In situ metabolomic mass spectrometry imaging: Recent advances and difficulties

Advancing mass spectrometry-based chemical imaging: A noncontact continuous flow surface probe in mass spectrometry for enhanced signal detection and spatial resolution

A new method has been developed for mass spectrometric imaging of small molecules and proteins on tissue or in thinly sliced materials. A laser desorption Venturi electrospray ionization-mass spectrometer was developed for molecular imaging. This method combines laser desorption (LD) and electrospray ionization (ESI) systems before a mass spectrometer (MS). To carry out laser desorption, samples are excited with a laser from the back side of a glass substrate. The desorbed molecules or particles are then captured by a solvent flow. In the ESI system, these desorbed particles and molecules are ionized. The spray part of the solvent system consists of two capillaries: one delivers solvent to the sample plate sides to capture desorbed molecules and particles, and the other carries the solution to the mass spectrometry side using the Venturi effect. A 2D stage facilitates sampling. The system is designed to minimize the sample size after desorption using a 355 nm diode laser, and it is optimized for molecules of various sizes, including organic molecules, amino acids, and proteins. Despite challenging atmospheric conditions for protein desorption, this specialized design enables the collection of protein spectra. The amino acids and other small molecules showed high sensitivity in the MSI measurements. This innovative MS imaging system can be directly applied to real tissue systems and other plant samples to visualize the molecular level distributions.

Metabolic heterogeneity in adrenocortical carcinoma impacts patient outcomes

Spatially resolved metabolomics enables the investigation of tumoral metabolites in situ. Inter- and intratumor heterogeneity are key factors associated with patient outcomes. Adrenocortical carcinoma (ACC) is an exceedingly rare tumor associated with poor survival. Its clinical prognosis is highly variable, but the contributions of tumor metabolic heterogeneity have not been investigated thus far to our knowledge. An in-depth understanding of tumor heterogeneity requires molecular feature-based identification of tumor subpopulations associated with tumor aggressiveness. Here, using spatial metabolomics by high-mass resolution MALDI Fourier transform ion cyclotron resonance mass spectrometry imaging, we assessed metabolic heterogeneity by de novo discovery of metabolic subpopulations and Simpson's diversity index. After identification of tumor subpopulations in 72 patients with ACC, we additionally performed a comparison with 25 tissue sections of normal adrenal cortex to identify their common and unique metabolic subpopulations. We observed variability of ACC tumor heterogeneity and correlation of high metabolic heterogeneity with worse clinical outcome. Moreover, we identified tumor subpopulations that served as independent prognostic factors and, furthermore, discovered 4 associated anticancer drug action pathways. Our research may facilitate comprehensive understanding of the biological implications of tumor subpopulations in ACC and showed that metabolic heterogeneity might impact chemotherapy.

Metabolomic Profiling in Children with Celiac Disease: Beyond the Gluten-Free Diet

Celiac disease (CD) is included in the group of complex or multifactorial diseases, i.e., those caused by the interaction of genetic and environmental factors. Despite a growing understanding of the pathophysiological mechanisms of the disease, diagnosis is still often delayed and there are no effective biomarkers for early diagnosis. The only current treatment, a gluten-free diet (GFD), can alleviate symptoms and restore intestinal villi, but its cellular effects remain poorly understood. To gain a comprehensive understanding of CD's progression, it is crucial to advance knowledge across various scientific disciplines and explore what transpires after disease onset. Metabolomics studies hold particular significance in unravelling the complexities of multifactorial and multisystemic disorders, where environmental factors play a significant role in disease manifestation and progression. By analyzing metabolites, we can gain insights into the reasons behind CD's occurrence, as well as better comprehend the impact of treatment initiation on patients. In this review, we present a collection of articles that showcase the latest breakthroughs in the field of metabolomics in pediatric CD, with the aim of trying to identify CD biomarkers for both early diagnosis and treatment monitoring. These advancements shed light on the potential of metabolomic analysis in enhancing our understanding of the disease and improving diagnostic and therapeutic strategies. More studies need to be designed to cover metabolic profiles in subjects at risk of developing the disease, as well as those analyzing biomarkers for follow-up treatment with a GFD.

Analysis of Metabolite Distribution in Rat Liver of High-Fat Model by Mass Spectrometry Imaging

Hyperlipidemia is a medical condition characterized by elevated levels of blood lipids, especially triglycerides (TG). However, it remains unclear whether TG levels remain consistently elevated throughout the entire developmental stage of the high-lipid state. In our animal experiment, we found that TG levels were significantly higher in the early stage of the high-lipid model but significantly decreased at the 14th week of the late stage, reaching levels similar to those of the control group. This suggests that TG levels in the high-lipid model are not always higher than those of the control group. To determine the reason for this observation, we used in situ mass spectrometry imaging (MSI) to detect the distribution of metabolites in the liver of rats. The metabolite distribution of the control rats at different stages was significantly different from that of the model rats, and the high-lipid model differed significantly from the control rats. We identified nine functional metabolites that showed differences throughout the period, namely, PA(20:3-OH/i-21:0), PA(20:4-OH/22:6), PG(20:5-OH/i-16:0), PG(22:6-2OH/i-13:0), PG(O-18:0/20:4), PGP(18:3-OH/i-12:0), PGP(PGJ2/i-15:0), SM(d18:0/18:1-2OH), and TG(14:0/14:0/16:0), among which TG was most significantly correlated with hyperlipidemia and high lipid. This study is unique in that it used MSI to reveal the changes in metabolites in situ, showing the distribution of different metabolites or the same metabolite in liver tissue. The findings highlight the importance of considering the animal’s age when using TG as a biomarker for hyperlipidemia. Additionally, the MSI images of the liver in the high-lipid model clearly indicated the distribution and differences of more significant metabolites, providing valuable data for further research into new biomarkers and mechanisms of hyperlipidemia. This new pathway of in situ, visualized, and data-rich metabolomics research provides a more comprehensive understanding of the characteristics of high lipid and its implications for disease prevention and treatment.

Different ion mobility-mass spectrometry coupling techniques to promote metabolomics

Metabolomics has become increasingly popular in recent years for many applications ranging from clinical diagnosis, human health to biotechnological questioning. Despite technological advances, metabolomic studies are still currently limited by the difficulty of identifying all metabolites, a class of compounds with great chemical diversity. Although lengthy chromatographic analyses are often used to obtain comprehensive data, many isobar and isomer metabolites still remain unresolved, which is a critical point for the compound identification. Currently, ion mobility spectrometry is being explored in metabolomics as a way to improve metabolome coverage, analysis throughput and isomer separation. In this review, all the steps of a typical workflow for untargeted metabolomics are discussed considering the use of an ion mobility instrument. An overview of metabolomics is first presented followed by a brief description of ion mobility instrumentation. The ion mobility potential for complex mixture analysis is discussed regarding its coupling with a mass spectrometer alone, providing gas-phase separation before mass analysis as well as its combination with different separation platforms (conventional hyphenation but also multidimensional ion mobility couplings), offering multidimensional separation. Various instrumental and analytical conditions for improving the ion mobility separation are also described. Finally, data mining, including software packages and visualization approaches, as well as the construction of ion mobility databases for the metabolite identification are examined.

