Detection of Metastatic Breast and Thyroid Cancer in Lymph Nodes by Desorption Electrospray Ionization Mass Spectrometry Imaging

Advances in mass spectrometry imaging for spatial cancer metabolomics

Mass spectrometry (MS) has become a central technique in cancer research. The ability to analyze various types of biomolecules in complex biological matrices makes it well suited for understanding biochemical alterations associated with disease progression. Different biological samples, including serum, urine, saliva, and tissues have been successfully analyzed using mass spectrometry. In particular, spatial metabolomics using MS imaging (MSI) allows the direct visualization of metabolite distributions in tissues, thus enabling in-depth understanding of cancer-associated biochemical changes within specific structures. In recent years, MSI studies have been increasingly used to uncover metabolic reprogramming associated with cancer development, enabling the discovery of key biomarkers with potential for cancer diagnostics. In this review, we aim to cover the basic principles of MSI experiments for the nonspecialists, including fundamentals, the sample preparation process, the evolution of the mass spectrometry techniques used, and data analysis strategies. We also review MSI advances associated with cancer research in the last 5 years, including spatial lipidomics and glycomics, the adoption of three-dimensional and multimodal imaging MSI approaches, and the implementation of artificial intelligence/machine learning in MSI-based cancer studies. The adoption of MSI in clinical research and for single-cell metabolomics is also discussed. Spatially resolved studies on other small molecule metabolites such as amino acids, polyamines, and nucleotides/nucleosides will not be discussed in the context.

Intraoperative Identification of Thyroid and Parathyroid Tissues During Human Endocrine Surgery Using the MasSpec Pen

Importance

Intraoperative identification of tissues through gross inspection during thyroid and parathyroid surgery is challenging yet essential for preserving healthy tissue and improving outcomes for patients.

Objective

To evaluate the performance and clinical applicability of the MasSpec Pen (MSPen) technology for discriminating thyroid, parathyroid, and lymph node tissues intraoperatively.

Design, Setting, and Participants

In this diagnostic/prognostic study, the MSPen was used to analyze 184 fresh-frozen thyroid, parathyroid, and lymph node tissues in the laboratory and translated to the operating room to enable in vivo and ex vivo tissue analysis by endocrine surgeons in 102 patients undergoing thyroidectomy and parathyroidectomy procedures. This diagnostic study was conducted between August 2017 and March 2020. Fresh-frozen tissues were analyzed in a laboratory. Clinical analyses occurred in an operating room at an academic medical center. Of the analyses performed on 184 fresh-frozen tissues, 131 were included based on sufficient signal and postanalysis pathologic diagnosis. From clinical tests, 102 patients undergoing surgery were included. A total of 1015 intraoperative analyses were performed, with 269 analyses subject to statistical classification. Statistical classifiers for discriminating thyroid, parathyroid, and lymph node tissues were generated using training sets comprising both laboratory and intraoperative data and evaluated on an independent test set of intraoperative data. Data were analyzed from July to December 2022.

Main Outcomes and Measures

Accuracy for each tissue type was measured for classification models discriminating thyroid, parathyroid, and lymph node tissues using MSPen data compared to gross analysis and final pathology results.

Results

Of the 102 patients in the intraoperative study, 80 were female (78%) and the median (IQR) age was 52 (42-66) years. For discriminating thyroid and parathyroid tissues, an overall accuracy, defined as agreement with pathology, of 92.4% (95% CI, 87.7-95.4) was achieved using MSPen data, with 82.6% (95% CI, 76.5-87.4) accuracy achieved for the independent test set. For distinguishing thyroid from lymph node and parathyroid from lymph node, overall training set accuracies of 97.5% (95% CI, 92.8-99.1) and 96.1% (95% CI, 91.2-98.3), respectively, were achieved.

Conclusions and Relevance

In this study, the MSPen showed high performance for discriminating thyroid, parathyroid, and lymph node tissues intraoperatively, suggesting this technology may be useful for providing near real-time feedback on tissue type to aid in surgical decision-making.

New tools for immunologists: models of lymph node function from cells to tissues

The lymph node is a highly structured organ that mediates the body's adaptive immune response to antigens and other foreign particles. Central to its function is the distinct spatial assortment of lymphocytes and stromal cells, as well as chemokines that drive the signaling cascades which underpin immune responses. Investigations of lymph node biology were historically explored in vivo in animal models, using technologies that were breakthroughs in their time such as immunofluorescence with monoclonal antibodies, genetic reporters, in vivo two-photon imaging, and, more recently spatial biology techniques. However, new approaches are needed to enable tests of cell behavior and spatiotemporal dynamics under well controlled experimental perturbation, particularly for human immunity. This review presents a suite of technologies, comprising in vitro, ex vivo and in silico models, developed to study the lymph node or its components. We discuss the use of these tools to model cell behaviors in increasing order of complexity, from cell motility, to cell-cell interactions, to organ-level functions such as vaccination. Next, we identify current challenges regarding cell sourcing and culture, real time measurements of lymph node behavior in vivo and tool development for analysis and control of engineered cultures. Finally, we propose new research directions and offer our perspective on the future of this rapidly growing field. We anticipate that this review will be especially beneficial to immunologists looking to expand their toolkit for probing lymph node structure and function.

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.

