Desorption Electrospray Ionization Mass Spectrometry Imaging Research Articles

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

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

Integration of ion-mobility high-resolution liquid chromatography/mass spectrometry-based untargeted metabolomics and desorption electrospray ionization-mass spectrometry imaging to unveil the ginsenosides variation induced by steaming for Panax ginseng, P. quinquefolius and P. notoginseng

The raw and steamed products of ginseng exhibit different efficacy, and elucidation of the underlying chemical transformation is significant for their rational application and quality control. This work was designed to holistically depict the chemical variation of Panax ginseng (PG), P. quinquefolius (PQ), and P. notoginseng (PN) induced by the steaming, and to unveil the steaming-associated markers diagnostic for differentiating between the raw and processed products, following a three-step strategy: 1) systematic ginsenosides characterization by hybrid scan (namely HDDIDDA) available on the Vion™ ion mobility-quadrupole time-of-flight mass spectrometer coupled with reversed-phase ultra-high performance liquid chromatography; 2) holistic depiction of the chemical variation and discovery of potential markers by the pattern recognition untargeted metabolomics analysis; and 3) construction of steaming-induced transformation network combined with desorption electrospray ionization-mass spectrometry imaging (DESI-MSI). Consequently, 542 ginsenosides were characterized from the raw and processed PG/PQ/PN. Steaming at 1–10 h could cause significant chemical variation, and separately 26, 28, and 18 potential markers were found for the steaming of PG, PQ, and PN. Steaming-induced transformation network mainly involved hydration of the malonyl group, hydrolysis of the glycosyl moiety, and dehydration at C-20. DESI-MSI further revealed spatial distribution of marker saponins in the cork layer, phloem, and xylem, and primarily confirmed the hydrolysis reactions occurring to ginseng steaming. Conclusively, the established strategy is practical to unveil the holistic ginsenosides variation of ginseng induced by the processing, which is useful in the quality control of both the herbal medicines and foods.

Mapping the Metabolic Characteristics and Perturbation of Adult Casper Zebrafish by Ambient Mass Spectrometry Imaging.

Casper, a type of transparent mutant-line zebrafish, was generated to overcome the opaque trunk of an adult zebrafish for tumor modeling to realize real-time visualization of transplanted cells in vivo. However, the molecular information at the metabolic level has not received much attention. Herein, a spatially resolved metabolomics method based on an airflow-assisted desorption electrospray ionization-mass spectrometry imaging (AFADESI-MSI) system for whole-body zebrafish was used to investigate small molecules and the distribution of adult casper (Mitfaw2/w2, roya9/a9) and the differences from wild-type zebrafish. Finally, the spatial distribution information of more than 1500 endogenous ions was obtained in positive and negative detection modes, and 186 metabolites belonging to a variety of structural categories were identified or annotated. Compared with wild-type samples, 85 variables, including 37 known metabolites, were screened out. In addition, the disordered metabolic pathways caused by the genetic mutation were excavated, involving downregulation of purine metabolism and arachidonic acid metabolism, upregulation of glycerophospholipid metabolism, and biosynthesis of unsaturated fatty acids. All these results were observed in the most intuitive way through MSI. This study revealed important metabolic characteristics of and perturbation in adult casper zebrafish, and provides indispensable fundamental knowledge for tumor research based on it.

Synthesis and tissue distribution of bufadienolides in Bufo bufo gargarizans

This study explored the biosynthesis of bufadienolides(BDs) in Bufo bufo gargarizans to solve the dilemma of the decreasing resources of B. bufo gargarizans and provide a theoretical basis for the sustainable utilization of the resources. Ultra-high performance liquid chromatography-Orbitrap-mass spectrometry(UHPLC-Orbitrap-MS) was employed to detect the synthesis sites of BDs in B. bufo gargarizans, and the results were verified by desorption electrospray ionization-mass spectrometry imaging(DESI-MSI) and homogenate incubation experiments. BDs in B. bufo gargarizans had the highest content in the liver and the highest concentration in the gallbladder, in addition to the parotid gland and skin, which suggested that the liver could synthesize BDs. The results of DESI-MSI also showed that BDs were mainly enriched in the liver rather than the immature parotid gland. The incubation experiment of liver homogenates demonstrated the liver of B. bufo gargarizans had the ability to synthesize BDs. This study showed that the liver was a major organ for the synthesis of BDs in B. bufo gargarizans during metamorphosis, development, and growth, which provided strong theoretical support for the biosynthesis of BDs and the sustainable utilization of B. bufo gargarizans resources.