Mass spectrometry in organic and bio-organic catalysis: Using thermochemical properties to lend insight into mechanism

In this review, we discuss gas phase experimentation centered on the measurement of acidity and proton affinity of substrates that are useful for understanding catalytic mechanisms. The review is divided into two parts. The first covers examples of organocatalysis, while the second focuses on biological catalysis. The utility of gas phase acidity and basicity values for lending insight into mechanisms of catalysis is highlighted.

Spatial metabolomics for evaluating response to neoadjuvant therapy in non-small cell lung cancer patients

BACKGROUND

The response to neoadjuvant chemotherapy (NAC) differs substantially among individual patients with non-small cell lung cancer (NSCLC). Major pathological response (MPR) is a histomorphological read-out used to assess treatment response and prognosis in patients NSCLC after NAC. Although spatial metabolomics is a promising tool for evaluating metabolic phenotypes, it has not yet been utilized to assess therapy responses in patients with NSCLC. We evaluated the potential application of spatial metabolomics in cancer tissues to assess the response to NAC, using a metabolic classifier that utilizes mass spectrometry imaging combined with machine learning.

METHODS

Resected NSCLC tissue specimens obtained after NAC (n = 88) were subjected to high-resolution mass spectrometry, and these data were used to develop an approach for assessing the response to NAC in patients with NSCLC. The specificities of the generated tumor cell and stroma classifiers were validated by applying this approach to a cohort of biologically matched chemotherapy-naïve patients with NSCLC (n = 85).

RESULTS

The developed tumor cell metabolic classifier stratified patients into different prognostic groups with 81.6% accuracy, whereas the stroma metabolic classifier displayed 78.4% accuracy. By contrast, the accuracies of MPR and TNM staging for stratification were 62.5% and 54.1%, respectively. The combination of metabolic and MPR classifiers showed slightly lower accuracy than either individual metabolic classifier. In multivariate analysis, metabolic classifiers were the only independent prognostic factors identified (tumor: P = 0.001, hazards ratio [HR] = 3.823, 95% confidence interval [CI] = 1.716-8.514; stroma: P = 0.049, HR = 2.180, 95% CI = 1.004-4.737), whereas MPR (P = 0.804; HR = 0.913; 95% CI = 0.445-1.874) and TNM staging (P = 0.078; HR = 1.223; 95% CI = 0.977-1.550) were not independent prognostic factors. Using Kaplan-Meier survival analyses, both tumor and stroma metabolic classifiers were able to further stratify patients as NAC responders (P < 0.001) and non-responders (P < 0.001).

CONCLUSIONS

Our findings indicate that the metabolic constitutions of both tumor cells and the stroma are valuable additions to the classical histomorphology-based assessment of tumor response.

Leaf microscopy applications in photosynthesis research: identifying the gaps

Leaf imaging via microscopy has provided critical insights into research on photosynthesis at multiple junctures, from the early understanding of the role of stomata, through elucidating C4 photosynthesis via Kranz anatomy and chloroplast arrangement in single cells, to detailed explorations of diffusion pathways and light utilization gradients within leaves. In recent decades, the original two-dimensional (2D) explorations have begun to be visualized in three-dimensional (3D) space, revising our understanding of structure-function relationships between internal leaf anatomy and photosynthesis. In particular, advancing new technologies and analyses are providing fresh insight into the relationship between leaf cellular components and improving the ability to model net carbon fixation, water use efficiency, and metabolite turnover rate in leaves. While ground-breaking developments in imaging tools and techniques have expanded our knowledge of leaf 3D structure via high-resolution 3D and time-series images, there is a growing need for more in vivo imaging as well as metabolite imaging. However, these advances necessitate further improvement in microscopy sciences to overcome the unique challenges a green leaf poses. In this review, we discuss the available tools, techniques, challenges, and gaps for efficient in vivo leaf 3D imaging, as well as innovations to overcome these difficulties.

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

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

Innovation in drug toxicology: Application of mass spectrometry imaging technology

Mass spectrometry imaging (MSI) is a powerful molecular imaging technology that can obtain qualitative, quantitative, and location information by simultaneously detecting and mapping endogenous or exogenous molecules in biological tissue slices without specific chemical labeling or complex sample pretreatment. This article reviews the progress made in MSI and its application in drug toxicology research, including the tissue distribution of toxic drugs and their metabolites, the target organs (liver, kidney, lung, eye, and central nervous system) of toxic drugs, the discovery of toxicity-associated biomarkers, and explanations of the mechanisms of drug toxicity when MSI is combined with the cutting-edge omics methodologies. The unique advantages and broad prospects of this technology have been fully demonstrated to further promote its wider use in the field of pharmaceutical toxicology.

Deciphering the mechanism of action of antitubercular compounds with metabolomics

Tuberculosis (TB), one of the oldest and deadliest bacterial diseases, continues to cause serious global economic, health, and social problems. Current TB treatments are lengthy, expensive, and routinely ineffective against emerging drug resistant strains. Thus, there is an urgent need for the identification and development of novel TB drugs possessing comprehensive and specific mechanisms of action (MoAs). Metabolomics is a valuable approach to elucidating the MoA, toxicity, and potency of promising chemical leads, which is a critical step of the drug discovery process. Recent advances in metabolomics methodologies for deciphering MoAs include high-throughput screening techniques, the integration of multiple omics methods, mass spectrometry imaging, and software for automated analysis. This review describes recently introduced metabolomics methodologies and techniques for drug discovery, highlighting specific applications to the discovery of new antitubercular drugs and the elucidation of their MoAs.

Quantum Chemistry Calculations for Metabolomics

A primary goal of metabolomics studies is to fully characterize the small-molecule composition of complex biological and environmental samples. However, despite advances in analytical technologies over the past two decades, the majority of small molecules in complex samples are not readily identifiable due to the immense structural and chemical diversity present within the metabolome. Current gold-standard identification methods rely on reference libraries built using authentic chemical materials ("standards"), which are not available for most molecules. Computational quantum chemistry methods, which can be used to calculate chemical properties that are then measured by analytical platforms, offer an alternative route for building reference libraries, i.e., in silico libraries for "standards-free" identification. In this review, we cover the major roadblocks currently facing metabolomics and discuss applications where quantum chemistry calculations offer a solution. Several successful examples for nuclear magnetic resonance spectroscopy, ion mobility spectrometry, infrared spectroscopy, and mass spectrometry methods are reviewed. Finally, we consider current best practices, sources of error, and provide an outlook for quantum chemistry calculations in metabolomics studies. We expect this review will inspire researchers in the field of small-molecule identification to accelerate adoption of in silico methods for generation of reference libraries and to add quantum chemistry calculations as another tool at their disposal to characterize complex samples.