Advances in non-invasive biosensing measures to monitor wound healing progression

Impaired wound healing is a significant financial and medical burden. The synthesis and deposition of extracellular matrix (ECM) in a new wound is a dynamic process that is constantly changing and adapting to the biochemical and biomechanical signaling from the extracellular microenvironments of the wound. This drives either a regenerative or fibrotic and scar-forming healing outcome. Disruptions in ECM deposition, structure, and composition lead to impaired healing in diseased states, such as in diabetes. Valid measures of the principal determinants of successful ECM deposition and wound healing include lack of bacterial contamination, good tissue perfusion, and reduced mechanical injury and strain. These measures are used by wound-care providers to intervene upon the healing wound to steer healing toward a more functional phenotype with improved structural integrity and healing outcomes and to prevent adverse wound developments. In this review, we discuss bioengineering advances in 1) non-invasive detection of biologic and physiologic factors of the healing wound, 2) visualizing and modeling the ECM, and 3) computational tools that efficiently evaluate the complex data acquired from the wounds based on basic science, preclinical, translational and clinical studies, that would allow us to prognosticate healing outcomes and intervene effectively. We focus on bioelectronics and biologic interfaces of the sensors and actuators for real time biosensing and actuation of the tissues. We also discuss high-resolution, advanced imaging techniques, which go beyond traditional confocal and fluorescence microscopy to visualize microscopic details of the composition of the wound matrix, linearity of collagen, and live tracking of components within the wound microenvironment. Computational modeling of the wound matrix, including partial differential equation datasets as well as machine learning models that can serve as powerful tools for physicians to guide their decision-making process are discussed.

Non-Invasive Differential Diagnosis of Cervical Neoplastic Lesions by the Lipid Profile Analysis of Cervical Scrapings

Cervical cancer is one of the most common cancers in women with pronounced stages of precancerous lesions. Accurate differential diagnosis of such lesions is one of the primary challenges of medical specialists, which is vital to improving patient survival. The aim of this study was to develop and test an algorithm for the differential diagnosis of cervical lesions based on lipid levels in scrapings from the cervical epithelium and cervicovaginal canal. The lipid composition of the samples was analyzed by high-performance chromato-mass spectrometry. Lipid markers were selected using the Mann–Whitney test with a cutoff value of 0.05 and by projections to latent structures discriminant analysis, where a projection threshold of one was chosen. The final selection of variables for binomial logistic regressions was carried out using the Akaike information criterion. As a result, a final neoplasia classification method, based on 20 logistic regression sub-models, has an accuracy of 79% for discrimination NILM/cervicitis/LSIL/HSIL/cancer. The model has a sensitivity of 83% and a specificity of 88% for discrimination of several lesions (HSIL and cancer). This allows us to discuss the prospective viability of further validation of the developed non-invasive method of differential diagnosis.

Molecular Pathological Diagnosis of Thyroid Tumors Using Spatially Resolved Metabolomics

The pathological diagnosis of benign and malignant follicular thyroid tumors remains a major challenge using the current histopathological technique. To improve diagnosis accuracy, spatially resolved metabolomics analysis based on air flow-assisted desorption electrospray ionization mass spectrometry imaging (AFADESI-MSI) technique was used to establish a molecular diagnostic strategy for discriminating four pathological types of thyroid tumor. Without any specific labels, numerous metabolite features with their spatial distribution information can be acquired by AFADESI-MSI. The underlying metabolic heterogeneity can be visualized in line with the cellular heterogeneity in native tumor tissue. Through micro-regional feature extraction and in situ metabolomics analysis, three sets of metabolic biomarkers for the visual discrimination of benign follicular adenoma and differentiated thyroid carcinomas were discovered. Additionally, the automated prediction of tumor foci was supported by a diagnostic model based on the metabolic profile of 65 thyroid nodules. The model prediction accuracy was 83.3% when a test set of 12 independent samples was used. This diagnostic strategy presents a new way of performing in situ pathological examinations using small molecular biomarkers and provides a model diagnosis for clinically indeterminate thyroid tumor cases.

Applications of spatially resolved omics in the field of endocrine tumors

Endocrine tumors derive from endocrine cells with high heterogeneity in function, structure and embryology, and are characteristic of a marked diversity and tissue heterogeneity. There are still challenges in analyzing the molecular alternations within the heterogeneous microenvironment for endocrine tumors. Recently, several proteomic, lipidomic and metabolomic platforms have been applied to the analysis of endocrine tumors to explore the cellular and molecular mechanisms of tumor genesis, progression and metastasis. In this review, we provide a comprehensive overview of spatially resolved proteomics, lipidomics and metabolomics guided by mass spectrometry imaging and spatially resolved microproteomics directed by microextraction and tandem mass spectrometry. In this regard, we will discuss different mass spectrometry imaging techniques, including secondary ion mass spectrometry, matrix-assisted laser desorption/ionization and desorption electrospray ionization. Additionally, we will highlight microextraction approaches such as laser capture microdissection and liquid microjunction extraction. With these methods, proteins can be extracted precisely from specific regions of the endocrine tumor. Finally, we compare applications of proteomic, lipidomic and metabolomic platforms in the field of endocrine tumors and outline their potentials in elucidating cellular and molecular processes involved in endocrine tumors.

The effect of sample age on the metabolic information extracted from formalin-fixed and paraffin embedded tissue samples using desorption electrospray ionization mass spectrometry imaging

Background: Metabolites, especially lipids, have been shown to be promising therapeutic targets. In conjugation with genes and proteins they can be used to identify phenotypes of disease and support the development of targeted treatments. The majority of clinically collected tissue samples are stored in formalin-fixed and paraffin embedded (FFPE) blocks due to their tissue conservation ability and indefinite storage capacity. For metabolic analysis, however, fresh frozen (FF) samples are currently preferred over FFPE samples due to concerns of metabolic information being lost when preparing the samples. With little or no sample preparation, desorption electrospray ionisation mass spectrometry imaging (DESI-MSI) allows for the study of spatial as well as spectral information. Methods: DESI-MSI analysis was performed on FFPE breast cancer tissue microarray samples from 213 patients collected between the years 1935-2013. Logistic regression (LR) models were built to classify samples based on age and FF samples were used for feature validation. Results: LR models developed on the FFPE samples achieved an average classification accuracy of 96% when predicting their age with a 10-year grouping. Closer examination of the metabolic change over time revealed that the mean signal intensities for the lower mass range (100 - 500 m/z) linearly decrease over time, while the mean intensities for the higher mass range (500 - 900 m/z), remained relatively constant. Conclusions: In our samples, which span over 70 years, sample age has a weak yet quantifiable impact on metabolite content in FFPE samples, while the higher mass range is seemingly unaffected. FFPE samples thus provide an alternative avenue for metabolic analysis of lipids.

