Combining Desorption Electrospray Ionization Mass Spectrometry Imaging and Machine Learning for Molecular Recognition of Myocardial Infarction

Modern machine-learning applications in ambient ionization mass spectrometry

This article provides a comprehensive overview of the applications of methods of machine learning (ML) and artificial intelligence (AI) in ambient ionization mass spectrometry (AIMS). AIMS has emerged as a powerful analytical tool in recent years, allowing for rapid and sensitive analysis of various samples without the need for extensive sample preparation. The integration of ML/AI algorithms with AIMS has further expanded its capabilities, enabling enhanced data analysis. This review discusses ML/AI algorithms applicable to the AIMS data and highlights the key advancements and potential benefits of utilizing ML/AI in the field of mass spectrometry, with a focus on the AIMS community.

Advances in multimodal mass spectrometry for single-cell analysis and imaging enhancement

Multimodal mass spectrometry (MMS) incorporates an imaging modality with probe-based mass spectrometry (MS) to enable precise, targeted data acquisition and provide additional biological and chemical data not available by MS alone. Two categories of MMS are covered; in the first, an imaging modality guides the MS probe to target individual cells and to reduce acquisition time by automatically defining regions of interest. In the second category, imaging and MS data are coupled in the data analysis pipeline to increase the effective spatial resolution using a higher resolution imaging method, correct for tissue deformation, and incorporate fine morphological features in an MS imaging dataset. Recent methodological and computational developments are covered along with their application to single-cell and imaging analyses.

Toward Omics-Scale Quantitative Mass Spectrometry Imaging of Lipids in Brain Tissue Using a Multiclass Internal Standard Mixture

Mass spectrometry imaging (MSI) has accelerated our understanding of lipid metabolism and spatial distribution in tissues and cells. However, few MSI studies have approached lipid imaging quantitatively and those that have focused on a single lipid class. We overcome this limitation by using a multiclass internal standard (IS) mixture sprayed homogeneously over the tissue surface with concentrations that reflect those of endogenous lipids. This enabled quantitative MSI (Q-MSI) of 13 lipid classes and subclasses representing almost 200 sum-composition lipid species using both MALDI (negative ion mode) and MALDI-2 (positive ion mode) and pixel-wise normalization of each lipid species in a manner analogous to that widely used in shotgun lipidomics. The Q-MSI approach covered 3 orders of magnitude in dynamic range (lipid concentrations reported in pmol/mm2) and revealed subtle changes in distribution compared to data without normalization. The robustness of the method was evaluated by repeating experiments in two laboratories using both timsTOF and Orbitrap mass spectrometers with an ∼4-fold difference in mass resolution power. There was a strong overall correlation in the Q-MSI results obtained by using the two approaches. Outliers were mostly rationalized by isobaric interferences or the higher sensitivity of one instrument for a particular lipid species. These data provide insight into how the mass resolving power can affect Q-MSI data. This approach opens up the possibility of performing large-scale Q-MSI studies across numerous lipid classes and subclasses and revealing how absolute lipid concentrations vary throughout and between biological tissues.

Integrated mass spectrometry imaging reveals spatial-metabolic alteration in diabetic cardiomyopathy and the intervention effects of ferulic acid

Diabetic cardiomyopathy (DCM) is a metabolic disease and a leading cause of heart failure among people with diabetes. Mass spectrometry imaging (MSI) is a versatile technique capable of combining the molecular specificity of mass spectrometry (MS) with the spatial information of imaging. In this study, we used MSI to visualize metabolites in the rat heart with high spatial resolution and sensitivity. We optimized the air flow-assisted desorption electrospray ionization (AFADESI)-MSI platform to detect a wide range of metabolites, and then used matrix-assisted laser desorption ionization (MALDI)-MSI for increasing metabolic coverage and improving localization resolution. AFADESI-MSI detected 214 and 149 metabolites in positive and negative analyses of rat heart sections, respectively, while MALDI-MSI detected 61 metabolites in negative analysis. Our study revealed the heterogenous metabolic profile of the heart in a DCM model, with over 105 region-specific changes in the levels of a wide range of metabolite classes, including carbohydrates, amino acids, nucleotides, and their derivatives, fatty acids, glycerol phospholipids, carnitines, and metal ions. The repeated oral administration of ferulic acid during 20 weeks significantly improved most of the metabolic disorders in the DCM model. Our findings provide novel insights into the molecular mechanisms underlying DCM and the potential of ferulic acid as a therapeutic agent for treating this condition.