The Lymph Node Reservoir: Physiology, HIV Infection, and Antiretroviral Therapy

Despite advances in treatment, finding a cure for HIV remains a top priority. Chronic HIV infection is associated with increased risk of comorbidities, such as diabetes and cardiovascular disease. Additionally, people living with HIV must remain adherent to daily antiretroviral therapy, because lapses in medication adherence can lead to viral rebound and disease progression. Viral recrudescence occurs from cellular reservoirs in lymphoid tissues. In particular, lymph nodes are central to the pathology of HIV due to their unique architecture and compartmentalization of immune cells. Understanding how antiretrovirals (ARVs) penetrate lymph nodes may explain why these tissues are maintained as HIV reservoirs, and how they contribute to viral rebound upon treatment interruption. In this report, we review (i) the physiology of the lymph nodes and their function as part of the immune and lymphatic systems, (ii) the pathogenesis and outcomes of HIV infection in lymph nodes, and (iii) ARV concentrations and distribution in lymph nodes, and the relationship between ARVs and HIV in this important reservoir.

Systemic Immunometabolism: Challenges and Opportunities

Over the past 10 years, the field of immunometabolism made great strides to unveil the crucial role of intracellular metabolism in regulating immune cell function. Emerging insights into how systemic inflammation and metabolism influence each other provide a critical additional dimension on the organismal level. Here, we discuss the concept of systemic immunometabolism and review the current understanding of the communication circuits that underlie the reciprocal impact of systemic inflammation and metabolism across organs in inflammatory and infectious diseases, as well as how these mechanisms apply to homeostasis. We present current challenges of systemic immunometabolic research, and in this context, highlight opportunities and put forward ideas to effectively explore organismal physiological complexity in both health and disease.

Metabolic Regulation of Tissue Stem Cells

Adult tissue stem cells mediate organ homeostasis and regeneration and thus are continually making decisions about whether to remain quiescent, proliferate, or differentiate into mature cell types. These decisions often integrate external cues, such as energy balance and the nutritional status of the organism. Metabolic substrates and byproducts that regulate epigenetic and signaling pathways are now appreciated to have instructive rather than bystander roles in regulating cell fate decisions. In this review, we highlight recent literature focused on how metabolites and dietary manipulations can impact cell fate decisions, with a focus on the regulation of adult tissue stem cells.

In Situ Metabolomics of the Honeybee Brain: The Metabolism of l-Arginine through the Polyamine Pathway in the Proboscis Extension Response (PER)

The proboscis extension response (PER) reflex may be used to condition the pairing of an odor with sucrose, which is applied to the antennae, in experiments to induce learning, where the odor represents a conditioned stimulus, while sucrose represents an unconditioned stimulus. A series of studies have been conducted on honeybees, relating learning and memory acquisition/retrieval using the PER as a strategy for accessing their ability to exhibit an unconditioned stimulus; however, the major metabolic processes involved in the PER are not well known. Thus, the aim of this investigation is profiling the metabolome of the honeybee brain involved in the PER. In this study, a semiquantitative approach of matrix-assisted laser desorption ionization (MALDI) mass spectral imaging (MSI) was used to profile the most abundant metabolites of the honeybee brain that support the PER. It was reported that execution of the PER requires the metabolic transformations of arginine, ornithine, and lysine as substrates for the production of putrescine, cadaverine, spermine, spermidine, 1,3-diaminopropane, and γ-aminobutyric acid (GABA). Considering the global metabolome of the brain of honeybee workers, the PER requires the consumption of large amounts of cadaverine and 1,3-diaminopropane, in parallel with the biosynthesis of high amounts of spermine, spermidine, and ornithine. To exhibit the PER, the brain of honeybee workers processes the conversion of l-arginine and l-lysine through the polyamine pathway, with different regional metabolomic profiles at the individual neuropil level. The outcomes of this study using this metabolic route as a reference are indicating that the antennal lobes and the calices (medial and lateral) were the most active brain regions for supporting the PER.

Mass Spectrometry Imaging Establishes 2 Distinct Metabolic Phenotypes of Aldosterone-Producing Cell Clusters in Primary Aldosteronism

Aldosterone-producing adenomas (APAs) are one of the main causes of primary aldosteronism and the most prevalent surgically correctable form of hypertension. Aldosterone-producing cell clusters (APCCs) comprise tight nests of zona glomerulosa cells, strongly positive for CYP11B2 (aldosterone synthase) in immunohistochemistry. APCCs have been suggested as possible precursors of APAs because they frequently carry driver mutations for constitutive aldosterone production, and a few adrenal lesions with histopathologic features of both APCCs and APAs have been identified. Our objective was to investigate the metabolic phenotypes of APCCs (n=27) compared with APAs (n=6) using in situ matrix-assisted laser desorption/ionization mass spectrometry imaging of formalin-fixed paraffin-embedded adrenals from patients with unilateral primary aldosteronism. Specific distribution patterns of metabolites were associated with APCCs and classified 2 separate APCC subgroups (subgroups 1 and 2) indistinguishable by CYP11B2 immunohistochemistry. Metabolic profiles of APCCs in subgroup 1 were tightly clustered and distinct from subgroup 2 and APAs. Multiple APCCs from the same adrenal displayed metabolic profiles of the same subgroup. Metabolites of APCC subgroup 2 were highly similar to the APA group and indicated enhanced metabolic pathways favoring cell proliferation compared with APCC subgroup 1. In conclusion, we demonstrate specific subgroups of APCCs with strikingly divergent distribution patterns of metabolites. One subgroup displays a metabolic phenotype convergent with APAs and may represent the progression of APCCs to APAs.

Single-Probe Mass Spectrometry Analysis of Metabolites in Single Cells

Mass spectrometry (MS) is an indispensable analytical technique for bioanalysis. Based on the measurement of mass/charge ratios (m/z) of ions, MS can be used for sensitive detection and accurate identification of species of interest. In traditional studies, MS is utilized to measure analytes in prepared solutions or gas-phase samples. Benefited from recent development of sampling and ionization approaches, MS has been extensively applied to the analysis of broad ranges of biological samples. We have developed a new device, the Single-probe, that can be used for in situ, real-time MS analysis of metabolites inside individual living cells. The Single-probe is a miniaturized multifunctional sampling and ionization device that is directly coupled to the mass spectrometer. With a sampling tip size smaller than 10 μm, we can insert the Single-probe tip into single cells to extract intracellular compounds, which are analyzed using MS in real-time. We have successfully used the Single-probe MS technique to detect a variety of endogenous and exogenous cellular metabolites in individual eukaryotic cells. Single cell mass spectrometry (SCMS) is a new scientific technology that has the potential to reshape approaches in biological and pharmaceutical bioanalytical research.

