Mass Spectrometry Imaging of Lipids Using MALDI Coupled with Plasma-Based Post-Ionization on a Trapped Ion Mobility Mass Spectrometer

Adhesive tapes as sampling probes and thermal desorption substrates, in search of direct analysis of particulate solid samples using Soft Ionization by Chemical Reaction In Transfer mass spectrometry

Direct analysis by mass spectrometry (MS) has emerged as a promising strategy for analyzing a broad range of samples. Among the different ionization techniques, plasma-based sources are simple alternatives that can be used to ionize a wide range of analytes, thus providing high analytical versatility. This article describes the design of a simple and cost-effective interface that uses adhesive tapes as both sampling probes and thermal desorption substrates. The particulate solid sample is adhered to the tape in a single and quick step, avoiding particle losses and protecting the interface from clogging. The probe is thermally desorbed in a dedicated chamber, built using commercial elements, in front of the MS inlet. The desorbed analytes are driven by the vacuum system (any supplementary carrier gas is not required), passing through the plasma-based source (Soft Ionization by Chemical Reaction In Transfer, SICRIT®), where they are ionized before being introduced in the spectrometer. The determination of caffeine in coffee has been evaluated as a proof of concept since the complexity of the matrix and the semivolatile character of the analyte can challenge the performance, allowing to check the potential of the new approach. The type of adhesive tape, the parameters related to the desorption and sample dilution have been studied in depth. Working under optimum conditions, LOD was settled down at 0.18 mg g-1. The intra-day and inter-day precision, expressed as relative standard deviation and calculated at three different concentration levels, were better than 12.5 and 13.7 %, respectively. Finally, the accuracy of this method was calculated by analyzing five different coffee samples and comparing the results with those provided by a reference method. The accuracy was in the range of 82.3-105.5 %. This interface is presented as a promising, simple, solvent-free approach for the direct analysis of solid samples. The interface has been finally applied to the analysis of drug mixtures to demonstrate its versatility.

Analysis of carbohydrates and glycoconjugates by matrix-assisted laser desorption/ionization mass spectrometry: An update for 2021-2022

The use of matrix-assisted laser desorption/ionization (MALDI) mass spectrometry for the analysis of carbohydrates and glycoconjugates is a well-established technique and this review is the 12th update of the original article published in 1999 and brings coverage of the literature to the end of 2022. As with previous review, this review also includes a few papers that describe methods appropriate to analysis by MALDI, such as sample preparation, even though the ionization method is not MALDI. The review follows the same format as previous reviews. It is divided into three sections: (1) general aspects such as theory of the MALDI process, matrices, derivatization, MALDI imaging, fragmentation, quantification and the use of computer software for structural identification. (2) Applications to various structural types such as oligo- and polysaccharides, glycoproteins, glycolipids, glycosides and biopharmaceuticals, and (3) other general areas such as medicine, industrial processes, natural products and glycan synthesis where MALDI is extensively used. Much of the material relating to applications is presented in tabular form. MALDI is still an ideal technique for carbohydrate analysis, particularly in its ability to produce single ions from each analyte and advancements in the technique and range of applications show little sign of diminishing.

Rapid Assessment of Samples from Large-Scale Clandestine Synthetic Drug Laboratories by Soft Ionization by Chemical Reaction in Transfer-High-Resolution Mass Spectrometry

The worldwide ongoing trend of synthetic drug production is also of increasing concern due to enormous amounts of chemical waste produced in clandestine laboratories. Typically, several tons of different types of production waste are stored in numerous containers and need to be characterized after dismantling a laboratory to assess production features, e.g., synthesis route and production scale, and to draw conclusions on the minimum number of batches produced. This forensic assessment is commonly done by a rather laborious gas chromatography - mass spectrometry approach. The aim of this work is to evaluate the suitability of the SICRIT (soft ionization by chemical reaction in transfer) ion source, which is based on the dielectric barrier discharge ionization principle, combined with high-resolution mass spectrometry (HRMS), for the rapid classification of liquid samples from amphetamine production in a seized large-scale clandestine drug laboratory. Among the different sample introduction methods tested, headspace analysis directly into the SICRIT ion source in conjunction with a heated inlet proved to be optimal. Identification of expected target substances (reaction educts, intermediates, byproducts, products) was possible as well as grouping related samples and assigning them to specific synthesis steps by multivariate data analysis in an unsupervised approach. In addition, supervised machine learning algorithms were evaluated to obtain a classification model for the assessment of production waste samples from one dismantled synthetic drug laboratory, and a random forest classifier showed the best performance with an accuracy of 97%. The potential of the novel SICRIT-HRMS approach for the assessment of synthetic drug laboratories was demonstrated.

