Mass spectrometry imaging: How will it affect clinical research in the future?

Dilillo, Marialaura; Heijs, Bram; McDonnell, Liam A.

doi:10.1080/14789450.2018.1521278

Cited by 14 publications

(12 citation statements)

References 71 publications

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“…Next to the use of glycan-targeting antibodies and specific lectins, protein N -glycan spatial distribution in tissues is currently investigated using advanced MS imaging (MSI) workflows. The spatial resolution of these methods is in the range of 5–20 μm and not yet advanced enough to study single cells ( Dilillo et al. 2018 ).…”

Section: Challenges and Perspectives In Technological Developmentsmentioning

confidence: 99%

Developments and perspectives in high-throughput protein glycomics: enabling the analysis of thousands of samples

et al. 2022

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Glycans expand the structural complexity of proteins by several orders of magnitude, resulting in a tremendous analytical challenge when including them in biomedical research. Recent glycobiological research is painting a picture in which glycans represent a crucial structural and functional component of the majority of proteins with alternative glycosylation of proteins and lipids being an important regulatory mechanism in many biological and pathological processes. Since inter-individual differences in glycosylation are extensive, large studies are needed to map the structures and understand the role of glycosylation in human (patho)physiology. Driven by these challenges, methods have emerged which can tackle the complexity of glycosylation in thousands of samples, also known as high-throughput glycomics. For facile dissemination and implementation of high-throughput glycomics technology, the sample preparation, analysis as well as data mining, needs to be stable over a long period of time (months/years), amenable to automation, and available to non-specialized laboratories. Current high-throughput glycomics methods mainly focus on protein N-glycosylation and allow to extensively characterize this subset of the human glycome in large numbers of various biological samples. The ultimate goal in high-throughput glycomics is to gain better knowledge and understanding of the complete human glycome, using methods that are easy to adapt and implement in (basic) biomedical research. Aiming to promote wider use and development of high-throughput glycomics, here we present currently available, emerging and prospective methods and some of their applications, revealing a largely unexplored molecular layer of the complexity of life.

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Section: Challenges and Perspectives In Technological Developmentsmentioning

confidence: 99%

Developments and perspectives in high-throughput protein glycomics: enabling the analysis of thousands of samples

et al. 2022

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“…One technology for discovering such molecular markers at scale is mass spectrometry, which characterizes molecular species in terms of their mass-to-charge ratio ( m/z ). It was demonstrated in 2003 that matrix assisted laser desorption/ionization (MALDI) mass spectrometry could facilitate the discovery of diagnostic and prognostic biomarkers for lung cancer when applied directly to clinical patient samples [5, 6]. Imaging mass spectrometry (IMS) is a multiplexed, label-free imaging technology that uses mass spectrometry for the molecular mapping of tissues down to cellular resolution [7, 8, 9].…”

Section: Introductionmentioning

confidence: 99%

Automated Biomarker Candidate Discovery in Imaging Mass Spectrometry Data Through Spatially Localized Shapley Additive Explanations

Tideman

Migas

Patterson

et al. 2020

Preprint

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The search for molecular species that are differentially expressed between biological states is an important step towards discovering promising biomarker candidates. In imaging mass spectrometry (IMS), performing this search manually is often impractical due to the large size and high-dimensionality of IMS datasets. Instead, we propose an interpretable machine learning workflow that automatically identifies biomarker candidates by their mass-to-charge ratios, and that quantitatively estimates their relevance to recognizing a given biological class using Shapley additive explanations (SHAP). The task of biomarker candidate discovery is translated into a feature ranking problem: given a classification model that assigns pixels to different biological classes on the basis of their mass spectra, the molecular species that the model uses as features are ranked in descending order of relative predictive importance such that the top-ranking features have a higher likelihood of being useful biomarkers. Besides providing the user with an experiment-wide measure of a molecular species’ biomarker potential, our workflow delivers spatially localized explanations of the classification model’s decision-making process in the form of a novel representation called SHAP maps. SHAP maps deliver insight into the spatial specificity of biomarker candidates by highlighting in which regions of the tissue sample each feature provides discriminative information and in which regions it does not. SHAP maps also enable one to determine whether the relationship between a biomarker candidate and a biological state of interest is correlative or anticorrelative. Our automated approach to estimating a molecular species’ potential for characterizing a user-provided biological class, combined with the untargeted and multiplexed nature of IMS, allows for the rapid screening of thousands of molecular species and the obtention of a broader biomarker candidate shortlist than would be possible through targeted manual assessment. Our biomarker candidate discovery workflow is demonstrated on mouse-pup and rat kidney case studies.HighlightsOur workflow automates the discovery of biomarker candidates in imaging mass spectrometry data by using state-of-the-art machine learning methodology to produce a shortlist of molecular species that are differentially expressed with regards to a user-provided biological class.A model interpretability method called Shapley additive explanations (SHAP), with observational Shapley values, enables us to quantify the local and global predictive importance of molecular species with respect to recognizing a user-provided biological class.By providing spatially localized explanations for a classification model’s decision-making process, SHAP maps deliver insight into the spatial specificity of biomarker candidates and enable one to determine whether (and where) the relationship between a biomarker candidate and the class of interest is correlative or anticorrelative.