Clinical Chemistry for Developing Countries: Mass Spectrometry

Early disease diagnosis is necessary to enable timely interventions. Implementation of this vital task in the developing world is challenging owing to limited resources. Diagnostic approaches developed for resource-limited settings have often involved colorimetric tests (based on immunoassays) due to their low cost. Unfortunately, the performance/sensitivity of such simplistic tests are often limited and significantly hinder opportunities for early disease detection. A new criterion for selecting diagnostic tests in low- and middle-income countries is proposed here that is based on performance-to-cost ratio. For example, modern mass spectrometry (MS) now involves analysis of the native sample in the open laboratory environment, enabling applications in many fields, including clinical research, forensic science, environmental analysis, and agriculture. In this critical review, we summarize recent developments in chemistry that enable MS to be applied effectively in developing countries. In particular, we argue that closed automated analytical systems may not offer the analytical flexibility needed in resource-limited settings. Alternative strategies proposed here have potential to be widely accepted in low- and middle-income countries through the utilization of the open-source ambient MS platform that enables microsampling techniques such as dried blood spot to be coupled with miniature mass spectrometers in a centralized analytical platform. Consequently, costs associated with sample handling and maintenance can be reduced by >50% of the total ownership cost, permitting analytical measurements to be operated at high performance-to-cost ratios in the developing world.

Direct Liquid Extraction and Ionization Techniques for Understanding Multimolecular Environments in Biological Systems (Secondary Publication)

A combination of direct liquid extraction using a small volume of solvent and electrospray ionization allows the rapid measurement of complex chemical components in biological samples and visualization of their distribution in tissue sections. This review describes the development of such techniques and their application to biological research since the first reports in the early 2000s. An overview of electrospray ionization, ion suppression in samples, and the acceleration of specific chemical reactions in charged droplets is also presented. Potential future applications for visualizing multimolecular environments in biological systems are discussed.

Solving the enigma: Mass spectrometry and small molecule probes to study sphingolipid function

Sphingolipids are highly bioactive lipids. Sphingolipid metabolism produces key membrane components (e.g. sphingomyelin) and a variety of signaling lipids with different biological functions (e.g. ceramide, sphingosine-1-phosphate). The coordinated activity of tens of different enzymes maintains proper levels and localization of these lipids with key roles in cellular processes. In this review, we highlight the signaling roles of sphingolipids in cell death and survival. We discuss recent findings on the role of specific sphingolipids during these processes, enabled by the use of lipidomics to study compositional and spatial regulation of these lipids and synthetic sphingolipid probes to study subcellular localization and interaction partners of sphingolipids to understand the function of these lipids.

Bringing Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging to the Clinics

Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) is an emerging analytical technique that promises to change tissue-based diagnostics. This article provides a brief introduction to MALDI MSI as well as clinical diagnostic workflows and opportunities to apply this powerful approach. It describes various MALDI MSI applications, from more clinically mature applications such as cancer to emerging applications such as infectious diseases and drug distribution. In addition, it discusses the analytical considerations that need to be considered when bringing these approaches to different diagnostic problems and settings.

Mass spectrometry in the lipid study of cancer

Introduction: Cancer is a heterogeneous disease that exploits various metabolic pathways to meet the demand for increased energy and structural components. Lipids are biomolecules that play essential roles as high energy sources, mediators, and structural components of biological membranes. Accumulating evidence has established that altered lipid metabolism is a hallmark of cancer.Areas covered: Mass spectrometry (MS) is a label-free analytical tool that can simultaneously identify and quantify hundreds of analytes. To date, comprehensive lipid studies exclusively rely on this technique. Here, we reviewed the use of MS in the study of lipids in various cancers and discuss its instrumental limitations and challenges.Expert opinion: MS and MS imaging have significantly contributed to revealing altered lipid metabolism in a variety of cancers. Currently, a single MS approach cannot profile the entire lipidome because of its lack of sensitivity and specificity for all lipid classes. For the metabolic pathway investigation, lipid study requires the integration of MS with other molecular approaches. Future developments regarding the high spatial resolution, mass resolution, and sensitivity of MS instruments are warranted.

Internal standard application strategies in mass spectrometry imaging by desorption electrospray ionization mass spectrometry

RATIONALE

We developed a model case study to evaluate three internal standard (IS) application strategies (methods I-III) using the psycholeptic phenobarbital (PB) and the isotopically labelled IS phenobarbital-D5 (PB-D5) from in vitro dosed tissues of the golden apple snail (Pomacea diffusa) by desorption electrospray ionization mass spectrometry imaging (DESI-MSI).

METHODS

In method I, the IS was deposited as microspots on top of 10 μm thick snail tissues; in method II, a thin IS film was applied; and in method III, the IS was spiked into the DESI solvent spray. DESI-MSI analyses were performed using a Thermo LTQ mass spectrometer equipped with a custom-built DESI source and two-dimensional moving stage. PB (m/z 231) and PB-D5 (m/z 236) were monitored in selected ion monitoring mode between m/z 227 and 239.

RESULTS

The analytical performance of two IS strategies (methods I and II) in DESI-MSI was evaluated based on an intra- and inter-day precision assay, an accuracy assessment, and statistical analysis. In the inter-day DESI-MSI assay, method I exhibited better precision (6.5%-7.4%) than method II (10.7%-17.6%) between 10 and 100 ng/μL. In the accuracy assessment, PB quality controls of 75 ng/μL were back-calculated as 71 ± 4 and 83 ± 9 ng/μL, resulting in relative errors of -5% and 11% for methods I and II, respectively. Method III did not work under the experimental design and was not evaluated.

