Distinguishing Non-Small Cell Lung Cancer Subtypes in Fine Needle Aspiration Biopsies by Desorption Electrospray Ionization Mass Spectrometry Imaging

Mass spectrometry imaging in pulmonary disorders

Mass Spectrometry Imaging (MSI) represents a novel and advancing technology that offers unparalleled in situ characterization of tissues. It provides comprehensive insights into the chemical structures, relative abundances, and spatial distributions of a vast array of both identified and unidentified endogenous and exogenous compounds, a capability not paralleled by existing analytical methodologies. Recent scholarly endeavors have increasingly explored the utility of MSI in the adjunct diagnosis and biomarker research of pulmonary disorders, including but not limited to lung cancer. Concurrently, MSI has proven instrumental in elucidating the spatiotemporal dynamics of various pharmacological agents. This review concisely delineates the fundamental principles underpinning MSI, its applications in pulmonary disease diagnosis, biomarker discovery, and drug distribution investigations. Additionally, it presents a forward-looking perspective on the prospective trajectories of MSI technological advancements.

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.

Lipid mediators of inhalation exposure-induced pulmonary toxicity and inflammation

Many inhalation exposures induce pulmonary inflammation contributing to disease progression. Inflammatory processes are actively regulated via mediators including bioactive lipids. Bioactive lipids are potent signaling molecules involved in both pro-inflammatory and resolution processes through receptor interactions. The formation and clearance of lipid signaling mediators are controlled by multiple metabolic enzymes. An imbalance of these lipids can result in exacerbated and sustained inflammatory processes which may result in pulmonary damage and disease. Dysregulation of pulmonary bioactive lipids contribute to inflammation and pulmonary toxicity following exposures. For example, inhalation of cigarette smoke induces activation of pro-inflammatory bioactive lipids such as sphingolipids, and ceramides contributing to chronic obstructive pulmonary disease. Additionally, exposure to silver nanoparticles causes dysregulation of inflammatory resolution lipids. As inflammation is a common consequence resulting from inhaled exposures and a component of numerous diseases it represents a broadly applicable target for therapeutic intervention. With new appreciation for bioactive lipids, technological advances to reliably identify and quantify lipids have occurred. In this review, we will summarize, integrate, and discuss findings from recent studies investigating the impact of inhaled exposures on pro-inflammatory and resolution lipids within the lung and their contribution to disease. Throughout the review current knowledge gaps in our understanding of bioactive lipids and their contribution to pulmonary effects of inhaled exposures will be presented. New methods being employed to detect and quantify disruption of pulmonary lipid levels following inhalation exposures will be highlighted. Lastly, we will describe how lipid dysregulation could potentially be addressed by therapeutic strategies to address inflammation.

Diagnostic method of mass spectrometry for detecting lymph node metastasis of non-small cell lung cancer

BACKGROUND

Histopathology by pathologists is essential in the diagnosis of non-small cell lung cancer (NSCLC). However, auxiliary diagnostic procedures for malignant tumor have continued to evolve. Despite the poor prognosis of patients with NSCLC, the application of the latest procedures and technologies to the field of lung cancer has lagged. Mass spectrometry was used to detect trace amounts of peptides in human tissue with high accuracy. The aim of this study was to establish a method for diagnostic mass spectrometry to identify lymph node metastasis by detecting cytokeratin (CK)19, a useful biomarker in lung cancer.

METHODS

We collected 81 lymph nodes with positive expression of CK19 in patients who underwent radical surgical resection in the Department of Thoracic Surgery at Iwate Medical University between May 2020 and December 2022. An X500R instrument was used for sample analysis. A positive result for lymph node metastasis as the detection at least two product ions (FGPGVAFR and ILGATIENSR) from CK19 was defined.

RESULTS

Our study indicated a high diagnostic efficiency for mass spectrometry, with 87.5% sensitivity and 91.2% specificity. The mutual concordance of mass spectrometry methods and histopathological diagnosis was 90.1%.

CONCLUSIONS

Mass spectrometry offers high diagnostic accuracy and can be clinically applied to auxiliary diagnostic procedures for lymph node metastasis from NSCLC.