Radioinduced Modulatory Response of Lipid Profiles of Wistar Rats Brain Monitored by DESI-MS Imaging

The ionizing radiation used in radiotherapy can break down deoxyribonucleic acid bonds, generate reactive oxygen and nitrogen species, alter intracellular proteins, and induce changes in the lipidome. This study delves into examining lipid classes and their spatial distribution in the brains of Wistar rats exposed to ionizing radiation. Healthy male isogenic Wistar rats (n = 15) were divided into a control group (CG, n = 3) and an irradiated group (IRG, n = 12). The individuals of the IRG group were subjected to total body irradiation (TBI) using a Co-60 source and a radiation dose of 5 Gy. The IRG group was subdivided into four subgroups corresponding to 168, 96, 48, and 24 h before sacrifice. The brains were sliced into the sagittal direction, and the slices were analyzed using the desorption electrospray ionization mass spectrometry imaging (DESI-MSI) approach. The mass spectrometry results and information from the Metabolite and Chemical Entity Database (METLINTM) and Lipid Metabolites and Pathways Strategy (LIPID MAPS) databases enabled the identification of lipids belonging to distinct classes. The DESI-MS images revealed distinctive spatial distributions of these lipids and the modulatory response induced by the radiation over time.

Noninvasive Detection of Skin Cancer by Imprint Desorption Electrospray Ionization Mass Spectrometry Imaging.

We report a technique for the noninvasive detection of skin cancer by imprint desorption electrospray ionization mass spectrometry imaging (DESI-MSI) using a transfer agent that is pressed against the tissue of interest. By noninvasively pressing a tape strip against human skin, metabolites, fatty acids, and lipids on the skin surface are transferred to the tape with little spatial distortion. Running DESI-MSI on the tape strip provides chemical images of the molecules on the skin surface, which are valuable for distinguishing cancer from healthy skin. Chemical components of the tissue imprint on the tape strip and the original basal cell carcinoma (BCC) section from the mass spectra show high consistency. By comparing MS images (about 150-μm resolution) of same molecules from the tape strip and from the BCC section, we confirm that chemical patterns are successfully transferred to the tape stripe. We also used the technique to distinguish cherry angiomas from normal human skin by comparing the molecular patterns from a tape strip. These results demonstrate the potential of the imprint DESI-MSI technique for the noninvasive detection of skin cancers as well as other skin diseases before and during clinical surgery.

Geographical distribution-based differentiation of cultivated Angelica dahurica, exploring the relationship between the secretory tract and the quality

Based on geographical distribution, cultivated Chinese Angelica dahurica has been divided into Angelica dahurica cv. ‘Hangbaizhi’ (HBZ) and Angelica dahurica cv. ‘Qibaizhi’ (QBZ). Long-term geographical isolation has led to significant quality differences between them. The secretory structure in medicinal plants, as a place for accumulating effective constituents and information transmission to the environment, links the environment with the quality of medicinal materials. However, the secretory tract differences between HBZ and QBZ has not been revealed. This study aimed to explore the relationship between the secretory tract and the quality of two kinds of A. dahurica. Root samples were collected at seven development phases. High-Performance Liquid Chromatography (HPLC) and Desorption Electrospray Ionization Mass Spectrometry Imaging (DESI-MSI) were used for the content determination and spatial location of coumarins. Paraffin section was used to observe and localize the root secretory tract. Origin, CaseViewer, and HDI software were used for data analysis and image processing. The results showed that compared to QBZ, HBZ, with better quality, has a larger area of root secretory tracts. Hence, the root secretory tract can be included in the quality evaluation indicators of A. dahurica. Additionally, DESI-MSI technology was used for the first time to elucidate the temporal and spatial distribution of coumarin components in A. dahurica root tissues. This study provides a theoretical basis for the quality evaluation and breeding of improved varieties of A. dahurica and references the DESI-MSI technology used to analyze the metabolic differences of various compounds, including coumarin and volatile oil, in different tissue parts of A. dahurica.

