Current State and Future Challenges of Mass Spectrometry Imaging for Clinical Research

Design and Implementation of a Desorption Electro-flow Focusing Sprayer on an Orbitrap Mass Spectrometer for DESI Mass Spectrometry Imaging at High Spatial Resolution and at High Speed

Since desorption electrospray ionization mass spectrometry (DESI-MS) was first presented in 2004, the fundamental design of the sprayer has undergone relatively minor modifications. This changed in 2022 when Takats and co-workers implemented the desorption electro-flow focusing (DEFFI) sprayer design by modifying the sprayer from a commercial DESI system, leading to significantly improved spatial resolution and robustness compared with the traditional DESI-MSI sprayer design. Here, we present the design of a new DEFFI sprayer that can be built from standard fittings and connectors in combination with an aluminum spray head that can be machined in most mechanic workshops. The new design represents a cost-efficient approach to improved DESI-MSI on mass spectrometers from all vendors, including high-resolution instruments such as Orbitraps and FT-ICR. The new DEFFI sprayer is demonstrated on a QExactive Orbitrap mass spectrometer, resulting in a massively improved ion yield compared with the classic DESI sprayer. The improved ion yield enables DESI-MSI at ion injection times down to 5 ms, allowing for DESI-MSI at a potentially very high speed. More importantly, the DEFFI sprayer delivers a more robust and focused spray, which is easier to use and requires less optimization. It provides high spatial resolution with limited effort compared with previous modifications of the traditional DESI design. Imaging of rat testis was performed at pixel sizes down to 12 μm, suggesting a spatial resolution of approximately 30 μm, which may have potential for further improvement.

Application of spatial omics in gastric cancer

Gastric cancer (GC), a globally prevalent and lethal malignancy, continues to be a key research focus. However, due to its considerable heterogeneity and complex pathogenesis, the treatment and diagnosis of gastric cancer still face significant challenges. With the rapid development of spatial omics technology, which provides insights into the spatial information within tumor tissues, it has emerged as a significant tool in gastric cancer research. This technology affords new insights into the pathology and molecular biology of gastric cancer for scientists. This review discusses recent advances in spatial omics technology for gastric cancer research, highlighting its applications in the tumor microenvironment (TME), tumor heterogeneity, tumor genesis and development mechanisms, and the identification of potential biomarkers and therapeutic targets. Moreover, this article highlights spatial omics' potential in precision medicine and summarizes existing challenges and future directions. It anticipates spatial omics' continuing impact on gastric cancer research, aiming to improve diagnostic and therapeutic approaches for patients. With this review, we aim to offer a comprehensive overview to scientists and clinicians in gastric cancer research, motivating further exploration and utilization of spatial omics technology. Our goal is to improve patient outcomes, including survival rates and quality of life.

Mass spectrometric approaches in discovering lipid biomarkers for COVID-19 by lipidomics: Future challenges and perspectives

Coronavirus disease 2019 (COVID-19) has emerged as a global health threat and has rapidly spread worldwide. Significant changes in the lipid profile before and after COVID-19 confirmed the significance of lipid metabolism in regulating the response to viral infection. Therefore, understanding the role of lipid metabolism may facilitate the development of new therapeutics for COVID-19. Owing to their high sensitivity and accuracy, mass spectrometry (MS)-based methods are widely used for rapidly identifying and quantifying of thousands of lipid species present in a small amount of sample. To enhance the capabilities of MS for the qualitative and quantitative analysis of lipids, different platforms have been combined to cover a wide range of lipidomes with high sensitivity, specificity, and accuracy. Currently, MS-based technologies are being established as efficient methods for discovering potential diagnostic biomarkers for COVID-19 and related diseases. As the lipidome of the host cell is drastically affected by the viral replication process, investigating lipid profile alterations in patients with COVID-19 and targeting lipid metabolism pathways are considered to be crucial steps in host-directed drug targeting to develop better therapeutic strategies. This review summarizes various MS-based strategies that have been developed for lipidomic analyzes and biomarker discoveries to combat COVID-19 by integrating various other potential approaches using different human samples. Furthermore, this review discusses the challenges in using MS technologies and future perspectives in terms of drug discovery and diagnosis of COVID-19.

Low-Temperature Plasma Pretreatment Enhanced Cholesterol Detection in Brain by Desorption Electrospray Ionization-Mass Spectrometry Imaging

Cholesterol is a primary lipid molecule in the brain that contains one-fourth of the total body cholesterol. Abnormal cholesterol homeostasis is associated with neurodegenerative disorders. Mass spectrometry imaging (MSI) technique is a powerful tool for studying lipidomics and metabolomics. Among the MSI techniques, desorption electrospray ionization-MSI (DESI-MSI) has been used advantageously to study brain lipidomics due to its soft and ambient ionization nature. However, brain cholesterol is poorly ionized. To this end, we have developed a new method for detecting brain cholesterol by DESI-MSI using low-temperature plasma (LTP) pretreatment as an ionization enhancement. In this method, the brain sections were treated with LTP for 1 and 2 min prior to DESI-MSI analyses. Interestingly, the MS signal intensity of cholesterol (at m/z 369.35 [M + H - H2O]+) was more than 2-fold higher in the 1 min LTP-treated brain section compared to the untreated section. In addition, we detected cholesterol, more specifically excluding isomers by targeted-DESI-MSI in multiple reaction monitoring (MRM) mode and similar results were observed: the signal intensity of each cholesterol transition (m/z 369.4 → 95.1, 109.1, 135.1, 147.1, and 161.1) was increased by more than 2-fold due to 1 min LTP treatment. Cholesterol showed characteristic distributions in the fiber tract region, including the corpus callosum and anterior commissure, anterior part of the brain where LTP markedly (p < 0.001) enhanced the cholesterol intensity. In addition, the distributions of some unknown analytes were exclusively detected in the LTP-treated section. Our study revealed LTP pretreatment as a potential strategy to ionize molecules that show poor ionization efficiency in the MSI technique.

A statistical approach to system suitability testing for mass spectrometry imaging

RATIONALE

Mass spectrometry imaging (MSI) elevates the power of conventional mass spectrometry (MS) to multidimensional space, elucidating both chemical composition and localization. However, the field lacks any robust quality control (QC) and/or system suitability testing (SST) protocols to monitor inconsistencies during data acquisition, both of which are integral to ensure the validity of experimental results. To satisfy this demand in the community, we propose an adaptable QC/SST approach with five analyte options amendable to various ionization MSI platforms (e.g., desorption electrospray ionization, matrix-assisted laser desorption/ionization [MALDI], MALDI-2, and infrared matrix-assisted laser desorption electrospray ionization [IR-MALDESI]).

METHODS

A novel QC mix was sprayed across glass slides to collect QC/SST regions-of-interest (ROIs). Data were collected under optimal conditions and on a compromised instrument to construct and refine the principal component analysis (PCA) model in R. Metrics, including mass measurement accuracy and spectral accuracy, were evaluated, yielding an individual suitability score for each compound. The average of these scores is utilized to inform if troubleshooting is necessary.

RESULTS

The PCA-based SST model was applied to data collected when the instrument was compromised. The resultant SST scores were used to determine a statistically significant threshold, which was defined as 0.93 for IR-MALDESI-MSI analyses. This minimizes the type-I error rate, where the QC/SST would report the platform to be in working condition when cleaning is actually necessary. Further, data scored after a partial cleaning demonstrate the importance of QC and frequent full instrument cleaning.

CONCLUSIONS

This study is the starting point for addressing an important issue and will undergo future development to improve the efficiency of the protocol. Ultimately, this work is the first of its kind and proposes this approach as a proof of concept to develop and implement universal QC/SST protocols for a variety of MSI platforms.

