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Solvent-based ambient ionization mass spectrometry (MS) techniques provide a powerful approach for direct chemical analysis and molecular profiling of biological tissues. While molecular profiling of tissues has been widely used for disease diagnosis, little is understood about how the interplay among solvent properties, matrix effects, and ion suppression can influence the detection of biological molecules. Here, we perform a systematic investigation of the extraction processes of lipids using an ambient ionization droplet microsampling platform to investigate how the physicochemical properties of the solvent systems and extraction time influence molecular extraction and detection. Direct molecular profiling and quantitative liquid chromatography-mass spectrometry (LC-MS) of discrete solvent droplets after surface sampling were investigated to provide insights into extraction and ionization mechanisms. The results of this study suggest that intermolecular interactions such as hydrogen bonding play a major role in extraction and detection of lipids using solvent-based ambient ionization techniques. In addition, extraction time was observed to impact the molecular profiles obtained, suggesting optimization of this parameter can be performed to favor detection of specific analytes.

Background Rapid identification of bacteria is critical to prevent antimicrobial resistance and ensure positive patient outcomes. We have developed the MasSpec Pen, a handheld mass spectrometry-based device that enables rapid analysis of biological samples. Here, we evaluated the MasSpec Pen for identification of bacteria from culture and clinical samples. Methods A total of 247 molecular profiles were obtained from 43 well-characterized strains of 8 bacteria species that are clinically relevant to osteoarticular infections, including Staphylococcus aureus, Group A and B Streptococcus, and Kingella kingae, using the MasSpec Pen coupled to a high-resolution mass spectrometer. The molecular profiles were used to generate statistical classifiers based on metabolites that were predictive of Gram stain category, genus, and species. Then, we directly analyzed samples from 4 patients, including surgical specimens and clinical isolates, and used the classifiers to predict the etiologic agent. Results High accuracies were achieved for all levels of classification with a mean accuracy of 93.3% considering training and validation sets. Several biomolecules were detected at varied abundances between classes, many of which were selected as predictive features in the classifiers including glycerophospholipids and quorum-sensing molecules. The classifiers also enabled correct identification of Gram stain type and genus of the etiologic agent from 3 surgical specimens and all classification levels for clinical specimen isolates. Conclusions The MasSpec Pen enables identification of several bacteria at different taxonomic levels in seconds from cultured samples and has potential for culture-independent identification of bacteria directly from clinical samples based on the detection of metabolic species.

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Precise diagnosis and subtyping of kidney tumors are imperative to optimize and personalize treatment decision for patients. Patients with the most common benign renal tumor, renal oncocytomas, may be overtreated with surgical resection because of limited preoperative diagnostic methods that can accurately identify the benign condition with certainty. In this study, desorption electrospray ionization (DESI)-mass spectrometry (MS) imaging was applied to study the metabolic and lipid profiles of various types of renal tissues, including normal kidney, renal oncocytoma, and renal cell carcinomas (RCC). A total of 73,992 mass spectra from 71 patient samples were obtained and used to build predictive models using the least absolute shrinkage and selection operator (Lasso). Overall accuracies of 99.47% per pixel and 100% per patient for prediction of the three tissue types were achieved. In particular, renal oncocytoma and chromophobe RCC, which present the most significant morphologic overlap and are sometimes indistinguishable using histology alone, were also investigated and the predictive models built yielded 100% accuracy in discriminating these tumor types. Discrimination of three subtypes of RCC was also achieved on the basis of DESI-MS imaging data. Importantly, several small metabolites and lipids species were identified as characteristic of individual tissue types and chemically characterized using tandem MS and high mass accuracy measurements. Collectively, our study shows that the metabolic data acquired by DESI-MS imaging in conjunction with statistical modeling allows discrimination of renal tumors and thus has the potential to be used in the clinical setting to improve treatment of patients with kidney tumor.Significance:Metabolic data acquired by mass spectrometry imaging in conjunction with statistical modeling allows discrimination of renal tumors and has the potential to be used in the clinic to improve treatment of patients.

This supplemental file contains data that supports the claims and results described in the main manuscript. Figures include full mass spectra data acquired for the representative thyroid samples, examples of fragmentation patterns obtained for all the main molecular classes identified, mass spectra results obtained for confirmatory experiments using lipid standards, examples of 2D DESI-MS ion images and mass spectra for several additional samples, optical images of immunohistochemistry results, mass spectra results for isolated mitochondria experiments, and a table with detailed identification results for all molecules described in the main manuscript.

Oncocytic tumors are characterized by an excessive eosinophilic, granular cytoplasm due to aberrant accumulation of mitochondria. Mutations in mitochondrial DNA occur in oncocytic thyroid tumors, but there is no information about their lipid composition, which might reveal candidate theranostic molecules. Here, we used desorption electrospray ionization mass spectrometry (DESI-MS) to image and chemically characterize the lipid composition of oncocytic thyroid tumors, as compared with nononcocytic thyroid tumors and normal thyroid samples. We identified a novel molecular signature of oncocytic tumors characterized by an abnormally high abundance and chemical diversity of cardiolipins (CL), including many oxidized species. DESI-MS imaging and IHC experiments confirmed that the spatial distribution of CLs overlapped with regions of accumulation of mitochondria-rich oncocytic cells. Fluorescent imaging and mitochondrial isolation showed that both mitochondrial accumulation and alteration in CL composition of mitochondria occurred in oncocytic tumors cells, thus contributing the aberrant molecular signatures detected. A total of 219 molecular ions, including CLs, other glycerophospholipids, fatty acids, and metabolites, were found at increased or decreased abundance in oncocytic, nononcocytic, or normal thyroid tissues. Our findings suggest new candidate targets for clinical and therapeutic use against oncocytic tumors. Cancer Res; 76(22); 6588–97. ©2016 AACR.

The supporting information file contains figures with experimental data such as tandem mass spectrometry mass spectra, 2D ion images, representative mass spectra of cancer and normal tissues, and mass spectra of standard compounds. Tables are also included which present identification of molecular markers, and predictive accuracy results for the molecular models.