Next-generation pathology practices with mass spectrometry imaging

Mass spectrometry imaging (MSI) is a powerful technique that reveals the spatial distribution of various molecules in biological samples, and it is widely used in pathology-related research. In this review, we summarize common MSI techniques, including matrix-assisted laser desorption/ionization and desorption electrospray ionization MSI, and their applications in pathological research, including disease diagnosis, microbiology, and drug discovery. We also describe the improvements of MSI, focusing on the accumulation of imaging data sets, expansion of chemical coverage, and identification of biological significant molecules, that have prompted the evolution of MSI to meet the requirements of pathology practices. Overall, this review details the applications and improvements of MSI techniques, demonstrating the potential of integrating MSI techniques into next-generation pathology practices.

Recent Advances and Applications of Ambient Mass Spectrometry Imaging in Cancer Research: An Overview

Cancer metabolic variability has a significant impact on both diagnosis and treatment outcomes. The discovery of novel biological indicators and metabolic dysregulation, can significantly rely on comprehension of the modified metabolism in cancer, is a research focus. Tissue histology is a critical feature in the diagnostic testing of many ailments, such as cancer. To assess the surgical margin of the tumour on patients, frozen section histology is a tedious, laborious, and typically arbitrary method. Concurrent monitoring of ion images in tissues facilitated by the latest advancements in mass spectrometry imaging (MSI) is far more efficient than optical tissue image analysis utilized in conventional histopathology examination. This article focuses on the "desorption electrospray ionization (DESI)-MSI" technique's most recent advancements and uses in cancer research. DESI-MSI can provide wealthy information based on the variances in metabolites and lipids in normal and cancerous tissues by acquiring ion images of the lipid and metabolite variances on biopsy samples. As opposed to a systematic review, this article offers a synopsis of the most widely employed cutting-edge DESI-MSI techniques in cancer research.

Desorption electrospray ionization mass spectrometry imaging (DESI-MSI) in disease diagnosis: an overview

Tissue analysis, which is essential to histology and is considered the benchmark for the diagnosis and prognosis of many illnesses, including cancer, is significant. During surgery, the surgical margin of the tumor is assessed using the labor-intensive, challenging, and commonly subjective technique known as frozen section histopathology. In the biopsy section, large numbers of molecules can now be visualized at once (ion images) following recent developments in [MSI] mass spectrometry imaging under atmospheric conditions. This is vastly superior to and different from the single optical tissue image processing used in traditional histopathology. This review article will focus on the advancement of desorption electrospray ionization mass spectrometry imaging [DESI-MSI] technique, which is label-free and requires little to no sample preparation. Since the proportion of molecular species in normal and abnormal tissues is different, DESI-MSI can capture ion images of the distributions of lipids and metabolites on biopsy sections, which can provide rich diagnostic information. This is not a systematic review but a summary of well-known, cutting-edge and recent DESI-MSI applications in cancer research between 2018 and 2023.

Ion-to-Image, i2i, a Mass Spectrometry Imaging Data Analysis Platform for Continuous Ionization Techniques

Mass spectrometry imaging (MSI) techniques generate data that reveal spatial distributions of molecules on a surface with high sensitivity and selectivity. However, processing large volumes of mass spectrometry data into useful ion images is not trivial. Furthermore, data from MSI techniques using continuous ionization sources where data are acquired in line scans require different data handling strategies compared to data collected from pulsed ionization sources where data are acquired in grids. In addition, for continuous ionization sources, the pixel dimensions are influenced by the mass spectrometer duty cycle, which, in turn, can be controlled by the automatic gain control (AGC) for each spectrum (pixel). Currently, there is a lack of data-handling software for MSI data generated with continuous ionization sources and AGC. Here, we present ion-to-image (i2i), which is a MATLAB-based application for MSI data acquired with continuous ionization sources, AGC, high resolution, and one or several scan filters. The source code and a compiled installer are available at https://github.com/LanekoffLab/i2i. The application includes both quantitative, targeted, and nontargeted data processing strategies and enables complex data sets to be processed in minutes. The i2i application has high flexibility for generating, processing, and exporting MSI data both from simple full scans and more complex scan functions interlacing MSn and SIM scan data sets, and we anticipate that it will become a valuable addition to the existing MSI software toolbox.

