Mass Spectrometry Imaging Techniques Research Articles

A Comprehensive Spatially Resolved Metabolomics Dataset for Lampreys

As one of the two most ancient groups of extant vertebrates, lamprey has become an important model organism in various fields of biology. In this paper, we present a comprehensive tissue-wide spatial metabolomics dataset for lampreys, where 14 distinct tissues were analyzed using liquid chromatography-mass spectrometry (LC-MS) in both positive and negative ion modes. The dataset has been fully validated using internal standard and pooled quality control samples and is readily accessible at the UCSD Metabolomics Workbench. This dataset serves as a valuable resource for researchers using lampreys as a model organism. Additionally, it acts as a benchmark metabolomics dataset for evaluating new algorithms and software tools and comparing them with previously published results. A lamprey spatial metabolomics database is also provided to support studies utilizing lampreys as an animal model, and to complement and validate other spatial metabolomics studies on lampreys conducted with mass spectrometry imaging or other techniques.

Determining the N-Glycan and Collagen/Extracellular Matrix Protein Compositions in a Novel Outcome Cohort of Prostate Cancer Tissue Microarrays Using MALDI-MSI.

Prostate cancer is a significant health concern, with metastasis posing major clinical challenges and resulting in poor patient outcome. Despite screening and treatment advances, a critical need for novel biomarkers to predict prostate cancer progression at the time of prostatectomy persists. Here, we assessed aberrant N-glycosylation patterns and alterations in extracellular matrix proteins as potential biomarkers of predicting prostate cancer severity in a unique patient outcome cohort. Tissue microarray slides were assembled from primary prostatectomy specimens that were categorized into "no evidence of disease (NED)" and "metastasis (MET)" designations based on >5-year disease progression outcomes. Serial mass spectrometry imaging techniques were performed to analyze N-glycans and extracellular matrix (ECM) components in formalin-fixed paraffin-embedded cores. The results revealed a significant upregulation of bisecting and multi-antennary core fucosylated N-glycans in MET tissues when compared to NED tissues. Alterations in ECM composition in both NED and MET cohorts were observed, particularly in collagen species and the amount of hydroxyproline content. Results suggest a coordinated alteration of ECM protein and glycosylation content in prostate cancer tissues can be predictive for post-prostatectomy disease progression.

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.

Advances in mass spectrometry imaging for plant metabolomics-Expanding the analytical toolbox.

Mass spectrometry imaging (MSI) has become increasingly popular in plant science due to its ability to characterize complex chemical, spatial, and temporal aspects of plant metabolism. Over the past decade, as the emerging and unique features of various MSI techniques have continued to support new discoveries in studies of plant metabolism closely associated with various aspects of plant function and physiology, spatial metabolomics based on MSI techniques has positioned it at the forefront of plant metabolic studies, providing the opportunity for far higher resolution than was previously available. Despite these efforts, profound challenges at the levels of spatial resolution, sensitivity, quantitative ability, chemical confidence, isomer discrimination, and spatial multi-omics integration, undoubtedly remain. In this Perspective, we provide a contemporary overview of the emergent MSI techniques widely used in the plant sciences, with particular emphasis on recent advances in methodological breakthroughs. Having established the detailed context of MSI, we outline both the golden opportunities and key challenges currently facing plant metabolomics, presenting our vision as to how the enormous potential of MSI technologies will contribute to progress in plant science in the coming years.

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

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

Desorption Electrospray Ionization Mass Spectrometry Imaging Techniques Depict a Reprogramming of Energy and Purine Metabolism in the Core Brain Regions of Chronic Social Defeat Stress Mice.

Depression is associated with pathological changes and metabolic abnormalities in multiple brain regions. The simultaneous comprehensive and in situ detection of endogenous molecules in all brain regions is essential for a comprehensive understanding of depression pathology, which is described in this paper. A method based on desorption electrospray ionization mass spectrometry imaging (DESI-MSI) technology was developed to classify mouse brain regions using characteristic lipid molecules and to detect the metabolites in mouse brain tissue samples simultaneously. The results showed that characteristic lipid molecules can be used to clearly distinguish each subdivision of the mouse brain, and the accuracy of this method is higher than that of the conventional staining method. The cerebellar cortex, medial prefrontal cortex, hippocampus, striatum, nucleus accumbens-core, and nucleus accumbens-shell exhibited the most significant differences in the chronic social defeat stress model. An analysis of metabolic pathways revealed that 13 kinds of molecules related to energy metabolism and purine metabolism exhibited significant changes. A DESI-MSI method was developed for the detection of pathological brain sections. We found, for the first time, that there are characteristic changes in the energy metabolism in the cortex and purine metabolism in the striatum, which is highly important for obtaining a deeper and more comprehensive understanding of the pathology of depression and discovering regulatory targets.