This supplemental file contains detailed descriptions of the methods employed in the research reported in the main manuscript, including the methods used for molecular imaging experiments using DESI-MS, immunohistochemistry and immunofluorescence assays, mitochondria isolation and analysis, and histopathologic evaluation of tissue samples.

Ovarian high-grade serous carcinoma (HGSC) results in the highest mortality among gynecological cancers, developing rapidly and aggressively. Dissimilarly, serous borderline ovarian tumors (BOT) can progress into low-grade serous carcinomas and have relatively indolent clinical behavior. The underlying biological differences between HGSC and BOT call for accurate diagnostic methodologies and tailored treatment options, and identification of molecular markers of aggressiveness could provide valuable biochemical insights and improve disease management. Here, we used desorption electrospray ionization (DESI) mass spectrometry (MS) to image and chemically characterize the metabolic profiles of HGSC, BOT, and normal ovarian tissue samples. DESI-MS imaging enabled clear visualization of fine papillary branches in serous BOT and allowed for characterization of spatial features of tumor heterogeneity such as adjacent necrosis and stroma in HGSC. Predictive markers of cancer aggressiveness were identified, including various free fatty acids, metabolites, and complex lipids such as ceramides, glycerophosphoglycerols, cardiolipins, and glycerophosphocholines. Classification models built from a total of 89,826 individual pixels, acquired in positive and negative ion modes from 78 different tissue samples, enabled diagnosis and prediction of HGSC and all tumor samples in comparison with normal tissues, with overall agreements of 96.4% and 96.2%, respectively. HGSC and BOT discrimination was achieved with an overall accuracy of 93.0%. Interestingly, our classification model allowed identification of three BOT samples presenting unusual histologic features that could be associated with the development of low-grade carcinomas. Our results suggest DESI-MS as a powerful approach for rapid serous ovarian cancer diagnosis based on altered metabolic signatures. Cancer Res; 77(11); 2903–13. ©2017 AACR.

Supplementary Figures and Supplementary Figure legends. Supplementary Fig. 1.- Tandem mass spectrum of m/z 810.527 and the tentative assignment, Supplementary Fig. 2.- Tandem mass spectrum of m/z 723.478 and the tentative assignment, Supplementary Fig. 3.- Comparison of mass spectra of kidney cortex tissue sections from normal patient, Supplementary Fig. 4.- Comparison of mass spectra from normal patients which contain both cortex and medulla, Supplementary Fig. 5.- PCA analysis of normal kidney tissues containing both cortex and medulla and the biplot, Supplementary Fig. 6.- The graphical summary from PCA of cortex and medulla tissue for six selected ions, Supplementary Fig. 7.- Tandem mass spectrum of m/z 887.557 and the tentative assignment, Supplementary Fig. 8.- Tentative peak assignment for m/z 215.032, Supplementary Fig. 9.- Tandem mass spectrum of m/z 175.025 and the tentative assignment, Supplementary Fig. 10.- Tandem mass spectrum of m/z 885.547 and the tentative assignment, Supplementary Fig. 11.- Tandem mass spectrum of m/z 773.532 and the tentative assignment, Supplementary Fig. 12.- Tandem mass spectrum of m/z 766.538 and the tentative assignment, Supplementary Fig. 13.- Tandem mass spectrum of m/z 1035.772 and the tentative assignment, Supplementary Fig. 14.- Tandem mass spectrum of m/z 737.490 and the tentative assignment, Supplementary Fig. 15.- Tandem mass spectrum of m/z 464.314, m/z 697.482, and m/z 842.589 and the tentative assignment, Supplementary Fig. 16.- Tandem mass spectrum of m/z 306.077 and the tentative assignment, Supplementary Fig. 17.- Tandem mass spectrum of m/z 712.485 and the tentative assignment, Supplementary Fig. 18.- Mass spectrometric profile from normal kidney tissue at mass range from m/z 723 to m/z 728, Supplementary Fig. 19.- PCA analysis of averaged mass spectra of ROchRCC tissues and ccRCCppRCC tissues and the biplot. Supplementary Tables. Supplementary Table 1. m/z values selected by the lasso as important for characterizing normal kidney, RO and RCC, Supplementary Table 2. m/z values selected by the lasso as important for characterizing RO and chRCC, Supplementary Table 3. CL and PE related species selected by the lasso for characterizing RO and chRCC, Supplementary Table 4. m/z values selected by the lasso as important for characterizing three renal cancer subtypes (ccRCC, chRCC, and ppRCC), Supplementary Table 5. The top loading plots of PC1 and PC2 for PCA of cortex and medulla in normal kidney tissue, Supplementary Table 6. The top loading plots of PC1 and PC2 for PCA of ROchRCC vs ppRCCccRCC. Supplementary Data Supplementary Results, Supplementary Methods, Supplementary Discussion

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Offering superb speed, chemical specificity, and analytical sensitivity, direct mass spectrometry (MS) technologies are highly amenable for the molecular analysis of complex tissues to aid in disease characterization and help identify new diagnostic, prognostic, and predictive markers. By enabling detection of clinically actionable molecular profiles from tissues and cells, direct MS technologies have the potential to guide treatment decisions and transform sample analysis within clinical workflows. In this review, we highlight recent health-related developments and applications of direct MS technologies that exhibit tangible potential to accelerate clinical research and disease diagnosis, including oncological and neurodegenerative diseases and microbial infections. We focus primarily on applications that employ direct MS technologies for tissue analysis, including MS imaging technologies to map spatial distributions of molecules in situ as well as handheld devices for rapid in vivo and ex vivo tissue analysis. Expected final online publication date for the Annual Review of Analytical Chemistry, Volume 16 is June 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

SummaryLipid metabolism plays a central role in prostate cancer. To date, the major focus on prostate cancer lipid metabolism has centered onde novolipogenesis and lipid uptake with little consideration for how cancer cells access these lipids once they are created or taken up and stored. Patient-derived phosphoproteomics identified adipose triglyceride lipase (ATGL), a previously suspected tumor suppressor, as a CAMKK2-AMPK signaling target that, conversely, promotes castration-resistant prostate cancer (CRPC) progression. Phosphorylation of ATGL increased its lipase activity, cancer cell proliferation, migration, and invasion. Shotgun lipidomics and mass spectrometry imaging demonstrated ATGL’s profound regulation of lipid metabolismin vitroandin vivo, remodeling membrane composition. Inhibition of ATGL induced metabolic plasticity, causing a glycolytic shift that could be exploited therapeutically by co-targeting both metabolic pathways. Together, these data nominate ATGL and intracellular lipolysis as potential therapeutic targets for the treatment of CRPC and provide insights for future combination therapies.