Headspace Injection Method for Intermittent Sampling and Profiling Analyses of Volatile Organic Compounds Using Dielectric Barrier Discharge Ionization (DBDI)

A direct headspace injection method is presented and optimized for the analysis of volatile organic compounds (VOCs) using dielectric barrier discharge ionization-mass spectrometry (DBDI-MS), incorporating an intermediate vial in which the sample headspace is injected. The setup is built of commonly available, cheap consumable parts and easily enables the incorporation of different gases for generating different ionization atmospheres. The method can be fully automated by using standard GC autosamplers, and its rapid analysis time is suitable for high-throughput applications. We show that this method is suitable for both profiling analysis of complex samples such as biofluids and quantitative measurements for real-time reaction monitoring. Our optimized method demonstrated improved reproducibility and sensitivity, with detection limits for compounds tested in the high nanomolar to the low micromolar range, depending on the compound. Key parameters for method optimization were identified such as sample vial volume, headspace-to-liquid ratio, incubation temperature, and equilibration time. These settings were systematically evaluated to maximize the signal intensity and improve repeatability between measurements. Two use cases are demonstrated: (i) quantitative measurement of ethanol production by a metal-organic framework from CO2 and (ii) profiling of biofluids following the consumption of asparagus.

Advancing atherosclerosis research: The Power of lipid imaging with MALDI-MSI

Atherosclerosis is a chronic inflammatory disease that is one of the leading causes of mortality globally. It is characterized by the formation of atheromatous plaques in the intima layer of larger arteries. The (fibro-)fatty plaques usually develop asymptomatically within the vessel until a serious event such as myocardial infarction or stroke occurs. Lipids play a pivotal role in disease progression, but while the causal role of cholesterol is beyond doubt, the distribution of numerous other lipids within the heterogeneous layers of atherosclerotic plaques, and their biological function remain unclear. A deeper understanding of the pathophysiological progression of the disease for prognostics, diagnostics, treatment, and prevention is of great need. Mass spectrometry imaging (MSI), in particular with matrix-assisted laser desorption/ionization (MALDI) offers an unprecedented untargeted characterization of the physiological microenvironment, unraveling the spatial distribution of numerous biochemical compounds. MALDI-MSI offers an advantageous balance of sample preparation, chemical sensitivity, and spatial resolution, and thus has been established as a key technology in modern biomedical analysis. This review focuses on the analysis of lipids in atherosclerotic lesions with MALDI-MSI, for which the past years showed major developments in the spatial characterization of lipids and their interaction within atherosclerotic plaques. We will cover main contributions with a focus on the recent decade, elaborate possibilities, limitations, main findings, and recent developments from sample handling to instrumentation, and estimate current challenges and potentials of MALDI-MSI with respect to a clinical application.

Resolving multi-image spatial lipidomic responses to inhaled toxicants by machine learning

Regional responses to inhaled toxicants are essential to understand the pathogenesis of lung disease under exposure to air pollution. We evaluate the effect of combined allergen sensitization and ozone exposure on eliciting spatial differences in lipid distribution in the mouse lung that may contribute to ozone-induced exacerbations in asthma. We demonstrate the ability to normalize and segment high resolution mass spectrometry imaging data by applying established machine learning algorithms. Interestingly, our segmented regions overlap with histologically validated lung regions, enabling regional analysis across biological replicates. Our data reveal differences in the abundance of spatially distinct lipids, support the potential role of lipid saturation in healthy lung function, and highlight sex differences in regional lung lipid distribution following ozone exposure. Our study provides a framework for future mass spectrometry imaging experiments capable of relative quantification across biological replicates and expansion to multiple sample types, including human tissue.