Spatial metabolomics and its application in the liver

Hepatocytes work in highly structured, repetitive hepatic lobules. Blood flow across the radial axis of the lobule generates oxygen, nutrient, and hormone gradients, which result in zoned spatial variability and functional diversity. This large heterogeneity suggests that hepatocytes in different lobule zones may have distinct gene expression profiles, metabolic features, regenerative capacity, and susceptibility to damage. Here, we describe the principles of liver zonation, introduce metabolomic approaches to study the spatial heterogeneity of the liver, and highlight the possibility of exploring the spatial metabolic profile, leading to a deeper understanding of the tissue metabolic organization. Spatial metabolomics can also reveal intercellular heterogeneity and its contribution to liver disease. These approaches facilitate the global characterization of liver metabolic function with high spatial resolution along physiological and pathological time scales. This review summarizes the state of the art for spatially resolved metabolomic analysis and the challenges that hinder the achievement of metabolome coverage at the single-cell level. We also discuss several major contributions to the understanding of liver spatial metabolism and conclude with our opinion on the future developments and applications of these exciting new technologies.

Multiple ion isolation and accumulation events for selective chemical noise reduction and dynamic range enhancement in MALDI imaging mass spectrometry

Abundant chemical noise in MALDI imaging mass spectrometry experiments can impede the detection of less abundant compounds of interest. This chemical noise commonly originates from the MALDI matrix as well as other endogenous compounds present in high concentrations and/or with high ionization efficiencies. MALDI imaging mass spectrometry of biological tissues measures numerous biomolecular compounds that exist in a wide range of concentrations in vivo. When ion trapping instruments are used, highly abundant ions can dominate the charge capacity and lead to space charge effects that hinder the dynamic range and detection of lowly abundant compounds of interest. Gas-phase fractionation has been previously utilized in mass spectrometry to isolate and enrich target analytes. Herein, we have characterized the use of multiple continuous accumulations of selected ions (Multi CASI) to reduce the abundance of chemical noise and diminish the effects of space charge in MALDI imaging mass spectrometry experiments. Multi CASI utilizes the mass-resolving capability of a quadrupole mass filter to perform multiple sequential ion isolation events prior to a single mass analysis of the combined ion population. Multi CASI was used to improve metabolite and lipid detection in the MALDI imaging mass spectrometry analysis of rat brain tissue.

Metabolic heterogeneity affects trastuzumab response and survival in HER2-positive advanced gastric cancer

BACKGROUND

Trastuzumab is the only first-line treatment targeted against the human epidermal growth factor receptor 2 (HER2) approved for patients with HER2-positive advanced gastric cancer. The impact of metabolic heterogeneity on trastuzumab treatment efficacy remains unclear.

METHODS

Spatial metabolomics via high mass resolution imaging mass spectrometry was performed in pretherapeutic biopsies of patients with HER2-positive advanced gastric cancer in a prospective multicentre observational study. The mass spectra, representing the metabolic heterogeneity within tumour areas, were grouped by K-means clustering algorithm. Simpson's diversity index was applied to compare the metabolic heterogeneity level of individual patients.

RESULTS

Clustering analysis revealed metabolic heterogeneity in HER2-positive gastric cancer patients and uncovered nine tumour subpopulations. High metabolic heterogeneity was shown as a factor indicating sensitivity to trastuzumab (p = 0.008) and favourable prognosis at trend level. Two of the nine tumour subpopulations associated with favourable prognosis and trastuzumab sensitivity, and one subpopulation associated with poor prognosis and trastuzumab resistance.

CONCLUSIONS

This work revealed that tumour metabolic heterogeneity associated with prognosis and trastuzumab response based on tissue metabolomics of HER2-positive gastric cancer. Tumour metabolic subpopulations may provide an association with trastuzumab therapy efficacy.

CLINICAL TRIAL REGISTRATION

The patient cohort was conducted from a multicentre observational study (VARIANZ;NCT02305043).

Advances in Imaging Mass Spectrometry for Biomedical and Clinical Research

Imaging mass spectrometry (IMS) allows for the untargeted mapping of biomolecules directly from tissue sections. This technology is increasingly integrated into biomedical and clinical research environments to supplement traditional microscopy and provide molecular context for tissue imaging. IMS has widespread clinical applicability in the fields of oncology, dermatology, microbiology, and others. This review summarizes the two most widely employed IMS technologies, matrix-assisted laser desorption/ionization (MALDI) and desorption electrospray ionization (DESI), and covers technological advancements, including efforts to increase spatial resolution, specificity, and throughput. We also highlight recent biomedical applications of IMS, primarily focusing on disease diagnosis, classification, and subtyping.

Inclusion of Porous Graphitic Carbon Chromatography Yields Greater Protein Identification and Compartment and Process Coverage and Enables More Reflective Protein-Level Label-Free Quantitation

The ubiquity of mass spectrometry-based bottom-up proteomic analyses as a component of biological investigation mandates the validation of methodologies that increase acquisition efficiency, improve sample coverage, and enhance profiling depth. Chromatographic separation is often ignored as an area of potential improvement, with most analyses relying on traditional reversed-phase liquid chromatography (RPLC); this consistent reliance on a single chromatographic paradigm fundamentally limits our view of the observable proteome. Herein, we build upon early reports and validate porous graphitic carbon chromatography (PGC) as a facile means to substantially enhance proteomic coverage without changes to sample preparation, instrument configuration, or acquisition methods. Analysis of offline fractionated cell line digests using both separations revealed an increase in peptide and protein identifications by 43% and 24%, respectively. Increased identifications provided more comprehensive coverage of cellular components and biological processes independent of protein abundance, highlighting the substantial quantity of proteomic information that may go undetected in standard analyses. We further utilize these data to reveal that label-free quantitative analyses using RPLC separations alone may not be reflective of actual protein constituency. Together, these data highlight the value and comprehension offered through PGC-MS proteomic analyses. RAW proteomic data have been uploaded to the MassIVE repository with the primary accession code MSV000091495.

Unlocking the Hidden Depths: Multi-Modal Integration of Imaging Mass Spectrometry-Based and Molecular Imaging Techniques

Multimodal imaging (MMI) has emerged as a powerful tool in clinical research, combining different imaging modes to acquire comprehensive information and enabling scientists and surgeons to study tissue identification, localization, metabolic activity, and molecular discovery, thus aiding in disease progression analysis. While multimodal instruments are gaining popularity, challenges such as non-standardized characteristics, custom software, inadequate commercial support, and integration issues with other instruments need to be addressed. The field of multimodal imaging or multiplexed imaging allows for simultaneous signal reproduction from multiple imaging strategies. Intraoperatively, MMI can be integrated into frameless stereotactic surgery. Recent developments in medical imaging modalities such as magnetic resonance imaging (MRI), and Positron Emission Topography (PET) have brought new perspectives to multimodal imaging, enabling early cancer detection, molecular tracking, and real-time progression monitoring. Despite the evidence supporting the role of MMI in surgical decision-making, there is a need for comprehensive studies to validate and perform integration at the intersection of multiple imaging technologies. They were integrating mass spectrometry-based technologies (e.g., imaging mass spectrometry (IMS), imaging mass cytometry (IMC), and Ion mobility mass spectrometry ((IM-IM) with medical imaging modalities, offering promising avenues for molecular discovery and clinical applications. This review emphasizes the potential of multi-omics approaches in tissue mapping using MMI integrated into desorption electrospray ionization (DESI) and matrix-assisted laser desorption ionization (MALDI), allowing for sequential analyses of the same section. By addressing existing knowledge gaps, this review encourages future research endeavors toward multi-omics approaches, providing a roadmap for future research and enhancing the value of MMI in molecular pathology for diagnosis.