Structural characterization of glycerophospholipids beyond the fatty acid level has become a major endeavor in lipidomics, presenting an opportunity to advance the understanding of the intricate relationship between lipid metabolism and disease state. Distinguishing subtle lipid structural features, however, remains a major challenge for high-throughput workflows that implement traditional tandem mass spectrometry (MS/MS) techniques, stunting the molecular depth of quantitative strategies. Here, reversed phase liquid chromatography is coupled to parallel reaction mass spectrometry utilizing the double bond localization capabilities of ultraviolet photodissociation (UVPD) mass spectrometry to produce double bond isomer specific responses that are leveraged for relative quantitation. The strategy provides lipidomic characterization at the double bond level for phosphatidylcholine phospholipids from biological extracts. In addition to quantifying monounsaturated lipids, quantitation of phospholipids incorporating isomeric polyunsaturated fatty acids is also achieved. Using this technique, phosphatidylcholine isomer ratios are compared across human normal and tumor breast tissue to reveal significant structural alterations related to disease state.

ImportanceDespite similar histologic appearance among high-grade serous ovarian cancers (HGSOCs), clinical observations suggest vast differences in gross appearance. There is currently no systematic framework by which to classify HGSOCs according to their gross morphologic characteristics.ObjectiveTo develop and characterize a gross morphologic classification system for HGSOC.Design, Setting, and ParticipantsThis cohort study included patients with suspected advanced-stage ovarian cancer who presented between April 1, 2013, and August 5, 2016, to the University of Texas MD Anderson Cancer Center, a large referral center. Patients underwent laparoscopic assessment of disease burden before treatment and received a histopathologic diagnosis of HGSOC. Researchers assigning morphologic subtype and performing molecular analyses were blinded to clinical outcomes. Data analysis was performed between April 2020 and November 2021.ExposuresGross tumor morphologic characteristics.Main Outcomes and MeasuresClinical outcomes and multiomic profiles of representative tumor samples of type I or type II morphologic subtypes were compared.ResultsOf 112 women (mean [SD] age 62.7 [9.7] years) included in the study, most patients (84% [94]) exhibited a predominant morphologic subtype and many (63% [71]) had a uniform morphologic subtype at all involved sites. Compared with those with uniform type I morphologic subtype, patients with uniform type II morphologic subtype were more likely to have a favorable Fagotti score (83% [19 of 23] vs 46% [22 of 48]; P = .004) and thus to be triaged to primary tumor reductive surgery. Similarly, patients with uniform type II morphologic subtype also had significantly higher mean (SD) estimated blood loss (639 [559; 95% CI, 391-887] mL vs 415 [527; 95% CI, 253-577] mL; P = .006) and longer mean (SD) operative time (408 [130; 95% CI, 350-466] minutes vs 333 [113; 95% CI, 298-367] minutes; P = .03) during tumor reductive surgery. Type I tumors had enrichment of epithelial-mesenchymal transition (false discovery rate [FDR] q-value, 3.10 × 10−24), hypoxia (FDR q-value, 1.52 × 10−5), and angiogenesis pathways (FDR q-value, 2.11 × 10−2), whereas type II tumors had enrichment of pathways related to MYC signaling (FDR q-value, 2.04 × 10−9) and cell cycle progression (FDR q-value, 1.10 × 10−5) by integrated proteomic and transcriptomic analysis. Abundances of metabolites and lipids also differed between the 2 morphologic subtypes.Conclusions and RelevanceThis study identified 2 novel, gross morphologic subtypes of HGSOC, each with unique clinical features and molecular signatures. The findings may have implications for triaging patients to surgery or chemotherapy, identifying outcomes, and developing tailored therapeutic strategies.

Cardiolipin (CL) is a mitochondrial lipid with diverse roles in cellular respiration, signaling, and organelle membrane structure. CL content and composition are essential for proper mitochondrial function. Deranged mitochondrial energy production and signaling are key components of glial cell cancers and altered CL molecular species have been observed in mouse brain glial cell xenograft tumors. The objective of this study was to describe CL structural diversity trends in human astrocytoma tumors of varying grades and correlate these trends with histological regions within the heterogeneous astrocytoma microenvironment. To this aim, we applied desorption electrospray ionization coupled with high field asymmetric ion mobility mass spectrometry (DESI-FAIMS-MS) to map CL molecular species in human normal cortex (N = 29), lower-grade astrocytoma (N = 19), and glioblastoma (N = 28) tissues. With this platform, we detected 46 CL species and 12 monolysocardiolipin species from normal cortex samples. CL profiles detected from glioblastoma tissues lacked diversity and abundance of longer chain polyunsaturated fatty acid containing CL species when compared to CL detected from normal and lower-grade tumors. CL profiles correlated with trends in tumor viability and tumor infiltration. Structural characterization of the CL species by tandem MS experiments revealed differences in fatty acid and double bond isomer composition among astrocytoma tissues compared with normal cortex and glioblastoma tissues. The GlioVis platform was used to analyze astrocytoma gene expression data from the CGGA dataset. Decreased expression of several mitochondrial respiratory enzyme encoding-genes was observed for higher-grade versus lower-grade tumors, however no significant difference was observed for cardiolipin synthesis enzyme CRLS1.

