Desorption Electrospray Ionization Mass Spectrometry Research Articles

Comparative analysis of sucrose-embedding for whole-body zebrafish MSI by IR-MALDESI.

Infrared matrix-assisted laser desorption electrospray ionization mass spectrometry imaging (IR-MALDESI) conventionally utilizes fresh-frozen biological tissues with an ice matrix to improve the detection of analytes. Sucrose-embedding with paraformaldehyde fixation has demonstrated feasibility as an alternative matrix for analysis by IR-MALDESI by preserving tissue features and enhancing ionization of lipids. However, investigating multi-organ systems provides broader context for a biological study and can elucidate more information about a disease state as opposed to a single organ. Danio rerio, or zebrafish, are model organisms for various disease states and can be imaged as a multi-organ sample to analyze morphological and metabolomic preservation as a result of sample preparation. Herein, whole-body zebrafish were imaged to compare sucrose-embedding with paraformaldehyde fixation against conventional fresh-frozen sample preparation. Serial sections were analyzed with and without an ice matrix to evaluate if sucrose functions as an alternative energy-absorbing matrix for IR-MALDESI applications across whole-body tissues. The resulting four conditions were compared in terms of total putative lipid annotations and category diversity, coverage across the entire m/z range, and ion abundance. Ultimately, sucrose-embedded zebrafish had an increase in putative lipid annotations for the combination of putative annotations with and without the application of an ice matrix relative to fresh-frozen tissues which require the application of an ice matrix. Upon the use of an ice matrix, a greater number of high mass putative lipid annotations (e.g., glycerophospholipids, glycerolipids, and sphingolipids) were identified. Conversely, without an ice matrix, sucrose-embedded sections elucidated more putative annotations in lower molecular weight lipids, including fatty acyls and sterol lipids. Similar to the mouse brain model, sucrose-embedding increased putative lipid annotation and abundance for whole-body zebrafish.

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.

Illustrate the distribution and metabolic regulatory effects of pterostilbene in cerebral ischemia-reperfusion rat brain by mass spectrometry imaging and spatial metabolomics

Pterostilbene is a promising molecule with superior pharmacological activities and pharmacokinetic characteristics compared to its structural analogue resveratrol, which could be used to treat ischemic stroke. However, its mechanism is still unclear. The cutting-edge air flow-assisted desorption electrospray ionization mass spectrometry imaging (AFADESI-MSI) and spatial metabolomics analysis were applied to investigate the distribution of pterostilbene in ischemic rat brain and the changes of related small molecule metabolic pathways to further explore the potential mechanisms of pterostilbene against cerebral ischemia-reperfusion injury. This research found that pterostilbene could significantly restore cerebral microcirculation blood flow, reduce infarct volume, improve neurological function and ameliorate neuronal damage in ischemic rats. Moreover, pterostilbene was widely and abundantly distributed in ischemic brain tissue, laying a solid foundation for the rescue of ischemic penumbra. Further study revealed that pterostilbene played a therapeutic role in restoring energy supply, rebalancing neurotransmitters, reducing abnormal polyamine accumulation and phospholipid metabolism. These findings offer an opportunity to illustrate novel mechanisms of pterostilbene in the treatment of cerebral ischemia/reperfusion injury resulting from ischemic stroke.

Customized Surface Design to Increase Throughput of Ambient Ionization Mass Spectrometry Lipidomics.

Increased access to cheap and rapid mass spectrometry testing of biofluids is desirable for the analysis of disorders and diseases that may be linked to alterations in metabolite or lipid levels. The objective of this study is to establish an easily customized high-throughput workflow for the analysis of biological samples using desorption electrospray ionization-mass spectrometry (DESI-MS). The guiding principles of this workflow are the use of low-cost, open-source, and readily accessible materials with high-throughput and reproducibility. The design consists of 3 steps: (1) PARAFILM surface customization of size, shape, and depth of features on PARAFILM via 3D printed molds; (2) sample spotting via high-throughput robotics using the relatively inexpensive and open-source Opentrons platform to reduce variability and increase reliability of sample spotting; and (3) an open-source point-and-click graphical user interface (MSI.EAGLE) for data analysis via the R statistical language building on the Cardinal package. Here we describe this workflow and test optimal surface ionization characteristics by comparison of serum extracts spotted on PARAFILM and on PTFE (porous and nonporous). Untargeted analysis across three surfaces suggests that they are all suitable for ionization of a wide range of metabolites and lipids, with 3983 m/z features detected. Differential analysis of polar vs nonpolar serum extracts suggests that ∼80% of ions are desorbed preferentially from different surfaces. PARAFILM is less impacted by the interference of background ions derived from the surface. The developed system allows for a wide range of researchers to access custom surface design workflows and high-throughput analyses in a highly cost-effective manner.