Spatial segmentation of mass spectrometry imaging data featuring selected principal components

Spatial segmentation aims to find homogeneous/heterogeneous subgroups of spectra or ion images in mass spectrometry imaging (MSI) data. The maps it generated inform researchers of vital characteristics of the data and thus provide the basis for strategizing further biological analysis. Dimensional reduction and clustering are two basic steps of segmentation. Due to the variations in the quality, resolution, density of spectral information, and sizes, not all datasets could be segmented ideally with combinations of different dimensional reduction and clustering algorithms. Here, we proposed a segmentation pipeline that utilized pattern compression by principal component analysis (PCA) and represented by principal components. Instead of preprocessed or raw MSI data, normalized principal components were used for the segmentation process. Multiple datasets of rat brains and mouse kidneys were tested, and the proposed segmentation pipeline presented the obvious advantage of easy-to-use and can be readily intergraded with other existing innovative pipelines.

Directly Mapping the Spatial Distribution of Organic Compounds on Mineral Rock Surfaces by DESI and LAESI Mass Spectrometry Imaging

Here, we present a new application of desorption electrospray ionization (DESI) and laser ablation electrospray ionization (LAESI) mass spectrometry imaging to assess the spatial location of organic compounds, both polar and nonpolar, directly from rock surfaces. Three carbonaceous rocks collected from an aquatic environment and a berea sandstone subjected to a small-scale oil recovery experiment were analyzed by DESI and LAESI. No rock pretreatment was required before DESI and LAESI analyses. DESI detected and spatially mapped several fatty acids and a disaccharide on the surfaces of carbonaceous rocks, and various nitrogenated and oxygenated compounds on the surfaces of berea sandstone. In contrast, LAESI using a 3.4 μm infrared laser beam was able to detect and map hydrocarbons on the surfaces of all rock samples. Both techniques can be combined to analyze polar and nonpolar compounds. DESI can be used first to detect polar compounds, as it does not destroy the rock surface, and LAESI can then be used to analyze nonpolar analytes, as it destroys a layer of the sample surface. Both techniques have the potential to be used in several scientific areas involving rocks and minerals, such as in the analysis of industry-derived contaminants in aquatic sediments or in small-scale rock-fluid interaction experiments.

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.

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

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.

Native top-down mass spectrometry for higher-order structural characterization of proteins and complexes

Progress in structural biology research has led to a high demand for powerful and yet complementary analytical tools for structural characterization of proteins and protein complexes. This demand has significantly increased interest in native mass spectrometry (nMS), particularly native top-down mass spectrometry (nTDMS) in the past decade. This review highlights recent advances in nTDMS for structural research of biological assemblies, with a particular focus on the extra multi-layers of information enabled by TDMS. We include a short introduction of sample preparation and ionization to nMS, tandem fragmentation techniques as well as mass analyzers and software/analysis pipelines used for nTDMS. We highlight unique structural information offered by nTDMS and examples of its broad range of applications in proteins, protein-ligand interactions (metal, cofactor/drug, DNA/RNA, and protein), therapeutic antibodies and antigen-antibody complexes, membrane proteins, macromolecular machineries (ribosome, nucleosome, proteosome, and viruses), to endogenous protein complexes. The challenges, potential, along with perspectives of nTDMS methods for the analysis of proteins and protein assemblies in recombinant and biological samples are discussed.

Induction of Liver Size Reduction in Zebrafish Larvae by the Emerging Synthetic Cannabinoid 4F-MDMB-BINACA and Its Impact on Drug Metabolism

Zebrafish (ZF; Danio rerio) larvae have become a popular in vivo model in drug metabolism studies. Here, we investigated the metabolism of methyl 2-[1-(4-fluorobutyl)-1H-indazole-3-carboxamido]-3,3-dimethylbutanoate (4F-MDMB-BINACA) in ZF larvae after direct administration of the cannabinoid via microinjection, and we visualized the spatial distributions of the parent compound and its metabolites by mass spectrometry imaging (MSI). Furthermore, using genetically modified ZF larvae, the role of cannabinoid receptor type 1 (CB1) and type 2 (CB2) on drug metabolism was studied. Receptor-deficient ZF mutant larvae were created using morpholino oligonucleotides (MOs), and CB2-deficiency had a critical impact on liver development of ZF larva, leading to a significant reduction of liver size. A similar phenotype was observed when treating wild-type ZF larvae with 4F-MDMB-BINACA. Thus, we reasoned that the cannabinoid-induced impaired liver development might also influence its metabolic function. Studying the metabolism of two synthetic cannabinoids, 4F-MDMB-BINACA and methyl 2-(1-(5-fluoropentyl)-1H-pyrrolo[2,3-b]pyridine-3-carboxamido)-3,3-dimethylbutanoate (7′N-5F-ADB), revealed important insights into the in vivo metabolism of these compounds and the role of cannabinoid receptor binding.

Unraveling metabolic alterations in transgenic mouse model of Alzheimer's disease using MALDI MS imaging with 4-aminocinnoline-3-carboxamide matrix

Revealing the metabolic abnormalities of central and peripheral systems in Alzheimer's disease (AD) mouse model is of paramount importance for understanding AD disease. Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) is a powerful label-free technique that has been extensively utilized for the interrogation of spatial changes of various metabolites in neurodegenerative disease. However, technical limitations still exist in MALDI MS, and there is a need to improve the performance of traditional MALDI for a deeper investigation of metabolic alterations in the AD mouse model. In this work, 4-aminocinnoline-3-carboxamide (4-AC) was developed into a novel dual-polarity MALDI matrix. Compared with traditionally used MALDI matrices such as 2,5-dihydroxybenzoic acid (DHB) and 9-aminoacridine (9-AA), 4-AC exhibited superior performance in UV absorption at 355 nm, ion yields, background interference, and vacuum stability, making it an ideal MALDI matrix for comprehensive evaluation of metabolic alteration in the brain and serum of APP/PS1 transgenic mouse model of AD. In total, 93 metabolites exhibited different levels of regional changes in the brain of AD mice as compared to the age-matched controls. Moreover, in the serum of AD mice, 81 altered metabolites distinguishing the AD group from the control were observed by using multivariate statistical analysis. It is expected that the application of the MALDI MSI method developed in this work to visualize the spatio-chemical change of various metabolites may improve our understanding of the etiopathogenesis of AD.

massNet: integrated processing and classification of spatially resolved mass spectrometry data using deep learning for rapid tumor delineation

MOTIVATION

Mass spectrometry imaging (MSI) provides rich biochemical information in a label-free manner and therefore holds promise to substantially impact current practice in disease diagnosis. However, the complex nature of MSI data poses computational challenges in its analysis. The complexity of the data arises from its large size, high-dimensionality and spectral nonlinearity. Preprocessing, including peak picking, has been used to reduce raw data complexity; however, peak picking is sensitive to parameter selection that, perhaps prematurely, shapes the downstream analysis for tissue classification and ensuing biological interpretation.

RESULTS

We propose a deep learning model, massNet, that provides the desired qualities of scalability, nonlinearity and speed in MSI data analysis. This deep learning model was used, without prior preprocessing and peak picking, to classify MSI data from a mouse brain harboring a patient-derived tumor. The massNet architecture established automatically learning of predictive features, and automated methods were incorporated to identify peaks with potential for tumor delineation. The model's performance was assessed using cross-validation, and the results demonstrate higher accuracy and a substantial gain in speed compared to the established classical machine learning method, support vector machine.