Poison frogs are well-known for their fascinating ability to store alkaloids in their skin as chemical defense against predators. Chemical methods used to study these alkaloids are limited by requirements for euthanasia or stress during sampling. Here, we demonstrate sensitive and biocompatible alkaloid detection and monitoring

Mammalian folate-dependent one-carbon (1C) metabolism provides the building blocks essential during development via amino acid interconversion, methyl-donor production, regeneration of redox factors, and de novo purine and thymidylate synthesis. Folate supplementation prevents many neural tube defects (NTDs) that occur during the embryonic process of neurulation. The mechanism by which folate functions during neurulation is not well understood, and not all NTDs are preventable by folate supplementation. Mthfd1l is a mitochondrial 1C metabolism enzyme that produces formate, a 1C donor that fuels biosynthesis and the methyl cycle in the cytoplasm. Homozygous deletion of the Mthfd1l gene in mice (Mthfd1lz/z) causes embryonic lethality, developmental delay, and folate-resistant NTDs. These mice also have defects in cranial mesenchyme formation. In this work, mass spectrometry imaging was used to obtain ion maps of the cranial mesenchyme that identified the spatial distribution and relative abundance of metabolites in wild-type and Mthfd1lz/z embryos. The relative abundances of purine and thymidylate derivatives, as well as amino acids, were diminished in the cranial mesenchyme of Mthfd1lz/z embryos. Loss of Mthfd1l activity in this region also led to abnormal levels of methionine and dysregulated energy metabolism. These alterations in metabolism suggest possible approaches to preventing NTDs in humans.

Food authenticity and safety are major public concerns due to the increasing number of food fraud cases. Meat fraud is an economically motivated practice of covertly replacing one type of meat with a cheaper alternative raising health, safety, and ethical concerns for consumers. In this study, we implement the MasSpec Pen technology for rapid and direct meat analysis and authentication. The MasSpec Pen is an easy-to-use handheld device connected to a mass spectrometer that employs a solvent droplet for gentle chemical analysis of samples. Here, MasSpec Pen analysis was performed directly on several meat and fish types including grain-fed beef, grass-fed beef, venison, cod, halibut, Atlantic salmon, sockeye salmon, and steelhead trout, with a total analysis time of 15 s per sample. Statistical models developed with the Lasso method using a training set of samples yielded per-sample accuracies of 95% for the beef model, 100% for the beef versus venison model, and 84% for the multiclass fish model. Predictors of meat type selected included several molecules previously reported in the skeletal muscles of animals, including carnosine, anserine, succinic acid, xanthine, and taurine. When testing the models on independent test sets of samples, per-sample accuracies of 100% were achieved for all models, demonstrating the robustness of our method for unadulterated meat authentication. MasSpec Pen feasibility testing for classifying venison and grass-fed beef samples adulterated with grain-fed beef achieved per-sample prediction accuracies of 100% for both classifiers using test sets of samples. Altogether, the results obtained in this study provide compelling evidence that the MasSpec Pen technology is a powerful alternative analytical method for meat analysis and investigation of meat fraud.

BACKGROUNDDistinguishing adenocarcinoma and squamous cell carcinoma subtypes of non-small cell lung cancers is critical to patient care. Preoperative minimally-invasive biopsy techniques, such as fine needle aspiration (FNA), are increasingly used for lung cancer diagnosis and subtyping. Yet, histologic distinction of lung cancer subtypes in FNA material can be challenging. Here, we evaluated the usefulness of desorption electrospray ionization mass spectrometry imaging (DESI-MSI) to diagnose and differentiate lung cancer subtypes in tissues and FNA samples.METHODSDESI-MSI was used to analyze 22 normal, 26 adenocarcinoma, and 25 squamous cell carcinoma lung tissues. Mass spectra obtained from the tissue sections were used to generate and validate statistical classifiers for lung cancer diagnosis and subtyping. Classifiers were then tested on DESI-MSI data collected from 16 clinical FNA samples prospectively collected from 8 patients undergoing interventional radiology guided FNA.RESULTSVarious metabolites and lipid species were detected in the mass spectra obtained from lung tissues. The classifiers generated from tissue sections yielded 100% accuracy, 100% sensitivity, and 100% specificity for lung cancer diagnosis, and 73.5% accuracy for lung cancer subtyping for the training set of tissues, per-patient. On the validation set of tissues, 100% accuracy for lung cancer diagnosis and 94.1% accuracy for lung cancer subtyping were achieved. When tested on the FNA samples, 100% diagnostic accuracy and 87.5% accuracy on subtyping were achieved per-slide.ConclusionsDESI-MSI can be useful as an ancillary technique to conventional cytopathology for diagnosis and subtyping of non-small cell lung cancers.

Developments in mass spectrometry technologies have driven a widespread interest and expanded their use in cancer-related research and clinical applications. In this review, we highlight the developments in mass spectrometry methods and instrumentation applied to direct tissue analysis that have been tailored at enhancing performance in clinical research as well as facilitating translation and implementation of mass spectrometry in clinical settings, with a focus on cancer-related studies. Notable studies demonstrating the capabilities of direct mass spectrometry analysis in biomarker discovery, cancer diagnosis and prognosis, tissue analysis during oncologic surgeries, and other clinically relevant problems that have the potential to substantially advance cancer patient care are discussed. Key challenges that need to be addressed before routine clinical implementation including regulatory efforts are also discussed. Overall, the studies highlighted in this review demonstrate the transformative potential of mass spectrometry technologies to advance clinical research and care for cancer patients. © 2020 Wiley Periodicals, Inc. Mass Spec Rev.

