Matrix-assisted laser desorption/ionization mass spectrometry imaging as a new tool for molecular histopathology in epilepsy surgery.

OBJECTIVE: Epilepsy surgery is a treatment option for patients with seizures that do not respond to pharmacotherapy. The histopathological characterization of the resected tissue has an important prognostic value to define postoperative seizure outcome in these patients. However, the diagnostic classification process based on microscopic assessment remains challenging, particularly in the case of focal cortical dysplasia (FCD). Imaging mass spectrometry is a spatial omics technique that could improve tissue phenotyping and patient stratification by investigating hundreds of biomolecules within a single tissue sample, without the need for target-specific reagents. METHODS: An in situ proteomic technique called matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) is here investigated as a potential new tool to expand conventional diagnosis on standard paraffin brain tissue sections. Unsupervised and region of interest-based MALDI-MSI analyses of sections from 10 FCD type IIb (FCDIIb) cases were performed, and the results were validated by immunohistochemistry. RESULTS: MALDI-MSI identified distinct histopathological features and the boundaries of the dysplastic lesion. The capability to visualize the spatial distribution of well-known diagnostic markers enabling multiplex measurements on single tissue sections was demonstrated. Finally, a fingerprint list of potential discriminant peptides that distinguish FCD core from peri-FCD tissue was generated. SIGNIFICANCE: This is the first study that explores the potential application of MALDI-MSI in epilepsy postsurgery fixed tissue, by utilizing the well-characterized FCDIIb features as a model. Extending these preliminary analyses to a larger cohort of patients will generate spectral libraries of molecular signatures that discriminate tissue features and will contribute to patient phenotyping.

Tissue derivatization for visualizing lactate and pyruvate in mouse testis tissues using matrix-assisted laser desorption/ionization-mass spectrometry imaging.

Pyruvate and lactate are the final metabolites of the glycolytic system that are formed under oxygen-rich and anaerobic conditions, respectively. They play an important role in energy metabolism. Obtaining a tissue distribution image of pyruvate and lactate holds great significance in molecular biology because the glycolytic system plays an essential role in diseases, such as tumors and diabetes; microbial activities, such as alcohol production and lactic acid fermentation; and maintaining homeostasis in the gut environment. However, it is difficult to obtain images of the distribution of in vivo metabolites because of the low detection sensitivities of current methods. In this study, a novel derivatization method for pyruvate and lactate was developed using matrix-assisted laser desorption/ionization-mass spectrometry imaging (MALDI-MSI) to detect pyruvate and lactate in vivo and obtain biodistribution images. We investigated derivatization methods using readily available 3-nitrophenylhydrazine (3NPH), the addition of which improves the sensitivity of pyruvate detection, and the distribution of pyruvate in mouse testes was successfully visualized. Furthermore, the distribution of lactate in the mouse testes could be visualized, and improved detection sensitivity for the main metabolites of the tricarboxylic acid cycle was demonstrated. This derivatization method can be used to detect carboxyl-containing metabolites, including pyruvate, via MALDI-MSI. Furthermore, 3NPH forms amide bonds with carbonyl, phosphate, and carboxyl groups, suggesting the possibility of visualizing its distribution in many metabolites.

Spatially lipidomic characterization of patient-derived organoids by whole-mount autofocusing SMALDI mass spectrometry imaging.

BACKGROUND: Patient-derived organoids (PDOs) are multi-cellular cultures with specific three-dimensional (3D) structures. Tumor organoids (TOs) offer a personalized perspective for assessing treatment response. However, the presence of normal organoid (NO) residuals poses a potential threat to their utility for personalized medicine. There is a crucial need for an effective platform capable of distinguishing between TO and NO in cancer organoid cultures. RESULTS: We introduced a whole-mount (WM) preparation protocol for in-situ visualization of the lipidomic distribution of organoids. To assess the efficacy of this method, nine breast cancer organoids (BCOs) and six normal breast organoids (NBOs) were analyzed. Poly-l-lysine (PLL) coated slides, equipped with 12 well chambers, were utilized as a carrier for the high-throughput analysis of PDOs. Optimizing the fixation time to 30 min, preserved the integrity of organoids and the fidelity of lipid compounds. The PDOs derived from the same organoid lines exhibited similar lipidomic profiles. BCOs and NBOs were obviously distinguished based on their lipidomic signatures detected by WM autofocusing (AF) scanning microprobe matrix-assisted laser desorption/ionization (SMALDI) mass spectrometry imaging (MSI). SIGNIFICANCE: A whole-mount (WM) preparation protocol was developed to visualize lipidomic distributions of the organoids' surface. Using poly-l-lysine coated slides for high-throughput analysis, the method preserved organoid integrity and distinguished breast cancer organoids (BCOs) from normal breast organoids (NBOs) based on their unique lipidomic profiles using autofocusing scanning microprobe matrix-assisted laser desorption/ionization (SMALDI) mass spectrometry imaging.