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

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

Characterisation of hepatic lipid signature distributed across the liver zonation using mass spectrometry imaging

Background & Aims

Lipid metabolism plays an important role in liver pathophysiology. The liver lobule asymmetrically distributes oxygen and nutrition, resulting in heterogeneous metabolic functions. Periportal and pericentral hepatocytes have different metabolic functions, which lead to generating liver zonation. We developed spatial metabolic imaging using desorption electrospray ionisation mass spectrometry to investigate lipid distribution across liver zonation with high reproducibility and accuracy.

Methods

Fresh frozen livers from healthy mice with control diet were analysed using desorption electrospray ionisation mass spectrometry imaging. Imaging was performed at 50 μm × 50 μm pixel size. Regions of interest (ROIs) were manually created by co-registering with histological data to determine the spatial hepatic lipids across liver zonation. The ROIs were confirmed by double immunofluorescence. The mass list of specific ROIs was automatically created, and univariate and multivariate statistical analysis were performed to identify statistically significant lipids across liver zonation.

Results

A wide range of lipid species was identified, including fatty acids, phospholipids, triacylglycerols, diacylglycerols, ceramides, and sphingolipids. We characterised hepatic lipid signatures in three different liver zones (periportal zone, midzone, and pericentral zone) and validated the reproducibility of our method for measuring a wide range of lipids. Fatty acids were predominantly detected in the periportal region, whereas phospholipids were distributed in both the periportal and pericentral zones. Interestingly, phosphatidylinositols, PI(36:2), PI(36:3), PI(36:4), PI(38:5), and PI(40:6) were located predominantly in the midzone (zone 2). Triacylglycerols and diacylglycerols were detected mainly in the pericentral region. De novo triacylglycerol biosynthesis appeared to be the most influenced pathway across the three zones.

Conclusions

The ability to accurately assess zone-specific hepatic lipid distribution in the liver could lead to a better understanding of lipid metabolism during the progression of liver disease.

Impact and Implications

Zone-specific hepatic lipid metabolism could play an important role in lipid homoeostasis during disease progression. Herein, we defined the zone-specific references of hepatic lipid species in the three liver zones using molecular imaging. The de novo triacylglycerol biosynthesis was highlighted as the most influenced pathway across the three zones.

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

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

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

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

Mass Spectrometry Imaging technology in metabolomics: a systematic review

Mass spectrometry imaging (MSI) is a powerful label-free analysis technique that can provide simultaneous spatial distribution of multiple compounds in a single experiment. By combining the sensitive and rapid screening of high-throughput mass spectrometry with spatial chemical information, metabolite analysis and morphological characteristics are presented in a single image. MSI can be used for qualitative and quantitative analysis of metabolic profiles and it can provide visual analysis of spatial distribution information of complex biological and microbial systems. Matrix assisted laser desorption ionization, laser ablation electrospray ionization and desorption electrospray ionization are commonly used in MSI. Here, we summarize and compare these three technologies, as well as the applications and prospects of MSI in metabolomics.

Comparing DESI-MSI and MALDI-MSI Mediated Spatial Metabolomics and Their Applications in Cancer Studies

Metabolic heterogeneity of cancer contributes significantly to its poor treatment outcomes and prognosis. As a result, studies continue to focus on identifying new biomarkers and metabolic vulnerabilities, both of which depend on the understanding of altered metabolism in cancer. In the recent decades, the rise of mass spectrometry imaging (MSI) enables the in situ detection of large numbers of small molecules in tissues. Therefore, researchers look to using MSI-mediated spatial metabolomics to further study the altered metabolites in cancer patients. In this review, we examined the two most commonly used spatial metabolomics techniques, MALDI-MSI and DESI-MSI, and some recent highlights of their applications in cancer studies. We also described AFADESI-MSI as a recent variation from the DESI-MSI and compare it with the two major techniques. Specifically, we discussed spatial metabolomics results in four types of heterogeneous malignancies, including breast cancer, esophageal cancer, glioblastoma and lung cancer. Multiple studies have effectively classified cancer tissue subtypes using altered metabolites information. In addition, distribution trends of key metabolites such as fatty acids, high-energy phosphate compounds, and antioxidants were identified. Therefore, while the visualization of finer distribution details requires further improvement of MSI techniques, past studies have suggested spatial metabolomics to be a promising direction to study the complexity of cancer pathophysiology.