From molecules to visuals: Empowering drug discovery and development with mass spectrometry imaging.

Over the past three decades, mass spectrometry imaging (MSI) has emerged as a valuable tool for the spatial localization of drugs and metabolites directly from tissue surfaces without the need for labels. MSI offers molecular specificity, making it increasingly popular in the pharmaceutical industry compared to conventional imaging techniques like quantitative whole-body autoradiography (QWBA) and immunohistochemistry, which are unable to distinguish parent drugs from metabolites. Across the industry, there has been a consistent uptake in the utilization of MSI to investigate drug and metabolite distribution patterns, and the integration of MSI with omics technologies in preclinical investigations. To continue the further adoption of MSI in drug discovery and development, we believe there are two key areas that need to be addressed. First, there is a need for accurate quantification of analytes from MSI distribution studies. Second, there is a need for increased interactions with regulatory agencies for guidance on the utility and incorporation of MSI techniques in regulatory filings. Ongoing efforts are being made to address these areas, and it is hoped that MSI will gain broader utilization within the industry, thereby becoming a critical ingredient in driving drug discovery and development.

The discovery of regional neurotoxicity-associated metabolic alterations induced by carbon quantum dots in brain of mice using a spatial metabolomics analysis

BackgroundRecently, carbon quantum dots (CQDs) have been widely used in various fields, especially in the diagnosis and therapy of neurological disorders, due to their excellent prospects. However, the associated inevitable exposure of CQDs to the environment and the public could have serious severe consequences limiting their safe application and sustainable development.ResultsIn this study, we found that intranasal treatment of 5 mg/kg BW (20 µL/nose of 0.5 mg/mL) CQDs affected the distribution of multiple metabolites and associated pathways in the brain of mice through the airflow-assisted desorption electrospray ionization mass spectrometry imaging (AFADESI-MSI) technique, which proved effective in discovery has proven to be significantly alerted and research into tissue-specific toxic biomarkers and molecular toxicity analysis. The neurotoxic biomarkers of CQDs identified by MSI analysis mainly contained aminos, lipids and lipid-like molecules which are involved in arginine and proline metabolism, biosynthesis of unsaturated fatty acids, and glutamine and glutamate metabolism, etc. as well as related metabolic enzymes. The levels or expressions of these metabolites and enzymes changed by CQDs in different brain regions would induce neuroinflammation, organelle damage, oxidative stress and multiple programmed cell deaths (PCDs), leading to neurodegeneration, such as Parkinson’s disease-like symptoms. This study enlightened risk assessments and interventions of QD-type or carbon-based nanoparticles on the nervous system based on toxic biomarkers regarding region-specific profiling of altered metabolic signatures.ConclusionThese findings provide information to advance knowledge of neurotoxic effects of CQDs and guide their further safety evaluation.

Lipid-related ion suppression on the herbicide atrazine in earthworm samples in ToF-SIMS and matrix-assisted laser desorption ionization mass spectrometry imaging and the role of gas-phase basicity.

In mass spectrometry imaging (MSI), ion suppression can lead to a misinterpretation of results. Particularly phospholipids, most of which exhibit high gas-phase basicity (GB), are known to suppress the detection of metabolites and drugs. This study was initiated by the observation that the signal of an herbicide, i.e., atrazine, was suppressed in MSI investigations of earthworm tissue sections. Herbicide accumulation in earthworms was investigated by time-of-flight secondary ion mass spectrometry and matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI). Additionally, earthworm tissue sections without accumulation of atrazine but with a homogeneous spray deposition of the herbicide were analyzed to highlight region-specific ion suppression. Furthermore, the relationship of signal intensity and GB in binary mixtures of lipids, amino acids, and atrazine was investigated in both MSI techniques. The GB of atrazine was determined experimentally through a linear plot of the obtained intensity ratios of the binary amino acid mixtures, as well as theoretically. The GBs values for atrazine of 896 and 906 kJ/mol in ToF-SIMS and 933 and 987 kJ/mol in MALDI-MSI were determined experimentally and that of 913 kJ/mol by quantum mechanical calculations. Compared with the GB of a major lipid component, phosphatidylcholine (GBPC = 1044.7 kJ/mol), atrazine's experimentally and computationally determined GBs in this work are significantly lower, making it prone to ion suppression in biological samples containing polar lipids.