AVAILABILITY AND IMPLEMENTATION

https://github.com/wabdelmoula/massNet. The data underlying this article are available in the NIH Common Fund's National Metabolomics Data Repository (NMDR) Metabolomics Workbench under project id (PR001292) with http://dx.doi.org/10.21228/M8Q70T.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Insights and prospects for ion mobility-mass spectrometry in clinical chemistry

INTRODUCTION

Ion mobility-mass spectrometry is an emerging technology in the clinical setting for high throughput and high confidence molecular characterization from complex biological samples. Ion mobility spectrometry can provide isomer separations on the basis of molecular structure, the ability of which is increasing through technological developments that afford enhanced resolving power. Integrating multiple separation dimensions, such as liquid chromatography-ion mobility-mass spectrometry (LC-IM-MS) provide dramatic enhancements in the mitigation of molecular interferences for high accuracy clinical measurements.

AREAS COVERED

Multidimensional separations with LC-IM-MS provide better selectivity and sensitivity in molecular analysis. Mass spectrometry imaging of tissues to inform spatial molecular distribution is improved by complementary ion mobility analyses. Biomarker identification in surgical environments is enhanced by intraoperative biochemical analysis with mass spectrometry and holds promise for integration with ion mobility spectrometry. New prospects in high resolving power ion mobility are enhancing analysis capabilities, such as distinguishing isomeric compounds.

EXPERT OPINION

Ion mobility-mass spectrometry holds many prospects for the field of isomer identification, molecular imaging, and intraoperative tumor margin delineation in clinical settings. These advantages are afforded while maintaining fast analysis times and subsequently high throughput. High resolving power ion mobility will enhance these advantages further, in particular for analyses requiring high confidence isobaric selectivity and detection.

Molecular tissue profiling by MALDI imaging: recent progress and applications in cancer research

Matrix-assisted laser desorption/ionization (MALDI) imaging is an emergent technology that has been increasingly adopted in cancer research. MALDI imaging is capable of providing global molecular mapping of the abundance and spatial information of biomolecules directly in the tissues without labeling. It enables the characterization of a wide spectrum of analytes, including proteins, peptides, glycans, lipids, drugs, and metabolites and is well suited for both discovery and targeted analysis. An advantage of MALDI imaging is that it maintains tissue integrity, which allows correlation with histological features. It has proven to be a valuable tool for probing tumor heterogeneity and has been increasingly applied to interrogate molecular events associated with cancer. It provides unique insights into both the molecular content and spatial details that are not accessible by other techniques, and it has allowed considerable progress in the field of cancer research. In this review, we first provide an overview of the MALDI imaging workflow and approach. We then highlight some useful applications in various niches of cancer research, followed by a discussion of the challenges, recent developments and future prospect of this technique in the field.

Proteomic Imaging of Vestibular Schwannomas and Normal Nerves. Histopathologic Correlations

OBJECTIVES

Proteomic analysis of vestibular schwannoma (VS), non-vestibular schwannoma (NVS), and normal nerve (NN) using mass spectrometry and imaging of matrix assisted laser desorption ionization-time of flight (MALDI-TOF).

METHODS

Retrospective, qualitative, and descriptive study on VS, NVS, and NN. Samples were provided by our Tumor Bank. They were analyzed histologically then sprayed by acid matrix. The laser beam of MALDI performed desorption-ionization of the sample. A mass spectrogram (MS) was drawn depending on time of flight of ionized peptides, and MALDI-imaging was obtained which is a summation color spectrum depending on sample's peptide content. The slice was reexamined histologically and results compared with MALDI-imaging.

RESULTS

Fifty schwannomas were sampled, of which 27 exploitable: 22 VS (17 Antoni type A and five type B) and five NVS (all Antoni type B). Eleven NN were analyzed. Among the 22 VS, near-total correlation between MALDI-imaging and pathology was found in two cases (9.1%), partial correlation in four (18.2%), and no correlation in 16 (72.7%); correlations were more frequent in VS of the Antoni type B. MS showed a peptide spike at 2,000 m/z in 7 (31.8%) and 5,000 m/z in 21 (95.5%). Among the five NVS, near-total correlation was found in three cases (60%), partial correlation in one (20%), and no correlation in one (20%). MS showed a peptide spike at 2,000 m/z in two (40%) and 5,000 m/z in all (100%). Among the 11 NN, near-total correlation was found in nine cases (81.8%), partial correlation in one (9.1%), and no correlation in one (9.1%). MS showed no peptide spike at 2,000 or 5,000 m/z. Behind homogeneous areas on histology, there was great heterogeneity on MALDI-imaging and MS, regarding VS and NVS, but not NN.

CONCLUSIONS

There was a lack of correlation between MALDI-imaging and pathology in VS (except Antoni type B) as compared with NVS and NN. The lack of correlation in VS of the type A as compared with type B VS and NVS could be attributed to the overexpression of degeneration-associated proteins/peptides in VS of the type B as well as NVS that are better correlated with histologic findings. The two peptide spikes detected in schwannoma and not in NN opens up the prospect of tumor biomarkers identifiable by sequencing. The proteomic polymorphism found in VS and NVS was absent on histology which is a new morphologic characteristic of schwannoma. Further studies should be performed in the future to confirm the benefit and usefulness of the MALDI in the analysis of VS and NVS.

Cross-Normalization of MALDI Mass Spectrometry Imaging Data Improves Site-to-Site Reproducibility

Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) is an established tool for the investigation of formalin-fixed paraffin-embedded (FFPE) tissue samples and shows a high potential for applications in clinical research and histopathological tissue classification. However, the applicability of this method to serial clinical and pharmacological studies is often hampered by inevitable technical variation and limited reproducibility. We present a novel spectral cross-normalization algorithm that differs from the existing normalization methods in two aspects: (a) it is based on estimating the full statistical distribution of spectral intensities and (b) it involves applying a non-linear, mass-dependent intensity transformation to align this distribution with a reference distribution. This method is combined with a model-driven resampling step that is specifically designed for data from MALDI imaging of tryptic peptides. This method was performed on two sets of tissue samples: a single human teratoma sample and a collection of five tissue microarrays (TMAs) of breast and ovarian tumor tissue samples (N = 241 patients). The MALDI MSI data was acquired in two labs using multiple protocols, allowing us to investigate different inter-lab and cross-protocol scenarios, thus covering a wide range of technical variations. Our results suggest that the proposed cross-normalization significantly reduces such batch effects not only in inter-sample and inter-lab comparisons but also in cross-protocol scenarios. This demonstrates the feasibility of cross-normalization and joint data analysis even under conditions where preparation and acquisition protocols themselves are subject to variation.

Matrix-Assisted Laser Desorption Ionization Mass Spectrometry Imaging by Freeze-Spot Deposition of the Matrix

Imaging mass spectrometry has emerged as a powerful metabolite measurement approach to capture the spatial dimension of metabolite distribution in a biological sample. In matrix-assisted laser desorption ionization-mass spectrometry imaging (MALDI-MSI), deposition of the chemical-matrix onto the sample serves to simultaneously extract biomolecules to the sample surface and concurrently render the sample amenable to MALDI. However, matrix application may mobilize sample metabolites and will dictate the efficiency of matrix crystallization, together limiting the lateral resolution which may be optimally achieved by MSI. Here, we describe a matrix application technique, herein referred to as the "freeze-spot" method, conceived as a low-cost preparative approach requiring minimal amounts of chemical matrix while maintaining the spatial dimension of sample metabolites for MALDI-MSI. Matrix deposition was achieved by pipette spot application of the matrix-solubilized within a solvent solution with a freezing point above that of a chilled sample stage to which the sample section is mounted. The matrix solution freezes on contact with the sample and the solvent is removed by sublimation, leaving a fine crystalline matrix on the sample surface. Freeze-spotting is quick to perform, found particularly useful for MALDI-MSI of small sample sections, and well suited to efficient and cost-effective method development pipelines, while capable of maintaining the lateral resolution required by MSI.