Absolute Quantification of Glutathione Using Top-Hat Optics for IR-MALDESI Mass Spectrometry Imaging.

Infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) uses an infrared laser to desorb neutral biomolecules with postionization via ESI at atmospheric pressure. The Gaussian profile of the laser with conventional optics results in the heating of adjacent nonablated tissue due to the energy profile being circular. A diffractive optical element (DOE) was incorporated into the optical train to correct for this disadvantage. The DOE produces a top-hat beam profile and square ablation spots, which have uniform energy distributions. Although beneficial to mass spectrometry imaging (MSI), it is unknown how the DOE affects the ability to perform quantitative MSI (qMSI). In this work, we evaluate the performance of the DOE optical train against our conventional optics to define the potential advantages of the top-hat beam profile. Absolute quantification of glutathione (GSH) was achieved by normalizing the analyte of interest to homoglutathione (hGSH), spotting a dilution series of stable isotope labeled glutathione (SIL-GSH), and analyzing by IR-MALDESI MSI with either the conventional optical train or with the DOE incorporated. Statistical comparison indicates that there was no significant difference between the quantification of GSH by the two optical trains as evidenced by similar calibration curves. Results support that both optical trains can be used for qMSI without a change in the ability to carry out absolute quantification but providing the benefits of the top-hat optical train (i.e., flat energy profile and square ablation spots)-for future qMSI studies.

High-resolution 3D spatial distribution of complex microbial colonies revealed by mass spectrometry imaging.

INTRODUCTION: Bacterial living states and the distribution of microbial colony signaling molecules are widely studied using mass spectrometry imaging (MSI). However, current approaches often treat 3D colonies as flat 2D disks, inadvertently omitting valuable details. The challenge of achieving 3D MSI in biofilms persists due to the unique properties of microbial samples. OBJECTIVES: The study aimed to develop a new biofilm sample preparation method that can realize high-resolution 3D MSI of bacterial colonies to reveal the spatial organization of bacterial colonies. METHODS: This article introduces the moisture-assisted cryo-section (MACS) method, enabling embedding-free sectioning parallel to the growth plane. The MACS method secures intact sections by controlling ambient humidity and slice thickness, preventing molecular delocalization. RESULTS: Combined with matrix-assisted laser desorption ionization mass spectrometry (MALDI)-MSI, the MACS method provides high-resolution insights into endogenic and exogenous molecule distributions in Pseudomonas aeruginosa (P. aeruginosa) biofilms, including isomeric pairs. Moreover, analyzed colonies are revived into 3D models, vividly depicting molecular distribution from inner to outer layers. Additionally, we investigated metabolite spatiotemporal dynamics in multiple colonies, observing changes over time and distinct patterns in single versus merged colonies. These findings shed light on the repel-merge process for multi-colony formation. Furthermore, our study monitored chemical responses inside biofilms after antibiotic treatment, showing increased antibiotic levels in the outer biofilm layer over time while maintaining low levels in the inner region. Moreover, the MACS method demonstrated its universality and applicability to other bacterial strains. CONCLUSION: These results unveil complex cell activities within biofilm colonies, offering insights into microbe communities. The MACS method is universally applicable to loosely packed microorganism colonies, overcoming the limitations of previously reported MSI methods. It has great potential for studying bacterial-infected cancer tissues and artificial organs, making it a valuable tool in microbiological research.

Microscopic distribution of taxanes in freeze-fixed stems of Taxus cuspidata.