Hepatotoxic effects of inhalation exposure to polycyclic aromatic hydrocarbons on lipid metabolism of C57BL/6 mice

Inhalation from ambient air and cigarette smoke is a common route of human exposure to polycyclic aromatic hydrocarbons (PAHs). Little information is available regarding hepatotoxicities of inhaled PAHs so for. In this study, we evaluated the toxic effects of intratracheally instilled benzo[a]pyrene (B[a]P) on hepatic lipid metabolism of C57BL/6 mice at relevant environmental exposure levels by using two different mass-based lipidomics approaches. The results of mass spectrometry imaging analysis showed that both the abundance and spatial distribution of several lysophosphatidylcholine (LysoPC), phosphatidylcholine (PC) and sphingomyelin (SM) in the liver section were different and changed after inhalation exposure to B[a]P. Liquid chromatography coupled with mass spectrometry-based lipidomics analysis and multivariate statistical analysis found that B[a]P exposure markedly altered glycerophospholipids, glycerolipids, and fatty acid metabolism in the mouse liver, with increasing of triacylglycerol (TG), phosphatidylinositol (PI) and PC, and decreasing of LysoPCs phosphatidylethanolamines (PEs), lysophosphatidylethanolamine (LysoPEs), free fatty acids (FFAs) and eicosanoids. B[a]P-induced lipid metabolic disorders showed a time-dependent effect, which generated three response trajectories with different change trends. Consequently, B[a]P exposure induced alteration of hepatic lipids by promoting the uptake from blood or the biosynthesis and transformation in the liver, might contribute to non-alcoholic fatty liver disease, hepatocyte membrane injury, inflammation, and signal system disturbance.

Maintenance of Deep Lung Architecture and Automated Airway Segmentation for 3D Mass Spectrometry Imaging

Mass spectrometry imaging (MSI) is a technique for mapping the spatial distributions of molecules in sectioned tissue. Histology-preserving tissue preparation methods are central to successful MSI studies. Common fixation methods, used to preserve tissue morphology, can result in artifacts in the resulting MSI experiment including delocalization of analytes, altered adduct profiles, and loss of key analytes due to irreversible cross-linking and diffusion. This is especially troublesome in lung and airway samples, in which histology and morphology is best interpreted from 3D reconstruction, requiring the large and small airways to remain inflated during analysis. Here, we developed an MSI-compatible inflation containing as few exogenous components as possible, forgoing perfusion, fixation, and addition of salt solutions upon inflation that resulted in an ungapped 3D molecular reconstruction through more than 300 microns. We characterized a series of polyunsaturated phospholipids (PUFA-PLs), specifically phosphatidylinositol (-PI) lipids linked to lethal inflammation in bacterial infection and mapped them in serial sections of inflated mouse lung. PUFA-PIs were identified using spatial lipidomics and determined to be determinant markers of major airway features using unsupervised hierarchical clustering. Deep lung architecture was preserved using this inflation approach and the resulting sections are compatible with multiple MSI modalities, automated interpretation software, and serial 3D reconstruction.

Multimodal Imaging of Amyloid Plaques: Fusion of the Single-Probe Mass Spectrometry Image and Fluorescence Microscopy Image

Alzheimer's disease (AD) is one of the most common neurodegenerative diseases. The formation of amyloid plaques by aggregated amyloid beta (Aβ) peptides is a primary event in AD pathology. Understanding the metabolomic features and related pathways is critical for studying plaque-related pathological events (e.g., cell death and neuron dysfunction). Mass spectrometry imaging (MSI), due to its high sensitivity and ability to obtain the spatial distribution of metabolites, has been applied to AD studies. However, limited studies of metabolites in amyloid plaques have been performed due to the drawbacks of the commonly used techniques such as matrix-assisted laser desorption/ionization MSI. In the current study, we obtained high spatial resolution (∼17 μm) MS images of the AD mouse brain using the Single-probe, a microscale sampling and ionization device, coupled to a mass spectrometer under ambient conditions. The adjacent slices were used to obtain fluorescence microscopy images to locate amyloid plaques. The MS image and the fluorescence microscopy image were fused to spatially correlate histological protein hallmarks with metabolomic features. The fused images produced significantly improved spatial resolution (∼5 μm), allowing for the determination of fine structures in MS images and metabolomic biomarkers representing amyloid plaques.

Prognostic Relevance of Steroid Sulfation in Adrenocortical Carcinoma Revealed by Molecular Phenotyping Using High-Resolution Mass Spectrometry Imaging

BACKGROUND

Adrenocortical carcinoma (ACC) is a rare tumor with variable prognosis even within the same tumor stage. Cancer-related sex hormones and their sulfated metabolites in body fluids can be used as tumor markers. The role of steroid sulfation in ACC has not yet been studied. MALDI mass spectrometry imaging (MALDI-MSI) is a novel tool for tissue-based chemical phenotyping.

METHODS

We performed phenotyping of formalin-fixed, paraffin-embedded tissue samples from 72 ACC by MALDI-MSI at a metabolomics level.

RESULTS

Tumoral steroid hormone metabolites-estradiol sulfate [hazard ratio (HR) 0.26; 95% CI, 0.10-0.69; P = 0.005] and estrone 3-sulfate (HR 0.22; 95% CI, 0.07-0.63; P = 0.003)-were significantly associated with prognosis in Kaplan-Meier analyses and after multivariable adjustment for age, tumor stage, and sex (HR 0.29; 95% CI, 0.11-0.79; P = 0.015 and HR 0.30; 95% CI, 0.10-0.91; P = 0.033, respectively). Expression of sulfotransferase SULT2A1 was associated with prognosis to a similar extent and was validated to be a prognostic factor in two published data sets. We discovered the presence of estradiol-17β 3,17-disulfate (E2S2) in a subset of tumors with particularly poor overall survival. Electron microscopy revealed novel membrane-delimited organelles in only these tumors. By applying cluster analyses of metabolomic data, 3 sulfation-related phenotypes exhibited specific metabolic features unrelated to steroid metabolism.

CONCLUSIONS

MALDI-MSI provides novel insights into the pathophysiology of ACC. Steroid hormone sulfation may be used for prognostication and treatment stratification. Sulfation-related metabolic reprogramming may be of relevance also in conditions beyond the rare ACC and can be directly investigated by the use of MALDI-MSI.