Hydralazine as a Versatile and Universal Matrix for High-Molecular Coverage and Dual-Polarity Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging

Few matrices have the potential to be universally applicable for imaging vast endogenous compounds ranging from micro to macromolecules. In this article, we present hydralazine (HZN) as a versatile and universal matrix for matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) of a wide range of endogenous compounds between 50.0 and 20,000.0 Da. HZN was prepared from its hydrochloride by alkalizing HZN·HCl with ammonia to enhance the optical absorptivity at the preferred MALDI UV laser wavelength. To further improve its performance for MALDI MS, HZN was doped with NH₄OH or TFA, resulting in matrix superior performance for imaging biologically relevant compounds in the negative and positive-ion modes, respectively. The analyte-matrix interaction was also enhanced by the optimized matrix solvent and the deposition amount. Compared with conventional matrices such as 2,5-dihydroxybenzoic acid, α-cyano-4-hydroxycinnamic acid, and 9-aminoacridine (9-AA), the HZN matrix provided higher sensitivity, broader molecular coverage, and improved signal intensities. Its broad acquisition range makes it versatile for imaging small molecular metabolites and lipids, as well as proteins. In addition, HZN was applied successfully for the visualization of tissue-specific distributions and changes of small molecules, lipids, and proteins in the kidney and liver sections of obese ob/ob and diabetic db/db mice. The use of the HZN matrix shows great potential application in the field of pathological research.

Venom Gland Mass Spectrometry Imaging of Saw-Scaled Viper, Echis carinatus sochureki, at High Lateral Resolution

The snake venom gland is the place for the synthesis, storage, and secretion of a complex mixture of proteins and peptides, i.e., the venom. The morphology of the gland has been revealed by classical histology and microscopic studies. However, knowledge about the gland's cellular secretory and functional processes is still incomplete and has so far been neglected by the omics disciplines. We used autofocusing atmospheric-pressure matrix-assisted laser desorption/ionization (AP-SMALDI) mass spectrometry imaging (MSI) to investigate endogenous biomolecular distributions in the venom glands of the saw-scaled viper, Echis carinatus sochureki, employing different sample preparation methods. Fresh-freezing and formalin-fixation were tested for the gland to obtain intact tissue sections. Subsequently, MSI was conducted with 12 μm pixel resolution for both types of preparations, and the lateral distributions of the metabolites were identified. Experiments revealed that lipids belonging to the classes of PC, SM, PE, PS, PA, and TG are present in the venom gland. PC (32:0) and SM (36:1) were found to be specifically located in the areas where cells are present. The snake venom metalloprotease inhibitor pEKW (m/z 444.2233) was identified in the venom by top-down LC-MS/MS and localized by MALDI-MSI in the gland across secretory epithelial cells. The peptide can inhibit the venom's enzymatic activity during long-term storage within the venom gland. With a high degree of spectral similarities, we concluded that formalin-fixed tissue, in addition to its high ability to preserve tissue morphology, can be considered as an alternative method to fresh-frozen tissue in the case of lipid and peptide MS imaging in venom gland tissues.

Classification of Molecular Subtypes of High-Grade Serous Ovarian Cancer by MALDI-Imaging

Simple Summary

High-grade serous ovarian cancer (HGSOC) accounts for 70% of ovarian carcinomas with sobering survival rates. The mechanisms mediating treatment efficacy are still poorly understood with no adequate biomarkers of response to treatment and risk assessment. This variability of treatment response might be due to its molecular heterogeneity. Therefore, identification of biomarkers or molecular signatures to stratify patients and offer personalized treatment is of utmost priority. Currently, comprehensive gene expression profiling is time- and cost-extensive and limited by tissue heterogeneity. Thus, it has not been implemented into clinical practice. This study demonstrates for the first time a spatially resolved, time- and cost-effective approach to stratifying HGSOC patients by combining novel matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS) technology with machine-learning algorithms. Eventually, MALDI-derived predictive signatures for treatment efficacy, recurrent risk, or, as demonstrated here, molecular subtypes might be utilized for emerging clinical challenges to ultimately improve patient outcomes.

Abstract

Despite the correlation of clinical outcome and molecular subtypes of high-grade serous ovarian cancer (HGSOC), contemporary gene expression signatures have not been implemented in clinical practice to stratify patients for targeted therapy. Hence, we aimed to examine the potential of unsupervised matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS) to stratify patients who might benefit from targeted therapeutic strategies. Molecular subtyping of paraffin-embedded tissue samples from 279 HGSOC patients was performed by NanoString analysis (ground truth labeling). Next, we applied MALDI-IMS paired with machine-learning algorithms to identify distinct mass profiles on the same paraffin-embedded tissue sections and distinguish HGSOC subtypes by proteomic signature. Finally, we devised a novel approach to annotate spectra of stromal origin. We elucidated a MALDI-derived proteomic signature (135 peptides) able to classify HGSOC subtypes. Random forest classifiers achieved an area under the curve (AUC) of 0.983. Furthermore, we demonstrated that the exclusion of stroma-associated spectra provides tangible improvements to classification quality (AUC = 0.988). Moreover, novel MALDI-based stroma annotation achieved near-perfect classifications (AUC = 0.999). Here, we present a concept integrating MALDI-IMS with machine-learning algorithms to classify patients according to distinct molecular subtypes of HGSOC. This has great potential to assign patients for personalized treatment.

Evaluation of UV-C Decontamination of Clinical Tissue Sections for Spatially Resolved Analysis by Mass Spectrometry Imaging (MSI)

Clinical tissue specimens are often unscreened, and preparation of tissue sections for analysis by mass spectrometry imaging (MSI) can cause aerosolization of particles potentially carrying an infectious load. We here present a decontamination approach based on ultraviolet-C (UV-C) light to inactivate clinically relevant pathogens such as herpesviridae, papovaviridae human immunodeficiency virus, or SARS-CoV-2, which may be present in human tissue samples while preserving the biodistributions of analytes within the tissue. High doses of UV-C required for high-level disinfection were found to cause oxidation and photodegradation of endogenous species. Lower UV-C doses maintaining inactivation of clinically relevant pathogens to a level of increased operator safety were found to be less destructive to the tissue metabolome and xenobiotics. These doses caused less alterations of the tissue metabolome and allowed elucidation of the biodistribution of the endogenous metabolites. Additionally, we were able to determine the spatially integrated abundances of the ATR inhibitor ceralasertib from decontaminated human biopsies using desorption electrospray ionization-MSI (DESI-MSI).

Challenges and Strategies of Chemical Analysis of Drugs of Abuse and Explosives by Mass Spectrometry

In analytical science, mass spectrometry (MS) is known as a "gold analytical tool" because of its unique character of providing the direct molecular structural information of the relevant analyte molecules. Therefore, MS technique has widely been used in all branches of chemistry along with in proteomics, metabolomics, genomics, lipidomics, environmental monitoring etc. Mass spectrometry-based methods are very much needed for fast and reliable detection and quantification of drugs of abuse and explosives in order to provide fingerprint information for criminal investigation as well as for public security and safety at public places, respectively. Most of the compounds exist as their neutral form in nature except proteins, peptides, nucleic acids that are in ionic forms intrinsically. In MS, ion source is the heart of the MS that is used for ionizing the electrically neutral molecules. Performance of MS in terms of sensitivity and selectivity depends mainly on the efficiency of the ionization source. Accordingly, much attention has been paid to develop efficient ion sources for a wide range of compounds. Unfortunately, none of the commercial ion sources can be used for ionization of different types of compounds. Moreover, in MS, analyte molecules must be released into the gaseous phase and then ionize by using a suitable ion source for detection/quantification. Under these circumstances, fabrication of new ambient ion source and ultrasonic cutter blade-based non-thermal and thermal desorption methods have been taken into account. In this paper, challenges and strategies of mass spectrometry analysis of the drugs of abuse and explosives through fabrication of ambient ionization sources and new desorption methods for non-volatile compounds have been described. We will focus the literature progress mostly in the last decade and present our views for the future study.