Minimally invasive robotic-assisted surgeries have been increasingly used as a first-line of treatment for patients undergoing oncologic surgeries. In-situ tissue identification is critical to guide tissue resection and assist decision-making. Traditional intraoperative histopathologic analysis of frozen tissue sections can be time-consuming and present logistical challenges which interrupt surgical workflows. We report the development and implementation of a laparoscopic, drop-in version of the MasSpec Pen device integrated into the da Vinci Xi Surgical system for in vivo tissue analysis in a robotic-assisted porcine surgery. We evaluated the performance of the drop-in MasSpec Pen during surgery by introducing the device into the animal upper gastrointestinal system and performing in vivo analyses of the stomach and liver, including charred and bloody tissues after electrocauterization. The molecular profiles obtained included ions tentatively identified as metabolites and lipids typically observed with MasSpec Pen analysis, without causing observable tissue damage. Statistical classifiers built to distinguish porcine liver and stomach tissues using the in vivo data yielded an overall tissue identification accuracy of 98% (n = 53 analyses). The results provide evidence that the drop-in MasSpec Pen developed can be used to acquire mass spectra in vivo during a robotic-assisted surgery and might be used as an in vivo tissue assessment tool to help guide surgical resections and streamline surgical workflows.

Free fatty acids (FA) are a vital component of cells and are critical to cellular structure and function, so much so that alterations in FA are often associated with cell malfunction and disease. Analysis of FA from biological samples can be achieved by mass spectrometry (MS), but these analyses are often not capable of distinguishing the fine structural alterations within FA isomers and often limited to global profiling of lipids without spatial resolution. Here, we present the use of ultraviolet photodissociation (UVPD) for the characterization of double bond positional isomers of charge inverted dication·FA complexes and the subsequent implementation of this method for online desorption electrospray ionization (DESI) MS imaging of FA isomers from human tissue sections. This method allows relative quantification of FA isomers from heterogeneous biological tissue sections, yielding spatially resolved information about alterations in double bond isomers within these samples. Applying this method to the analysis of the monounsaturated FA 18:1 within breast cancer subtypes uncovered a correlation between double bond positional isomer abundance and the hormone receptor status of the tissue sample, an important factor in the prognosis and treatment of breast cancer patients. This result further validates similar studies that suggest FA synthase activity and FA isomer abundances are significantly altered within breast cancer tissue.

Complete tumor removal during breast conserving surgery (BCS) remains a challenge for even the most experienced surgeons due to difficulties associated with detecting residual disease at the margin. Additionally, there is limited technology to evaluate resected tissue while the patient is under anesthesia. Mass spectrometry (MS) has been previously used for rapid molecular characterization of healthy and diseased tissue and has shown potential for intraoperative use. We have developed an MS-based technology that couples a handheld and biocompatible device, the MasSpec Pen, to a mass spectrometer for non-destructive ex vivo and in vivo analysis of cancer tissue in a matter of seconds. We have used the MasSpec Pen with multivariate statistical analysis to discriminate the molecular patterns of normal breast and lymph node from invasive cancer tissue, achieving accuracies over 88%. Our results showcase the potential of the MasSpec Pen to provide surgical guidance during BCS. A total of 213 banked human tissues including normal breast and lymph node, IDC, and IDC to lymph node were obtained from the Cooperative Human Tissue Network and stored at -80°C prior to analysis. Samples were then thawed and analyzed using a mass spectrometer coupled to the MasSpec Pen. During MasSpec Pen analysis, a single water droplet is held in contact with the tissue to extract molecules that are then analyzed by a mass spectrometer, yielding a molecular fingerprint. The molecular information obtained is analyzed by the statistical model to provide a predictive diagnosis in seconds. The least absolute shrinkage and selection operator (Lasso) statistical method was used to generate predictive models comprised of molecular features indicative of healthy and diseased tissue. Analyzed regions of tissue were demarcated with a surgical ink marker, snap frozen, and sectioned at a thickness of 5µm. Tissue sections were H&E stained for pathological evaluation. The MasSpec Pen was used to analyze 79 normal and 64 breast cancer tissues. Various molecular species involved in cell metabolism, including metabolites, fatty acids, and glycerophospholipids were observed in the mass spectra. For example, glutamate was detected at a high relative abundance in breast cancer tissue while hexose was detected at a high abundance in normal breast tissue. The primary breast cancer model yielded a sensitivity and specificity over 95% for the training set (n=68) and validation set (n=22). The diagnostic capabilities of the model were further evaluated by predicting on an independent test set of samples (n=53). A sensitivity and specificity of 83.9% and 100% were achieved, demonstrating the ability of the MasSpec Pen to discriminate between normal and cancerous breast tissue. The MasSpec Pen was used to detect metastatic breast cancer in lymph node. Normal lymph nodes (n=26) and nodes with metastatic disease (n=44) were analyzed with the MasSpec Pen. For the metastatic breast cancer model, overall accuracies of 88.7% and 94.1% were achieved for the training set (n=46) and validation set (n=17), respectively. We envision the MasSpec Pen to be used in the operating room (OR) for the in vivo analysis of breast cancer. Recently, we have implemented the MasSpec Pec in the OR to evaluate the feasibility of this technology for intraoperative analysis of breast and lymph node tissue. Similar molecular species are detected in spectra obtained from the in vivo and ex vivo analysis of human breast and lymph node tissue when compared to spectra obtained from banked tissue. Continuous effort is being made to continue patient accrual and testing of the technology. Collectively, these results showcase the capability of the MasSpec Pen for breast cancer detection and its potential as an intraoperative tool for rapid breast cancer diagnosis and surgical margin evaluation during BCS. Citation Format: Kyana Y Garza, Jialing Zhang, John Q Lin, Stacey Carter, James Suliburk, Chandandeep Nagi, Livia S Eberlin. Advanced development of the MasSpec Pen technology to aid in breast cancer surgical margin evaluation and diagnosis during surgery [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P1-20-04.

