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

Spatiotemporal metabolomic approaches to the cancer-immunity panorama: a methodological perspective

Metabolic reprogramming drives the development of an immunosuppressive tumor microenvironment (TME) through various pathways, contributing to cancer progression and reducing the effectiveness of anticancer immunotherapy. However, our understanding of the metabolic landscape within the tumor-immune context has been limited by conventional metabolic measurements, which have not provided comprehensive insights into the spatiotemporal heterogeneity of metabolism within TME. The emergence of single-cell, spatial, and in vivo metabolomic technologies has now enabled detailed and unbiased analysis, revealing unprecedented spatiotemporal heterogeneity that is particularly valuable in the field of cancer immunology. This review summarizes the methodologies of metabolomics and metabolic regulomics that can be applied to the study of cancer-immunity across single-cell, spatial, and in vivo dimensions, and systematically assesses their benefits and limitations.

Mass Spectrometry Advancements and Applications for Biomarker Discovery, Diagnostic Innovations, and Personalized Medicine

Mass spectrometry (MS) has revolutionized clinical chemistry, offering unparalleled capabilities for biomolecule analysis. This review explores the growing significance of mass spectrometry (MS), particularly when coupled with liquid chromatography (LC), in identifying disease biomarkers and quantifying biomolecules for diagnostic and prognostic purposes. The unique advantages of MS in accurately identifying and quantifying diverse molecules have positioned it as a cornerstone in personalized-medicine advancement. MS-based technologies have transformed precision medicine, enabling a comprehensive understanding of disease mechanisms and patient-specific treatment responses. LC-MS has shown exceptional utility in analyzing complex biological matrices, while high-resolution MS has expanded analytical capabilities, allowing the detection of low-abundance molecules and the elucidation of complex biological pathways. The integration of MS with other techniques, such as ion mobility spectrometry, has opened new avenues for biomarker discovery and validation. As we progress toward precision medicine, MS-based technologies will be crucial in addressing the challenges of individualized patient care, driving innovations in disease diagnosis, prognosis, and treatment strategies.

Bacterial metabolomics: current applications for human welfare and future aspects

An imbalanced microbiome is linked to several diseases, such as cancer, inflammatory bowel disease, obesity, and even neurological disorders. Bacteria and their by-products are used for various industrial and clinical purposes. The metabolites under discussion were chosen based on their biological impacts on host and gut microbiota interactions as established by metabolome research. The separation of bacterial metabolites by using statistics and machine learning analysis creates new opportunities for applications of bacteria and their metabolites in the environmental and medical sciences. Thus, the metabolite production strategies, methodologies, and importance of bacterial metabolites for human well-being are discussed in this review.

Dual mass spectrometry imaging and spatial metabolomics to investigate the metabolism and nephrotoxicity of nitidine chloride

Evaluating toxicity and decoding the underlying mechanisms of active compounds are crucial for drug development. In this study, we present an innovative, integrated approach that combines air flow-assisted desorption electrospray ionization mass spectrometry imaging (AFADESI-MSI), time-of-flight secondary ion mass spectrometry (ToF-SIMS), and spatial metabolomics to comprehensively investigate the nephrotoxicity and underlying mechanisms of nitidine chloride (NC), a promising anti-tumor drug candidate. Our quantitive AFADESI-MSI analysis unveiled the region specific of accumulation of NC in the kidney, particularly within the inner cortex (IC) region, following single and repeated dose of NC. High spatial resolution ToF-SIMS analysis further allowed us to precisely map the localization of NC within the renal tubule. Employing spatial metabolomics based on AFADESI-MSI, we identified over 70 discriminating endogenous metabolites associated with chronic NC exposure. These findings suggest the renal tubule as the primary target of NC toxicity and implicate renal transporters (organic cation transporters, multidrug and toxin extrusion, and organic cation transporter 2 (OCT2)), metabolic enzymes (protein arginine N-methyltransferase (PRMT) and nitric oxide synthase), mitochondria, oxidative stress, and inflammation in NC-induced nephrotoxicity. This study offers novel insights into NC-induced renal damage, representing a crucial step towards devising strategies to mitigate renal damage caused by this compound.