CONCLUSIONS

Three IS application strategies were investigated and compared for a routine quantitative DESI-MSI approach. Methods I and II were not statistically significantly different as shown by a Bland-Altman plot, suggesting that these two methods can be used interchangeably. However, method III requires further research for future quantitative DESI-MSI analyses.

Mass Spectrometry-Based Shotgun Lipidomics for Cancer Research

Shotgun lipidomics is an analytical approach for large-scale and systematic analysis of the composition, structure, and quantity of cellular lipids directly from lipid extracts of biological samples by mass spectrometry. This approach possesses advantages of high throughput and quantitative accuracy, especially in absolute quantification. As cancer research deepens at the level of quantitative biology and metabolomics, the demand for lipidomics approaches such as shotgun lipidomics is becoming greater. In this chapter, the principles, approaches, and some applications of shotgun lipidomics for cancer research are overviewed.

Phospholipase A2 superfamily in cancer

Phospholipase A2 enzymes (PLA₂s) comprise a superfamily that is generally divided into six subfamilies known as cytosolic PLA₂s (cPLA₂s), calcium-independent PLA₂s (iPLA₂s), secreted PLA₂s (sPLA₂s), lysosomal PLA₂s, platelet-activating factor (PAF) acetylhydrolases, and adipose specific PLA₂s. Each subfamily consists of several isozymes that possess PLA₂ activity. The first three PLA₂ subfamilies play important roles in inflammation-related diseases and cancer. In this review, the roles of well-studied enzymes sPLA₂-IIA, cPLA₂α and iPLA₂β in carcinogenesis and cancer development were discussed. sPLA₂-IIA seems to play conflicting roles and can act as a tumor suppressor or a tumor promoter according to the cancer type, but cPLA₂α and iPLA₂β play protumorigenic role in most cancers. The mechanisms of PLA₂-mediated signal transduction and crosstalk between cancer cells and endothelial cells in the tumor microenvironment are described. Moreover, the mechanisms by which PLA₂s mediate lipid reprogramming and glycerophospholipid remodeling in cancer cells are illustrated. PLA₂s as the upstream regulators of the arachidonic acid cascade are generally high expressed and activated in various cancers. Therefore, they can be considered as potential pharmacological targets and biomarkers in cancer. The detailed information summarized in this review may aid in understanding the roles of PLA₂s in cancer, and provide new clues for the development of novel agents and strategies for tumor prevention and treatment.

Addressing Cellular Heterogeneity in Cancer through Precision Proteomics

Cells exhibit a broad spectrum of functions driven by differences in molecular phenotype. Understanding the heterogeneity between and within cell types has led to advances in our ability to diagnose and manipulate biological systems. Heterogeneity within and between tumors still poses a challenge to the development and efficacy of therapeutics. In this Perspective we review the toolkit of protein-level experimental approaches for investigating cellular heterogeneity. We describe how innovative approaches and technical developments have supported the advent of bottom-up single-cell proteomic analysis and present opportunities and challenges within cancer research. Finally, we introduce the concept of "precision proteomics" and discuss how the advantages and limitations of various experimental approaches render them suitable for different biological systems and questions.

The Potential of Metabolomics in the Diagnosis of Thyroid Cancer

Thyroid cancer is the most common endocrine system malignancy. However, there is still a lack of reliable and specific markers for the detection and staging of this disease. Fine needle aspiration biopsy is the current gold standard for diagnosis of thyroid cancer, but drawbacks to this technique include indeterminate results or an inability to discriminate different carcinomas, thereby requiring additional surgical procedures to obtain a final diagnosis. It is, therefore, necessary to seek more reliable markers to complement and improve current methods. "Omics" approaches have gained much attention in the last decade in the field of biomarker discovery for diagnostic and prognostic characterisation of various pathophysiological conditions. Metabolomics, in particular, has the potential to identify molecular markers of thyroid cancer and identify novel metabolic profiles of the disease, which can, in turn, help in the classification of pathological conditions and lead to a more personalised therapy, assisting in the diagnosis and in the prediction of cancer behaviour. This review considers the current results in thyroid cancer biomarker research with a focus on metabolomics.

In situ probing changes in fatty-acyl chain length and desaturation of lipids in cancerous areas using mass spectrometry imaging

Aberrant changes in the expression levels and structure of lipids may shape tumor microenvironment. In this study, we have performed mass spectrometry imaging and profiling analysis of 63 tissues of five types of cancer, namely, breast, colorectal, esophageal, lung, and gastric cancer, using in situ liquid extraction electrosonic spray ionization mass spectrometry imaging. Alteration of fatty-acyl chain length of unsaturated phosphatidylcholines, phosphatidylinositols, and phosphatidylserines and of chain length of (un)saturated fatty acids are associated with different cancerous areas of five types of cancer. The ratios of the same fatty-acyl carbon atom lipids with one double bond difference and the ratios of the same chain-length fatty acids with one double bond difference exhibited significant differences among the cancerous areas of five types of cancer. Our data may reveal that there were different lipid metabolism networks among five types of cancer.

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.

DESI-MSI and METASPACE indicates lipid abnormalities and altered mitochondrial membrane components in diabetic renal proximal tubules

INTRODUCTION

Diabetic kidney disease (DKD) is the most prevalent complication in diabetic patients, which contributes to high morbidity and mortality. Urine and plasma metabolomics studies have been demonstrated to provide valuable insights for DKD. However, limited information on spatial distributions of metabolites in kidney tissues have been reported.

OBJECTIVES

In this work, we employed an ambient desorption electrospray ionization-mass spectrometry imaging (DESI-MSI) coupled to a novel bioinformatics platform (METASPACE) to characterize the metabolome in a mouse model of DKD.