Merging optical images of tissue sections with the spatial distributions of molecules seen by imaging mass spectrometry is a powerful approach to better understand the metabolic roles of the mapped molecules. Here, we use histologically friendly desorption electrospray ionization–mass spectrometry (DESI-MS) to map the lipid distribution in tissue sections of ovaries from cows (N = 8), sows (N = 3), and mice (N = 12). Morphologically friendly DESI-MS imaging allows the same sections to be examined for morphological information. Independent of the species, ovarian follicles, corpora lutea, and stroma could be differentiated by principal component analysis, showing that lipid profiles are well conserved among species. As examples of specific findings, arachidonic acid and the phosphatidylinositol PI(38:4), were both found concentrated in the follicles and corpora lutea, structures that promoted ovulation and implantation, respectively. Adrenic acid was spatially located in the corpora lutea, suggesting the importance of this fatty acid in the ovary luteal phase. In summary, lipid information captured by DESI-MS imaging could be related to ovarian structures and data were all conserved among cows, sows, and mice. Further application of DESI-MS imaging to either physiological or pathophysiological models of reproductive conditions will likely expand knowledge of the roles of specific lipids and pathways in ovarian activity and mammalian fertility.

Mass spectrometry (MS) has emerged as a valuable technology for molecular and spatial evaluation of biological samples. Ambient ionization MS techniques, in particular, allow direct analysis of tissue samples with minimal pretreatment. Here, we describe the design and optimization of an alternative ambient liquid extraction MS approach for metabolite and lipid profiling and imaging from biological samples. The system combines a piezoelectric picoliter dispenser to form solvent nanodroplets onto the sample surface with controlled and tunable spatial resolution and a conductive capillary to directly aspirate/ionize the nanodroplets for efficient analyte transmission and detection. Using this approach, we performed spatial profiling of mouse brain tissue sections with different droplet sizes (390, 420, and 500 μm). MS analysis of normal and cancerous human brain and ovarian tissues yielded rich metabolic profiles that were characteristic of disease state and enabled visualization of tissue regions with different histologic composition. This method was also used to analyze the lipid profiles of human ovarian cell lines. Overall, our results demonstrate the capabilities of this system for spatially controlled MS analysis of biological samples.

Mass spectrometry imaging provides a powerful approach for the direct analysis and spatial visualization of molecules in tissue sections. Using matrix-assisted laser desorption/ionization mass spectrometry, intact protein imaging has been widely investigated for biomarker analysis and diagnosis in a variety of tissue types and diseases. However, blood-rich or highly vascular tissues present a challenge in molecular imaging due to the high ionization efficiency of hemoglobin, which leads to ion suppression of endogenous proteins. Here, we describe a protocol to selectively reduce hemoglobin signal in blood-rich tissues that can easily be integrated into mass spectrometry imaging workflows.

The outbreak of COVID-19 has created an unprecedent global crisis. While the polymerase chain reaction (PCR) is the gold standard method for detecting active SARS-CoV-2 infection, alternative high-throughput diagnostic tests are of a significant value to meet universal testing demands. Here, we describe a new design of the MasSpec Pen technology integrated to electrospray ionization (ESI) for direct analysis of clinical swabs and investigate its use for COVID-19 screening. The redesigned MasSpec Pen system incorporates a disposable sampling device refined for uniform and efficient analysis of swab tips via liquid extraction directly coupled to an ESI source. Using this system, we analyzed nasopharyngeal swabs from 244 individuals including symptomatic COVID-19 positive, symptomatic negative, and asymptomatic negative individuals, enabling rapid detection of rich lipid profiles. Two statistical classifiers were generated based on the lipid information acquired. Classifier 1 was built to distinguish symptomatic PCR-positive from asymptomatic PCR-negative individuals, yielding a cross-validation accuracy of 83.5%, sensitivity of 76.6%, and specificity of 86.6%, and validation set accuracy of 89.6%, sensitivity of 100%, and specificity of 85.3%. Classifier 2 was built to distinguish symptomatic PCR-positive patients from negative individuals including symptomatic PCR-negative patients with moderate to severe symptoms and asymptomatic individuals, yielding a cross-validation accuracy of 78.4%, specificity of 77.21%, and sensitivity of 81.8%. Collectively, this study suggests that the lipid profiles detected directly from nasopharyngeal swabs using MasSpec Pen-ESI mass spectrometry (MS) allow fast (under a minute) screening of the COVID-19 disease using minimal operating steps and no specialized reagents, thus representing a promising alternative high-throughput method for screening of COVID-19.

Intraoperative delineation of tumor margins is critical for effective pancreatic cancer surgery. Yet, intraoperative frozen section analysis of tumor margins is a time-consuming and often challenging procedure that can yield confounding results due to histologic heterogeneity and tissue-processing artifacts. We have previously described the development of the MasSpec Pen technology as a handheld mass spectrometry–based device for nondestructive tissue analysis. Here, we evaluated the usefulness of the MasSpec Pen for intraoperative diagnosis of pancreatic ductal adenocarcinoma based on alterations in the metabolite and lipid profiles in in vivo and ex vivo tissues. We used the MasSpec Pen to analyze 157 banked human tissues, including pancreatic ductal adenocarcinoma, pancreatic, and bile duct tissues. Classification models generated from the molecular data yielded an overall agreement with pathology of 91.5%, sensitivity of 95.5%, and specificity of 89.7% for discriminating normal pancreas from cancer. We built a second classifier to distinguish bile duct from pancreatic cancer, achieving an overall accuracy of 95%, sensitivity of 92%, and specificity of 100%. We then translated the MasSpec Pen to the operative room and predicted on in vivo and ex vivo data acquired during 18 pancreatic surgeries, achieving 93.8% overall agreement with final postoperative pathology reports. Notably, when integrating banked tissue data with intraoperative data, an improved agreement of 100% was achieved. The result obtained demonstrate that the MasSpec Pen provides high predictive performance for tissue diagnosis and compatibility for intraoperative use, suggesting that the technology may be useful to guide surgical decision-making during pancreatic cancer surgeries.