MALDI-MSI-A Step Forward in Overcoming the Diagnostic Challenges in Ovarian Tumors

This study presents the use of matrix-assisted laser desorption and ionization mass spectrometry imaging (MALDI-MSI) directly on the tissue of two ovarian tumors that often present a diagnostic challenge, a low-grade serous borderline ovarian tumor and ovarian fibrothecoma. Different spatial distribution of m/z values within the tissue samples was observed, and regiospecific peaks were identified. Among the 106 peaks in the borderline ovarian tumor five, regiospecific peaks (m/z: 2861.35; 2775.79; 3368.34; 3438.43; 4936.37) were selected using FlexImaging software. Subsequently, the distribution of those selected peaks was visualized on the fibrothecoma tissue section, which demonstrated the differences in the tissue homo-/heterogeneous structure of both tumors. The comparison with the histopathological staining of the ovarian borderline tumor tissue section, obtained during serial sectioning, showed a close correlation of the molecular map with the morphological and histopathological features of the tissue and allowed the identification of different tissue types within the sample. This study highlights the potential significance of MSI in enabling morphological characterization of ovarian tumors as well as correct diagnosis and further prognosis than thus far seen in the literature. Osteopontin, tropomyosin and orosomucoid are only a couple of the molecules investigated using MALDI-MSI in ovarian cancer research. This study, in line with the available literature, proves the potential of MALDI-MSI to overcome the current limitations of classic histopathological examination giving a more in-depth insight into the tissue structure and thus lead to the more accurate differential diagnosis of ovarian tumors, especially in the most challenging cases.

Metabolite Imaging at the Margin: Visualizing Metabolic Tumor Gradients Using Mass Spectrometry

Glioblastoma multiforme (GBM) tumors are highly metabolic and vascularized, yet little has been reported regarding the spatial localization of metabolic activity within these tumors. A mass spectrometry imaging (MSI) study by Randall and colleagues in this issue provides provocative observations of metabolic gradients in xenograft GBM models. The intensity of acylcarnitines is dramatically increased at tumor margins, which interface with normal tissue, but not in tumor margins at the edge of the brain. A secondary examination of drug metabolites suggests that the observed metabolic gradients are pharmacologically relevant. These findings underscore previously undescribed spatial metabolic heterogeneity in GBM biology and opportunities for MSI investigations.See related article by Randall et al., p. 1258.

Mass Spectrometry Analysis of Intact Proteins from Crude Samples

Analysis of intact proteins by native mass spectrometry has emerged as a powerful tool for obtaining insight into subunit diversity, post-translational modifications, stoichiometry, structural arrangement, stability, and overall architecture. Typically, such an analysis is performed following protein purification procedures, which are time consuming, costly, and labor intensive. As this technology continues to move forward, advances in sample handling and instrumentation have enabled the investigation of intact proteins in situ and in crude samples, offering rapid analysis and improved conservation of the biological context. This emerging field, which involves various ion source platforms such as matrix-assisted laser desorption ionization (MALDI) and electrospray ionization (ESI) for both spatial imaging and solution-based analysis, is expected to impact many scientific fields, including biotechnology, pharmaceuticals, and clinical sciences. In this Perspective, we discuss the information that can be retrieved by such experiments as well as the current advantages and technical challenges associated with the different sampling strategies. Furthermore, we present future directions of these MS-based methods, including current limitations and efforts that should be made to make these approaches more accessible. Considering the vast progress we have witnessed in recent years, we anticipate that the advent of further innovations enabling minimal handling of MS samples will make this field more robust, user friendly, and widespread.

Artificial Intelligence to Decode Cancer Mechanism: Beyond Patient Stratification for Precision Oncology

The multitude of multi-omics data generated cost-effectively using advanced high-throughput technologies has imposed challenging domain for research in Artificial Intelligence (AI). Data curation poses a significant challenge as different parameters, instruments, and sample preparations approaches are employed for generating these big data sets. AI could reduce the fuzziness and randomness in data handling and build a platform for the data ecosystem, and thus serve as the primary choice for data mining and big data analysis to make informed decisions. However, AI implication remains intricate for researchers/clinicians lacking specific training in computational tools and informatics. Cancer is a major cause of death worldwide, accounting for an estimated 9.6 million deaths in 2018. Certain cancers, such as pancreatic and gastric cancers, are detected only after they have reached their advanced stages with frequent relapses. Cancer is one of the most complex diseases affecting a range of organs with diverse disease progression mechanisms and the effectors ranging from gene-epigenetics to a wide array of metabolites. Hence a comprehensive study, including genomics, epi-genomics, transcriptomics, proteomics, and metabolomics, along with the medical/mass-spectrometry imaging, patient clinical history, treatments provided, genetics, and disease endemicity, is essential. Cancer Moonshot℠ Research Initiatives by NIH National Cancer Institute aims to collect as much information as possible from different regions of the world and make a cancer data repository. AI could play an immense role in (a) analysis of complex and heterogeneous data sets (multi-omics and/or inter-omics), (b) data integration to provide a holistic disease molecular mechanism, (c) identification of diagnostic and prognostic markers, and (d) monitor patient's response to drugs/treatments and recovery. AI enables precision disease management well beyond the prevalent disease stratification patterns, such as differential expression and supervised classification. This review highlights critical advances and challenges in omics data analysis, dealing with data variability from lab-to-lab, and data integration. We also describe methods used in data mining and AI methods to obtain robust results for precision medicine from "big" data. In the future, AI could be expanded to achieve ground-breaking progress in disease management.

Classification of Inflammatory Bowel Disease from Formalin-Fixed, Paraffin-Embedded Tissue Biopsies via Imaging Mass Spectrometry

PURPOSE

Discrimination between ulcerative colitis (UC) and Crohn's disease (CD) by histologic features alone can be challenging and often leads to inaccurate initial diagnoses in inflammatory bowel disease (IBD) patients. This is mostly due to an overlap of clinical and histologic features. However, exact diagnosis is not only important for patient treatment but it also has a socioeconomic impact. It is therefore important to develop and improve diagnostic tools complementing traditional histomorphological approaches.

EXPERIMENTAL DESIGN

In this retrospective proof-of-concept study, the utilization of MALDI imaging is explored in combination with multi-variate data analysis methods to classify formalin-fixed, paraffin-embedded (FFPE) colon biopsies from UC (87 biopsies, 14 patients), CD (71 biopsies, 14 patients), and normal colonic (21 biopsies, 14 patients) tissues.

RESULTS

The proposed method results in an overall balanced accuracy of 85.7% on patient and of 80.4% on sample level, thus demonstrating that the assessment of IBD from FFPE tissue specimens via MALDI imaging is feasible.

CONCLUSIONS AND CLINICAL RELEVANCE

The results emphasize the high potential of this method to distinguish IBD subtypes in FFPE tissue sections, which is a prerequisite for further investigations in retrospective multicenter studies, as well as for a future implementation into clinical routine.

Recent advances of ambient ionization mass spectrometry imaging in clinical research

The structural information and spatial distribution of molecules in biological tissues are closely related to the potential molecular mechanisms of disease origin, transfer, and classification. Ambient ionization mass spectrometry imaging is an effective tool that provides molecular images while describing in situ information of biomolecules in complex samples, in which ionization occurs at atmospheric pressure with the samples being analyzed in the native state. Ambient ionization mass spectrometry imaging can directly analyze tissue samples at a fairly high resolution to obtain molecules in situ information on the tissue surface to identify pathological features associated with a disease, resulting in the wide applications in pharmacy, food science, botanical research, and especially clinical research. Herein, novel ambient ionization techniques, such as techniques based on spray and solid-liquid extraction, techniques based on plasma desorption, techniques based on laser desorption ablation, and techniques based on acoustic desorption were introduced, and the data processing of ambient ionization mass spectrometry imaging was briefly reviewed. Besides, we also highlight recent applications of this imaging technology in clinical researches and discuss the challenges in this imaging technology and the perspectives on the future of the clinical research.