Introduction: Taxus species contain the anticancer alkaloid paclitaxel, as well as other taxanes similar in structure and potentially in effect to paclitaxel. Tissue-specific distribution patterns and seasonal variations of taxanes in some Taxus species have been reported; however, it is still under-presented for the taxanes in Taxus cuspidata. Methods: The radial distributions of eight taxanes in the transverse surface of freeze-fixed T. cuspidata stems from the late summer and the spring seasons were investigated by cryo-time-of-flight secondary ion mass spectrometry and scanning electron microscopy (cryo-TOF-SIMS/SEM) visualization and liquid chromatography-mass spectrometry (LC-MS) quantitative analysis. By optical microscopic observation, seasonal differences in the amounts and distribution patterns of target taxanes were further characterized in specific tissues. Results and Discussion: The overall amount of taxanes was higher in the late summer than in the spring. Also, taxanes' radial distribution was generally found at higher concentration in the phloem, the cambium and lower level in the periderm, the latest-forming xylem, with different taxanes showing several patterns with distinction between seasons, which were considered related to seasonal plant physiological behaviors. In addition, the distribution of baccatin III (BAC) was investigated at the cellular level, which was regarded in specific cells suggesting its transport in the radial and axial directions in the T. cuspidata stem. Characterizing the microscopic distribution of taxanes in the T. cuspidata stem is expected to play a role in the further study of their biosynthesis and in planta behaviors.

Metabolomics integrated with mass spectrometry imaging reveals novel action of tetramethylpyrazine in migraine.

Migraine as a common neurological disorder still lacks effective therapies. Tetramethylpyrazine (TMP) is the main bioactive component from Ligusticum chuanxiong hort., a traditional edible-medicinal herb. This study aimed to investigate the action of TMP on migraine by metabolomics with mass spectrometry imaging (MSI) analysis and molecular exploring, including random forest model analysis, KEGG enrichment analysis and metabolite-metabolite interaction network analysis. The results indicated that 26 key representative metabolic biomarkers were identified, especially γ-glu-cys, which were highly related to glutathione (GSH) metabolism. MSI found the abundance of eleven endogenous metabolites were modulated by TMP, particularly glucose, the most important energy metabolism molecule, and GSH were increased that maintains intracellular redox balance, which was consistent with activation of Nrf2 signals by TMP. These findings provide insights into the effectiveness of metabolomics integrated with MSI in explaining the metabolic mechanisms of TMP, and afford valuable information for healthy development of TMP in migraine.

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.

The glycosylation landscape of prostate cancer tissues and biofluids.

An overview of the role of glycosylation in prostate cancer (PCa) development and progression is presented, focusing on recent advancements in defining the N-glycome through glycomic profiling and glycoproteomic methodologies. Glycosylation is a common post-translational modification typified by oligosaccharides attached N-linked to asparagine or O-linked to serine or threonine on carrier proteins. These attached sugars have crucial roles in protein folding and cellular recognition processes, such that altered glycosylation is a hallmark of cancer pathogenesis and progression. In the past decade, advancements in N-glycan profiling workflows using Matrix Assisted Laser Desorption/Ionization Mass Spectrometry Imaging (MALDI-MSI) technology have been applied to define the spatial distribution of glycans in PCa tissues. Multiple studies applying N-glycan MALDI-MSI to pathology-defined PCa tissues have identified significant alterations in N-glycan profiles associated with PCa progression. N-glycan compositions progressively increase in number, and structural complexity due to increased fucosylation and sialylation. Additionally, significant progress has been made in defining the glycan and glycopeptide compositions of prostatic-derived glycoproteins like prostate-specific antigen in tissues and biofluids. The glycosyltransferases involved in these changes are potential drug targets for PCa, and new approaches in this area are summarized. These advancements will be discussed in the context of the further development of clinical diagnostics and therapeutics targeting glycans and glycoproteins associated with PCa progression. Integration of large scale spatial glycomic data for PCa with other spatial-omic methodologies is now feasible at the tissue and single-cell levels.

Metabolomic profiling and accurate diagnosis of basal cell carcinoma by MALDI imaging and machine learning.

Basal cell carcinoma (BCC), the most common keratinocyte cancer, presents a substantial public health challenge due to its high prevalence. Traditional diagnostic methods, which rely on visual examination and histopathological analysis, do not include metabolomic data. This exploratory study aims to molecularly characterize BCC and diagnose tumour tissue by applying matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) and machine learning (ML). BCC tumour development was induced in a mouse model and tissue sections containing BCC (n = 12) were analysed. The study design involved three phases: (i) Model training, (ii) Model validation and (iii) Metabolomic analysis. The ML algorithm was trained on MS data extracted and labelled in accordance with histopathology. An overall classification accuracy of 99.0% was reached for the labelled data. Classification of unlabelled tissue areas aligned with the evaluation of a certified Mohs surgeon for 99.9% of the total tissue area, underscoring the model's high sensitivity and specificity in identifying BCC. Tentative metabolite identifications were assigned to 189 signals of importance for the recognition of BCC, each indicating a potential tumour marker of diagnostic value. These findings demonstrate the potential for MALDI-MSI coupled with ML to characterize the metabolomic profile of BCC and to diagnose tumour tissue with high sensitivity and specificity. Further studies are needed to explore the potential of implementing integrated MS and automated analyses in the clinical setting.