Mass Spectrometry Imaging Techniques: Non-Ambient and Ambient Ionization Approaches

Molecular information can be acquired from sample surfaces in real time using a revolutionary molecular imaging technique called mass spectrometry imaging (MSI). The technique can concurrently provide high spatial resolution information on the spatial distribution and relative proportion of many different compounds. Thus, many scientists have been drawn to the innovative capabilities of the MSI approach, leading to significant focus in various fields during the past few decades. This review describes the sampling protocol, working principle and applications of a few non-ambient and ambient ionization mass spectrometry imaging techniques. The non-ambient techniques include secondary ionization mass spectrometry and matrix-assisted laser desorption ionization, while the ambient techniques include desorption electrospray ionization, laser ablation electrospray ionization, probe electro-spray ionization, desorption atmospheric pressure photo-ionization and femtosecond laser desorption ionization. The review additionally addresses the advantages and disadvantages of ambient and non-ambient MSI techniques in relation to their suitability, particularly for biological samples used in tissue diagnostics. Last but not least, suggestions and conclusions are made regarding the challenges and future prospects of MSI.

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.

Recent Analytical Methodologies in Lipid Analysis

Lipids represent a large group of biomolecules that are responsible for various functions in organisms. Diseases such as diabetes, chronic inflammation, neurological disorders, or neurodegenerative and cardiovascular diseases can be caused by lipid imbalance. Due to the different stereochemical properties and composition of fatty acyl groups of molecules in most lipid classes, quantification of lipids and development of lipidomic analytical techniques are problematic. Identification of different lipid species from complex matrices is difficult, and therefore individual analytical steps, which include extraction, separation, and detection of lipids, must be chosen properly. This review critically documents recent strategies for lipid analysis from sample pretreatment to instrumental analysis and data interpretation published in the last five years (2019 to 2023). The advantages and disadvantages of various extraction methods are covered. The instrumental analysis step comprises methods for lipid identification and quantification. Mass spectrometry (MS) is the most used technique in lipid analysis, which can be performed by direct infusion MS approach or in combination with suitable separation techniques such as liquid chromatography or gas chromatography. Special attention is also given to the correct evaluation and interpretation of the data obtained from the lipid analyses. Only accurate, precise, robust and reliable analytical strategies are able to bring complex and useful lipidomic information, which may contribute to clarification of some diseases at the molecular level, and may be used as putative biomarkers and/or therapeutic targets.

Lewis Acidic Metal-Organic Framework Assisted Ambient Liquid Extraction Mass Spectrometry Imaging for Enhancing the Coverage of Poorly Ionizable Lipids in Brain Tissue

The spatial distribution of lipidomes in tissues is of great importance in studies of living processes, diseases, and therapies. Mass spectrometry imaging (MSI) has become a critical technique for spatial lipidomics. However, MSI of low-abundance or poorly ionizable lipids is still challenging because of the ion suppression from high-abundance lipids. Here, a metal-organic framework (MOF) Zr6O4(OH)4(1,3,5-Tris(4-carboxyphenyl) benzene)2(triflate)6(Zr6OTf-BTB) was prepared and used for selective on-tissue adsorption of phospholipids to reduce ion suppression from them to poorly ionizable lipids. The results show that Zr6OTf-BTB with strong Lewis acidic sites and a large specific surface area (647.9 m2·g-1) could selectively adsorb phospholipids under 1% FA-MeOH. Adsorption efficiencies of phospholipids are 88.4-144.9 times higher than those of other neutral lipids. Moreover, the adsorption capacity and the adsorption kinetic rate constant of the new material to phospholipids are higher than those of Zr6-BTB (242.72 vs 73.96 mg·g-1, 0.0442 vs 0.0220 g·mg-1·min-1). A Zr6OTf-BTB sheet was prepared by a lamination technique for on-tissue phospholipid adsorption from brain tissue. Then, the tissue section on the Zr6OTf-BTB sheet was directly imaged via ambient liquid extraction-MSI with 1% FA-MeOH as the sampling solvent. The results showed that phospholipids could be 100% removed directly on tissue, and the detection coverage of the Zr6OTf-BTB-enhanced MSI method to ceramides (Cers) and hexosylceramides (HexCers) was increased by 5-26 times compared with direct tissue MSI (26 vs 1 and 17 vs 3). The new method provides an efficient and convenient way to eliminate the ion suppression from phospholipids in MSI, largely improving the detection coverage of low-abundance and poorly ionizable lipids.