Investigation into Drug-Induced Liver Damage Using Multimodal Mass Spectrometry Imaging

Drug toxicity during the development of candidate pharmaceuticals is the leading cause of discontinuation in preclinical drug discovery and development. Traditionally, the cause of the toxicity is often determined by histological examination, clinical pathology, and the detection of drugs and/or metabolites by liquid chromatography-mass spectrometry (LC-MS). While these techniques individually provide information on the pathological effects of the drug and the detection of metabolites, they cannot provide specific molecular spatial information without additional experiments. Matrix-assisted laser desorption/ionization-mass spectrometry imaging (MALDI-MSI) is a powerful, label-free technique for the simultaneous detection of pharmaceuticals, metabolites, and endogenous chemical species in tissue sections, which makes it suitable for mechanistic toxicological studies to directly correlate the distribution of the drug and its metabolites with histological findings. This capability was demonstrated by the analysis of the liver from dogs dosed with discontinued drug compound B and its N-desmethyl metabolite, compound A. Histological examination showed multifocal hepatocellular necrosis, bile duct hyperplasia, periportal fibrosis, and chronic inflammation. MALDI-MSI analysis of liver tissue dosed with only compound A indicated that liver lesions were associated exclusively with the parent compound, whereas liver lesions with compound B showed the presence of the parent compound and its two metabolites (compound A and an N-oxide metabolite). Using both positive and negative ion modes, simultaneous detection and identification of endogenous molecular markers of the connective tissue, blood vessels, liver parenchyma, and bile duct epithelium was achieved, allowing optimal visualization of histological lesions by mass spectrometry imaging.

The Society for Immunotherapy of Cancer Perspective on Tissue-Based Technologies for Immuno-Oncology Biomarker Discovery and Application

With immuno-oncology becoming the standard of care for a variety of cancers, identifying biomarkers that reliably classify patient response, resistance, or toxicity becomes the next critical barrier toward improving care. Multiparametric, multi-omics, and computational platforms generating an unprecedented depth of data are poised to usher in the discovery of increasingly robust biomarkers for enhanced patient selection and personalized treatment approaches. Deciding which developing technologies to implement in clinical settings ultimately, applied either alone or in combination, relies on weighing pros and cons, from minimizing patient sampling to maximizing data outputs, and assessing the reproducibility and representativeness of findings, while lessening data fragmentation toward harmonization. These factors are all assessed while taking into consideration the shortest turnaround time. The Society for Immunotherapy of Cancer Biomarkers Committee convened to identify important advances in biomarker technologies and to address advances in biomarker discovery using multiplexed IHC and immunofluorescence, their coupling to single-cell transcriptomics, along with mass spectrometry-based quantitative and spatially resolved proteomics imaging technologies. We summarize key metrics obtained, ease of interpretation, limitations and dependencies, technical improvements, and outward comparisons of these technologies. By highlighting the most interesting recent data contributed by these technologies and by providing ways to improve their outputs, we hope to guide correlative research directions and assist in their evolution toward becoming clinically useful in immuno-oncology.

Evaluation of combined workflows for multimodal mass spectrometry imaging of elements and lipids from the same tissue section

The wide range of mass spectrometry imaging (MSI) technologies enables the spatial distributions of many analyte classes to be investigated. However, as each approach is best suited to certain analytes, combinations of different MSI techniques are increasingly being explored to obtain more chemical information from a sample. In many cases, performing a sequential analysis of the same tissue section is ideal to enable a direct correlation of multimodal data. In this work, we explored different workflows that allow sequential lipid and elemental imaging on the same tissue section using atmospheric pressure laser desorption/ionisation-plasma post-ionisation-MSI (AP-MALDI-PPI-MSI) and laser ablation-inductively coupled plasma-MSI (LA-ICP-MSI), respectively. It is found that performing lipid imaging first using matrix-coated samples, followed by elemental imaging on matrix-coated samples, provides high-quality MSI datasets for both lipids and elements, with the resulting distributions being similar to those obtained when each is performed in isolation. The effect of matrix removal prior to elemental imaging, and of performing elemental imaging first were also investigated but found to generally yield lower quality elemental imaging data but comparable lipid imaging data. Finally, we used the ability to acquire both elemental and lipid imaging data from the same section to investigate the spatial correlations between different lipids (including ceramides, phosphatidylethanolamine, and hexosylceramides) and elements within mouse brain tissue.