[Research progress of deep learning applications in mass spectrometry imaging data analysis].

Mass spectrometry imaging (MSI) is a promising method for characterizing the spatial distribution of compounds. Given the diversified development of acquisition methods and continuous improvements in the sensitivity of this technology, both the total amount of generated data and complexity of analysis have exponentially increased, rendering increasing challenges of data postprocessing, such as large amounts of noise, background signal interferences, as well as image registration deviations caused by sample position changes and scan deviations, and etc. Deep learning (DL) is a powerful tool widely used in data analysis and image reconstruction. This tool enables the automatic feature extraction of data by building and training a neural network model, and achieves comprehensive and in-depth analysis of target data through transfer learning, which has great potential for MSI data analysis. This paper reviews the current research status, application progress and challenges of DL in MSI data analysis, focusing on four core stages: data preprocessing, image reconstruction, cluster analysis, and multimodal fusion. The application of a combination of DL and mass spectrometry imaging in the study of tumor diagnosis and subtype classification is also illustrated. This review also discusses trends of development in the future, aiming to promote a better combination of artificial intelligence and mass spectrometry technology.

Study on the Anti-Inflammatory Mechanism of Coumarins in Peucedanum decursivum Based on Spatial Metabolomics Combined with Network Pharmacology.

Peucedanum decursivum (Miq.) Maxim (P. decursivum) is a traditional Chinese medicinal plant with pharmacological effects such as anti-inflammatory and anti-tumor effects, the root of which is widely used as medicine. Determining the spatial distribution and pharmacological mechanisms of metabolites is necessary when studying the effective substances of medicinal plants. As a means of obtaining spatial distribution information of metabolites, mass spectrometry imaging has high sensitivity and allows for molecule visualization. In this study, matrix-assisted laser desorption mass spectrometry (MALDI-TOF-MSI) and network pharmacology were used for the first time to visually study the spatial distribution and anti-inflammatory mechanism of coumarins, which are metabolites of P. decursivum, to determine their tissue localization and mechanism of action. A total of 27 coumarins were identified by MALDI-TOF-MSI, which mainly concentrated in the cortex, periderm, and phloem of the root of P. decursivum. Network pharmacology studies have identified key targets for the anti-inflammatory effect of P. decursivum, such as TNF, PTGS2, and PRAKA. GO enrichment and KEGG pathway analyses indicated that coumarins in P. decursivum mainly participated in biological processes such as inflammatory response, positive regulation of protein kinase B signaling, chemical carcinogenesis receptor activation, pathways in cancer, and other biological pathways. The molecular docking results indicated that there was good binding between components and targets. This study provides a basis for understanding the spatial distribution and anti-inflammatory mechanism of coumarins in P. decursivum.

Characterisation of Canine and Feline Breast Tumours, Their Metastases, and Corresponding Primary Cell Lines Using LA-REIMS and DESI-MS Imaging.

Breast cancer, a complex disease with a significant prevalence to form metastases, necessitates novel therapeutic strategies to improve treatment outcomes. Here, we present the results of a comparative molecular study of primary breast tumours, their metastases, and the corresponding primary cell lines using Desorption Electrospray Ionisation (DESI) and Laser-Assisted Rapid Evaporative Ionisation Mass Spectrometry (LA-REIMS) imaging. Our results show that ambient ionisation mass spectrometry technology is suitable for rapid characterisation of samples, providing a lipid- and metabolite-rich spectrum within seconds. Our study demonstrates that the lipidomic fingerprint of the primary tumour is not significantly distinguishable from that of its metastasis, in parallel with the similarity observed between their respective primary cell lines. While significant differences were observed between tumours and the corresponding cell lines, distinct lipidomic signatures and several phospholipids such as PA(36:2), PE(36:1), and PE(P-38:4)/PE(O-38:5) for LA-REIMS imaging and PE(P-38:4)/PE(O-38:5), PS(36:1), and PI(38:4) for DESI-MSI were identified in both tumours and cells. We show that the tumours' characteristics can be found in the corresponding primary cell lines, offering a promising avenue for assessing tumour responsiveness to therapeutic interventions. A comparative analysis by DESI-MSI and LA-REIMS imaging revealed complementary information, demonstrating the utility of LA-REIMS in the molecular imaging of cancer.