Identification of Diagnostic Metabolic Signatures in Thyroid Tumors Using Mass Spectrometry Imaging.

"Gray zone" thyroid follicular tumors are difficult to diagnose, especially when distinguishing between benign follicular thyroid adenoma (FTA) and malignant carcinoma (FTC). Thus, proper classification of thyroid follicular diseases may improve clinical prognosis. In this study, the diagnostic performance of metabolite enzymes was evaluated using imaging mass spectrometry to distinguish FTA from FTC and determine the association between metabolite enzyme expression with thyroid follicular borderline tumor diagnosis. Air flow-assisted desorption electrospray ionization mass spectrometry imaging (AFAIDESI-MSI) was used to build a classification model for thyroid follicular tumor characteristics among 24 samples. We analyzed metabolic enzyme marker expression in an independent validation set of 133 cases and further evaluated the potential biological behavior of 19 thyroid borderline lesions. Phospholipids and fatty acids (FAs) were more abundant in FTA than FTC (p < 0.001). The metabolic enzyme panel, which included FA synthase and Ca2+-independent PLA2, was further validated in follicular thyroid tumors. The marker combination showed optimal performance in the validation group (area under the ROC, sensitivity, and specificity: 73.6%, 82.1%, and 60.6%, respectively). The findings indicate that AFAIDESI-MSI, in combination with low metabolic enzyme expression, could play a role in the diagnosis of thyroid follicular borderline tumors for strict follow-up.

Application of infrared matrix-assisted laser desorption electrospray ionization mass spectrometry for morphine imaging in brain tissue

Here, we present a method developed for the analysis of spatial distributions of morphine in mouse brain tissue using infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) coupled to a Q Exactive Plus mass spectrometer. The method is also capable of evaluating spatial distributions of the antiretroviral drug abacavir. To maximize sensitivity to morphine, we analyze various Orbitrap mass spectrometry acquisition modes utilizing signal abundance and frequency of detection as evaluation criteria. We demonstrate detection of morphine in mouse brain and establish that the selected ion monitoring mode provides 2.5 times higher sensitivity than the full-scan mode. We find that distributions of morphine and abacavir are highly correlated with the Pearson correlation coefficient R = 0.87. Calibration showed that instrument response is linear up to 40 pg/mm2 (3.8 μg/g of tissue).Graphical abstract

Multiple selected ion monitoring mode for sensitive imaging of eicosanoids in tissues using nanospray desorption electrospray ionization (nano-DESI) mass spectrometry

Nanospray desorption electrospray ionization mass spectrometry imaging (nano-DESI MSI) enables sensitive label-free mapping of multiple classes of molecules in biological samples. Previously, we demonstrated that combining nano-DESI MSI with selected ion monitoring (SIM) increases the sensitivity of the technique in a narrow m/z range. In this study, we use a series of adjacent multiple SIM (mSIM) windows to acquire nano-DESI MSI data with high sensitivity over a broader m/z range. We demonstrate that this approach substantially improves the quality of imaging data obtained for low-abundance eicosanoids, important lipid mediators derived from eicosapolyenoic acid products of arachidonic acid oxidation. Despite their importance as mediators of inflammation, asthma, fever, pain, hypertension, and stroke, eicosanoids have not been extensively investigated using MSI. This is mainly attributed to their low ionization efficiency and low, subnanomolar, concentrations in tissue, which make it difficult to detect these species in MSI experiments alongside abundant phospholipids. We demonstrate substantial enhancements in the signal-to-noise ratios (S/N) of eicosanoids using imaging in mSIM mode. We have also developed data processing and visualization tools to support this type of MSI data acquisition. S/N enhancements observed in mSIM mode are independent of the properties of analytes making this approach applicable to other low-abundance species in biological and environmental samples.

The royal food of termites shows king and queen specificity.