Mass spectrometry imaging-based metabolomics to visualize the spatially resolved reprogramming of carnitine metabolism in breast cancer

New insights into tumor-associated metabolic reprogramming have provided novel vulnerabilities that can be targeted for cancer therapy. Here, we propose a mass spectrometry imaging (MSI)-based metabolomic strategy to visualize the spatially resolved reprogramming of carnitine metabolism in heterogeneous breast cancer. Methods: A wide carnitine coverage MSI method was developed to investigate the spatial alternations of carnitines in cancer tissues of xenograft mouse models and human samples. Spatial expression of key metabolic enzymes that are closely associated with the altered carnitines was examined in adjacent cancer tissue sections. Results: A total of 17 carnitines, including L-carnitine, 6 short-chain acylcarnitines, 3 middle-chain acylcarnitines, and 7 long-chain acylcarnitines were imaged. L-carnitine and short-chain acylcarnitines are significantly reprogrammed in breast cancer. A classification model based on the carnitine profiles of 170 cancer samples and 128 normal samples enables an accurate identification of breast cancer. CPT 1A, CPT 2, and CRAT, which are extensively involved in carnitine system-mediated fatty acid β-oxidation pathway were also found to be abnormally expressed in breast cancer. Remarkably, the expressions of CPT 2 and CRAT were found for the first time to be altered in breast cancer. Conclusion: These data not only expand our understanding of the complex tumor metabolic reprogramming, but also provide the first evidence that carnitine metabolism is reprogrammed at both the metabolite and enzyme levels in breast cancer.

Microfluidic Probe for In-Situ Extraction of Adherent Cancer Cells to Detect Heterogeneity Difference by Electrospray Ionization Mass Spectrometry

The pathological studies of cancer tissues and cell molecules could provide an early diagnosis for the treatment of cancer. In this work, we have designed a microfluidic surface extractor (MSE). The MSE has been coupled with electrospray mass spectrometry (extraction reagent, methanol; optimum flow rate, 0.5 mL/h) to analyze the phospholipid content of different tumor cells. Three types of cancer cell lines, including A549 cells, HepG2 cells, and U87 cells, were investigated, and the principle component analysis (PCA: linear discriminant analysis (LDA), PC1 97.2%; PC2, 2.8%) was carried out to analyze the difference in the lipid contents. The LDA revealed heterogeneity among the cancer cells. The designed MSE could have potential applications in the clinical analysis of cancer tissues.

DIUTHAME enables matrix-free mass spectrometry imaging of frozen tissue sections

RATIONALE

A recently developed matrix-free laser desorption/ionization method, DIUTHAME (desorption ionization using through-hole alumina membrane), was examined for the feasibility of mass spectrometry imaging (MSI) applied to frozen tissue sections. The permeation behavior of DIUTHAME is potentially useful for MSI as positional information may not be distorted during the extraction of analytes from a sample.

METHODS

The through-hole porous alumina membranes used in the DIUTHAME chips were fabricated by wet anodization, were 5 μm thick, and had the desired values of 200 nm through-hole diameter and 50% open aperture ratio. Mouse brain frozen tissue sections on indium tin oxide (ITO)-coated slides were covered using the DIUTHAME chips and were subjected to MSI experiments in commercial time-of-flight mass spectrometers equipped with solid-state UV lasers after thawing and drying without matrix application.

RESULT

Mass spectra and mass images were successfully obtained from the frozen tissue sections using DIUTHAME as the ionization method. The mass spectra contained rich peaks in the phospholipid mass range free from the chemical background owing to there being no matrix-derived peaks in that range. DIUTHAME-MSI delivered high-quality mass images that reflected the anatomy of the brain tissue.

CONCLUSIONS

Analytes can be extracted from frozen tissue by capillary action of the through-holes in DIUTHAME and moisture contained in the tissue without distorting positional information of the analytes. The sample preparation for frozen tissue sections in DIUTHAME-MSI is simple, requiring no specialized skills or dedicated apparatus for matrix application. DIUTHAME can facilitate MSI at a low mass, as there is no interference from matrix-derived peaks, and should provide high-quality, reproducible mass images more easily than MALDI-MSI.

Targeted imaging of integrins in cancer tissues using photocleavable Ru(ii) polypyridine complexes as mass-tags

Targeted epitope-based mass spectrometry imaging (MSI) utilizes laser cleavable mass-tags bound to targeting moieties for detecting proteins in tissue sections. Our work constitutes the first proof-of-concept of a novel laser desorption ionization (LDI)-MSI strategy using photocleavable Ru(ii) polypyridine complexes as mass-tags for imaging of integrins αvβ3 in human cancer tissues.

Schirmer Paper Noninvasive Microsampling for Direct Mass Spectrometry Analysis of Human Tears

Rapid and sensitive detection of metabolites and chemical residues in human tears is highly beneficial for understanding eye health. In this study, Schirmer paper was used for noninvasive microsampling of human tears, and then paper spray mass spectrometry (PSMS) was performed for direct analysis of human tears. Schirmer PSMS was successfully used for rapid diagnosis of dry-eye syndrome by detecting the volume and metabolites of human tears. Drugs of abuse, therapeutic drugs, and pharmacodynamics in human tears were also investigated by Schirmer PSMS. Furthermore, specific markers of environmental exposures in the air to human eyes, including volatile organic compounds, aerosol, and smoke, were unambiguously sampled and detected in human tears using Schirmer PSMS. Excellent analytical performances were achieved, including single-use, low-sample consumption (1.0 μL), rapid analysis (the whole analytical procedure completed within 3 min), high sensitivity (absolute limit of detection less than or equal to 0.5 pg, signal-to-noise ratio greater than or equal to 3), good reproducibility (relative standard deviation less than 10%, n = 3), and accurate quantitation (average deviation less than 3%, n = 3). Overall, our results showed that Schirmer PSMS is a highly effective method for direct tear analysis and is expected to be a convenient tool for human tear analysis in significant clinical applications.

The choice of tissue fixative is a key determinant for mass spectrometry imaging based tumor metabolic reprogramming characterization

The application of mass spectrometry imaging (MSI) for the study of spatiotemporal alterations of the metabolites in tumors has brought a number of significant biological results. At present, metabolite profiling based on MSI is typically performed on frozen tissue sections; however, the majority of clinical specimens need to be fixed in tissue fixative to avoid autolysis and to preserve antigenicity. In this study, we present the global impacts of different fixatives on the MS imaging of gastric cancer tissue metabolites. The MSI performances of 17 kinds of metabolites, such as amino acids, polyamines, cholines, organic acids, polypeptides, nucleotides, nucleosides, nitrogen bases, cholesterols, fatty acids, and phospholipids, in untreated, 10% formalin-, 4% paraformaldehyde-, acetone-, and 95% ethanol-fixed gastric cancer tissues were thoroughly explored for the first time. Furthermore, we also investigated the spatial expressions of 6 metabolic enzymes, namely, GLS, FASN, CHKA, PLD2, cPLA2, and EGFR, closely related to tumor-associated metabolites. Immunohistochemical staining carried out on the same tissue sections' which have undergone MSI analysis' suggests that enzymatic characterization is feasible after metabolite imaging. Combining the spatial signatures of metabolites and pathway-related metabolic enzymes in heterogeneous tumor tissues offers an insight to understand the complex tumor metabolism. Graphical abstract.