METHODS

DESI-MSI was performed for spatial untargeted metabolomics analysis in kidneys of mouse models (F1 C57BL/6J-Ins2Akita male mice at 17 weeks of age) of type 1 diabetes (T1D, n = 5) and heathy controls (n = 6).

RESULTS

Multivariate analyses (i.e., PCA and PLS-DA (a 2000 permutation test: P < 0.001)) showed clearly separated clusters for the two groups of mice on the basis of 878 measured m/z's in kidney cortical tissues. Specifically, mice with T1D had increased relative abundances of pseudouridine, accumulation of free polyunsaturated fatty acids (PUFAs), and decreased relative abundances of cardiolipins in cortical proximal tubules when compared with healthy controls.

CONCLUSION

Results from the current study support potential key roles of pseudouridine and cardiolipins for maintaining normal RNA structure and normal mitochondrial function, respectively, in cortical proximal tubules with DKD. DESI-MSI technology coupled with METASPACE could serve as powerful new tools to provide insight on fundamental pathways in DKD.

In Situ DESI-MSI Lipidomic Profiles of Breast Cancer Molecular Subtypes and Precursor Lesions

Clinically meaningful molecular subtypes for classification of breast cancers have been established, however, initiation and progression of these subtypes remain poorly understood. The recent development of desorption electrospray ionization-mass spectrometry imaging (DESI-MSI) facilitates the convergence of analytical chemistry and traditional pathology, allowing chemical profiling with minimal tissue pretreatment in frozen samples. Here, we characterized the chemical composition of molecular subtypes of breast cancer with DESI-MSI. Regions of interest were identified, including invasive breast cancer (IBC), ductal carcinoma in situ (DCIS), and adjacent benign tissue (ABT), and metabolomic profiles at 200 μm elaborated using Biomap software and the Lasso method. Top ions identified in IBC regions included polyunsaturated fatty acids, deprotonated glycerophospholipids, and sphingolipids. Highly saturated lipids, as well as antioxidant molecules [taurine (m/z 124.0068), uric acid (m/z 167.0210), ascorbic acid (m/z 175.0241), and glutathione (m/z 306.0765)], were able to distinguish IBC from ABT. Moreover, luminal B and triple-negative subtypes showed more complex lipid profiles compared with luminal A and HER2 subtypes. DCIS and IBC were distinguished on the basis of cell signaling and apoptosis-related ions [fatty acids (341.2100 and 382.3736 m/z) and glycerophospholipids (PE (P-16:0/22:6, m/z 746.5099, and PS (38:3), m/z 812.5440)]. In summary, DESI-MSI identified distinct lipid composition between DCIS and IBC and across molecular subtypes of breast cancer, with potential implications for breast cancer pathogenesis. SIGNIFICANCE: These findings present the first in situ metabolomic findings of the four molecular subtypes of breast cancer, DCIS, and normal tissue, and add to the understanding of their pathogenesis.

Applications of Mass Spectrometry for Clinical Diagnostics: The Influence of Turnaround Time

This critical review discusses how the need for reduced clinical turnaround times has influenced chemical instrumentation. We focus on the development of modern mass spectrometry (MS) and its application in clinical diagnosis. With increased functionality that takes advantage of novel front-end modifications and computational capabilities, MS can now be used for non-traditional clinical analyses, including applications in clinical microbiology for bacteria differentiation and in surgical operation rooms. We summarize here recent developments in the field that have enabled such capabilities, which include miniaturization for point-of-care testing, direct complex mixture analysis via ambient ionization, chemical imaging and profiling, and systems integration.

Mass spectrometry imaging to detect lipid biomarkers and disease signatures in cancer

BACKGROUND

Current methods to identify, classify, and predict tumor behavior mostly rely on histology, immunohistochemistry, and molecular determinants. However, better predictive markers are required for tumor diagnosis and evaluation. Due, in part, to recent technological advancements, metabolomics and lipid biomarkers have become a promising area in cancer research. Therefore, there is a necessity for novel and complementary techniques to identify and visualize these molecular markers within tumors and surrounding tissue.

RECENT FINDINGS

Since its introduction, mass spectrometry imaging (MSI) has proven to be a powerful tool for mapping analytes in biological tissues. By adding the label-free specificity of mass spectrometry to the detailed spatial information of traditional histology, hundreds of lipids can be imaged simultaneously within a tumor. MSI provides highly detailed lipid maps for comparing intra-tumor, tumor margin, and healthy regions to identify biomarkers, patterns of disease, and potential therapeutic targets. In this manuscript, recent advancement in sample preparation and MSI technologies are discussed with special emphasis on cancer lipid research to identify tumor biomarkers.

CONCLUSION

MSI offers a unique approach for biomolecular characterization of tumor tissues and provides valuable complementary information to histology for lipid biomarker discovery and tumor classification in clinical and research cancer applications.

Metabolomics technology and bioinformatics for precision medicine

Precision medicine is rapidly emerging as a strategy to tailor medical treatment to a small group or even individual patients based on their genetics, environment and lifestyle. Precision medicine relies heavily on developments in systems biology and omics disciplines, including metabolomics. Combination of metabolomics with sophisticated bioinformatics analysis and mathematical modeling has an extreme power to provide a metabolic snapshot of the patient over the course of disease and treatment or classifying patients into subpopulations and subgroups requiring individual medical treatment. Although a powerful approach, metabolomics have certain limitations in technology and bioinformatics. We will review various aspects of metabolomics technology and bioinformatics, from data generation, bioinformatics analysis, data fusion and mathematical modeling to data management, in the context of precision medicine.