Improvement of Sensitivity and Reproducibility for Imaging of Endogenous Metabolites by Matrix-Assisted Laser Desorption/Ionization-Mass Spectrometry

Matrix-assisted laser desorption/ionization (MALDI)-mass spectrometry imaging (MSI) is a powerful technique to visualize the distributions of biomolecules without any labeling. In MALDI-MSI experiments, the choice of matrix deposition method is important for acquiring favorable MSI data with high sensitivity and high reproducibility. Generally, manual or automated spray-coating and automated sublimation methods are used, but these methods have some drawbacks with respect to detection sensitivity, spatial resolution, and data reproducibility. Herein, we present an optimized matrix deposition method of sublimation coupled with recrystallization using 9-aminoacridine (9-AA) as a matrix capable of ionizing endogenous metabolites. The matrix recrystallization process after sublimation was optimized for the solvent concentration and reaction temperature for matrix-metabolite co-crystallization. This optimized method showed excellent reproducibility and spatial resolution compared to the automatic spray-coating method. Furthermore, the recrystallization step after sublimation remarkably improved the detectability of metabolites, including amino acids, nucleotide derivatives, and lipids, compared with the conventional sublimation method. To date, there have been no other reports of 9-AA-based sublimation combined with recrystallization. The present method provides an easy, sensitive, and reproducible matrix deposition method for MALDI-MSI of endogenous metabolites. Graphical Abstract.

From observing to predicting single-cell structure and function with high-throughput/high-content microscopy

In the past 15 years, cell-based microscopy has evolved its focus from observing cell function to aiming to predict it. In particular-powered by breakthroughs in computer vision, large-scale image analysis and machine learning-high-throughput and high-content microscopy imaging have enabled to uniquely harness single-cell information to systematically discover and annotate genes and regulatory pathways, uncover systems-level interactions and causal links between cellular processes, and begin to clarify and predict causal cellular behaviour and decision making. Here we review these developments, discuss emerging trends in the field, and describe how single-cell 'omics and single-cell microscopy are imminently in an intersecting trajectory. The marriage of these two fields will make possible an unprecedented understanding of cell and tissue behaviour and function.

Anticancer Drug Affects Metabolomic Profiles in Multicellular Spheroids: Studies Using Mass Spectrometry Imaging Combined with Machine Learning

Multicellular spheroids (hereinafter referred to as spheroids) are 3D biological models. The metabolomic profiles inside spheroids provide crucial information reflecting the molecular phenotypes and microenvironment of cells. To study the influence of an anticancer drug on the spatially resolved metabolites, spheroids were cultured using HCT-116 colorectal cancer cells, treated with the anticancer drug Irinotecan under a series of time- and concentration-dependent conditions. The Single-probe mass spectrometry imaging (MSI) technique was utilized to conduct the experiments. The MSI data were analyzed using advanced data analysis methods to efficiently extract metabolomic information. Multivariate curve resolution alternating least square (MCR-ALS) was used to decompose each MS image into different components with grouped species. To improve the efficiency of data analysis, both supervised (Random Forest) and unsupervised (cluster large applications (CLARA)) machine learning (ML) methods were employed to cluster MS images according to their metabolomic features. Our results indicate that anticancer drug significantly affected the abundances of a variety of metabolites in different regions of spheroids. This integrated experiment and data analysis approach can facilitate the studies of metabolites in different types of 3D tumor models and tissues and potentially benefit the drug discovery, therapeutic resistance, and other biological research fields.

Metabolomics in chronic lung diseases

Chronic lung diseases represent a significant global burden. Their increasing incidence and complexity render a comprehensive, multidisciplinary and personalized approach to each patient, critically important. Most recently, unique biochemical pathways and disease markers have been identified through large-scale metabolomic studies. Metabolomics is the study of metabolic pathways and the measurement of unique biomolecules in a living system. Analysing samples from different compartments such as bronchoalveolar lavage fluid (BALF) and plasma has proven useful for the characterization of a number of pathological conditions and offers promise as a clinical tool. For example, several studies using mass spectrometry (MS) have shown alterations in the sphingolipid metabolism of chronic obstructive pulmonary disease (COPD) sufferers. In this article, we present a practical review of the application of metabolomics to the study of chronic lung diseases (CLD): COPD, idiopathic pulmonary fibrosis (IPF) and asthma. The insights, which the analytical strategies employed in metabolomics, have provided to the dissection of the biochemistry of CLD and future clinical biomarkers are explored.

Spatial and molecular changes of mouse brain metabolism in response to immunomodulatory treatment with teriflunomide as visualized by MALDI-MSI

Multiple sclerosis (MS) is an immune-mediated neurodegenerative disease of the central nervous system (CNS). One of the most promising recent medications for MS is teriflunomide. Its primary mechanism of action is linked to effects on the peripheral immune system by inhibiting dihydroorotate dehydrogenase (DHODH)-catalyzed de novo pyrimidine synthesis and reducing the expansion of lymphocytes in the peripheral immune system. Some in vitro studies suggested, however, that it can also have a direct effect on the CNS compartment. This potential alternative mode of action depends on the drug's capacity to traverse the blood-brain barrier (BBB) and to exert an effect on the complex network of brain biochemical pathways. In this paper, we demonstrate the application of high-resolution/high-accuracy matrix-assisted laser desorption/ionization Fourier-transform ion cyclotron resonance mass spectrometry for molecular imaging of the mouse brain coronal sections from animals treated with teriflunomide. Specifically, in order to assess the effect of teriflunomide on the mouse CNS compartment, we investigated the feasibility of teriflunomide to traverse the BBB. Secondly, we systematically evaluated the spatial and semi-quantitative brain metabolic profiles of 24 different endogenous compounds after 4-day teriflunomide administration. Even though the drug was not detected in the examined cerebral sections (despite the high detection sensitivity of the developed method), in-depth study of the endogenous metabolic compartment revealed noticeable alterations as a result of teriflunomide administration compared to the control animals. The observed differences, particularly for purine and pyrimidine nucleotides as well as for glutathione and carbohydrate metabolism intermediates, shed some light on the potential impact of teriflunomide on the mouse brain metabolic networks. Graphical Abstract.

Pharmacometabolic response to pirfenidone in pulmonary fibrosis detected by MALDI-FTICR-MSI

Idiopathic pulmonary fibrosis (IPF) is a fatal condition that reduces life expectancy and shows a limited response to available therapies. Pirfenidone has been approved for treatment of IPF, but little is known about the distinct metabolic changes that occur in the lung upon pirfenidone administration.Here, we performed a proof-of-concept study using high-resolution quantitative matrix-assisted laser desorption/ionisation Fourier-transform ion cyclotron resonance mass spectrometry imaging (MALDI-FTICR-MSI) to simultaneously detect, visualise and quantify in situ endogenous and exogenous metabolites in lungs of mice subjected to experimental fibrosis and human patients with IPF, and to assess the effect of pirfenidone treatment on metabolite levels.Metabolic pathway analysis and endogenous metabolite quantification revealed that pirfenidone treatment restores redox imbalance and glycolysis in IPF tissues, and downregulates ascorbate and aldarate metabolism, thereby likely contributing to in situ modulation of collagen processing. As such, we detected specific alterations in metabolite pathways in fibrosis and, importantly, metabolic recalibration following pirfenidone treatment.Together, these results highlight the suitability of high-resolution MALDI-FTICR-MSI for deciphering the therapeutic effects of pirfenidone and provide a preliminary analysis of the metabolic changes that occur during pirfenidone treatment in vivo These data may therefore contribute to improvement of currently available therapies for IPF.