Mass Spectrometry Imaging and Histology for the Analysis of Budding Intestinal Organoids.

Three-dimensional (3D) organoids have been at the forefront of regenerative medicine and cancer biology fields for the past decade. However, the fragile nature of organoids makes their spatial analysis challenging due to their budding structures and composition of single layer of cells. The standard sample preparation approaches can collapse the organoid morphology. Therefore, in this study, we evaluated several approaches to optimize a method compatible with both mass spectrometry imaging (MSI) and immunohistological techniques. Murine intestinal organoids were used to evaluate embedding in gelatin, carboxymethylcellulose (CMC)-gelatin-CMC-sucrose, or hydroxypropyl methylcellulose (HPMC) and polyvinylpyrrolidone (PVP) solutions. Organoids were assessed with and without aldehyde fixation and analyzed for lipid distributions by MSI coupled with hematoxylin and eosin (H&E) staining and immunofluorescence (IF) in consecutive sections from the same sample. While chemical fixation preserves morphology for better histological outcomes, it can lead to suppression of the matrix-assisted laser desorption/ionization (MALDI) lipid signal. By contrast, leaving organoid samples unfixed enhanced MALDI lipid signal. The method that performed best for both MALDI and histological analysis was embedding unfixed samples in HPMC and PVP. This approach allowed assessment of cell proliferation by Ki67 while also identifying putative phosphatidylethanolamine (PE(18:0/18:1)), which was confirmed further by tandem MS approaches. Overall, these protocols will be amenable to multiplexing imaging mass spectrometry analysis with several histological assessments and help advance our understanding of the biological processes that take place in district subsets of cells in budding organoid structures.

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.

DeepION: A Deep Learning-Based Low-Dimensional Representation Model of Ion Images for Mass Spectrometry Imaging.

Mass spectrometry imaging (MSI) is a high-throughput imaging technique capable of the qualitative and quantitative in situ detection of thousands of ions in biological samples. Ion image representation is a technique that produces a low-dimensional vector embedded with significant spectral and spatial information on an ion image, which further facilitates the distance-based similarity measurement for the identification of colocalized ions. However, given the low signal-to-noise ratios inherent in MSI data coupled with the scarcity of annotated data sets, achieving an effective ion image representation for each ion image remains a challenge. In this study, we propose DeepION, a novel deep learning-based method designed specifically for ion image representation, which is applied to the identification of colocalized ions and isotope ions. In DeepION, contrastive learning is introduced to ensure that the model can generate the ion image representation in a self-supervised manner without manual annotation. Since data augmentation is a crucial step in contrastive learning, a unique data augmentation strategy is designed by considering the characteristics of MSI data, such as the Poisson distribution of ion abundance and a random pattern of missing values, to generate plentiful ion image pairs for DeepION model training. Experimental results of rat brain tissue MSI show that DeepION outperforms other methods for both colocalized ion and isotope ion identification, demonstrating the effectiveness of ion image representation. The proposed model could serve as a crucial tool in the biomarker discovery and drug development of the MSI technique.

Effect of optimal rapid thermal alloying of ohmic contacts to microwave pseudomorphic HEMTs

Gallium Arsenide (GaAs) pseudomorphic High Electron Mobility Transistors (pHEMTs) are currently used as active devices in manufacturing Monolithic Microwave Integrated Circuits (MMICs) for microwave and millimetre wave applications like Transmit-Receive (T/R) modules in Radars and communication. Low contact resistances (Rc) of pHEMT devices are necessary to obtain low access resistances to the device channel, for enhancement of transconductance and cut-off frequency. Devices formed by optimal and suboptimal conditions of rapid thermal alloying by infra-red optical radiation are investigated using cross-section transmission electron microscopy (XTEM), backside secondary ion mass spectrometry (backside SIMS) and thermal imaging techniques to understand the role of optimal alloy formation and its role in enhancement of device parameters.