Precision biomarker discovery powered by microscopy image fusion-assisted high spatial resolution ambient ionization mass spectrometry imaging

Mass spectrometry imaging (MSI) using the ambient ionization technique enables a direct chemical investigation of biological samples with minimal sample pretreatment. However, detailed morphological information of the sample is often lost due to its limited spatial resolution. In this study, predictive high-resolution molecular imaging was produced by the fusion of ambient ionization MSI with optical microscopy of routine hematoxylin and eosin (H&E) staining. Specifically, desorption electrospray ionization (DESI) and nanospray desorption electrospray ionization (nanoDESI) mass spectrometry were employed to visualize lipid and protein species on mice tissue sections. The resulting molecular distributions obtained by ambient ionization MSI-microscopy fusion were verified with matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) MSI and immunohistochemistry (IHC) staining. Label-free molecular imaging with 5-μm spatial resolution can be acquired using DESI and nanoDESI, whereas the typical spatial resolution of ambient ionization MSI was ∼100 μm. In this regard, sharpened molecular histology of tissue sections was achieved, providing complementary references to the pathology. Such a multi-modal integration enables the discovery of potential tumor biomarkers. After image fusion, more than a dozen potential biomarkers on a metastatic mouse lung tissue section and Luminal B breast tumor tissue section were identified.

Preoperative metabolic classification of thyroid nodules using mass spectrometry imaging of fine-needle aspiration biopsies

Thyroid neoplasia is common and requires appropriate clinical workup with imaging and fine-needle aspiration (FNA) biopsy to evaluate for cancer. Yet, up to 20% of thyroid nodule FNA biopsies will be indeterminate in diagnosis based on cytological evaluation. Genomic approaches to characterize the malignant potential of nodules showed initial promise but have provided only modest improvement in diagnosis. Here, we describe a method using metabolic analysis by desorption electrospray ionization mass spectrometry (DESI-MS) imaging for direct analysis and diagnosis of follicular cell-derived neoplasia tissues and FNA biopsies. DESI-MS was used to analyze 178 tissue samples to determine the molecular signatures of normal, benign follicular adenoma (FTA), and malignant follicular carcinoma (FTC) and papillary carcinoma (PTC) thyroid tissues. Statistical classifiers, including benign thyroid versus PTC and benign thyroid versus FTC, were built and validated with 114,125 mass spectra, with accuracy assessed in correlation with clinical pathology. Clinical FNA smears were prospectively collected and analyzed using DESI-MS imaging, and the performance of the statistical classifiers was tested with 69 prospectively collected clinical FNA smears. High performance was achieved for both models when predicting on the FNA test set, which included 24 nodules with indeterminate preoperative cytology, with accuracies of 93% and 89%. Our results strongly suggest that DESI-MS imaging is a valuable technology for identification of malignant potential of thyroid nodules.

Review and perspectives on the applications of mass spectrometry imaging under ambient conditions

Ambient mass spectrometry (AMS)-based techniques are performed under ambient conditions in which the ionization and desorption occur in the open environment allowing the direct analysis of molecules with minimal or no sample preparation. A selected group of AMS techniques demonstrate imaging capabilities that can provide information about the localization of molecules on complex sample surfaces such as biological tissues. 2D, 3D, and multimodal imaging have unlocked an array of applications to systematically address complex problems in many areas of research such as drug monitoring, natural products, forensics, and cancer diagnostics. In the present review, we summarize recent advances in the field with respect to the implementation of new ambient ionization techniques and current applications in the last 5 years. In more detail, we mainly focus on imaging applications in topics related to animal whole bodies and tissues, single cells, cancer diagnostics and biomarkers, microbial cultures and co-cultures, plant and natural product metabolomics, and forensic applications. Finally, we discuss new areas of research, future perspectives, and the overall direction that the field may take in the years to come.

Oncometabolites as biomarkers in thyroid cancer: a systematic review

INTRODUCTION

Thyroid cancer (TC) is an important common endocrine malignancy, and its incidence has increased in the past decades. The current TC diagnosis and classification tools are fine-needle aspiration (FNA) and histological examination following thyroidectomy. The metabolite profile alterations of thyroid cells (oncometabolites) can be considered for current TC diagnosis and management protocols.

METHODS

This systematic review focuses on metabolite alterations within the plasma, FNA specimens, and tissue of malignant TC contrary to benign, goiter, or healthy TC samples. A systematic search of MEDLINE (PubMed), Scopus, Embase, and Web of Science databases was conducted, and the final 31 studies investigating metabolite biomarkers of TC were included.

RESULTS

A total of 15 targeted studies and 16 untargeted studies revealed several potential metabolite signatures of TC such as glucose, fructose, galactose, mannose, 2-keto-d-gluconic acid and rhamnose, malonic acid and inosine, cholesterol and arachidonic acid, glycosylation (immunoglobulin G [IgG] Fc-glycosylation), outer mitochondrial membrane 20 (TOMM20), monocarboxylate transporter 4 (MCT4), choline, choline derivatives, myo-/scyllo-inositol, lactate, fatty acids, several amino acids, cell membrane phospholipids, estrogen metabolites such as 16 alpha-OH E1/2-OH E1 and catechol estrogens (2-OH E1), and purine and pyrimidine metabolites, which were suggested as the TC oncometabolite.

CONCLUSION

Citrate was suggested as the first most significant biomarker and lactate as the second one. Further research is needed to confirm these biomarkers as the TC diagnostic oncometabolite.