Graphene Oxide/TiO2 Nanocomposite-Assisted Two-Step Ambient Liquid Extraction Mass Spectrometry Imaging for Comprehensively Enhancing Lipid Coverage in Spatial Lipidomics

It is challenging to have comprehensive spatial lipidomic analysis by mass spectrometry imaging (MSI) due to the strong ion suppression and peak interference from high-abundance polar lipids to low-abundance poorly ionizable lipids. In this work, we proposed a new MSI approach via ambient liquid extraction techniques assisted by a new mixed-mode adsorptive material, graphene oxide (GO)/TiO2 nanocomposite. The material combines chelation affinity from TiO2 and hydrophobic interaction from GO. By finely tuning the adsorption solvent as 9% H2O-6% ammonia-85% methanol/acetonitrile (1:1, v/v), simultaneous enrichment of poorly ionizable glycolipids and glycerides with separation from high-abundance phospholipids was achieved on the material. In 10 mg/mL glucose-6% ammonia-94% methanol, all of the adsorbed glycolipids and glycerides could be desorbed from the material efficiently. Then, GO/TiO2 nanocomposite was coated onto the sample plate for thaw-mounting the tissue section, and ambient liquid extraction probe was used to have pixel-to-pixel desorption of the lipids on the section in two steps with the above two solvents. The results show that most of the phospholipids were imaged in the first step MSI, and glycolipids and glycerides were selectively imaged in the second step MSI, largely reducing ion suppression and peak interference. Compared with direct ambient liquid extraction MSI, much more glycolipid species (22 vs 9), glyceride species (10 vs 5), phosphatidylethanolamines (11 vs 3), and lysophospholipids (12 vs 2) were detected via GO/TiO2 nanocomposite-assisted two-step MSI. The ion images of most lipids show much higher signals and imaging quality with the new method than with the traditional method. Thus, comprehensive enhancement of lipid coverage in MSI by on-tissue separation was achieved here for the first time, providing more information about spatial lipidomics studies.

Microbial Metabolomics' Latest SICRIT: Soft Ionization by Chemical Reaction In-Transfer Mass Spectrometry

Microbial metabolomics studies are a common approach for identifying microbial strains that have a capacity to produce new chemistries both in vitro and in situ. A limitation to applying microbial metabolomics to the discovery of new chemical entities is the rediscovery of known compounds, or "known unknowns." One factor contributing to this rediscovery is that the majority of laboratories use one ionization source─electrospray ionization (ESI)─to conduct metabolomics studies. Although ESI is an efficient, widely adopted ionization method, its widespread use may contribute to the reidentification of known metabolites. Here, we present the use of a dielectric barrier discharge ionization (DBDI) for microbial metabolomics applications through the use of soft ionization chemical reaction in-transfer (SICRIT). Additionally, we compared SICRIT to ESI using two different Vibrio species: Vibrio fischeri, a symbiotic marine bacterium, and Vibrio cholerae, a pathogenic bacterium. Overall, we found that the SICRIT source ionizes a different set of metabolites than ESI, and it has the ability to ionize lipids more efficiently than ESI in the positive mode. This work highlights the value of using more than one ionization source for the detection of metabolites.