Spatial Distribution and Characterization of the Small-Molecule Metabolites and In Situ Hydrolyzed Oligosaccharides in the Rhizome of Glycyrrhiza uralensis by Desorption Electrospray Ionization-Mass Spectrometry Imaging and High-Resolution Liquid Chromatography-Mass Spectrometry.

Characterization and spatial distribution studies of the metabolome in plants are crucial for revealing the physiology of plants and developing functional foods. Using the rhizome of Glycyrrhiza uralensis as a case, we integrated desorption electrospray ionization-mass spectrometry imaging (DESI-MSI) and high-resolution liquid chromatography/mass spectrometry approaches aimed at characterizing and locating both the small molecules and the macromolecular polysaccharides. Under the optimal conditions, 21 flavonoids and 12 triterpenoids were detected and characterized in different tissues of the rhizome and another 19 components were characterized exclusively by DESI-MSI. Combined with hydrophilic interaction chromatography/ion mobility-quadrupole time-of-flight mass spectrometry, eight different degrees of polymerization of oligosaccharides (after in situ acid hydrolysis) were characterized from the rhizome of G. uralensis. Majority of these metabolites are located in the cortex, phloem, and medulla, which lays the foundation for understanding the physiology of G. uralensis. The useful information can benefit the sustainable utilization and further development of Glycyrrhiza resource.

Identifying lipid traces of atherogenic mechanisms in human carotid plaque

Background and aimsLipids play an important role in atherosclerotic plaque development and are interesting candidate predictive biomarkers. However, the link between circulating lipids, accumulating lipids in the vessel wall, and plaque destabilization processes in humans remains largely unknown. This study aims to provide new insights into the role of lipids in atherosclerosis using lipidomics and mass spectrometry imaging to investigate lipid signatures in advanced human carotid plaque and plasma samples. MethodsWe used lipidomics and desorption electrospray ionization mass spectrometry imaging (DESI-MSI) to investigate lipid signatures of advanced human carotid plaque and plasma obtained from patients who underwent carotid endarterectomy (n = 14 out of 17 whose plaque samples were analyzed by DESI-MSI). Multivariate data analysis and unsupervised clustering were applied to identify lipids that were the most discriminative species between different patterns in plaque and plasma. These patterns were interpreted by quantitative comparison with conventional histology. ResultsLipidomics detected more than 300 lipid species in plasma and plaque, with markedly different relative abundances. DESI-MSI visualized the spatial distribution of 611 lipid-related m/z features in plaques, of which 330 m/z features could be assigned based on exact mass, comparison to the lipidomic data, and high mass resolution MSI. Matching spatial lipid patterns to histological areas of interest revealed several molecular species that were colocalized with pertinent disease processes in plaque including specific sphingomyelin and ceramide species with calcification, phospholipids and free fatty acids with inflammation, and triacylglycerols and phosphatidylinositols with fibrin-rich areas. ConclusionsBy comparing lipid species in plaque and plasma, we identified those circulating species that were also prominently present in plaque. Quantitative comparison of lipid spectral patterns with histology revealed the presence of specific lipid species in destabilized plaque areas, corroborating previous in vitro and animal studies.