Imaging with mass spectrometry, the next frontier in sphingolipid research? A discussion on where we stand and the possibilities ahead

In the last ten years, mass spectrometry (MS) has become the favored analytical technique for sphingolipid (SPL) analysis and measurements. Indeed MS has the unique ability to both acquire sensitive and quantitative measurements and to resolve the molecular complexity characteristic of SPL molecules, both across the different SPL families and within the same SPL family. Currently, two complementary MS-based approaches are used for lipid research: analysis of lipid extracts, mainly by infusion electrospray ionization (ESI), and mass spectrometry imaging (MSI) from a sample surface (i.e. intact tissue sections, cells, model membranes, thin layer chromatography plates) (Fig. 1). The first allows for sensitive and quantitative information about total lipid molecular species from a given specimen from which lipids have been extracted and chromatographically separated prior to the analysis; the second, albeit generally less quantitative and less specific in the identification of molecular species due to the complexity of the sample, allows for spatial information of lipid molecules from biological specimens. In the field of SPL research, MS analysis of lipid extracts from biological samples has been commonly utilized to implicate the role of these lipids in specific biological functions. On the other hand, the utilization of MSI in SPL research represents a more recent development that has started to provide interesting descriptive observations regarding the distribution of specific classes of SPLs within tissues. Thus, it is the aim of this review to discuss how MSI technology has been employed to extend the study of SPL metabolism and the type of information that has been obtained from model membranes, single cells and tissues. We envision this discussion as a complementary compendium to the excellent technical reviews recently published about the specifics of MSI technologies, including their application to SPL analysis (Fuchs et al., 2010; Berry et al., 2011; Ellis et al., 2013; Eberlin et al., 2011; Kraft and Klitzing, 2014).

Assessment of Metabolic Signature for Cancer Diagnosis Using Desorption Electrospray Ionization Mass Spectrometric Imaging

Metabolic reprogramming is a hallmark of tumor development. A technique that can map this complex biochemical shift by taking a snapshot of various metabolites in a tissue specimen (biopsy) is of high utility in the context of cancer diagnosis. Desorption electrospray ionization mass spectrometric imaging (DESI-MSI) is such a powerful and emerging analytical technique to simultaneously visualize the distributions of hundreds of metabolites, lipids, and other small molecules in the biological tissue. In DESI-MSI, a fine spray of high-velocity charged microdroplets rapidly extracts molecular species from the tissue surface and subsequently transfers them to the mass spectrometer, while the sample is continuously moved in two dimensions under the impinging spray of microdroplets. This allows a detailed multiplex molecular mapping of the tissue. DESI-MSI enables simultaneous examination of hundreds of putative metabolic biomarkers, an approach that lends much more predictive power than simply evaluating one or a few candidate biomarkers. The speed, versatility, lack of complicated sample preparation, and operation at ambient conditions make DESI-MSI extremely promising as a rapid diagnostic and prognostic tool.

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

Lipid profile changes in heart muscle have been previously linked to cardiac ischemia and myocardial infarction, but the spatial distribution of lipids and metabolites in ischemic heart remains to be fully investigated. We performed desorption electrospray ionization mass spectrometry imaging of hearts from in vivo myocardial infarction mouse models. In these mice, myocardial ischemia was induced by blood supply restriction via a permanent ligation of left anterior descending coronary artery. We showed that applying the machine learning algorithm of gradient boosting tree ensemble to the ambient mass spectrometry imaging data allows us to distinguish segments of infarcted myocardium from normally perfused hearts on a pixel by pixel basis. The machine learning algorithm selected 62 molecular ion peaks important for classification of each 200 μm-diameter pixel of the cardiac tissue map as normally perfused or ischemic. This approach achieved very high average accuracy (97.4%), recall (95.8%), and precision (96.8%) at a spatial resolution of ∼200 μm. In addition, we determined the chemical identity of 27 species, mostly small metabolites and lipids, selected by the algorithm as the most significant for cardiac pathology classification. This molecular signature of myocardial infarction may provide new mechanistic insights into cardiac ischemia, assist with infarct size assessment, and point toward novel therapeutic interventions.

Multicenter Study Using Desorption-Electrospray-Ionization-Mass-Spectrometry Imaging for Breast-Cancer Diagnosis

The histological and molecular subtypes of breast cancer demand distinct therapeutic approaches. Invasive ductal carcinoma (IDC) is subtyped according to estrogen-receptor (ER), progesterone-receptor (PR), and HER2 status, among other markers. Desorption-electrospray-ionization-mass-spectrometry imaging (DESI-MSI) is an ambient-ionization MS technique that has been previously used to diagnose IDC. Aiming to investigate the robustness of ambient-ionization MS for IDC diagnosis and subtyping over diverse patient populations and interlaboratory use, we report a multicenter study using DESI-MSI to analyze samples from 103 patients independently analyzed in the United States and Brazil. The lipid profiles of IDC and normal breast tissues were consistent across different patient races and were unrelated to country of sample collection. Similar experimental parameters used in both laboratories yielded consistent mass-spectral data in mass-to-charge ratios ( m/ z) above 700, where complex lipids are observed. Statistical classifiers built using data acquired in the United States yielded 97.6% sensitivity, 96.7% specificity, and 97.6% accuracy for cancer diagnosis. Equivalent performance was observed for the intralaboratory validation set (99.2% accuracy) and, most remarkably, for the interlaboratory validation set independently acquired in Brazil (95.3% accuracy). Separate classification models built for ER and PR statuses as well as the status of their combined hormone receptor (HR) provided predictive accuracies (>89.0%), although low classification accuracies were achieved for HER2 status. Altogether, our multicenter study demonstrates that DESI-MSI is a robust and reproducible technology for rapid breast-cancer-tissue diagnosis and therefore is of value for clinical use.

Ambient ionization mass spectrometry imaging for disease diagnosis: Excitements and challenges

Tissue analysis in histology is extremely important and also considered to be a gold standard to diagnose and prognosticate several diseases including cancer. Intraoperative evaluation of surgical margin of tumor also relies on frozen section histopathology, which is time consuming, challenging and often subjective. Recent development in the ambient ionization mass spectrometry imaging (MSI) technique has enabled us to simultaneously visualize hundreds to thousands of molecules (ion images) in the biopsy specimen, which are strikingly different and more powerful than the single optical tissue image analysis in conventional histopathology. This paper will highlight the emergence of the desorption electrospray ionization MSI (DESI-MSI) technique, which is label-free, requires minimal or no sample preparation and operates under ambient conditions. DESI-MSI can record ion images of lipid/metabolite distributions on biopsy specimens, providing a wealth of diagnostic information based on differential distributions of these molecular species in healthy and unhealthy tissues. Remarkable success of this technology in rapidly evaluating the cancer margin intraoperatively with very high accuracy also promises to bring this imaging technique from bench to bedside.