MSIGen: An Open-Source Python Package for Processing and Visualizing Mass Spectrometry Imaging Data

Mass spectrometry imaging (MSI) provides information about the spatial localization of molecules in complex samples with high sensitivity and molecular selectivity. Although point-wise data acquisition, in which mass spectra are acquired at predefined points in a grid pattern, is common in MSI, several MSI techniques use line-wise data acquisition. In line-wise mode, the imaged surface is continuously sampled along consecutive parallel lines and MSI data are acquired as a collection of line scans across the sample. Furthermore, aside from the standard imaging mode in which full mass spectra are acquired, other acquisition modes have been developed to enhance molecular specificity, enable separation of isobaric and isomeric species, and improve sensitivity to facilitate the imaging of low abundance species. These methods, including MS/MS-MSI in both MS2 and MS3 modes, multiple-reaction monitoring (MRM)-MSI, and ion mobility spectrometry (IMS)-MSI have all demonstrated their capabilities, but their broader implementation is limited by the existing MSI analysis software. Here, we present MSIGen, an open-source Python package for the visualization of MSI experiments performed in line-wise acquisition mode containing MS1, MS2, MRM, and IMS data, which is available at https://github.com/LabLaskin/MSIGen. The package supports multiple vendor-specific and open-source data formats and contains tools for targeted extraction of ion images, normalization, and exportation as images, arrays, or publication-style images. MSIGen offers multiple interfaces, allowing for accessibility and easy integration with other workflows. Considering its support for a wide variety of MSI imaging modes and vendor formats, MSIGen is a valuable tool for the visualization and analysis of MSI data.

Lipids associated with atherosclerotic plaque instability revealed by mass spectrometry imaging of human carotid arteries

BACKGROUND AND AIMS

Lipids constitute one of the main components of atherosclerosis lesions and are the mediators of many mechanisms involved in plaque progression and stability. Here we tested the hypothesis that lipids known to be involved in plaque development exhibited associations with plaque vulnerability. We used spatial lipidomics to overcome plaque heterogeneity and to compare lipids from specific regions of symptomatic and asymptomatic human carotid atherosclerotic plaques.

METHODS

Carotid atherosclerotic plaques were collected from symptomatic and asymptomatic patients. Plaque lipids were analyzed with the spatial lipidomics technique matrix-assisted laser desorption/ionization mass spectrometry imaging, and histology and immunofluorescence were used to segment the plaques into histomolecularly distinct regions.

RESULTS

Macrophage-rich regions from symptomatic lesions were found to be enriched in phosphatidylcholines (synthesized to counteract excess free cholesterol), while the same region from asymptomatic plaques were enriched in polyunsaturated cholesteryl esters and triglycerides, characteristic of functional lipid droplets. Vascular smooth muscle cells (VSMCs) of the fibrous cap of asymptomatic plaques were enriched in lysophosphatidylcholines and cholesteryl esters, know to promote VSMC proliferation and migration, crucial for the buildup of the fibrous cap stabilizing the plaque.

CONCLUSIONS

The investigation of the region-specific lipid composition of symptomatic and asymptomatic human atherosclerotic plaques revealed specific lipid markers of plaque outcome, which could be linked to known biological characteristics of stable plaques.

Microbial metabolomics' latest SICRIT: Soft ionization by Chemical Reaction in-Transfer mass spectrometry

Microbial metabolomics studies are a common approach to identifying microbial strains that have a capacity to produce new chemistries both in vitro and in situ. A limitation to applying microbial metabolomics to the discovery of new chemical entities is the rediscovery of known compounds, or "known unknowns." One contributing factor to this rediscovery is the majority of laboratories use one ionization source-electrospray ionization (ESI)-to conduct metabolomics studies. Although ESI is an efficient, widely adopted ionization method, its widespread use may contribute to the re-identification of known metabolites. Here, we present the use of a dielectric barrier discharge ionization (DBDI) for microbial metabolomics applications through the use of soft ionization chemical reaction in-transfer (SICRIT). Additionally, we compared SICRIT to ESI using two different Vibrio species-Vibrio fischeri, a symbiotic marine bacterium, and Vibrio cholerae, a pathogenic bacterium. Overall, we found that the SICRIT source ionizes a different set of metabolites than ESI, and it has the ability to ionize lipids more efficiently than ESI in positive mode. This work highlights the value of using more than one ionization source for the detection of metabolites.