Mass spectrometry imaging in plants, microbes, and food: a review.

Plant health, which affects the nutritional quality and safety of derivative food products, is influenced by symbiotic interactions with microorganisms. These interactions influence the local molecular profile at the tissue level. Therefore, studying the distribution of molecules within plants, microbes, and plant-based food is crucial to assess plant health, ensure the safety and quality of the agricultural products that become part of our food supply, and plan agricultural management practices. Within this framework, the molecular distribution within plant-based samples can be visualized with mass spectrometry imaging (MSI). This review describes key MSI methodologies, highlighting the role they play in unraveling the localization of metabolites, lipids, proteins, pigments, and elemental components across plants, microbes, and food products. Furthermore, investigations that involve multimodal molecular imaging approaches combining MSI with other imaging techniques are described. The advantages and limitations of the different MSI techniques that influence their applicability in diverse agro-food studies are described to enable informed choices for tailored analyses. For example, some MSI technologies involve meticulous sample preparation while others compromise spatial resolution to gain throughput. Key parameters such as sensitivity, ionization bias and fragmentation, reference database and compound class specificity are described and discussed in this review. With the ongoing refinements in instrumentation, data analysis, and integration of complementary techniques, MSI deepens our insight into the molecular biology of the agricultural ecosystem. This in turn empowers the quest for sustainable and productive agricultural practices.

Probing the stability of metal-organic frameworks by structure-responsive mass spectrometry imaging.

The widespread application of metal-organic frameworks (MOFs) is seriously hindered by their structural instability and it is still very challenging to probe the stability of MOFs during application by current techniques. Here, we report a novel structure-responsive mass spectrometry (SRMS) imaging technique to probe the stability of MOFs. We discovered that intact CuBTC (as a model of MOFs) could generate the characteristic peaks of organic ligands and carbon cluster anions in laser desorption/ionization mass spectrometry, but these peaks were significantly changed when the structure of CuBTC was dissociated, thus enabling a label-free probing of the stability. Furthermore, SRMS can be performed in imaging mode to visualize the degradation kinetics and reveal the spatial heterogeneity of the stability of CuBTC. This technique was successfully applied in different application scenarios (in water, moist air, and CO2) and also validated with different MOFs. It thus provides a versatile new tool for better design and application of environment-sensitive materials.

Mass spectrometry imaging as a promising analytical technique for herbal medicines: an updated review.

Herbal medicines (HMs) have long played a pivotal role in preventing and treating various human diseases and have been studied widely. However, the complexities present in HM metabolites and their unclear mechanisms of action have posed significant challenges in the modernization of traditional Chinese medicine (TCM). Over the past two decades, mass spectrometry imaging (MSI) has garnered increasing attention as a robust analytical technique that enables the simultaneous execution of qualitative, quantitative, and localization analyses without complex sample pretreatment. With advances in technical solutions, MSI has been extensively applied in the field of HMs. MSI, a label-free ion imaging technique can comprehensively map the spatial distribution of HM metabolites in plant native tissues, thereby facilitating the effective quality control of HMs. Furthermore, the spatial dimension information of small molecule endogenous metabolites within animal tissues provided by MSI can also serve as a supplement to uncover pharmacological and toxicological mechanisms of HMs. In the review, we provide an overview of the three most common MSI techniques. In addition, representative applications in HM are highlighted. Finally, we discuss the current challenges and propose several potential solutions. We hope that the summary of recent findings will contribute to the application of MSI in exploring metabolites and mechanisms of action of HMs.

Database-Driven Spatially Resolved Lipidomics Highlights Heterogeneous Metabolic Alterations in Type 2 Diabetic Mice.

Spatially resolved lipidomics is pivotal for detecting and interpreting lipidomes within spatial contexts using the mass spectrometry imaging (MSI) technique. However, comprehensive and efficient lipid identification in MSI remains challenging. Herein, we introduce a high-coverage, database-driven approach combined with air-flow-assisted desorption electrospray ionization (AFADESI)-MSI to generate spatial lipid profiles across whole-body mice. Using liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS), we identified 2868 unique lipids in the serum and various organs of mice. Subsequently, we systematically evaluated the distinct ionization properties of the lipids between LC-MS and MSI and created a detailed MSI database containing 14 123 ions. This method enabled the visualization of aberrant fatty acid and phospholipid metabolism across organs in a diabetic mouse model. As a powerful extension incorporated into the MSIannotator tool, our strategy facilitates the rapid and accurate annotation of lipids, providing new research avenues for probing spatially resolved heterogeneous metabolic changes in response to diseases.