Unveiling the distribution of chemical constituents at different body parts and maturity stages of Ganoderma lingzhi by combining metabolomics with desorption electrospray ionization mass spectrometry imaging (DESI)

Ganoderma lingzhi is an important medicinal fungus, which is widely used as dietary supplement and for pharmaceutical industries. However, the spatial distribution and dynamic accumulation pattern of active components such as ganoderic acids (GAs) among different parts of G. lingzhi fruiting body are still unclear. In this study, desorption electrospray ionization mass spectrometry imaging (DESI-MSI) with untargeted metabolomics analysis was applied to investigate the metabolites distribution within G. lingzhi fruiting body at four different maturity stages (squaring, opening, maturation and harvesting stage). A total of 132 metabolites were characterized from G. lingzhi, including 115 triterpenoids, 11 fatty acids and other component. Most of the GAs content in the cap was significantly higher than that in the stipe, with six components such as ganoderic acid B being extremely significant. GAs in the cap was mainly present in the bottom edge of the mediostratum layer, such as ganoderic A-I and ganoderic GS-1, while in the stipe, they were mainly distributed in the shell layer and the context layer, such as ganoderic A-F. Most ganoderic acids content in both the stipe and the cap of G. lingzhi was gradually decreased with the development of G. lingzhi. The GAs in the stipe was gradually transferred from the shell layer to the content layer, while the distribution of GAs among different tissues of the cap was not significantly changed. In addition, linoleic acid, 9-HODE, 9-KODE and other fatty acids were mainly accumulated in the opening and maturing stage of the caps. This study further clarifies the spatial dynamic distribution of GAs in G. lingzhi fruiting body at four different maturity stages (squaring, opening, maturation and harvesting stage), which provides a basis for the rational utilization of the medicinal parts of G. lingzhi. Furthermore, mass spectrometry imaging combined with non-target metabolome analysis provides a powerful tool for the spatial distribution of active substances in the different regions of the medicinal edible fungi.

Spatial metabolomics method to reveal differential metabolomes in microregions of Panax quinquefolius roots by using ultra-performance liquid chromatography quadrupole/time of flight-mass spectrometry and desorption electrospray ionization mass spectrometry imaging

Panax quinquefolius is a natural homology medicine and food that is rich in bioactive ingredients, such as ginsenosides and polysaccharides. The combination of ultra-performance liquid chromatography quadrupole/time of flight-mass spectrometry (UPLC-Q-TOF/MS) and desorption electrospray ionization mass spectrometry imaging (DESI-MSI) was used for the first time in a spatial metabolomics analysis to comprehensively evaluate the differential components in different microregions of P. quinquefolius. UPLC-Q-TOF/MS and DESI-MSI combined with principal component analysis and orthogonal partial least squares-discriminant analysis were used to screen differential metabolites. UPLC-Q-TOF/MS and DESI-MSI screened 27 and 23 differential metabolites, respectively, among which 15 differential metabolites were identified by both methods. It was found that some components, such as ginsenoside Rg1 and malonyl-ginsenoside Rc, were mainly distributed in P of the transverse slice of P. quinquefolius roots, while ginsenoside Ro and malonyl-ginsenoside Rd were mainly distributed in C. The methods and results of this study could be used to understand the precise localization, biosynthesis, and biological functions of special metabolites in P. quinquefolius.

Characterization of ginsenosides from Panax japonicus var. major (Zhu-Zi-Shen) based on ultra-high performance liquid chromatography/quadrupole time-of-flight mass spectrometry and desorption electrospray ionization-mass spectrometry imaging