Advances and Emerging Medical Applications of Direct Mass Spectrometry Technologies for Tissue Analysis

Offering superb speed, chemical specificity, and analytical sensitivity, direct mass spectrometry (MS) technologies are highly amenable for the molecular analysis of complex tissues to aid in disease characterization and help identify new diagnostic, prognostic, and predictive markers. By enabling detection of clinically actionable molecular profiles from tissues and cells, direct MS technologies have the potential to guide treatment decisions and transform sample analysis within clinical workflows. In this review, we highlight recent health-related developments and applications of direct MS technologies that exhibit tangible potential to accelerate clinical research and disease diagnosis, including oncological and neurodegenerative diseases and microbial infections. We focus primarily on applications that employ direct MS technologies for tissue analysis, including MS imaging technologies to map spatial distributions of molecules in situ as well as handheld devices for rapid in vivo and ex vivo tissue analysis.

Desorption electrospray ionization mass spectrometry imaging in discovery and development of novel therapies

Desorption electrospray ionization mass spectrometry imaging (DESI-MSI) is one of the least specimen destructive ambient ionization mass spectrometry tissue imaging methods. It enables rapid simultaneous mapping, measurement, and identification of hundreds of molecules from an unmodified tissue sample. Over the years, since its first introduction as an imaging technique in 2005, DESI-MSI has been extensively developed as a tool for separating tissue regions of various histopathologic classes for diagnostic applications. Recently, DESI-MSI has also emerged as a versatile technique that enables drug discovery and can guide the efficient development of drug delivery systems. For example, it has been increasingly employed for uncovering unique patterns of in vivo drug distribution, the discovery of potentially treatable biochemical pathways, revealing novel druggable targets, predicting therapeutic sensitivity of diseased tissues, and identifying early tissue response to pharmacological treatment. These and other recent advances in implementing DESI-MSI as the tool for the development of novel therapies are highlighted in this review.

High-throughput analysis of tissue microarrays using automated desorption electrospray ionization mass spectrometry

Tissue microarrays (TMAs) are commonly used for the rapid analysis of large numbers of tissue samples, often in morphological assessments but increasingly in spectroscopic analysis, where specific molecular markers are targeted via immunostaining. Here we report the use of an automated high-throughput system based on desorption electrospray ionization (DESI) mass spectrometry (MS) for the rapid generation and online analysis of high-density (6144 samples/array) TMAs, at rates better than 1 sample/second. Direct open-air analysis of tissue samples (hundreds of nanograms) not subjected to prior preparation, plus the ability to provide molecular characterization by tandem mass spectrometry (MS/MS), make this experiment versatile and applicable to both targeted and untargeted analysis in a label-free manner. These capabilities are demonstrated in a proof-of-concept study of frozen brain tissue biopsies where we showcase (i) a targeted MS/MS application aimed at identification of isocitrate dehydrogenase mutation in glioma samples and (ii) an untargeted MS tissue type classification using lipid profiles and correlation with tumor cell percentage estimates from histopathology. The small sample sizes and large sample numbers accessible with this methodology make for a powerful analytical system that facilitates the identification of molecular markers for later use in intraoperative applications to guide precision surgeries and ultimately improve patient outcomes.

Fully Automated Unconstrained Analysis of High-Resolution Mass Spectrometry Data with Machine Learning

Mass spectrometry (MS) is a convenient, highly sensitive, and reliable method for the analysis of complex mixtures, which is vital for materials science, life sciences fields such as metabolomics and proteomics, and mechanistic research in chemistry. Although it is one of the most powerful methods for individual compound detection, complete signal assignment in complex mixtures is still a great challenge. The unconstrained formula-generating algorithm, covering the entire spectra and revealing components, is a "dream tool" for researchers. We present the framework for efficient MS data interpretation, describing a novel approach for detailed analysis based on deisotoping performed by gradient-boosted decision trees and a neural network that generates molecular formulas from the fine isotopic structure, approaching the long-standing inverse spectral problem. The methods were successfully tested on three examples: fragment ion analysis in protein sequencing for proteomics, analysis of the natural samples for life sciences, and study of the cross-coupling catalytic system for chemistry.