Advanced progress of spatial metabolomics in head and neck cancer research

Head and neck cancer ranks as the sixth most prevalent malignancy, constituting 5 % of all cancer cases. Its inconspicuous onset often leads to advanced stage diagnoses, prompting the need for early detection to enhance patient prognosis. Currently, research into early diagnostic markers relies predominantly on genomics, proteomics, transcriptomics, and other methods, which, unfortunately, necessitate tumor tissue homogenization, resulting in the loss of temporal and spatial information. Emerging as a recent addition to the omics toolkit, spatial metabolomics stands out. This method conducts in situ mass spectrometry analyses on fresh tissue specimens while effectively preserving their spatiotemporal information. The utilization of spatial metabolomics in life science research offers distinct advantages. This article comprehensively reviews the progress of spatial metabolomics in head and neck cancer research, encompassing insights into cancer cell metabolic reprogramming. Various mass spectrometry imaging techniques, such as secondary ion mass spectrometry, stroma-assisted laser desorption/ionization, and desorption electrospray ionization, enable in situ metabolite analysis for head and neck cancer. Finally, significant emphasis is placed on the application of presently available techniques for early diagnosis, margin assessment, and prognosis of head and neck cancer.

Spatial omics: An innovative frontier in aging research

Disentangling the impact of aging on health and disease has become critical as population aging progresses rapidly. Studying aging at the molecular level is complicated by the diverse aging profiles and dynamics. However, the examination of cellular states within aging tissues in situ is hampered by the lack of high-resolution spatial data. Emerging spatial omics technologies facilitate molecular and spatial analysis of tissues, providing direct access to precise information on various functional regions and serving as a favorable tool for unraveling the heterogeneity of aging. In this review, we summarize the recent advances in spatial omics application in multi-organ aging research, which has enhanced the understanding of aging mechanisms from multiple standpoints. We also discuss the main challenges in spatial omics research to date, the opportunities for further developing the technology, and the potential applications of spatial omics in aging and aging-related diseases.

Recent developments and applications of ambient mass spectrometry imaging in pharmaceutical research: an overview

The application of ambient mass spectrometry imaging "MSI" is expanding in the areas of fundamental research on drug delivery and multiple phases of the process of identifying and developing drugs. Precise monitoring of a drug's pharmacological workflows, such as intake, distribution, metabolism, and discharge, is made easier by MSI's ability to determine the concentrations of the initiating drug and its metabolites across dosed samples without losing spatial data. Lipids, glycans, and proteins are just a few of the many phenotypes that MSI may be used to concurrently examine. Each of these substances has a particular distribution pattern and biological function throughout the body. MSI offers the perfect analytical tool for examining a drug's pharmacological features, especially in vitro and in vivo effectiveness, security, probable toxic effects, and putative molecular pathways, because of its high responsiveness in chemical and physical environments. The utilization of MSI in the field of pharmacy has further extended from the traditional tissue examination to the early stages of drug discovery and development, including examining the structure-function connection, high-throughput capabilities in vitro examination, and ex vivo research on individual cells or tumor spheroids. Additionally, an enormous array of endogenous substances that may function as tissue diagnostics can be scanned simultaneously, giving the specimen a highly thorough characterization. Ambient MSI techniques are soft enough to allow for easy examination of the native sample to gather data on exterior chemical compositions. This paper provides a scientific and methodological overview of ambient MSI utilization in research on pharmaceuticals.