BackgroundPanax japonicus var. major (PJM) belongs to the well-known ginseng species used in west China for hundreds of years, which has the effects of lung tonifying and yin nourishing, and exerts the analgesic, antitussive, and hemostatic activities. Compared with the other Panax species, the chemical composition and the spatial tissue distribution of the bioactive ginsenosides in PJM have seldom been investigated.MethodsUltra-high performance liquid chromatography/quadrupole time-of-flight mass spectrometry (UHPLC/QTOF-MS) and desorption electrospray ionization-mass spectrometry imaging (DESI-MSI) were integrated for the systematic characterization and spatial tissue distribution studies of ginsenosides in the rhizome of PJM. Considering the great difficulty in exposing the minor saponins, apart from the conventional Auto MS/MS (M1), two different precursor ions list-including data-dependent acquisition (PIL-DDA) approaches, involving the direct input of an in-house library containing 579 known ginsenosides (M2) and the inclusion of the target precursors screened from the MS1 data by mass defect filtering (M3), were developed. The in situ spatial distribution of various ginsenosides in PJM was profiled based on DESI-MSI with a mass range of m/z 100–1500 in the negative ion mode, with the imaging data processed by the High Definition Imaging (HDI) software.ResultsUnder the optimized condition, 272 ginsenosides were identified or tentatively characterized, and 138 thereof were possibly not ever reported from the Panax genus. They were composed by 75 oleanolic acid type, 22 protopanaxadiol type, 52 protopanaxatriol type, 16 octillol type, 19 malonylated, 35 C-17 side-chain varied, and 53 others. In addition, the DESI-MSI experiment unveiled the differentiated distribution of saponins, but the main location in the cork layer and phloem of the rhizome. The abundance of the oleanolic acid ginsenosides was high in the rhizome slice of PJM, which was consistent with the results obtained by UHPLC/QTOF-MS.ConclusionComprehensive characterization of the ginsenosides in the rhizome of PJM was achieved, with a large amount of unknown structures unveiled primarily. We, for the first time, reported the spatial tissue distribution of different subtypes of ginsenosides in the rhizome slice of PJM. These results can benefit the quality control and further development of PJM and the other ginseng species.

Adipose tissue composition determines its computed tomography radiodensity.

Adipose tissue radiodensity in computed tomography (CT) performed before surgeries can predict surgical difficulty. Despite its clinical importance, little is known about what influences radiodensity. This study combines desorption electrospray ionization mass spectrometry imaging (DESI-MSI) and electrospray ionization (ESI) with machine learning to unveil how chemical composition of adipose tissue determines its radiodensity. Patients in the study underwent abdominal surgeries. Before surgery, CT radiodensity of fat near operated sites was measured. Fifty-three fat samples were collected and analyzed by DESI-MSI, ESI, and histology, and then sorted by radiodensity, demographic parameters, and adipocyte size. A non-negative matrix factorization (NMF) algorithm was developed to differentiate between high and low radiodensities. No associations between radiodensity and patient age, gender, weight, height, or fat origin were found. Body mass index showed negative correlation with radiodensity. A substantial difference in chemical composition between adipose tissues of high and low radiodensities was observed. More radiodense tissues exhibited greater abundance of high molecular weight species, such as phospholipids of various types, ceramides, cholesterol esters and diglycerides, and about 70% smaller adipocyte size. Less radiodense tissue showed high abundance of short acyl-tail fatty acids. This study unveils the connection between abdominal adipose tissue radiodensity and its chemical composition. Because the radiodensity of the fat around the surgical site is associated with surgical difficulty, it is important to understand how adipose tissue composition affects this parameter. We conclude that fat tissue with a higher content of various phospholipids and waxy lipids is more CT radiodense. This study establishes the connection between the CT radiodensity of adipose tissue and its chemical composition. Clinicians may use this information for preoperative planning of surgical procedures, potentially modifying their surgical approach (for example, performing partial nephrectomy openly rather than laparoscopically). • Adipose tissue radiodensity values in computed tomography images taken prior to the surgery can potentially predict surgery difficulty. • Fifty-three human specimens were analyzed by advanced mass spectrometry, molecular imaging, and machine learning to establish the key features that determine Hounsfield units' values of adipose tissue. • The findings of this research will enable clinicians to better prepare for surgical procedures and select operative strategies.

Aggregated Molecular Phenotype Scores: Enhancing Assessment and Visualization of Mass Spectrometry Imaging Data for Tissue-Based Diagnostics.