Society in eusocial insects is based on the reproductive division of labor, with a small number of reproductive individuals supported by a large number of nonreproductive individuals. Because inclusive fitness of all colony members depends on the survival and fertility of reproductive members, sterile members provide royals with special treatment. Here, we show that termite kings and queens each receive special food of a different composition from workers. Sequential analysis of feeding processes demonstrated that workers exhibit discriminative trophallaxis, indicating their decision-making capacity in allocating food to the kings and queens. Liquid chromatography tandem-mass spectrometry analyses of the stomodeal food and midgut contents revealed king- and queen-specific compounds, including diacylglycerols and short-chain peptides. Desorption electrospray ionization mass spectrometry imaging analyses of 13C-labeled termites identified phosphatidylinositol and acetyl-l-carnitine in the royal food. Comparison of the digestive tract structure showed remarkable differences in the volume ratio of the midgut-to-hindgut among castes, indicating that digestive division of labor underlies reproductive division of labor. Our demonstration of king- and queen-specific foods in termites provides insight into the nutritional system that underpins the extraordinary reproduction and longevity of royals in eusocial insects.

Mass spectrometry imaging of hair identifies daily maraviroc adherence in HPTN 069/ACTG A5305.

Objective measures of adherence for antiretrovirals used as pre-exposure prophylaxis (PrEP) are critical for improving preventative efficacy in both clinical trials and real-world application. Current objective adherence measures either reflect only recent behavior (eg days for plasma or urine) or cumulative behavior (eg months for dried blood spots). Here, we measured the accumulation of the antiretroviral drug maraviroc (MVC) in hair strands by infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) mass spectrometry imaging (MSI) to evaluate adherence behavior longitudinally at high temporal resolution. An MSI threshold for classifying daily adherence was established using clinical samples from healthy volunteers following directly observed dosing of 1 to 7 doses MVC/week. We then used the benchmarked MSI assay to classify adherence to MVC-based PrEP regimens in hair samples collected throughout the 48-week HPTN069/ACTGA5305 study. We found that only ~32% of investigated hair samples collected during the study's active dosing period showed consistent daily PrEP adherence throughout a retrospective period of 30 days, and also found that profiles of daily individual adherence from MSI hair analysis could identify when patients were and were not taking study drug. The assessment of adherence from MSI hair strand analysis was 62% lower than adherence classified using paired plasma samples, the latter of which may be influenced by white-coat adherence. These findings demonstrate the ability of MSI hair analysis to examine daily variability of adherence behavior over a longer-term measurement and offer the potential for longitudinal comparison with risk behavior to target patient-specific adherence interventions and improve outcomes.

Recent Advances in Mass Spectrometry-Based Spatially Resolved Molecular Imaging of Drug Disposition and Metabolomics.

Mass spectrometric imaging is a nontargeted, tag-free, high-throughput, and highly responsive analytical approach. The highly accurate molecular visualization detection technology enables qualitative and quantitative analyses of biologic tissues or cells scanned by mass spectrometry in situ, extracting known and unknown multiple compounds, and simultaneously assessing relative contents of targeting molecules by monitoring their molecular ions and pinpointing the spatial locations of those molecules distributed. Five mass spectrometric imaging techniques and their characteristics are introduced in the review, including matrix-assisted laser desorption ionization mass spectrometry, secondary ion mass spectrometry, desorption electrospray ionization mass spectrometry, laser ablation electrospray ionization mass spectrometry, and laser ablation inductively coupled plasma mass spectrometry. The mass spectrometry-based techniques provide the possibility for spatial metabolomics with the capability of high throughput and precision detection. The approaches have been widely employed to spatially image not only metabolome of endogenous amino acids, peptides, proteins, neurotransmitters, and lipids but also the disposition of exogenous chemicals, such as pharmaceutical agents, environmental pollutants, toxicants, natural products, and heavy metals. The techniques also provide us with spatial distribution imaging of analytes in single cells, tissue microregions, organs, and whole animals. SIGNIFICANCE STATEMENT: The review article includes an overview of five commonly used mass spectrometers for spatial imaging and describes the advantages and disadvantages of each. Examples of the technology applications cover drug disposition, diseases, and omics. Technical aspects of relative and absolute quantification by mass spectrometric imaging and challenges for future new applications are discussed as well. The reviewed knowledge may benefit the development of new drugs and provide a better understanding of biochemical processes related to physiology and diseases.

Colonic mucosal biopsy location can not affect the results of mucosal metabolomics and mucosal microbiota analysis in IBS.