Recent Advances of Ambient Mass Spectrometry Imaging and Its Applications in Lipid and Metabolite Analysis

Ambient mass spectrometry imaging (AMSI) has attracted much attention in recent years. As a kind of unlabeled molecular imaging technique, AMSI can enable in situ visualization of a large number of compounds in biological tissue sections in ambient conditions. In this review, the developments of various AMSI techniques are discussed according to one-step and two-step ionization strategies. In addition, recent applications of AMSI for lipid and metabolite analysis (from 2016 to 2021) in disease diagnosis, animal model research, plant science, drug metabolism and toxicology research, etc., are summarized. Finally, further perspectives of AMSI in spatial resolution, sensitivity, quantitative ability, convenience and software development are proposed.

Lipid Profiling in Cancer Diagnosis with Hand-Held Ambient Mass Spectrometry Probes: Addressing the Late-Stage Performance Concerns

Untargeted lipid fingerprinting with hand-held ambient mass spectrometry (MS) probes without chromatographic separation has shown promise in the rapid characterization of cancers. As human cancers present significant molecular heterogeneities, careful molecular modeling and data validation strategies are required to minimize late-stage performance variations of these models across a large population. This review utilizes parallels from the pitfalls of conventional protein biomarkers in reaching bedside utility and provides recommendations for robust modeling as well as validation strategies that could enable the next logical steps in large scale assessment of the utility of ambient MS profiling for cancer diagnosis. Six recommendations are provided that range from careful initial determination of clinical added value to moving beyond just statistical associations to validate lipid involvements in disease processes mechanistically. Further guidelines for careful selection of suitable samples to capture expected and unexpected intragroup variance are provided and discussed in the context of demographic heterogeneities in the lipidome, further influenced by lifestyle factors, diet, and potential intersect with cancer lipid pathways probed in ambient mass spectrometry profiling studies.

Automated biomarker candidate discovery in imaging mass spectrometry data through spatially localized Shapley additive explanations

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.

Imaging of Polar and Nonpolar Lipids Using Desorption Electrospray Ionization/Post-photoionization Mass Spectrometry

Desorption electrospray ionization mass spectrometry imaging (DESI-MSI) can record 2D distribution of polar lipids in tissue slices at ambient condition. However, sensitivity of DESI-MSI for nonpolar lipids is restricted by low ionization efficiency and severe ion suppression. Here, a compact post-photoionization assembly combined with DESI (DESI/PI) was developed for simultaneous imaging polar and nonpolar lipids in tissue sections by switching off/on a portable krypton lamp. Compared with DESI, higher signal intensities of nonpolar compounds could be detected with DESI/PI. We describe the fabrication, optimization, implementation, and data transformation for imaging both the polar and nonpolar lipids in mouse brain tissue using an Agilent 6224 Accurate-Mass TOF mass spectrometer. More than ten nonpolar lipids including cholesterol and GalCer lipids were detected by DESI/PI in the positive ion mode, compared with that by DESI. In the negative-ion mode, ion yields of DESI/PI for lipids (HexCer, PE, and PE-P) were also increased by several folds.

Multimodal MALDI Imaging Mass Spectrometry Reveals Spatially Correlated Lipid and Protein Changes in Mouse Heart with Acute Myocardial Infarction