Mass spectrometry imaging (MSI) has gained increasing popularity for tissue-based diagnostics due to its ability to identify and visualize molecular characteristics unique to different phenotypes within heterogeneous samples. Data from MSI experiments are often assessed and visualized using various supervised and unsupervised statistical approaches. However, these approaches tend to fall short in identifying and concisely visualizing subtle, phenotype-relevant molecular changes. To address these shortcomings, we developed aggregated molecular phenotype (AMP) scores. AMP scores are generated using an ensemble machine learning approach to first select features differentiating phenotypes, weight the features using logistic regression, and combine the weights and feature abundances. AMP scores are then scaled between 0 and 1, with lower values generally corresponding to class 1 phenotypes (typically control) and higher scores relating to class 2 phenotypes. AMP scores, therefore, allow the evaluation of multiple features simultaneously and showcase the degree to which these features correlate with various phenotypes. Due to the ensembled approach, AMP scores are able to overcome limitations associated with individual models, leading to high diagnostic accuracy and interpretability. Here, AMP score performance was evaluated using metabolomic data collected from desorption electrospray ionization MSI. Initial comparisons of cancerous human tissues to their normal or benign counterparts illustrated that AMP scores distinguished phenotypes with high accuracy, sensitivity, and specificity. Furthermore, when combined with spatial coordinates, AMP scores allow visualization of tissue sections in one map with distinguished phenotypic borders, highlighting their diagnostic utility.

Prediction of coexisting invasive carcinoma on ductal carcinoma in situ (DCIS) lesions by mass spectrometry imaging.

Due to limited biopsy samples, ~20% of DCIS lesions confirmed by biopsy are upgraded to invasive ductal carcinoma (IDC) upon surgical resection. Avoiding underestimation of IDC when diagnosing DCIS has become an urgent challenge in an era discouraging overtreatment of DCIS. In this study, the metabolic profiles of 284 fresh frozen breast samples, including tumor tissues and adjacent benign tissues (ABTs) and distant surrounding tissues (DSTs), were analyzed using desorption electrospray ionization-mass spectrometry (DESI-MS) imaging. Metabolomics analysis using DESI-MS data revealed significant differences in metabolite levels, including small-molecule antioxidants, long-chain polyunsaturated fatty acids (PUFAs) and phospholipids between pure DCIS and IDC. However, the metabolic profile in DCIS with invasive carcinoma components clearly shifts to be closer to adjacent IDC components. For instance, DCIS with invasive carcinoma components showed lower levels of antioxidants and higher levels of free fatty acids compared to pure DCIS. Furthermore, the accumulation of long-chain PUFAs and the phosphatidylinositols (PIs) containing PUFA residues may also be associated with the progression of DCIS. These distinctive metabolic characteristics may offer valuable indications for investigating the malignant potential of DCIS. By combining DESI-MS data with machine learning (ML) methods, various breast lesions were discriminated. Importantly, the pure DCIS components were successfully distinguished from the DCIS components in samples with invasion in postoperative specimens by a Lasso prediction model, achieving an AUC value of 0.851. In addition, pixel-level prediction based on DESI-MS data enabled automatic visualization of tissue properties across whole tissue sections. Summarily, DESI-MS imaging on histopathological sections can provide abundant metabolic information about breast lesions. By analyzing the spatial metabolic characteristics in tissue sections, this technology has the potential to facilitate accurate diagnosis and individualized treatment of DCIS by inferring the presence of IDC components surrounding DCIS lesions. © 2023 The Pathological Society of Great Britain and Ireland.

Spatially Resolved Metabolomics Method for Mapping the Global Molecular Landscape of Whole-Body Zebrafish (Danio rerio) Using Ambient Mass Spectrometry Imaging.