The outbreak of COVID-19 has created an unprecedent global crisis. While PCR is the gold standard method for detecting active SARS-CoV-2 infection, alternative high-throughput diagnostic tests are of significant value to meet universal testing demands. Here, we describe a new design of the MasSpec Pen technology integrated to electrospray ionization (ESI) for direct analysis of clinical swabs and investigate its use for COVID-19 screening. The redesigned MasSpec Pen system incorporates a disposable sampling device refined for uniform and efficient analysis of swab tips via liquid extraction directly coupled to a ESI source. Using this system, we analyzed nasopharyngeal swabs from 244 individuals including symptomatic COVID-19 positive, symptomatic negative, and asymptomatic negative individuals, enabling rapid detection of rich lipid profiles. Two statistical classifiers were generated based on the lipid information aquired. Classifier 1 was built to distinguish symptomatic PCR-positive from asymptomatic PCR-negative individuals, yielding cross-validation accuracy of 83.5%, sensitivity of 76.6%, and specificity of 86.6%, and validation set accuracy of 89.6%, sensitivity of 100%, and specificity of 85.3%. Classifier 2 was built to distinguish symptomatic PCR-positive patients from negative individuals including symptomatic PCR-negative patients with moderate to severe symptoms and asymptomatic individuals, yielding a cross-validation accuracy of 78.4% accuracy, specificity of 77.21%, and sensitivity of 81.8%. Collectively, this study suggests that the lipid profiles detected directly from nasopharyngeal swabs using MasSpec Pen-ESI MS allows fast (under a minute) screening of COVID-19 disease using minimal operating steps and no specialized reagents, thus representing a promising alternative high-throughput method for screening of COVID-19.

Endometriosis is a pathologic condition affecting approximately 10% of women in their reproductive years. Characterized by abnormal growth of uterine endometrial tissue in other body areas, endometriosis can cause severe abdominal pain and/or infertility. Despite devastating consequences to patients’ quality of life, the causes of endometriosis are not fully understood and validated diagnostic markers for endometriosis have not been identified. Molecular analyses of ectopic and eutopic endometrial tissues could lead to enhanced understanding of the disease. Here, we apply desorption electrospray ionization (DESI) mass spectrometry (MS) imaging to chemically and spatially characterize the molecular profiles of 231 eutopic and ectopic endometrial tissues from 89 endometriosis patients. DESI-MS imaging allowed clear visualization of endometrial glandular and stromal regions within tissue samples. Statistical models built from DESI-MS imaging data allowed classification of endometriosis lesions with overall accuracies of 89.4%, 98.4%, and 98.8% on training, validation, and test sample sets, respectively. Further, molecular markers that are significantly altered in ectopic endometrial tissues when compared to eutopic tissues were identified, including fatty acids and glycerophosphoserines. Our study showcases the value of MS imaging to investigate the molecular composition of endometriosis lesions and pinpoints metabolic markers that may provide new knowledge on disease pathogenesis.

BACKGROUND Accurate tissue diagnosis during ovarian cancer surgery is critical to maximize cancer excision and define treatment options. Yet, current methods for intraoperative tissue evaluation can be time intensive and subjective. We have developed a handheld and biocompatible device coupled to a mass spectrometer, the MasSpec Pen, which uses a discrete water droplet for molecular extraction and rapid tissue diagnosis. Here we evaluated the performance of this technology for ovarian cancer diagnosis across different sample sets, tissue types, and mass spectrometry systems. METHODS MasSpec Pen analyses were performed on 192 ovarian, fallopian tube, and peritoneum tissue samples. Samples were evaluated by expert pathologists to confirm diagnosis. Performance using an Orbitrap and a linear ion trap mass spectrometer was tested. Statistical models were generated using machine learning and evaluated using validation and test sets. RESULTS High performance for high-grade serous carcinoma (n = 131; clinical sensitivity, 96.7%; specificity, 95.7%) and overall cancer (n = 138; clinical sensitivity, 94.0%; specificity, 94.4%) diagnoses was achieved using Orbitrap data. Variations in the mass spectra from normal tissue, low-grade, and high-grade serous ovarian cancers were observed. Discrimination between cancer and fallopian tube or peritoneum tissues was also achieved with accuracies of 92.6% and 87.9%, respectively, and 100% clinical specificity for both. Using ion trap data, excellent results for high-grade serous cancer vs normal ovarian differentiation (n = 40; clinical sensitivity, 100%; specificity, 100%) were obtained. CONCLUSIONS The MasSpec Pen, together with machine learning, provides robust molecular models for ovarian serous cancer prediction and thus has potential for clinical use for rapid and accurate ovarian cancer diagnosis.

The diagnosis of tissue samples traditionally has been performed by anatomic pathologists using a combination of cellular staining and light microscopy. With these techniques, pathologists can characterize various tissue features including cell morphology, structure, and composition to subsequently confirm whether a disease process is present. Although the histopathologic “gold-standard” methods are invaluable for routine tissue diagnosis, the results can be subjective, owing to a combination of factors such as variability in staining quality, nature of the sample, and human interpretation, and inconclusive for diseases that present indistinguishable histologic features. There is a need for new technologies that can be used as complementary tools in pathology for objective tissue analysis and disease diagnosis. Mass spectrometry (MS)7 imaging has been heralded as an upcoming advance in tissue analysis. The ability of MS to rapidly identify a variety of biomolecules present in a sample is highly attractive to the clinical laboratory. Indeed, MS coupled to chromatographic separation techniques is currently used to detect and/or quantify small molecules such as pharmacological agents and hormones in blood and urine. The advent of MS techniques that allow direct tissue analysis, including MALDI-MS and secondary ion MS, has allowed laboratories to extend the use of MS beyond biofluids into tissue samples. Using MALDI MS, for example, thin tissue sections can be analyzed in a nontargeted manner for the abundance and spatial distribution of biomolecules. As such, MALDI-MS imaging is being increasingly applied in clinical research, especially in the context of cancer diagnostics based on tissue proteomic and lipidomic signatures. The potential of MS in revolutionizing tissue analysis and diagnosis has driven several advances to accelerate its feasibility and utility in the clinical setting. The development of ambient ionization MS (AIMS), for example, has brought MS-based tissue imaging even closer to routine clinical use. Unlike MALDI and …