To compare and analyze the mucosal metabolites and mucosal microbiota of different parts of colon in patients with IBS. A total of 10 patients with IBS-D and six healthy controls (HC) were enrolled. All enrolled participants underwent two biopsies of the ileocecal and sigmoid colon during colonoscopy. Metabolomic profiling of one piece of tissue was conducted using desorption electrospray ionization-mass spectrometry (DESI-MS), and the gut flora of the other piece was examined using 16S rRNA sequencing. The metabolic profiles and flora of the ileocecal and sigmoid colonic mucosa in each group were further analyzed in this study. (1) Principal components analysis (PCA) indicated that mucosal metabolites did not differ in different parts of the colon in either the IBS-D or HC groups. (2) In the mucosal microbiome analyses, no differences between the microbiota of the two parts of the colon were found by using Principal Co-ordinates Analysis (PCoA). In IBS group, comparing with sigmoid mucosa, the chao1 richness indice was higher and the Shannon index was lower in the ileocecal mucosa (p = 0.40, p = 0.22). However, in the HC group, microbiome analysis of the ileocecal mucosa showed lower values for Chao 1 and Shannon indices than those of the sigmoid colon mucosa (p = 0.06, p = 0.86). (3) Compared with the HC group, 1,113 metabolic signal peaks were upregulated, whereas 594 metabolites were downregulated in the IBS-D samples. Moreover, the PCA of the metabolites showed significant separation between the IBS-D and HC groups. (4) Chao1 expression was significantly higher in the mucosal microbiota with IBS-D than in the HC (p = 0.03). The Shannon index was lower in IBS-D, but the difference was not statistically significant (p = 0.53). PCoA revealed a significant difference in the microflora structure between the IBS-D and HC groups. The mucosal metabolic profile and mucosal flora structure of the colon were similar, despite different locations in IBS and healthy subjects. IBS had abnormal colonic mucosal metabolism and flora disturbances.

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.

Intra-operative desorption-electrospray ionization mass-spectrometry for real-time diagnosis, margin assessment, and maximization of brain cancer resection.

2071 Background: Despite advances in our understanding of brain cancer, the ability to discern between infiltrated and non-infiltrated tissue with current adjuncts, remains elusive. Isocitrate dehydrogenase (IDH) mutation is unique to IDH-mut glioma cells and can be used as a surrogate for tumor infiltration. Further, emerging evidence has shown unique signatures in brain cancer lipidomic profiling, with lipid metabolism playing a key role in brain cancer biology. Here, we aimed to evaluate the impact of desorption electrospray ionization-mass spectrometry (DESI-MS) as a near real-time method to determine infiltration status and the use of DESI-MS to perform intra-operative histological diagnosis. Methods: This is a prospective multi-institution cohort study using intra-operative DESI-MS on freshly obtained tumor samples to evaluate 2-hydroxyglutarate (2HG), with post-hoc validation by review by a board-certified neuropathologist. For tumor cell estimation and tissue diagnosis, high-throughput DESI-MS was used for rapid analysis of high-density arrays using lipidomic profiling. Analysis of the data was performed using a pre-written algorithm employing a combination of Python- and MATLAB-based custom software allowing results in less than a second per sample. Results: A total of 496 tumor biopsies from 72 patients have been analyzed. Earlier iterations focused on absolute quantitation of 2HG in 247 biopsies from 49 patients to determine IDH genotype yielding sensitivity, specificity, and accuracy values of 89, 100, and 94% for core biopsies (71). Improvement in accuracy was obtained with relative measurement of 2HG in comparison to glutamate allowing for detection of IDH-mut infiltration at the margin, resulting in 93% sensitivity, 100% specificity, and 98% accuracy with turnover times averaging 3 minutes in 183 biopsies from 23 patients. Margin assessment beyond the limits of neuro-navigation and surgeon assessment showed tumor infiltration in 88% of margin biopsies from IDH mutant tumors (30 of 34 biopsies) with 100% accuracy. Intra-operative tissue diagnosis was pursued in 66 unique tumor samples using DESI-MS lipidomic profiling, including samples of normal brain parenchyma, gliomas, meningiomas and pituitary adenomas, split into two microarray sets (36 and 40 samples). Using estimated principal components as input features, high accuracy ( &gt; 90%; ROC AUCs ≥ 0.89) was obtained for supervised tissue classification using simple machine learning algorithms. Conclusions: We present a novel system for intra-operative evaluation of tumor infiltration at the margin in gliomas and for intra-operative tissue diagnosis of brain cancer. Our method identified tumor infiltration beyond the surgical margins and serves as a foundation for future uses employing DESI-MS to allow for molecularly guided surgery in IDH-mut gliomas.

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.

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.

Unsaturated free fatty acid emulsion infusion into carotid artery enhances drug delivery to the rat brain.