Desorption Electrospray Ionization Coupled with Ultraviolet Photodissociation for Characterization of Phospholipid Isomers in Tissue Sections

Desorption electrospray ionization (DESI) mass spectrometry imaging has become a powerful strategy for analysis of tissue sections, enabling differentiation of normal and diseased tissue based on changes in the lipid profiles. The most common DESI workflow involves collection of MS1 spectra as the DESI spray is rastered over a tissue section. Relying on MS1 spectra inherently limits the ability to differentiate isobaric and isomeric species or evaluate variations in the relative abundances of key isomeric lipids, such as double-bond positional isomers which may distinguish normal and diseased tissues. Here, 193 nm ultraviolet photodissociation (UVPD), a technique capable of differentiating double-bond positional isomers, is coupled with DESI to map differences in the double-bond isomer composition in tissue sections in a fast, high throughput manner compatible with imaging applications.

Imaging the Unimaginable: Desorption Electrospray Ionization - Imaging Mass Spectrometry (DESI-IMS) in Natural Product Research

Imaging mass spectrometry (IMS) has recently established itself in the field of "spatial metabolomics." Merging the sensitivity and fast screening of high-throughput mass spectrometry with spatial and temporal chemical information, IMS visualizes the production, location, and distribution of metabolites in intact biological models. Since metabolite profiling and morphological features are combined in single images, IMS offers an unmatched chemical detail on complex biological and microbiological systems. Thus, IMS-type "spatial metabolomics" emerges as a powerful and complementary approach to genomics, transcriptomics, and classical metabolomics studies. In this review, we summarize the current state-of-the-art IMS methods with a strong focus on desorption electrospray ionization (DESI)-IMS. DESI-IMS utilizes the original principle of electrospray ionization, but in this case solvent droplets are rastered and desorbed directly on the sample surface. The rapid and minimally destructive DESI-IMS chemical screening is achieved at ambient conditions and enables the accurate view of molecules in tissues at the µm-scale resolution. DESI-IMS analysis does not require complex sample preparation and allows repeated measurements on samples from different biological sources, including microorganisms, plants, and animals. Thanks to its easy workflow and versatility, DESI-IMS has successfully been applied to many different research fields, such as clinical analysis, cancer research, environmental sciences, microbiology, chemical ecology, and drug discovery. Herein we discuss the present applications of DESI-IMS in natural product research.

Mass Spectrometry for Synthesis and Analysis

Mass spectrometry is the science and technology of ions. As such, it is concerned with generating ions, measuring their properties, following their reactions, isolating them, and using them to build and transform materials. Instrumentation is an essential element of these activities, and analytical applications are one driving force. Work from the Aston Laboratories at Purdue University's Department of Chemistry is described here, with an emphasis on accelerated reactions of ions in solution and small-scale synthesis; ion/surface collision processes, including surface-induced dissociation (SID) and ion soft landing; and applications to tissue imaging. Our special interest in chirality and the chemistry behind the origins of life is also featured together with the exciting area of tissue diagnostics.

Integration of Ion Mobility MSE after Fully Automated, Online, High-Resolution Liquid Extraction Surface Analysis Micro-Liquid Chromatography

Direct analysis by mass spectrometry (imaging) has become increasingly deployed in preclinical and clinical research due to its rapid and accurate readouts. However, when it comes to biomarker discovery or histopathological diagnostics, more sensitive and in-depth profiling from localized areas is required. We developed a comprehensive, fully automated online platform for high-resolution liquid extraction surface analysis (HR-LESA) followed by micro–liquid chromatography (LC) separation and a data-independent acquisition strategy for untargeted and low abundant analyte identification directly from tissue sections. Applied to tissue sections of rat pituitary, the platform demonstrated improved spatial resolution, allowing sample areas as small as 400 μm to be studied, a major advantage over conventional LESA. The platform integrates an online buffer exchange and washing step for removal of salts and other endogenous contamination that originates from local tissue extraction. Our carry over–free platform showed high reproducibility, with an interextraction variability below 30%. Another strength of the platform is the additional selectivity provided by a postsampling gas-phase ion mobility separation. This allowed distinguishing coeluted isobaric compounds without requiring additional separation time. Furthermore, we identified untargeted and low-abundance analytes, including neuropeptides deriving from the pro-opiomelanocortin precursor protein and localized a specific area of the pituitary gland (i.e., adenohypophysis) known to secrete neuropeptides and other small metabolites related to development, growth, and metabolism. This platform can thus be applied for the in-depth study of small samples of complex tissues with histologic features of ∼400 μm or more, including potential neuropeptide markers involved in many diseases such as neurodegenerative diseases, obesity, bulimia, and anorexia nervosa.

High-resolution mass spectra processing for the identification of different pathological tissue types of brain tumors

The purpose of the work is to demonstrate the possibilities of identifying the different types of pathological tissue identification directly through tissue mass spectrometry. Glioblastoma parts dissected during neurosurgical operation were investigated. Tumor fragments were investigated by the immunohistochemistry method and were identified as necrotic tissue with necrotized vessels, necrotic tissue with tumor stain, tumor with necrosis (tumor tissue as major), tumor, necrotized tumor (necrotic tissues as major), parts of tumor cells, boundary brain tissue, and brain tissue hyperplasia. The technique of classification of tumor tissues based on mass spectrometric profile data processing is suggested in this paper. Classifiers dividing the researched sample to the corresponding tissue type were created as a result of the processing. Classifiers of necrotic and tumor tissues are shown to yield a combined result when the tissue is heterogeneous and consists of both tumor cells and necrotic tissue.