Resolving multi-image spatial lipidomic responses to inhaled toxicants by machine learning

Regional responses to inhaled toxicants are essential to understand the pathogenesis of lung disease under exposure to air pollution. We evaluated the effect of combined allergen sensitization and ozone exposure on eliciting spatial differences in lipid distribution in the mouse lung that may contribute to ozone-induced exacerbations in asthma. Lung lobes from male and female BALB/c mice were cryosectioned and acquired by high resolution mass spectrometry imaging (MSI). Processed MSI peak annotations were validated by LC-MS/MS data from scraped tissue slides and microdissected lung tissue. Images were normalized and segmented into clusters. Interestingly, segmented clusters overlapped with stained serial tissue sections, enabling statistical analysis across biological replicates for morphologically relevant lung regions. Spatially distinct lipids had higher overall degree of unsaturated fatty acids in distal lung regions compared to proximal regions. Furthermore, the airway and alveolar epithelium exhibited significantly decreased sphingolipid and glycerophospholipid abundance in females, but not in males. We demonstrate the potential role of lipid saturation in healthy lung function and highlight sex differences in regional lung lipid distribution following ozone exposure. Our study provides a framework for future MSI experiments capable of relative quantification across biological replicates and expansion to multiple sample types, including human tissue.

High-Specificity Imaging Mass Spectrometry

Imaging mass spectrometry (IMS) enables highly multiplexed, untargeted tissue mapping for a broad range of molecular classes, facilitating in situ biological discovery. Yet, challenges persist in molecular specificity, which is the ability to discern one molecule from another, and spatial specificity, which is the ability to link untargeted imaging data to specific tissue features. Instrumental developments have dramatically improved IMS spatial resolution, allowing molecular observations to be more readily associated with distinct tissue features across spatial scales, ranging from larger anatomical regions to single cells. High-performance mass analyzers and systems integrating ion mobility technologies are also becoming more prevalent, further improving molecular coverage and the ability to discern chemical identity. This review provides an overview of recent advancements in high-specificity IMS that are providing critical biological context to untargeted molecular imaging, enabling integrated analyses, and addressing advanced biomedical research applications.

High-resolution ion mobility separations coupled to mass spectrometry: What's next?

Herein, I provide a personal perspective on high-resolution multipass ion mobility spectrometry-mass spectrometry (IMS-MS), with a specific emphasis on cyclic (cIMS) and structures for lossless ion manipulations (SLIM IMS)-based separations. My overarching goal for this perspective was to detail what I believe will be the key important areas in which IMS-MS will help shape the bioanalytical community and especially omics-based research.

Imaging with mass spectrometry: Which ionization technique is best?

The use of mass spectrometry (MS) to acquire molecular images of biological tissues and other substrates has developed into an indispensable analytical tool over the past 25 years. Imaging mass spectrometry technologies are widely used today to study the in situ spatial distributions for a variety of analytes. Early MS images were acquired using secondary ion mass spectrometry and matrix-assisted laser desorption/ionization. Researchers have also designed and developed other ionization techniques in recent years to probe surfaces and generate MS images, including desorption electrospray ionization (DESI), nanoDESI, laser ablation electrospray ionization, and infrared matrix-assisted laser desorption electrospray ionization. Investigators now have a plethora of ionization techniques to select from when performing imaging mass spectrometry experiments. This brief perspective will highlight the utility and relative figures of merit of these techniques within the context of their use in imaging mass spectrometry.