Acute myocardial infarction (MI) is a cardiovascular disease that remains a major cause of morbidity and mortality worldwide despite advances in its prevention and treatment. During acute myocardial ischemia, the lack of oxygen switches the cell metabolism to anaerobic respiration, with lactate accumulation, ATP depletion, Na⁺ and Ca²⁺ overload, and inhibition of myocardial contractile function, which drastically modifies the lipid, protein, and small metabolite profile in the myocardium. Imaging mass spectrometry (IMS) is a powerful technique to comprehensively elucidate the spatial distribution patterns of lipids, peptides, and proteins in biological tissue sections. In this work, we demonstrate an application of multimodal chemical imaging using matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS), which provided comprehensive molecular information in situ within the same mouse heart tissue sections with myocardial infarction. MALDI-IMS (at 30 μm per pixel) revealed infarct-associated spatial alterations of several lipid species of sphingolipids, glycerophospholipids, lysophospholipids, and cardiolipins along with the acyl carnitines. Further, we performed multimodal MALDI-IMS (IMS3) where dual polarity lipid imaging was combined with subsequent protein MALDI-IMS analysis (at 30 μm per pixel) within the same tissue sections, which revealed accumulations of core histone proteins H4, H2A, and H2B along with post-translational modification products, acetylated H4 and H2A, on the borders of the infarcted region. This methodology allowed us to interpret the lipid and protein molecular pathology of the very same infarcted region in a mouse model of myocardial infarction. Therefore, the presented data highlight the potential of multimodal MALDI imaging mass spectrometry of the same tissue sections as a powerful approach for simultaneous investigation of spatial infarct-associated lipid and protein changes of myocardial infarction.

Cardiac tissue engineering: state-of-the-art methods and outlook

The purpose of this review is to assess the state-of-the-art fabrication methods, advances in genome editing, and the use of machine learning to shape the prospective growth in cardiac tissue engineering. Those interdisciplinary emerging innovations would move forward basic research in this field and their clinical applications. The long-entrenched challenges in this field could be addressed by novel 3-dimensional (3D) scaffold substrates for cardiomyocyte (CM) growth and maturation. Stem cell-based therapy through genome editing techniques can repair gene mutation, control better maturation of CMs or even reveal its molecular clock. Finally, machine learning and precision control for improvements of the construct fabrication process and optimization in tissue-specific clonal selections with an outlook of cardiac tissue engineering are also presented.

Trends in mass spectrometry imaging for cardiovascular diseases

Mass spectrometry imaging (MSI) is a widely established technology; however, in the cardiovascular research field, its use is still emerging. The technique has the advantage of analyzing multiple molecules without prior knowledge while maintaining the relation with tissue morphology. Particularly, MALDI-based approaches have been applied to obtain in-depth knowledge of cardiac (dys)function. Here, we discuss the different aspects of the MSI protocols, from sample handling to instrumentation used in cardiovascular research, and critically evaluate these methods. The trend towards structural lipid analysis, identification, and “top-down” protein MSI shows the potential for implementation in (pre)clinical research and complementing the diagnostic tests. Moreover, new insights into disease progression are expected and thereby contribute to the understanding of underlying mechanisms related to cardiovascular diseases.

Imaging of Polar and Nonpolar Species Using Compact Desorption Electrospray Ionization/Postphotoionization Mass Spectrometry

Desorption electrospray ionization (DESI) mass spectrometry imaging (MSI) can simultaneously record the 2D distribution of polar biomolecules in tissue slices at ambient conditions. However, sensitivity of DESI-MSI for nonpolar compounds is restricted by low ionization efficiency and strong ion suppression. In this study, a compact postphotoionization assembly combined with DESI (DESI/PI) was developed for imaging polar and nonpolar molecules in tissue sections by switching off/on a portable krypton lamp. Compared with DESI, higher signal intensities of nonpolar compounds could be detected with DESI/PI. To further increase the ionization efficiency and transport of charged ions of DESI/PI, the desorption solvent composition and gas flow in the ionization tube were optimized. In mouse brain tissue, more than 2 orders of magnitude higher signal intensities for certain neutral biomolecules like creatine, cholesterol, and GalCer lipids were obtained by DESI/PI in the positive ion mode, compared with that of DESI. In the negative ion mode, ion yields of DESI/PI for glutamine and some lipids (HexCer, PE, and PE-O) were also increased by several-fold. Moreover, nonpolar constituents in plant tissue, such as catechins in leaf shoots of tea, could also be visualized by DESI/PI. Our results indicate that DESI/PI can expand the application field of DESI to nonpolar molecules, which is important for comprehensive imaging of biomolecules in biological tissues with moderate spatial resolution at ambient conditions.