Visualization of Asparaptine in Asparagus (Asparagus officinalis) Using MALDI-IMS

Asparaptine, an inhibitor of angiotensin-converting enzyme (ACE), is a newly discovered compound in asparagus. Asparaptine is a conjugate of arginine and asparagusic acid; this compound is suggested to be effective for preventing high blood pressure. For this reason, asparaptine has attracted remarkable attention in recent years, and it was therefore necessary to carry out further research. No studies to date have investigated the localization of asparaptine in asparagus. In this study, the localization of asparaptine in asparagus was clarified using matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS), a powerful method to visualize molecules.

Fluorinated Gold Nanoparticles for Nanostructure Imaging Mass Spectrometry

Nanostructure imaging mass spectrometry (NIMS) with fluorinated gold nanoparticles (f-AuNPs) is a nanoparticle assisted laser desorption/ionization approach that requires low laser energy and has demonstrated high sensitivity. Here we describe NIMS with f-AuNPs for the comprehensive analysis of metabolites in biological tissues. F-AuNPs assist in desorption/ionization by laser-induced release of the fluorocarbon chains with minimal background noise. Since the energy barrier required to release the fluorocarbons from the AuNPs is minimal, the energy of the laser is maintained in the low μJ/pulse range, thus limiting metabolite in-source fragmentation. Electron microscopy analysis of tissue samples after f-AuNP NIMS shows a distinct "raising" of the surface as compared to matrix assisted laser desorption ionization ablation, indicative of a gentle desorption mechanism aiding in the generation of intact molecular ions. Moreover, the use of perfluorohexane to distribute the f-AuNPs on the tissue creates a hydrophobic environment minimizing metabolite solubilization and spatial dislocation. The transfer of the energy from the incident laser to the analytes through the release of the fluorocarbon chains similarly enhances the desorption/ionization of metabolites of different chemical nature, resulting in heterogeneous metabolome coverage. We performed the approach in a comparative study of the colon of mice exposed to three different diets. F-AuNP NIMS allows the direct detection of carbohydrates, lipids, bile acids, sulfur metabolites, amino acids, nucleotide precursors as well as other small molecules of varied biological origins. Ultimately, the diversified molecular coverage obtained provides a broad picture of a tissue's metabolic organization.

Characterization of microbial mixtures by mass spectrometry

MS applications in microbiology have increased significantly in the past 10 years, due in part to the proliferation of regulator-approved commercial MALDI MS platforms for rapid identification of clinical infections. In parallel, with the expansion of MS technologies in the "omics" fields, novel MS-based research efforts to characterize organismal as well as environmental microbiomes have emerged. Successful characterization of microorganisms found in complex mixtures of other organisms remains a major challenge for researchers and clinicians alike. Here, we review recent MS advances toward addressing that challenge. These include sample preparation methods and protocols, and established, for example, MALDI, as well as newer, for example, atmospheric pressure ionization (API) techniques. MALDI mass spectra of intact cells contain predominantly information on the highly expressed house-keeping proteins used as biomarkers. The API methods are applicable for small biomolecule analysis, for example, phospholipids and lipopeptides, and facilitate species differentiation. MS hardware and techniques, for example, tandem MS, including diverse ion source/mass analyzer combinations are discussed. Relevant examples for microbial mixture characterization utilizing these combinations are provided. Chemometrics and bioinformatics methods and algorithms, including those applied to large scale MS data acquisition in microbial metaproteomics and MS imaging of biofilms, are highlighted. Select MS applications for polymicrobial culture analysis in environmental and clinical microbiology are reviewed as well.

MassImager: A software for interactive and in-depth analysis of mass spectrometry imaging data

Mass spectrometry imaging (MSI) has become a powerful tool to probe molecule events in biological tissue. However, it is a widely held viewpoint that one of the biggest challenges is an easy-to-use data processing software for discovering the underlying biological information from complicated and huge MSI dataset. Here, a user-friendly and full-featured MSI software including three subsystems, Solution, Visualization and Intelligence, named MassImager, is developed focusing on interactive visualization, in-situ biomarker discovery and artificial intelligent pathological diagnosis. Simplified data preprocessing and high-throughput MSI data exchange, serialization jointly guarantee the quick reconstruction of ion image and rapid analysis of dozens of gigabytes datasets. It also offers diverse self-defined operations for visual processing, including multiple ion visualization, multiple channel superposition, image normalization, visual resolution enhancement and image filter. Regions-of-interest analysis can be performed precisely through the interactive visualization between the ion images and mass spectra, also the overlaid optical image guide, to directly find out the region-specific biomarkers. Moreover, automatic pattern recognition can be achieved immediately upon the supervised or unsupervised multivariate statistical modeling. Clear discrimination between cancer tissue and adjacent tissue within a MSI dataset can be seen in the generated pattern image, which shows great potential in visually in-situ biomarker discovery and artificial intelligent pathological diagnosis of cancer. All the features are integrated together in MassImager to provide a deep MSI processing solution at the in-situ metabolomics level for biomarker discovery and future clinical pathological diagnosis.

Metabolomics-A Promising Approach to Pituitary Adenomas

Background: Metabolomics-the novel science that evaluates the multitude of low-molecular-weight metabolites in a biological system, provides new data on pathogenic mechanisms of diseases, including endocrine tumors. Although development of metabolomic profiling in pituitary disorders is at an early stage, it seems to be a promising approach in the near future in identifying specific disease biomarkers and understanding cellular signaling networks. Objectives: To review the metabolomic profile and the contributions of metabolomics in pituitary adenomas (PA). Methods: A systematic review was conducted via PubMed, Web of Science Core Collection and Scopus databases, summarizing studies that have described metabolomic aspects of PA. Results: Liquid chromatography tandem mass spectrometry (LC-MS/MS) and nuclear magnetic resonance (NMR) spectrometry, which are traditional techniques employed in metabolomics, suggest amino acids metabolism appears to be primarily altered in PA. N-acetyl aspartate, choline-containing compounds and creatine appear as highly effective in differentiating PA from healthy tissue. Deoxycholic and 4-pyridoxic acids, 3-methyladipate, short chain fatty acids and glucose-6-phosphate unveil metabolite biomarkers in patients with Cushing's disease. Phosphoethanolamine, N-acetyl aspartate and myo-inositol are down regulated in prolactinoma, whereas aspartate, glutamate and glutamine are up regulated. Phosphoethanolamine, taurine, alanine, choline-containing compounds, homocysteine, and methionine were up regulated in unclassified PA across studies. Intraoperative use of ultra high mass resolution matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI), which allows localization and delineation between functional PA and healthy pituitary tissue, may contribute to achievement of complete tumor resection in addition to preservation of pituitary cell lines and vasopressin secretory cells, thus avoiding postoperative diabetes insipidus. Conclusion: Implementation of ultra high performance metabolomics analysis techniques in the study of PA will significantly improve diagnosis and, potentially, the therapeutic approach, by identifying highly specific disease biomarkers in addition to novel molecular pathogenic mechanisms. Ultra high mass resolution MALDI-MSI emerges as a helpful clinical tool in the neurosurgical treatment of pituitary tumors. Therefore, metabolomics appears to be a science with a promising prospect in the sphere of PA, and a starting point in pituitary care.