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“…Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) is a technique that can directly identify and localize hundreds to thousands of molecules in a tissue sample simultaneously, including metabolites, lipids, glycans, peptides, proteins, glycolipids, and drugs and their metabolites [1]. The molecular distribution can then be correlated with the histomorphology.…”

Section: Introductionmentioning

confidence: 99%

“…Unlike conventional molecular analysis methods of tissue samples [e. g., immunohistochemical (IHC) and histochemical techniques], which specifically detect individual compound localizations via antibody applications or compound classes such as polysaccharides [2], MSI-based techniques provide a simultaneous label-free detection of various types of species in the same tissue section. The cell-specific molecular signatures accessible through MSI are hugely complementary to histopathological, IHC and other established molecular (e. g., genomics-related) data [1,3] and therefore offer a major advantage for synergizing molecular information and morphology. Consequently, MSI has rapidly and substantially impacted basic and clinical research by identifying molecular changes associated with disease.…”

Section: Introductionmentioning

confidence: 99%

In situ Metabolite Mass Spectrometry Imaging: New Insights into the Adrenal Gland

Feuchtinger

Walch

et al. 2020

Horm Metab Res

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The adrenal gland integrates catecholamine-producing neuroendocrine cells and steroid-producing cells with mesenchymal origin in a structured manner under one capsule and is a key regulator for vital bioactivity. In addition to adrenal-specific disease, dysregulation of adrenal hormones is associated with systemic effects, leading to undesirable metabolic and cardiovascular consequences. Mass spectrometry imaging (MSI) technique can simultaneously measure a broad range of biomolecules, including metabolites and hormones, which has enabled the study of tissue metabolic and hormone alterations in adrenal and adrenal-related diseases. Furthermore, this technique coupled with labeled immunohistochemistry staining has enabled the study of the pathophysiological adaptation of the adrenal gland under normal and abnormal conditions at different molecular levels. This review discusses the recent applications of in situ MSI in the adrenal gland. For example, the combination of formalin-fixed paraffin-embedded tissue microarray and MSI to tissues from patient cohorts has facilitated the discovery of clinically relevant prognostic biomolecules and generated promising hypotheses for new sights into physiology and pathophysiology of adrenal gland. MSI also has enabled the discovery of clinically significant tissue molecular (i. e., biomarker) and pathway changes in adrenal disease, particularly in adrenal tumors. In addition, MSI has advanced the ability to optimally identify and detect adrenal gland specific molecules. Thus, as a novel analytical methodology, MSI has provided unprecedented capabilities for in situ tissue study.

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Mass spectrometry imaging: How will it affect clinical research in the future?

Cited by 14 publications

References 71 publications

Developments and perspectives in high-throughput protein glycomics: enabling the analysis of thousands of samples

Developments and perspectives in high-throughput protein glycomics: enabling the analysis of thousands of samples

Automated Biomarker Candidate Discovery in Imaging Mass Spectrometry Data Through Spatially Localized Shapley Additive Explanations

In situ Metabolite Mass Spectrometry Imaging: New Insights into the Adrenal Gland

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