in the spatial domain. It offers unique perspectives for the examination of entire organs or other tissue specimens. Owing to increasing capabilities of modern MSI devices, the use of 3D and multi-modal MSI becomes feasible in routine applications-resulting in hundreds of gigabytes of data. To fully leverage such MSI acquisitions, interactive tools for 3D image reconstruction, visualization, and analysis are required, which preferably should be open-source to allow scientists to develop custom extensions.

FINDINGS

We introduce M2aia (MSI applications for interactive analysis in MITK), a software tool providing interactive and memory-efficient data access and signal processing of multiple large MSI datasets stored in imzML format. M2aia extends MITK, a popular open-source tool in medical image processing. Besides the steps of a typical signal processing workflow, M2aia offers fast visual interaction, image segmentation, deformable 3D image reconstruction, and multi-modal registration. A unique feature is that fused data with individual mass axes can be visualized in a shared coordinate system. We demonstrate features of M2aia by reanalyzing an N-glycan mouse kidney dataset and 3D reconstruction and multi-modal image registration of a lipid and peptide dataset of a mouse brain, which we make publicly available.

CONCLUSIONS

To our knowledge, M2aia is the first extensible open-source application that enables a fast, user-friendly, and interactive exploration of large datasets. M2aia is applicable to a wide range of MSI analysis tasks.

Experimental and Data Analysis Considerations for Three-Dimensional Mass Spectrometry Imaging in Biomedical Research

Mass spectrometry imaging (MSI) enables the visualization of molecular distributions on complex surfaces. It has been extensively used in the field of biomedical research to investigate healthy and diseased tissues. Most of the MSI studies are conducted in a 2D fashion where only a single slice of the full sample volume is investigated. However, biological processes occur within a tissue volume and would ideally be investigated as a whole to gain a more comprehensive understanding of the spatial and molecular complexity of biological samples such as tissues and cells. Mass spectrometry imaging has therefore been expanded to the 3D realm whereby molecular distributions within a 3D sample can be visualized. The benefit of investigating volumetric data has led to a quick rise in the application of single-sample 3D-MSI investigations. Several experimental and data analysis aspects need to be considered to perform successful 3D-MSI studies. In this review, we discuss these aspects as well as ongoing developments that enable 3D-MSI to be routinely applied to multi-sample studies.

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.

Three-dimensional (3D) imaging of lipids in skin tissues with infrared matrix-assisted laser desorption electrospray ionization (MALDESI) mass spectrometry

Three-dimensional (3D) mass spectrometry imaging (MSI) has become a growing frontier as it has the potential to provide a 3D representation of analytes in a label-free, untargeted, and chemically specific manner. The most common 3D MSI is accomplished by the reconstruction of 2D MSI from serial cryosections; however, this presents significant challenges in image alignment and registration. An alternative method would be to sequentially image a sample by consecutive ablation events to create a 3D image. In this study, we describe the use of infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) in ablation-based 3D MSI for analyses of lipids within fresh frozen skin tissue. Depth resolution using different laser energy levels was explored with a confocal laser scanning microscope to establish the imaging parameters for skin. The lowest and highest laser energy level resulted in a depth resolution of 7 μm and 18 μm, respectively. A total of 594 lipids were putatively detected and detailed lipid profiles across different skin layers were revealed in a 56-layer 3D imaging experiment. Correlated with histological information, the skin structure was characterized with differential lipid distributions with a lateral resolution of 50 μm and a z resolution of 7 μm.

Registration of Image Modalities for Analyses of Tissue Samples Using 3D Image Modelling

PURPOSE

Biopsies are a diagnostic tool for the diagnosis of histopathological, molecular biological, proteomic, and imaging data, to narrow down disease patterns or identify diseases. Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) provides an emerging state-of-the-art technique for molecular imaging of biological tissue. The aim of this study is the registration of MALDI MSI data sets and data acquired from different histological stainings to create a 3D model of biopsies and whole organs.

EXPERIMENTAL DESIGN

The registration of the image modalities is achieved by using a variant of the authors' global, deformable Schatten-q-Norm registration approach. Utilizing a connected-component segmentation for background removal followed by a principal-axis based linear pre-registration, the images are adjusted into a homogeneous alignment. This registration approach is accompanied by the 3D reconstruction of histological and MALDI MSI data.

RESULTS

With this, a system of automatic registration for cross-process evaluation, as well as for creating 3D models, is developed and established. The registration of MALDI MSI data with different histological image data is evaluated by using the established global image registration system.

CONCLUSIONS AND CLINICAL RELEVANCE

In conclusion, this multimodal image approach offers the possibility of molecular analyses of tissue specimens in clinical research and diagnosis.

Interactive Visual Analysis of Mass Spectrometry Imaging Data Using Linear and Non-Linear Embeddings

Mass spectrometry imaging (MSI) is an imaging technique used in analytical chemistry to study the molecular distribution of various compounds at a micro-scale level. For each pixel, MSI stores a mass spectrum obtained by measuring signal intensities of thousands of mass-to-charge ratios (m/z-ratios), each linked to an individual molecular ion species. Traditional analysis tools focus on few individual m/z-ratios, which neglects most of the data. Recently, clustering methods of the spectral information have emerged, but faithful detection of all relevant image regions is not always possible. We propose an interactive visual analysis approach that considers all available information in coordinated views of image and spectral space visualizations, where the spectral space is treated as a multi-dimensional space. We use non-linear embeddings of the spectral information to interactively define clusters and respective image regions. Of particular interest is, then, which of the molecular ion species cause the formation of the clusters. We propose to use linear embeddings of the clustered data, as they allow for relating the projected views to the given dimensions. We document the effectiveness of our approach in analyzing matrix-assisted laser desorption/ionization (MALDI-2) imaging data with ground truth obtained from histological images.