Improving the Signal Intensity of Cryosections Using a Conductive Adhesive Film in Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging.

The matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) technique was used to obtain the molecular images of cryosections without labeling. Although MALDI-MSI has been widely used to detect small molecules from biological tissues, issues remain due to the technical process of cryosectioning and limited mass spectrometry parameters. The use of a conductive adhesive film is a unique method to obtain high-quality sections from cutting tissue, such as bone, muscle, adipose tissue, and whole body of mice or fish, and we have reported the utilization of the film for MALDI-MSI in previous. However, some signal of the small molecules using the conductive adhesive films was still lower than on the indium tin oxide (ITO) glass slide. Here, the sample preparation and analytical conditions for MALDI-MSI using an advanced conductive adhesive film were optimized to obtain strong signals from whole mice heads. The effects of tissue thickness and laser ionization power on signal intensity were verified using MALDI-MSI. The phospholipid signal intensity was measured for samples with three tissue thicknesses (5, 10, and 20 μm); compared to the signals from the samples on the ITO glass slides, the signals with conductive adhesive films exhibited significantly higher intensities when a laser with a higher range of power was used to ionize the small molecules. Thus, the technique using the advanced conductive adhesive film showed an improvement in MALDI-MSI analysis.

Imaging of N-Linked Glycans in Biological Tissue Sections Using Nanospray Desorption Electrospray Ionization (nano-DESI) Mass Spectrometry.

N-linked glycans are complex biomolecules vital to cellular functions that have been linked to a wide range of pathological conditions. Mass spectrometry imaging (MSI) has been used to study the localization of N-linked glycans in cells and tissues. However, their structural diversity presents a challenge for MSI techniques, which stimulates the development of new approaches. In this study, we demonstrate for the first time spatial mapping of N-linked glycans in biological tissues using nanospray desorption electrospray ionization mass spectrometry imaging (nano-DESI MSI). Nano-DESI MSI is an ambient ionization technique that has been previously used for imaging of metabolites, lipids, and proteins in biological tissue samples without special sample pretreatment. N-linked glycans are released from glycoproteins using an established enzymatic digestion with peptide N-glycosidase F, and their spatial localization is examined using nano-DESI MSI. We demonstrate imaging of N-linked glycans in formalin-fixed paraffin-embedded human hepatocellular carcinoma and human prostate tissues in both positive and negative ionization modes. We examine the localization of 38 N-linked glycans consisting of high mannose, hybrid fucosylated, and sialyated glycans. We demonstrate that negative mode nano-DESI MSI is well-suited for imaging of underivatized sialylated N-linked glycans. On-tissue MS/MS of different adducts of N-linked glycans proves advantageous for elucidation of the glycan sequence. This study demonstrates the applicability of liquid extraction techniques for spatial mapping of N-linked glycans in biological samples, providing an additional tool for glycobiology research.

Mass Spectrometry Imaging of Biomaterials.

The science related to biomaterials and tissue engineering accounts for a growing part of our knowledge. Surface modifications of biomaterials, their performance in vitro, and the interaction between them and surrounding tissues are gaining more and more attention. It is because we are interested in finding sophisticated materials that help us to treat or mitigate different disorders. Therefore, efficient methods for surface analysis are needed. Several methods are routinely applied to characterize the physical and chemical properties of the biomaterial surface. Mass Spectrometry Imaging (MSI) techniques are able to measure the information about molecular composition simultaneously from biomaterial and adjacent tissue. That is why it can answer the questions connected with biomaterial characteristics and their biological influence. Moreover, this kind of analysis does not demand any antibodies or dyes that may influence the studied items. It means that we can correlate surface chemistry with a biological response without any modification that could distort the image. In our review, we presented examples of biomaterials analyzed by MSI techniques to indicate the utility of SIMS, MALDI, and DESI-three major ones in the field of biomaterials applications. Examples include biomaterials used to treat vascular system diseases, bone implants with the effects of implanted material on adjacent tissues, nanofibers and membranes monitored by mass spectrometry-related techniques, analyses of drug-eluting long-acting parenteral (LAPs) implants and microspheres where MSI serves as a quality control system.