Direct and Rapid Visualization of the Spatial Distribution of Cholesterol in Alzheimer's and Cancer Tissue via MALDI Mass Spectrometry Imaging.

Cholesterol is a vital component of the central nervous system and tissues, and understanding its spatial distribution is crucial for biology, pathophysiology, and diagnostics. However, direct imaging of cholesterol using matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) remains challenging and time-consuming due to the difficulty in ionizing the sterol molecule. To tackle this issue, a MALDI-MSI method is established for direct and rapid analysis of the spatial distribution of cholesterol in Alzheimer's disease (AD), different cancer tissues and organs via MALDI-MSI. This excellent imaging performance depends on the study and systemic optimization of various conditions that affect the imaging of MALDI-MSI. In this case, we report the distribution and levels of cholesterol across specific structures of the AD mouse brain and different tumor tissue and organs. According to the results, the content of cholesterol in the AD mouse cerebellum, especially in the arborvitae, was significantly higher than that in the wild type (WT) model. Furthermore, we successfully visualize the distribution of cholesterol in other organs, such as the heart, liver, spleen, kidney, pancreas, as well as tumor tissues parenchyma and interstitium using MALDI-MSI. Notably, the attribution of cholesterol MS/MS hydrocarbon fragments was systematically investigated. Our presented optimization strategy and established MALDI-MSI method can be easily generalized for different animal tissues or live samples, thereby facilitating the potential for applications of MALDI-MSI in clinical, medical and biological research.

The Spatial Extracellular Proteomic Tumor Microenvironment Distinguishes Molecular Subtypes of Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) mortality rates continue to increase faster than those of other cancer types due to high heterogeneity, which limits diagnosis and treatment. Pathological and molecular subtyping have identified that HCC tumors with poor outcomes are characterized by intratumoral collagenous accumulation. However, the translational and post-translational regulation of tumor collagen, which is critical to the outcome, remains largely unknown. Here, we investigate the spatial extracellular proteome to understand the differences associated with HCC tumors defined by Hoshida transcriptomic subtypes of poor outcome (Subtype 1; S1; n = 12) and better outcome (Subtype 3; S3; n = 24) that show differential stroma-regulated pathways. Collagen-targeted mass spectrometry imaging (MSI) with the same-tissue reference libraries, built from untargeted and targeted LC-MS/MS was used to spatially define the extracellular microenvironment from clinically-characterized, formalin-fixed, paraffin-embedded tissue sections. Collagen α-1(I) chain domains for discoidin-domain receptor and integrin binding showed distinctive spatial distribution within the tumor microenvironment. Hydroxylated proline (HYP)-containing peptides from the triple helical regions of fibrillar collagens distinguished S1 from S3 tumors. Exploratory machine learning on multiple peptides extracted from the tumor regions could distinguish S1 and S3 tumors (with an area under the receiver operating curve of ≥0.98; 95% confidence intervals between 0.976 and 1.00; and accuracies above 94%). An overall finding was that the extracellular microenvironment has a high potential to predict clinically relevant outcomes in HCC.

Mass spectrometry imaging in pulmonary disorders.

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

A co-culture system of macrophages with breast cancer tumoroids to study cell interactions and therapeutic responses.

3D tumoroids have revolutionized in vitro/ex vivo cancer biology by recapitulating the complex diversity of tumors. While tumoroids provide new insights into cancer development and treatment response, several limitations remain. As the tumor microenvironment, especially the immune system, strongly influences tumor development, the absence of immune cells in tumoroids may lead to inappropriate conclusions. Macrophages, key players in tumor progression, are particularly challenging to integrate into the tumoroids. In this study, we established three optimized and standardized methods for co-culturing human macrophages with breast cancer tumoroids: a semi-liquid model and two matrix-embedded models tailored for specific applications. We then tracked interactions and macrophage infiltration in these systems using flow cytometry and light sheet microscopy and showed that macrophages influenced not only tumoroid molecular profiles but also chemotherapy response. This underscores the importance of increasing the complexity of 3D models to more accurately reflect in vivo conditions.