Desorption Electrospray Ionization Mass Spectrometry Assay for Label-Free Characterization of SULT2B1b Enzyme Kinetics

The sulfotransferase (SULT) 2B1b, which catalyzes the sulfonation of 3β-hydroxysteroids, has been identified as a potential target for prostate cancer treatment. However, a major limitation for SULT2B1b-targeted drug discovery is the lack of robust assays compatible with high-throughput screening and inconsistency in reported kinetic data. For this reason, we developed a novel label-free assay based on high-throughput (>1 Hz) desorption electrospray ionization mass spectrometry (DESI-MS) for the direct quantitation of the sulfoconjugated product (CV<10 %; <1 ng analyte). The performance of this DESI-based assay was compared against a new fluorometric coupled-enzyme method that we also developed. Both methodologies provided consistent kinetic data for the reaction of SULT2B1b with its major substrates, indicating the affinity trend pregnenolone>DHEA>cholesterol, for both the phospho-mimetic and wild-type SULT2B1b forms. The novel DESI-MS assay developed here is likely generalizable to other drug discovery efforts and is particularly promising for identification of SULT2B1b inhibitors with potential as prostate cancer therapeutics.

Multiplatform Metabolomics Studies of Human Cancers With NMR and Mass Spectrometry Imaging

The status of metabolomics as a scientific branch has evolved from proof-of-concept to applications in science, particularly in medical research. To comprehensively evaluate disease metabolomics, multiplatform approaches of NMR combining with mass spectrometry (MS) have been investigated and reported. This mixed-methods approach allows for the exploitation of each individual technique's unique advantages to maximize results. In this article, we present our findings from combined NMR and MS imaging (MSI) analysis of human lung and prostate cancers. We further provide critical discussions of the current status of NMR and MS combined human prostate and lung cancer metabolomics studies to emphasize the enhanced metabolomics ability of the multiplatform approach.

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

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

CCR2 and PTPRC are regulators of tumor microenvironment and potential prognostic biomarkers of lung adenocarcinoma

Background

Tumor microenvironment (TME) plays an essential role in lung adenocarcinoma (LUAD) development and metastasis. With the development of TME research, it has been proved that differences in tumor-infiltrating immune cells (TICs) and gene expression profile are related to the prognosis of cancer. The aim of our study was to identify key genes affecting immune state in TME of LUAD.

Methods

The RNA-seq data and clinical characteristics of 594 LUAD patients were downloaded from the TCGA database. ImmuneScore, StromalScore and ESTIMATEScore of each LUAD sample were calculated using ESTIMATE algorithm. Based on the median of different scores, LUAD samples were divided into high and low score groups. Differentially expressed genes (DEGs) between groups were obtained, and univariate Cox regression analysis and protein-protein interaction (PPI) network were used to screen the shared DEGs generating in the intersection analysis. Finally, the CIBORSORT algorithm was performed to calculate the relative contents of TICs for each LUAD sample, and the correlation analysis between TICs and key genes was used to determine the influence of key genes to the TME.

Results

In the presented study, we found that three different scores were positively correlated with the prognosis of LUAD patients, and correlation analysis showed the different scores were closely related to tumor progression and metastasis. After performing the intersection analysis, a total of 585 up-regulated and 107 down-regulated DEGs between the high and low score groups were obtained, all of which were enriched in immune-related functions. Having used univariate COX regression analysis and PPI network, the key genes, CCR2 and PTPRC, affecting the immune status of TME and the prognosis of LUAD were acquired. Analysis based on the CIBERSORT algorithm suggested that CCR2 and PTPRC were correlated with a variety of TICs, and closely related to the clinical characteristics of the LUAD patients.

Conclusions

Our research showed that CCR2 and PTPRC may be potential prognostic markers in LUAD, which may affect the function of γδT cells and other immune cells by participating in the regulation of TME immune state.