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.

Chemometric Strategies for Sensitive Annotation and Validation of Anatomical Regions of Interest in Complex Imaging Mass Spectrometry Data

Imaging mass spectrometry (IMS) is a promising new chemical imaging modality that generates a large body of complex imaging data, which in turn can be approached using multivariate analysis approaches for image analysis and segmentation. Processing IMS raw data is critically important for proper data interpretation and has significant effects on the outcome of data analysis, in particular statistical modeling. Commonly, data processing methods are chosen based on rational motivations rather than comparative metrics, though no quantitative measures to assess and compare processing options have been suggested. We here present a data processing and analysis pipeline for IMS data interrogation, processing and ROI annotation, segmentation, and validation. This workflow includes (1) objective evaluation of processing methods for IMS datasets based on multivariate analysis using PCA. This was then followed by (2) ROI annotation and classification through region-based active contours (AC) segmentation based on the PCA component scores matrix. This provided class information for subsequent (3) OPLS-DA modeling to evaluate IMS data processing based on the quality metrics of their respective multivariate models and for robust quantification of ROI-specific signal localization. This workflow provides an unbiased strategy for sensitive annotation of anatomical regions of interest combined with quantitative comparison of processing procedures for multivariate analysis allowing robust ROI annotation and quantification of the associated molecular histology.

Innate and adaptive nasal mucosal immune responses following experimental human pneumococcal colonization

Streptococcus pneumoniae (Spn) is a common cause of respiratory infection, but also frequently colonizes the nasopharynx in the absence of disease. We used mass cytometry to study immune cells from nasal biopsy samples collected following experimental human pneumococcal challenge in order to identify immunological mechanisms of control of Spn colonization. Using 37 markers, we characterized 293 nasal immune cell clusters, of which 7 were associated with Spn colonization. B cell and CD161⁺CD8⁺ T cell clusters were significantly lower in colonized than in noncolonized subjects. By following a second cohort before and after pneumococcal challenge we observed that B cells were depleted from the nasal mucosa upon Spn colonization. This associated with an expansion of Spn polysaccharide–specific and total plasmablasts in blood. Moreover, increased responses of blood mucosa-associated invariant T (MAIT) cells against in vitro stimulation with pneumococcus prior to challenge associated with protection against establishment of Spn colonization and with increased mucosal MAIT cell populations. These results implicate MAIT cells in the protection against pneumococcal colonization and demonstrate that colonization affects mucosal and circulating B cell populations.

Automatic 3D Nonlinear Registration of Mass Spectrometry Imaging and Magnetic Resonance Imaging Data

Multimodal integration between mass spectrometry imaging (MSI) and radiology-established modalities such as magnetic resonance imaging (MRI) would allow the investigations of key questions in complex biological systems such as the central nervous system. Such integration would provide complementary multiscale data to bridge the gap between molecular and anatomical phenotypes, potentially revealing new insights into molecular mechanisms underlying anatomical pathologies presented on MRI. Automatic coregistration between 3D MSI/MRI is a computationally challenging process due to dimensional complexity, MSI data sparsity, lack of direct spatial-correspondences, and nonlinear tissue deformation. Here, we present a new computational approach based on stochastic neighbor embedding to nonlinearly align 3D MSI to MRI data, identify and reconstruct biologically relevant molecular patterns in 3D, and fuse the MSI datacube to the MRI space. We demonstrate our method using multimodal high-spectral resolution matrix-assisted laser desorption ionization (MALDI) 9.4 T MSI and 7 T in vivo MRI data, acquired from a patient-derived, xenograft mouse brain model of glioblastoma following administration of the EGFR inhibitor drug of Erlotinib. Results show the distribution of some identified molecular ions of the EGFR inhibitor erlotinib, a phosphatidylcholine lipid, and cholesterol, which were reconstructed in 3D and mapped to the MRI space. The registration quality was evaluated on two normal mouse brains using the Dice coefficient for the regions of brainstem, hippocampus, and cortex. The method is generic and can therefore be applied to hyperspectral images from different mass spectrometers and integrated with other established in vivo imaging modalities such as computed tomography (CT) and positron emission tomography (PET).

Platforms for rapid cancer characterization by ambient mass spectrometry: advancements, challenges and opportunities for improvement towards intrasurgical use

Ambient Mass Spectrometry (MS) analysis is widely used to characterize biological and non-biological samples. Advancements that allow rapid analysis of samples by ambient methods such as Desorption Electrospray Ionization Mass Spectrometry (DESI-MS) and Rapid Evaporative Ionization Mass Spectrometry (REIMS) are discussed. A short, non-comprehensive overview of ambient MS is provided that only contains example applications due to space limitations. A spatially encoded mass spectrometry analysis concept to plan cancer resection is introduced. The application of minimally destructive tissue ablation probes to survey the surgical field for sites of pathology using on-line analysis methods is discussed. The technological challenges that must be overcome for ambient MS to become a robust method for intrasurgical pathology assessments are reviewed.