A Machine Learning-Driven Comparison of Ion Images Obtained by MALDI and MALDI-2 Mass Spectrometry Imaging

Matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) enables label-free imaging of biomolecules in biological tissues. However, many species remain undetected due to their poor ionization efficiencies. MALDI-2 (laser-induced post-ionization) is the most widely used post-ionization method for improving analyte ionization efficiencies. Mass spectra acquired using MALDI-2 constitute a combination of ions generated by both MALDI and MALDI-2 processes. Until now, no studies have focused on a detailed comparison between the ion images (as opposed to the generated m/z values) produced by MALDI and MALDI-2 for mass spectrometry imaging (MSI) experiments. Herein, we investigated the ion images produced by both MALDI and MALDI-2 on the same tissue section using correlation analysis (to explore similarities in ion images for ions common to both MALDI and MALDI-2) and a deep learning approach. For the latter, we used an analytical workflow based on the Xception convolutional neural network, which was originally trained for human-like natural image classification but which we adapted to elucidate similarities and differences in ion images obtained using the two MSI techniques. Correlation analysis demonstrated that common ions yielded similar spatial distributions with low-correlation species explained by either poor signal intensity in MALDI or the generation of additional unresolved signals using MALDI-2. Using the Xception-based method, we identified many regions in the t-SNE space of spatially similar ion images containing MALDI and MALDI-2-related signals. More notably, the method revealed distinct regions containing only MALDI-2 ion images with unique spatial distributions that were not observed using MALDI. These data explicitly demonstrate the ability of MALDI-2 to reveal molecular features and patterns as well as histological regions of interest that are not visible when using conventional MALDI.

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.

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.

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

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

Solvent-Assisted Laser Desorption Flexible Microtube Plasma Mass Spectrometry for Direct Analysis of Dried Samples on Paper

The present study investigated the potential for solvent-assisted laser desorption coupled with flexible microtube plasma ionization mass spectrometry (SALD-FμTP-MS) as a rapid analytical technique for direct analysis of surface-deposited samples. Paper was used as the demonstrative substrate, and an infrared hand-held laser was employed for sample desorption, aiming to explore cost-effective sampling and analysis methods. SALD-FμTP-MS offers several advantages, particularly for biofluid analysis, including affordability, the ability to analyze low sample volumes (<10 μL), expanded chemical coverage, sample and substrate stability, and in situ analysis and high throughput potential. The optimization process involved exploring the use of viscous solvents with high boiling points as liquid matrices. This approach aimed to enhance desorption and ionization efficiencies. Ethylene glycol (EG) was identified as a suitable solvent, which not only improved sensitivity but also ensured substrate stability during analysis. Furthermore, the addition of cosolvents such as acetonitrile/water (1:1) and ethyl acetate further enhanced sensitivity and reproducibility for a standard solution containing amphetamine, imazalil, and cholesterol. Optimized conditions for reproducible and sensitive analysis were determined as 1000 ms of laser exposure time using a 1 μL solvent mixture of 60% EG and 40% acetonitrile (ACN)/water (1:1). A mixture of 60% EG and 40% ACN/water (1:1) resulted in signal enhancements and relative standard deviations of 12, 20, and 13% for the evaluated standards, respectively. The applicability of SALD-FμTP-MS was further evaluated by successfully analyzing food, water, and biological samples, highlighting the potential of SALD-FμTP-MS analysis, particularly for thermolabile and polarity diverse compounds.

Evaluating Software Tools for Lipid Identification from Ion Mobility Spectrometry-Mass Spectrometry Lipidomics Data

The unambiguous identification of lipids is a critical component of lipidomics studies and greatly impacts the interpretation and significance of analyses as well as the ultimate biological understandings derived from measurements. The level of structural detail that is available for lipid identifications is largely determined by the analytical platform being used. Mass spectrometry (MS) coupled with liquid chromatography (LC) is the predominant combination of analytical techniques used for lipidomics studies, and these methods can provide fairly detailed lipid identification. More recently, ion mobility spectrometry (IMS) has begun to see greater adoption in lipidomics studies thanks to the additional dimension of separation that it provides and the added structural information that can support lipid identification. At present, relatively few software tools are available for IMS-MS lipidomics data analysis, which reflects the still limited adoption of IMS as well as the limited software support. This fact is even more pronounced for isomer identifications, such as the determination of double bond positions or integration with MS-based imaging. In this review, we survey the landscape of software tools that are available for the analysis of IMS-MS-based lipidomics data and we evaluate lipid identifications produced by these tools using open-access data sourced from the peer-reviewed lipidomics literature.