Host-based lipid inflammation drives pathogenesis in Francisella infection

Mass spectrometry imaging (MSI) was used to elucidate host lipids involved in the inflammatory signaling pathway generated at the host-pathogen interface during a septic bacterial infection. Using Francisella novicida as a model organism, a bacterial lipid virulence factor (endotoxin) was imaged and identified along with host phospholipids involved in the splenic response in murine tissues. Here, we demonstrate detection and distribution of endotoxin in a lethal murine F. novicida infection model, in addition to determining the temporally and spatially resolved innate lipid inflammatory response in both 2D and 3D renderings using MSI. Further, we show that the cyclooxygenase-2-dependent lipid inflammatory pathway is responsible for lethality in F. novicida infection due to overproduction of proinflammatory effectors including prostaglandin E2. The results of this study emphasize that spatial determination of the host lipid components of the immune response is crucial to identifying novel strategies to effectively address highly pathogenic and lethal infections stemming from bacterial, fungal, and viral origins.

Mass Spectrometry Imaging in Oncology Drug Discovery

Over the last decade mass spectrometry imaging (MSI) has been integrated in to many areas of drug discovery and development. It can have significant impact in oncology drug discovery as it allows efficacy and safety of compounds to be assessed against the backdrop of the complex tumour microenvironment. We will discuss the roles of MSI in investigating compound and metabolite biodistribution and defining pharmacokinetic -pharmacodynamic relationships, analysis that is applicable to all drug discovery projects. We will then look more specifically at how MSI can be used to understand tumour metabolism and other applications specific to oncology research. This will all be described alongside the challenges of applying MSI to industry research with increased use of metrology for MSI.

In Situ Metabolomics in Cancer by Mass Spectrometry Imaging

Metabolomics is a rapidly evolving and a promising research field with the expectation to improve diagnosis, therapeutic treatment prediction, and prognosis of particular diseases. Among all techniques used to assess the metabolome in biological systems, mass spectrometry imaging is the method of choice to qualitatively and quantitatively analyze metabolite distribution in tissues with a high spatial resolution, thus providing molecular data in relation to cancer histopathology. The technique is ideally suited to study tissues molecular content and is able to provide molecular biomarkers or specific mass signatures which can be used in classification or the prognostic evaluation of tumors. Recently, it was shown that FFPE tissue samples are also suitable for metabolic analyses. This progress in methodology allows access to a highly valuable resource of tissues believed to widen and strengthen metabolic discovery-driven studies.

Spatially resolved metabolic distribution for unraveling the physiological change and responses in tomato fruit using matrix-assisted laser desorption/ionization-mass spectrometry imaging (MALDI-MSI)

Information on spatiotemporal metabolic behavior is indispensable for a precise understanding of physiological changes and responses, including those of ripening processes and wounding stress, in fruit, but such information is still limited. Here, we visualized the spatial distribution of metabolites within tissue sections of tomato (Solanum lycopersicum L.) fruit using a matrix-assisted laser desorption/ionization-mass spectrometry imaging (MALDI-MSI) technique combined with a matrix sublimation/recrystallization method. This technique elucidated the unique distribution patterns of more than 30 metabolite-derived ions, including primary and secondary metabolites, simultaneously. To investigate spatiotemporal metabolic alterations during physiological changes at the whole-tissue level, MALDI-MSI was performed using the different ripening phenotypes of mature green and mature red tomato fruits. Although apparent alterations in the localization and intensity of many detected metabolites were not observed between the two tomatoes, the amounts of glutamate and adenosine monophosphate, umami compounds, increased in both mesocarp and locule regions during the ripening process. In contrast, malate, a sour compound, decreased in both regions. MALDI-MSI was also applied to evaluate more local metabolic responses to wounding stress. Accumulations of a glycoalkaloid, tomatine, and a low level of its glycosylated metabolite, esculeoside A, were found in the wound region where cell death had been induced. Their inverse levels were observed in non-wounded regions. Furthermore, the amounts of both compounds differed in the developmental stages. Thus, our MALDI-MSI technique increased the understanding of the physiological changes and responses of tomato fruit through the determination of spatiotemporally resolved metabolic alterations. Graphical abstract ᅟ.

Infrared matrix-assisted laser desorption electrospray ionization mass spectrometry imaging analysis of biospecimens

Infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) mass spectrometry imaging (MSI) is a technique well suited for analysis of biological specimens. This tutorial review focuses on recent advancements and applications of IR-MALDESI MSI to better understand key biological questions. Through optimization of user-defined source parameters, comprehensive and quantitative MSI data can be obtained for a variety of analytes. The effect of an ice matrix layer is well defined in the context of desorption dynamics and resulting ion abundance. Optimized parameters and careful control of conditions affords quantitative MSI data which provides valuable information for targeted, label-free drug distribution studies and untargeted metabolomic datasets. Challenges and limitations of MSI using IR-MALDESI are addressed in the context of the bioimaging field.

More than Pictures: When MS Imaging Meets Histology

Attaining high-resolution spatial information is a recurrent challenge in biological research, particularly in the case of small-molecule distribution. Mass spectrometry imaging (MSI) is an innovative molecular histology technique that could provide such information. It allows in situ and label-free measurement of both the abundance and distribution of a variety of molecules at the tissue or single cell level. The application of MSI in plant research has received considerable attention; thus, in this review, we describe the current state of MSI in plants. In particular, we present an overview of MSI approaches, highlight the recent technical and methodological developments, and discuss a range of applications contributing to the field of plant science.