To determine whether the blood-brain barrier (BBB) opens to enhance drug delivery during the acute stage of unsaturated fat embolism. We infused oleic, linoleic, and linolenic acid emulsions through the right common carotid artery of rats, followed by trypan blue for gross and lanthanum for electron microscopic (EM) examination. Doxorubicin and temozolomide were also administered, and then the rats were euthanized at 30min, 1h, and 2h. Trypan blue hue was analyzed to semiquantitatively measure BBB opening. Desorption electrospray ionization-mass spectrometry (DESI-MS) imaging was used to evaluate drug delivery. Trypan blue staining observed in each group 30min after emulsion infusion increased at 1h and decreased after 2h in the oleic acid group. The linoleic and linolenic acid groups showed weak staining over time. The hue and trypan blue analysis results were corroborative. EM showed tight junction opening, whereas DESI-MS imaging showed increased doxorubicin and temozolomide signal intensities in ipsilateral hemispheres of all three groups. We demonstrated that oleic, linoleic, and linolenic acid emulsions opened the BBB, promoting drug delivery to the brain. Hue analysis and DESI-MS imaging are appropriate for analysis of doxorubicin and temozolomide concentrations in brain tissue.

Rapid imaging of unsaturated lipids at isomer level using photoepoxidation

Unsaturated lipids play an essential role in living organisms, and their different isomers show significant functional differences. Therefore, in situ characterization of unsaturated lipids in tissues needs to be extended to isomer level. However, the exposure of tissue sections to an open environment for a long time may cause cell autolysis or corruption, and current unsaturated lipid imaging methods still face challenges in efficiency. This paper proposes an imaging method based on photoepoxidation coupled with air-flow-assisted desorption electrospray ionization mass spectrometry (AFADESI-MS) to rapidly realize the spatial characterization of unsaturated lipids at the isomer level. The technique has a fast response speed, high epoxide yield (>80%), and high diagnostic ion abundance. After 0.5 min of photoepoxidation, the derivation product yield ratio reached 24.6%. This method rapidly identified six glycerophospholipid isomers containing an 18:1 acyl chain in normal rat liver tissue. Then the imaging method was applied in nude mice lung cancer tissue and human thyroid cancer tissue, with only 3 min photoepoxidation. Results successfully characterized the location and range of unsaturated lipid isomers and revealed their enrichment in tumor tissue. In addition, the experiment shows that the variational trend of the ratio of unsaturated lipid isomers in different types of tumor samples is different. Based on the advantages of efficiency and convenience, this method is prospective for screening unsaturated lipid markers and pathological research of related diseases.

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.

Dynamic variations in the chemical constituents of Tiebangchui stir-fried with Zanba by integrating UPLC-Q-TOF-MS based metabolomics and DESI-MSI

Tiebangchui, which mainly originates from the dried root of Aconitum pendulum Busch, is an important traditional Tibetan medicine and has long been used for the treatment of various types of cold and pain. However, the excellent pharmacological activities of Tiebangchui are accompanied by high toxicity caused by Aconitum alkaloids. To ensure clinical medication safety, Tiebangchui is only used after processing. Stir-frying with Zanba is an effective and unique processing method in traditional Tibetan medicine, but chemical variations in raw and processed Tiebangchui are rarely reported. In this study, an integrated method combining ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS) and desorption electrospray ionization mass spectrometry imaging (DESI-MSI) was applied to explore chemical constituent variations, transformation mechanisms, and in situ spatial distributions of metabolites in raw Tiebangchui and its series of processed products. Three important diterpenoid alkaloids were quantified by HPLC. Results revealed visible differences between raw Tiebangchui and its Zanba stir-fried products, and 60 min was demonstrated to be the proper processing time. A total of 64 components were detected, and 32 of them were considered as metabolic markers to distinguish raw Tiebangchui and its processed products stir-fried for different time. Chemical changes were also visualized by DESI-MSI: the contents of aconitine and 3-deoxyaconitine significantly decreased, and those of benzoylaconine, benzoyldeoxyaconine, 16-epi-pyrodeoxyaconitine, and 16-epi-pyroaconitine markedly increased. Transformations from diester-diterpenoid alkaloids to monoester-diterpenoid alkaloids through hydrolysis and pyrolysis were found to be the major detoxication processes during Zanba stir-frying. In summary, UPLC-Q-TOF-MS based metabolomics, DESI-MSI, and quantitative combined approach could be an efficient method to characterize chemical variations in Tiebangchui and its Zanba processed products with different stir-frying time. The wide application of this method would contribute to the process monitoring and safe control of Tiebangchui and other toxic Aconitum medicine.