Lipid signature associated with chronic colon inflammation reveals a dysregulation in colonocyte differentiation process.

Inflammatory Bowel Disease (IBD) comprises a heterogeneous group of chronic inflammatory conditions of the gastrointestinal tract that include ulcerative colitis (UC) and Crohn's disease. Although the etiology is not well understood, IBD is characterized by a loss of the normal epithelium homeostasis that disrupts the intestinal barrier of these patients. Previous work by our group demonstrated that epithelial homeostasis along the colonic crypts involves a tight regulation of lipid profiles. To evaluate whether lipidomic profiles conveyed the functional alterations observed in the colonic epithelium of IBD, we performed matrix-assisted laser desorption ionization-mass spectrometry imaging (MALDI-MSI) analyses of endoscopic biopsies from inflamed and non-inflamed segments obtained from UC patients. Our results indicated that lipid profiling of epithelial cells discriminated between healthy and UC patients. We also demonstrated that epithelial cells of the inflamed mucosa were characterized by a decrease in mono- and di-unsaturated fatty acid-containing phospholipids and higher levels of arachidonic acid-containing species, suggesting an alteration of the lipid gradients occurring concomitantly to the epithelial differentiation. This result was reinforced by the immunofluorescence analysis of EPHB2 and HPGD, markers of epithelial cell differentiation, sustaining that altered lipid profiles were at least partially due to a faulty differentiation process. Overall, our results showed that lipid profiling by MALDI-MSI faithfully conveys molecular and functional alterations associated with the inflamed epithelium, providing the foundation for a novel molecular characterization of UC patients.

Differential distribution of characteristic constituents in peel and pulp of Aurantii Fructus Immaturus (Citrus aurantium L.) using MALDI mass spectrometry imaging.

Aurantii Fructus Immaturus (AFI) was structurally divided into two parts named "peel" and "pulp". The exocarp and mesocarp of materials named "peel". The endocarp separated into multiple compartments and the cystic hair attached to it named "pulp". In order to explore the distribution and content of constituents in AFI, an efficient method to explore the distribution of constituents was developed based on matrix assisted laser desorption/ionization fourier transform ion cyclotron resonance mass spectrometry imaging (MALDI-FTICR-MSI). After simple processing, thirty-two constituents with distinct localization in the mass range of 101-1200 Da were identified by MALDI-FTICR-MSI. In addition, the identified four flavnoids (poncirin, sinensetin, 3,5,6,7,8,3',4'-heptemthoxyflavone, and tangeritin) were analyzed for differences between using LC-MS. Quantitative analysis results supported the quantitative results from MALDI-FT-ICR-MSI. The results implied that different parts had different constituents in AFI, and demonstrated MALDI-MSI have high potential in the direct analysis of constituents.

Multimodal Mass Spectrometry Imaging of an Osteosarcoma Multicellular Tumour Spheroid Model to Investigate Drug-Induced Response.

A multimodal mass spectrometry imaging (MSI) approach was used to investigate the chemotherapy drug-induced response of a Multicellular Tumour Spheroid (MCTS) 3D cell culture model of osteosarcoma (OS). The work addresses the critical demand for enhanced translatable early drug discovery approaches by demonstrating a robust spatially resolved molecular distribution analysis in tumour models following chemotherapeutic intervention. Advanced high-resolution techniques were employed, including desorption electrospray ionisation (DESI) mass spectrometry imaging (MSI), to assess the interplay between metabolic and cellular pathways in response to chemotherapeutic intervention. Endogenous metabolite distributions of the human OS tumour models were complemented with subcellularly resolved protein localisation by the detection of metal-tagged antibodies using Imaging Mass Cytometry (IMC). The first application of matrix-assisted laser desorption ionization-immunohistochemistry (MALDI-IHC) of 3D cell culture models is reported here. Protein localisation and expression following an acute dosage of the chemotherapy drug doxorubicin demonstrated novel indications for mechanisms of region-specific tumour survival and cell-cycle-specific drug-induced responses. Previously unknown doxorubicin-induced metabolite upregulation was revealed by DESI-MSI of MCTSs, which may be used to inform mechanisms of chemotherapeutic resistance. The demonstration of specific tumour survival mechanisms that are characteristic of those reported for in vivo tumours has underscored the increasing value of this approach as a tool to investigate drug resistance.