High-mass-resolution MALDI mass spectrometry imaging of metabolites from formalin-fixed paraffin-embedded tissue

Formalin-fixed and paraffin-embedded (FFPE) tissue specimens are the gold standard for histological examination, and they provide valuable molecular information in tissue-based research. Metabolite assessment from archived tissue samples has not been extensively conducted because of a lack of appropriate protocols and concerns about changes in metabolite content or chemical state due to tissue processing. We present a protocol for the in situ analysis of metabolite content from FFPE samples using a high-mass-resolution matrix-assisted laser desorption/ionization fourier-transform ion cyclotron resonance mass spectrometry imaging (MALDI-FT-ICR-MSI) platform. The method involves FFPE tissue sections that undergo deparaffinization and matrix coating by 9-aminoacridine before MALDI-MSI. Using this platform, we previously detected ∼1,500 m/z species in the mass range m/z 50-1,000 in FFPE samples; the overlap compared with fresh frozen samples is 72% of m/z species, indicating that metabolites are largely conserved in FFPE tissue samples. This protocol can be reproducibly performed on FFPE tissues, including small samples such as tissue microarrays and biopsies. The procedure can be completed in a day, depending on the size of the sample measured and raster size used. Advantages of this approach include easy sample handling, reproducibility, high throughput and the ability to demonstrate molecular spatial distributions in situ. The data acquired with this protocol can be used in research and clinical practice.

Impact of tissue surface properties on the desorption electrospray ionization imaging of organic acids in grapevine stem

RATIONALE

Desorption electrospray ionization (DESI) imaging is a fast analytical technique used to assess spatially resolved biological processes over unmodified sample surfaces. Although DESI profiling experiments have demonstrated that the properties of the sample surface significantly affect the outcomes of DESI analyses, the potential implications of these phenomena in imaging applications have not yet been explored extensively.

METHODS

The distribution of endogenous and exogenous organic acids in pith and out pith region of grapevine stems was investigated by using DESI imaging, ion chromatography and direct infusion methods. Several common normalization strategies to account for the surface effect, including TIC normalization, addition of the internal standard in the spray solvent and deposition of the standard over the sample surface, were critically evaluated.

RESULTS

DESI imaging results show that, in our case, the measured distributions of these small organic acids are not consistent with their 'true' localizations within the tissues. Furthermore, our results indicate that the common normalization strategies are not able to completely compensate for the observed surface effect.

CONCLUSIONS

Variations in the tissue surface properties across the tissue sample can greatly affect the semi-quantitative detection of organic acids. Attention should be paid when interpreting DESI imaging results and an independent analytical validation step is important in untargeted DESI imaging investigations.

Mass spectrometric imaging of metabolites in kidney tissues from rats treated with furosemide

In the kidney, metabolic processes are different among the cortex (COR), outer medulla (OM), and inner medulla (IM). Using matrix-assisted laser desorption/ionization (MALDI) and imaging mass spectrometry (IMS), we examined the change of metabolites in the COR, OM, and IM of the rat kidney after furosemide treatment compared with vehicle-treated controls. Osmotic minipumps were implanted in male Sprague-Dawley rats to deliver 12 mg·day(-1)·rat(-1) of furosemide. Vehicle-treated (n = 14) and furosemide-treated (furosemide rats, n = 15) rats in metabolic cages received a fixed amount of rat chow (15 g·220 g body wt(-1)·day(-1) for each rat) with free access to water intake for 6 days. At day 6, higher urine output (32 ± 4 vs. 9 ± 1 ml/day) and lower urine osmolality (546 ± 44 vs. 1,677 ± 104 mosmol/kgH2O) were observed in furosemide rats. Extracts of COR, OM, and IM were analyzed by ultraperformance liquid chromatography coupled with quadrupole time-of-flight (TOF) mass spectrometry, where multivariate analysis revealed significant differences between the two groups. Several metabolites, including acetylcarnitine, betaine, carnitine, choline, and glycerophosphorylcholine (GPC), were significantly changed. The changes of metabolites were further identified by MALDI-TOF/TOF and IMS. Their spatial distribution and relative quantitation in the kidneys were analyzed by IMS. Carnitine compounds were increased in COR and IM, whereas carnitine and acetylcarnitine were decreased in OM. Choline compounds were increased in COR and OM but decreased in IM from furosemide rats. Betaine and GPC were decreased in OM and IM. Taken together, MALDI-TOF/TOF and IMS successfully provide the spatial distribution and relative quantitation of metabolites in the kidney.

Laser Ablation with Vacuum Capture for MALDI Mass Spectrometry of Tissue

We have developed a laser ablation sampling technique for matrix-assisted laser desorption ionization (MALDI) mass spectrometry and tandem mass spectrometry (MS/MS) analyses of in-situ digested tissue proteins. Infrared laser ablation was used to remove biomolecules from tissue sections for collection by vacuum capture and analysis by MALDI. Ablation and transfer of compounds from tissue removes biomolecules from the tissue and allows further analysis of the collected material to facilitate their identification. Laser ablated material was captured in a vacuum aspirated pipette-tip packed with C18 stationary phase and the captured material was dissolved, eluted, and analyzed by MALDI. Rat brain and lung tissue sections 10 μm thick were processed by in-situ trypsin digestion after lipid and salt removal. The tryptic peptides were ablated with a focused mid-infrared laser, vacuum captured, and eluted with an acetonitrile/water mixture. Eluted components were deposited on a MALDI target and mixed with matrix for mass spectrometry analysis. Initial experiments were conducted with peptide and protein standards for evaluation of transfer efficiency: a transfer efficiency of 16% was obtained using seven different standards. Laser ablation vacuum capture was applied to freshly digested tissue sections and compared with sections processed with conventional MALDI imaging. A greater signal intensity and lower background was observed in comparison with the conventional MALDI analysis. Tandem time-of-flight MALDI mass spectrometry was used for compound identification in the tissue.

Metabolomics, Standards, and Metabolic Modeling for Synthetic Biology in Plants

Life on earth depends on dynamic chemical transformations that enable cellular functions, including electron transfer reactions, as well as synthesis and degradation of biomolecules. Biochemical reactions are coordinated in metabolic pathways that interact in a complex way to allow adequate regulation. Biotechnology, food, biofuel, agricultural, and pharmaceutical industries are highly interested in metabolic engineering as an enabling technology of synthetic biology to exploit cells for the controlled production of metabolites of interest. These approaches have only recently been extended to plants due to their greater metabolic complexity (such as primary and secondary metabolism) and highly compartmentalized cellular structures and functions (including plant-specific organelles) compared with bacteria and other microorganisms. Technological advances in analytical instrumentation in combination with advances in data analysis and modeling have opened up new approaches to engineer plant metabolic pathways and allow the impact of modifications to be predicted more accurately. In this article, we review challenges in the integration and analysis of large-scale metabolic data, present an overview of current bioinformatics methods for the modeling and visualization of metabolic networks, and discuss approaches for interfacing bioinformatics approaches with metabolic models of cellular processes and flux distributions in order to predict phenotypes derived from specific genetic modifications or subjected to different environmental conditions.