Zebrafish (Danio rerio) represent an effective model biological material for human disease research, even for personalized precision medicine. Thus, it is necessary to fully characterize their molecular information in order to obtain a global metabolic profile. Here, a spatially resolved metabolomics method for whole-body zebrafish analysis was established based on an air-flow-assisted desorption electrospray ionization-mass spectrometry imaging (AFADESI-MSI) system. Using the optimized experimental conditions, the method provided high-quality visual distribution information for >1000 functional metabolites, thereby organ-specific metabolites characterizing nine regions were obtained comprehensively, including the eyes, brain, gill, heart, liver, kidney, intestine, muscle, and spinal cord. Then, combined with metabolic pathway analysis, a global metabolic network with in situ information on zebrafish was mapped for the first time. We also tried to use the recently published MSI database to annotate the metabolites in this study; however, the annotation rate was only 33.7 and 10.4% in positive and negative modes, respectively. This further demonstrated the necessity of establishing a suitable AFADESI-MSI method for zebrafish samples. These results offer comprehensive and in-depth molecular information about zebrafish at the metabolic level, which facilitates the use of zebrafish models to understand metabolic reprogramming in human diseases and the development of zebrafish disease models.

Molecular characterization of human peripheral nerves using desorption electrospray ionization mass spectrometry imaging.

Desorption electrospray ionization mass spectrometry imaging (DESI-MSI) is a molecular imaging method that can be used to elucidate the small-molecule composition of tissues and map their spatial information using two-dimensional ion images. This technique has been used to investigate the molecular profiles of variety of tissues, including within the central nervous system, specifically the brain and spinal cord. To our knowledge, this technique has yet to be applied to tissues of the peripheral nervous system (PNS). Data generated from such analyses are expected to advance the characterization of these structures. The study aimed to: (i) establish whether DESI-MSI can discriminate the molecular characteristics of peripheral nerves and distinguish them from surrounding tissues and (ii) assess whether different peripheral nerve subtypes are characterized by unique molecular profiles. Four different nerves for which are known to carry various nerve fiber types were harvested from a fresh cadaveric donor: mixed, motor and sensory (sciatic and femoral); cutaneous, sensory (sural); and autonomic (vagus). Tissue samples were harvested to include the nerve bundles in addition to surrounding connective tissue. Samples were flash-frozen, embedded in optimal cutting temperature compound in cross-section, and sectioned at 14 μm. Following DESI-MSI analysis, identical tissue sections were stained with hematoxylin and eosin. In this proof-of-concept study, a combination of multivariate and univariate statistical methods was used to evaluate molecular differences between the nerve and adjacent tissue and between nerve subtypes. The acquired mass spectral profiles of the peripheral nerve samples presented trends in ion abundances that seemed to be characteristic of nerve tissue and spatially corresponded to the associated histology of the tissue sections. Principal component analysis (PCA) supported the separation of the samples into distinct nerve and adjacent tissue classes. This classification was further supported by the K-means clustering analysis, which showed separation of the nerve and background ions. Differences in ion expression were confirmed using ANOVA which identified statistically significant differences in ion expression between the nerve subtypes. The PCA plot suggested some separation of the nerve subtypes into four classes which corresponded with the nerve types. This was supported by the K-means clustering. Some overlap in classes was noted in these two clustering analyses. This study provides emerging evidence that DESI-MSI is an effective tool for metabolomic profiling of peripheral nerves. Our results suggest that peripheral nerves have molecular profiles that are distinct from the surrounding connective tissues and that DESI-MSI may be able to discriminate between nerve subtypes. DESI-MSI of peripheral nerves may be a valuable technique that could be used to improve our understanding of peripheral nerve anatomy and physiology. The ability to utilize ambient mass spectrometry techniques in real time could also provide an unprecedented advantage for surgical decision making, including in nerve-sparing procedures in the future.

Distinguishing Renal Cell Carcinoma From Normal Kidney Tissue Using Mass Spectrometry Imaging Combined With Machine Learning.