In situ mass spectrometry analysis of intact proteins and protein complexes from biological substrates

Advances in sample preparation, ion sources and mass spectrometer technology have enabled the detection and characterisation of intact proteins. The challenges associated include an appropriately soft ionisation event, efficient transmission and detection of the often delicate macromolecules. Ambient ion sources, in particular, offer a wealth of strategies for analysis of proteins from solution environments, and directly from biological substrates. The last two decades have seen rapid development in this area. Innovations include liquid extraction surface analysis, desorption electrospray ionisation and nanospray desorption electrospray ionisation. Similarly, developments in native mass spectrometry allow protein-protein and protein-ligand complexes to be ionised and analysed. Identification and characterisation of these large ions involves a suite of hyphenated mass spectrometry techniques, often including the coupling of ion mobility spectrometry and fragmentation techniques. The latter include collision, electron and photon-induced methods, each with their own characteristics and benefits for intact protein identification. In this review, recent developments for in situ protein analysis are explored, with a focus on ion sources and tandem mass spectrometry techniques used for identification.

Using the Chemical Noise Background in MALDI Mass Spectrometry Imaging for Mass Alignment and Calibration

Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) is an established tool for the investigation of formalin fixed paraffin embedded (FFPE) tissue samples and shows a high potential for applications in clinical research and histopathological diagnosis. The applicability and accuracy of this method, however, heavily depends on the quality of the acquired data, and in particular mass misalignment in axial time-of-flight (TOF) MSI continues to be a serious issue. We present a mass alignment and recalibration method that is specifically designed to operate on MALDI peptide imaging data. The proposed method exploits statistical properties of the characteristic chemical noise background observed in peptide imaging experiments. By comparing these properties to a theoretical peptide mass model, the effective mass shift of each spectrum is estimated and corrected. The method was evaluated on a cohort of 31 FFPE tissue samples, pursuing a statistical validation approach to estimate both the reduction of relative misalignment, as well as the increase in absolute mass accuracy. Our results suggest that a relative mass precision of approximately 5 ppm and an absolute accuracy of approximately 20 ppm are achievable using our method.

Emerging role of clinical mass spectrometry in pathology

Mass spectrometry-based assays have been increasingly implemented in various disciplines in clinical diagnostic laboratories for their combined advantages in multiplexing capacity and high analytical specificity and sensitivity. It is now routinely used in areas including reference methods development, therapeutic drug monitoring, toxicology, endocrinology, paediatrics, immunology and microbiology to identify and quantify biomolecules in a variety of biological specimens. As new ionisation methods, instrumentation and techniques are continuously being improved and developed, novel mass spectrometry-based clinical applications will emerge for areas such as proteomics, metabolomics, haematology and anatomical pathology. This review will summarise the general principles of mass spectrometry and specifically highlight current and future clinical applications in anatomical pathology.

Multimodal analysis of formalin-fixed and paraffin-embedded tissue by MALDI imaging and fluorescence in situ hybridization for combined genetic and metabolic analysis

Multimodal tissue analyses that combine two or more detection technologies provide synergistic value compared to single methods and are employed increasingly in the field of tissue-based diagnostics and research. Here, we report a technical pipeline that describes a combined approach of HER2/CEP17 fluorescence in situ hybridization (FISH) analysis with MALDI imaging on the very same section of formalin-fixed and paraffin-embedded (FFPE) tissue. FFPE biopsies and a tissue microarray of human gastroesophageal adenocarcinoma were analyzed by MALDI imaging. Subsequently, the very same section was hybridized by HER2/CEP17 FISH. We found that tissue morphology of both, the biopsies and the tissue microarray, was unaffected by MALDI imaging and the HER2 and CEP17 FISH signals were analyzable. In comparison with FISH analysis of samples without MALDI imaging, we observed no difference in terms of fluorescence signal intensity and gene copy number. Our combined approach revealed adenosine monophosphate, measured by MALDI imaging, as a prognostic marker. HER2 amplification, which was detected by FISH, is a stratifier between good and poor patient prognosis. By integrating both stratification parameters on the basis of our combined approach, we were able to strikingly improve the prognostic effect. Combining molecules detected by MALDI imaging with the gene copy number detected by HER2/CEP17 FISH, we found a synergistic effect, which enhances patient prognosis. This study shows that our combined approach allows the detection of genetic and metabolic properties from one very same FFPE tissue section, which are specific for HER2 and hence suitable for prognosis. Furthermore, this synergism might be useful for response prediction in tumors.

Metabolic Profiling of the Diabetic Heart: Toward a Richer Picture

The increasing global prevalence of diabetes has been accompanied by a rise in diabetes-related conditions. This includes diabetic cardiomyopathy (DbCM), a progressive form of heart disease that occurs with both insulin-dependent (type-1) and insulin-independent (type-2) diabetes and arises in the absence of hypertension or coronary artery disease. Over time, DbCM can develop into overt heart failure. Like other forms of cardiomyopathy, DbCM is accompanied by alterations in metabolism which could lead to further progression of the pathology, with metabolic derangement postulated to precede functional changes in the diabetic heart. Moreover in the case of type-2 diabetes, underlying insulin resistance is likely to prevent the canonical substrate switch of the failing heart away from fatty acid oxidation toward increased use of glycolysis. Analytical chemistry techniques, collectively known as metabolomics, are useful tools for investigating the condition. In this article, we provide a comprehensive review of those studies that have employed metabolomic techniques, namely chromatography, mass spectrometry and nuclear magnetic resonance spectroscopy, to profile metabolic remodeling in the diabetic heart of human patients and animal models. These studies collectively demonstrate that glycolysis and glucose oxidation are suppressed in the diabetic myocardium and highlight a complex picture regarding lipid metabolism. The diabetic heart typically shows an increased reliance on fatty acid oxidation, yet triacylglycerols and other lipids accumulate in the diabetic myocardium indicating probable lipotoxicity. The application of lipidomic techniques to the diabetic heart has identified specific lipid species that become enriched and which may in turn act as plasma-borne biomarkers for the condition. Metabolomics is proving to be a powerful approach, allowing a much richer analysis of the metabolic alterations that occur in the diabetic heart. Careful physiological interpretation of metabolomic results will now be key in order to establish which aspects of the metabolic derangement are causal to the progression of DbCM and might form the basis for novel therapeutic intervention.

Rapid Identification of Ischemic Injury in Renal Tissue by Mass-Spectrometry Imaging

The increasing analytical speed of mass-spectrometry imaging (MSI) has led to growing interest in the medical field. Acute kidney injury is a severe disease with high morbidity and mortality. No reliable cut-offs are known to estimate the severity of acute kidney injury. Thus, there is a need for new tools to rapidly and accurately assess acute ischemia, which is of clinical importance in intensive care and in kidney transplantation. We investigated the value of MSI to assess acute ischemic kidney tissue in a porcine model. A perfusion model was developed where paired kidneys received warm (severe) or cold (minor) ischemia ( n = 8 per group). First, ischemic tissue damage was systematically assessed by two blinded pathologists. Second, MALDI-MSI of kidney tissues was performed to study the spatial distributions and compositions of lipids in the tissues. Histopathological examination revealed no significant difference between kidneys, whereas MALDI-MSI was capable of a detailed discrimination of severe and mild ischemia by differential expression of characteristic lipid-degradation products throughout the tissue within 2 h. In particular, lysolipids, including lysocardiolipins, lysophosphatidylcholines, and lysophosphatidylinositol, were dramatically elevated after severe ischemia. This study demonstrates the significant potential of MSI to differentiate and identify molecular patterns of early ischemic injury in a clinically acceptable time frame. The observed changes highlight the underlying biochemical processes of acute ischemic kidney injury and provide a molecular classification tool that can be deployed in assessment of acute ischemic kidney injury.