Matrix Selection for the Visualization of Small Molecules and Lipids in Brain Tumors Using Untargeted MALDI-TOF Mass Spectrometry Imaging

Matrix-assisted laser desorption/ionization mass spectrometry imaging allows for the study of metabolic activity in the tumor microenvironment of brain cancers. The detectable metabolites within these tumors are contingent upon the choice of matrix, deposition technique, and polarity setting. In this study, we compared the performance of three different matrices, two deposition techniques, and the use of positive and negative polarity in two different brain cancer types and across two species. Optimal combinations were confirmed by a comparative analysis of lipid and small-molecule abundance by using liquid chromatography-mass spectrometry and RNA sequencing to assess differential metabolites and enzymes between normal and tumor regions. Our findings indicate that in the tumor-bearing brain, the recrystallized α-cyano-4-hydroxycinnamic acid matrix with positive polarity offered superior performance for both detected metabolites and consistency with other techniques. Beyond these implications for brain cancer, our work establishes a workflow to identify optimal matrices for spatial metabolomics studies.

High-Throughput Deconvolution of Native Protein Mass Spectrometry Imaging Data Sets for Mass Domain Analysis

Protein mass spectrometry imaging (MSI) with electrospray-based ambient ionization techniques, such as nanospray desorption electrospray ionization (nano-DESI), generates data sets in which each pixel corresponds to a mass spectrum populated by peaks corresponding to multiply charged protein ions. Importantly, the signal associated with each protein is split among multiple charge states. These peaks can be transformed into the mass domain by spectral deconvolution. When proteins are imaged under native/non-denaturing conditions to retain non-covalent interactions, deconvolution is particularly valuable in helping interpret the data. To improve the acquisition speed, signal-to-noise ratio, and sensitivity, native MSI is usually performed using mass resolving powers that do not provide isotopic resolution, and conventional algorithms for deconvolution of lower-resolution data are not suitable for these large data sets. UniDec was originally developed to enable rapid deconvolution of complex protein mass spectra. Here, we developed an updated feature set harnessing the high-throughput module, MetaUniDec, to deconvolve each pixel of native MSI data sets and transform m/z-domain image files to the mass domain. New tools enable the reading, processing, and output of open format .imzML files for downstream analysis. Transformation of data into the mass domain also provides greater accessibility, with mass information readily interpretable by users of established protein biology tools such as sodium dodecyl sulfate polyacrylamide gel electrophoresis.

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.

Untangling Alzheimer's disease with spatial multi-omics: a brief review

Alzheimer's disease (AD) is the most common form of neurological dementia, specified by extracellular β-amyloid plaque deposition, neurofibrillary tangles, and cognitive impairment. AD-associated pathologies like cerebral amyloid angiopathy (CAA) are also affiliated with cognitive impairment and have overlapping molecular drivers, including amyloid buildup. Discerning the complexity of these neurological disorders remains a significant challenge, and the spatiomolecular relationships between pathogenic features of AD and AD-associated pathologies remain poorly understood. This review highlights recent developments in spatial omics, including profiling and molecular imaging methods, and how they are applied to AD. These emerging technologies aim to characterize the relationship between how specific cell types and tissue features are organized in combination with mapping molecular distributions to provide a systems biology view of the tissue microenvironment around these neuropathologies. As spatial omics methods achieve greater resolution and improved molecular coverage, they are enabling deeper characterization of the molecular drivers of AD, leading to new possibilities for the prediction, diagnosis, and mitigation of this debilitating disease.

A Multimodal Desorption Electrospray Ionisation Workflow Enabling Visualisation of Lipids and Biologically Relevant Elements in a Single Tissue Section

The colocation of elemental species with host biomolecules such as lipids and metabolites may shed new light on the dysregulation of metabolic pathways and how these affect disease pathogeneses. Alkali metals have been the subject of extensive research, are implicated in various neurodegenerative and infectious diseases and are known to disrupt lipid metabolism. Desorption electrospray ionisation (DESI) is a widely used approach for molecular imaging, but previous work has shown that DESI delocalises ions such as potassium (K) and chlorine (Cl), precluding the subsequent elemental analysis of the same section of tissue. The solvent typically used for the DESI electrospray is a combination of methanol and water. Here we show that a novel solvent system, (50:50 (%v/v) MeOH:EtOH) does not delocalise elemental species and thus enables elemental mapping to be performed on the same tissue section post-DESI. Benchmarking the MeOH:EtOH electrospray solvent against the widely used MeOH:H2O electrospray solvent revealed that the MeOH:EtOH solvent yielded increased signal-to-noise ratios for selected lipids. The developed multimodal imaging workflow was applied to a lung tissue section containing a tuberculosis granuloma, showcasing its applicability to elementally rich samples displaying defined structural information.