Accurately distinguishing renal cell carcinoma (RCC) from normal kidney tissue is critical for identifying positive surgical margins (PSMs) during partial and radical nephrectomy, which remains the primary intervention for localized RCC. Techniques that detect PSM with higher accuracy and faster turnaround time than intraoperative frozen section (IFS) analysis can help decrease reoperation rates, relieve patient anxiety and costs, and potentially improve patient outcomes. Here, we extended our combined desorption electrospray ionization mass spectrometry imaging (DESI-MSI) and machine learning methodology to identify metabolite and lipid species from tissue surfaces that can distinguish normal tissues from clear cell RCC (ccRCC), papillary RCC (pRCC), and chromophobe RCC (chRCC) tissues. From 24 normal and 40 renal cancer (23 ccRCC, 13 pRCC, and 4 chRCC) tissues, we developed a multinomial lasso classifier that selects 281 total analytes from over 27,000 detected molecular species that distinguishes all histological subtypes of RCC from normal kidney tissues with 84.5% accuracy. On the basis of independent test data reflecting distinct patient populations, the classifier achieves 85.4% and 91.2% accuracy on a Stanford test set (20 normal and 28 RCC) and a Baylor-UT Austin test set (16 normal and 41 RCC), respectively. The majority of the model's selected features show consistent trends across data sets affirming its stable performance, where the suppression of arachidonic acid metabolism is identified as a shared molecular feature of ccRCC and pRCC. Together, these results indicate that signatures derived from DESI-MSI combined with machine learning may be used to rapidly determine surgical margin status with accuracies that meet or exceed those reported for IFS.

Mass Spectrometry Imaging of Lumpectomy Specimens Deciphers Diacylglycerols as Potent Biomarkers for the Diagnosis of Breast Cancer.

Detecting breast tumor markers with a fast turnaround time from frozen sections should foster intraoperative histopathology in breast-conserving surgery, reducing the need for a second operation. Hence, rapid label-free discrimination of the spatially resolved molecular makeup between cancer and adjacent normal breast tissue is of growing importance. We performed desorption electrospray ionization mass spectrometry imaging (DESI-MSI) of fresh-frozen excision specimens, including cancer and paired adjacent normal sections, obtained from the lumpectomy of 73 breast cancer patients. The results demonstrate that breast cancer tissue posits sharp metabolic upregulation of diacylglycerol, a lipid second messenger that activates protein kinase C for promoting tumor growth. We identified four specific sn-1,2-diacylglycerols that outperformed all other lipids simultaneously mapped by the positive ion mode DESI-MSI for distinguishing cancers from adjacent normal specimens. This result contrasts with several previous DESI-MSI studies that probed metabolic dysregulation of glycerophospholipids, sphingolipids, and free fatty acids for cancer diagnoses. A random forest-based supervised machine learning considering all detected ion signals also deciphered the highest diagnostic potential of these four diacylglycerols with the top four importance scores. This led us to construct a classifier with 100% overall prediction accuracy of breast cancer by using the parsimonious set of four diacylglycerol biomarkers only. The metabolic pathway analysis suggested that increased catabolism of phosphatidylcholine in breast cancer contributes to diacylglycerol overexpression. These results open up opportunities for mapping diacylglycerol signaling in breast cancer in the context of novel therapeutic and diagnostic developments, including the intraoperative assessment of breast cancer margin status.

Chemical imaging reveals diverse functions of tricarboxylic acid metabolites in root growth and development

Understanding how plants grow is critical for agriculture and fundamental for illuminating principles of multicellular development. Here, we apply desorption electrospray ionization mass spectrometry imaging (DESI-MSI) to the chemical mapping of the developing maize root. This technique reveals a range of small molecule distribution patterns across the gradient of stem cell differentiation in the root. To understand the developmental logic of these patterns, we examine tricarboxylic acid (TCA) cycle metabolites. In both Arabidopsis and maize, we find evidence that elements of the TCA cycle are enriched in developmentally opposing regions. We find that these metabolites, particularly succinate, aconitate, citrate, and α-ketoglutarate, control root development in diverse and distinct ways. Critically, the developmental effects of certain TCA metabolites on stem cell behavior do not correlate with changes in ATP production. These results present insights into development and suggest practical means for controlling plant growth.