Development of a Laser Microdissection-Coupled Quantitative Shotgun Lipidomic Method to Uncover Spatial Heterogeneity

Lipid metabolic disturbances are associated with several diseases, such as type 2 diabetes or malignancy. In the last two decades, high-performance mass spectrometry-based lipidomics has emerged as a valuable tool in various fields of biology. However, the evaluation of macroscopic tissue homogenates leaves often undiscovered the differences arising from micron-scale heterogeneity. Therefore, in this work, we developed a novel laser microdissection-coupled shotgun lipidomic platform, which combines quantitative and broad-range lipidome analysis with reasonable spatial resolution. The multistep approach involves the preparation of successive cryosections from tissue samples, cross-referencing of native and stained images, laser microdissection of regions of interest, in situ lipid extraction, and quantitative shotgun lipidomics. We used mouse liver and kidney as well as a 2D cell culture model to validate the novel workflow in terms of extraction efficiency, reproducibility, and linearity of quantification. We established that the limit of dissectible sample area corresponds to about ten cells while maintaining good lipidome coverage. We demonstrate the performance of the method in recognizing tissue heterogeneity on the example of a mouse hippocampus. By providing topological mapping of lipid metabolism, the novel platform might help to uncover region-specific lipidomic alterations in complex samples, including tumors.

Role of Oxylipins in the Inflammatory-Related Diseases NAFLD, Obesity, and Type 2 Diabetes

Oxygenated polyunsaturated fatty acids (oxylipins) are bioactive molecules established as important mediators during inflammation. Different classes of oxylipins have been found to have opposite effects, e.g., pro-inflammatory prostaglandins and anti-inflammatory resolvins. Production of the different classes of oxylipins occurs during distinct stages of development and resolution of inflammation. Chronic inflammation is involved in the progression of many pathophysiological conditions and diseases such as non-alcoholic fatty liver disease, insulin resistance, diabetes, and obesity. Determining oxylipin profiles before, during, and after inflammatory-related diseases could provide clues to the onset, development, and prevention of detrimental conditions. This review focusses on recent developments in our understanding of the role of oxylipins in inflammatory disease, and outlines novel technological advancements and approaches to study their action.

Advances in mass spectrometry imaging for toxicological analysis and safety evaluation of pharmaceuticals

Safety issues caused by pharmaceuticals have frequently occurred worldwide, posing a tremendous threat to human health. As an essential part of drug development, the toxicological analysis and safety evaluation is of great significance. In addition, the risk of pharmaceuticals accumulation in the environment and the monitoring of the toxicity from natural medicines have also received ongoing concerns. Due to a lack of spatial distribution information provided by common analytical methods, analyses that provide spatial dimensions could serve as complementary safety evaluation methods for better prediction and evaluation of drug toxicity. With advances in technical solutions and software algorithms, mass spectrometry imaging (MSI) has received increasing attention as a popular analytical tool that enables the simultaneous implementation of qualitative, quantitative, and localization without complex sample pretreatment and labeling steps. In recent years, MSI has become more attractive, powerful, and sensitive and has been applied in several scientific fields that can meet the safety assessment requirements. This review aims to cover a detailed summary of the various MSI technologies utilized in the biomedical and pharmaceutical area, including technical principles, advantages, current status, and future trends. Representative applications and developments in the safety-related issues of different pharmaceuticals and natural medicines are also described to provide a reference for pharmaceutical research, improve rational clinical medicine use, and ensure public safety.

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.