Thyroid neoplasia is common and requires appropriate clinical workup with imaging and fine-needle aspiration (FNA) biopsy to evaluate for cancer. Yet, up to 20% of thyroid nodule FNA biopsies will be indeterminate in diagnosis based on cytological evaluation. Genomic approaches to characterize the malignant potential of nodules showed initial promise but have provided only modest improvement in diagnosis. Here, we describe a method using metabolic analysis by desorption electrospray ionization mass spectrometry (DESI-MS) imaging for direct analysis and diagnosis of follicular cell-derived neoplasia tissues and FNA biopsies. DESI-MS was used to analyze 178 tissue samples to determine the molecular signatures of normal, benign follicular adenoma (FTA), and malignant follicular carcinoma (FTC) and papillary carcinoma (PTC) thyroid tissues. Statistical classifiers, including benign thyroid versus PTC and benign thyroid versus FTC, were built and validated with 114,125 mass spectra, with accuracy assessed in correlation with clinical pathology. Clinical FNA smears were prospectively collected and analyzed using DESI-MS imaging, and the performance of the statistical classifiers was tested with 69 prospectively collected clinical FNA smears. High performance was achieved for both models when predicting on the FNA test set, which included 24 nodules with indeterminate preoperative cytology, with accuracies of 93% and 89%. Our results strongly suggest that DESI-MS imaging is a valuable technology for identification of malignant potential of thyroid nodules.

Chemotherapy resistance represents a major problem in ovarian cancer prognosis and reliable indicators of chemotherapy sensitivity remain an unmet need. Here, we examined metabolic profiles of pre- and post-neoadjuvant chemotherapy (NACT) tumor samples from patients with high-grade serous ovarian cancer (HGSC). We performed spatially resolved, desorption electrospray ionization-mass spectrometry (DESI-MS) on sections from clinically annotated HGSC samples collected according to a laparoscopic triage algorithm from patients pre- and post-NACT. Patients were considered to be either excellent (ER) if they presented complete response or only microscopic disease left at interval surgery, or poor responders (PR) if they presented stable or progressive disease after NACT. Tumor tissues from 50 patients (29 ER and 21 PR, 13 ER matched pre and post NACT, 13 PR matched pre and post-NACT) were eligible for the analyses Data relative to the detected metabolites, were collected from epithelium and stroma separately. We first identified differentially upregulated metabolites in ER and PR groups and performed pathway analysis. The over-represented pathways in the chemo-naïve PR tissues included the nucleotide metabolism pathway in epithelial areas, and degradation of cysteine and homocysteine, metabolism of amino acids and derivatives, as well as sulfur amino acids metabolism in the stromal areas (p values FDR adjusted 0.045). In general, both the epithelium and stroma of PR tissues of chemo-naïve tumors, contained a higher number and a higher variety of lipid species compared with ER tissues. In post-NACT tumor epithelial areas, there was global downregulation of metabolites across all patient samples, compared to the pre-chemo specimens; this was more evident in ER patients. In post-NACT tumors from PR patients, glutathione synthesis and recycling pathway, and sulfite oxidation to sulfate pathway and many glycerolipids species were upregulated in the epithelium, while phosphatidic acids were upregulated in the stroma. Lastly, we evaluated the predictive power of the metabolic data extracted by the epithelial areas of chemo-naïve tissues, in classifying patients as ER or PR. We used 10-fold cross-validation to create a ridge regression model for the classification of ER and PR samples from pre-NACT tumor tissues. This model, based on 78 differentially expressed metabolites (small molecules and lipids), correctly classified patients as ER or PR with an accuracy of 76.2% (sensitivity 84.6% and specificity 62.5%). Our findings indicate altered metabolic pathways in ovarian cancer tissues based on response to NACT. The identification of differentially activated pathways, such as sulfate metabolism and glutathione metabolism in tumors with poor response, suggest novel actionable targets to overcome chemotherapy resistance in patients with HGSC. Citation Format: Sara Corvigno, Meredith Spraldin, Michael Keating, Sunil Badal, Igor Pereira, Elaine Stur, Nicholas Bateman, Waleed Barakat, Thomas Conrads, Sanghoon Lee, George Maxwell, Susan K. Lutgendorf, Premal Thaker, Jinsong Liu, Nicole Fleming, Katheleen Darcy, Livia S. Eberlin, Anil K. Sood. Metabolic profiling of pre and post treatment ovarian cancer tissues using in situ mass spectrometry imaging [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2331.

Mammalian folate-dependent one-carbon (1C) metabolism provides the building blocks essential during development via amino acid interconversion, methyl-donor production, regeneration of redox factors, and de novo purine and thymidylate synthesis. Folate supplementation prevents many neural tube defects (NTDs) that occur during the embryonic process of neurulation. The mechanism by which folate functions during neurulation is not well understood, and not all NTDs are preventable by folate supplementation. Mthfd1l is a mitochondrial 1C metabolism enzyme that produces formate, a 1C donor that fuels biosynthesis and the methyl cycle in the cytoplasm. Homozygous deletion of the Mthfd1l gene in mice (Mthfd1l

Minimally invasive robotic-assisted surgeries have been increasingly used as a first-line of treatment for patients undergoing oncologic surgeries. In-situ tissue identification is critical to guide tissue resection and assist decision-making. Traditional intraoperative histopathologic analysis of frozen tissue sections can be time-consuming and present logistical challenges which interrupt surgical workflows. We report the development and implementation of a laparoscopic, drop-in version of the MasSpec Pen device integrated into the da Vinci Xi Surgical system for

Plasma and tissue from breast cancer patients are valuable for diagnostic/prognostic purposes and are accessible by multiple mass spectrometry (MS) tools. Liquid chromatography-mass spectrometry (LC-MS) and ambient mass spectrometry imaging (MSI) were shown to be robust and reproducible technologies for breast cancer diagnosis. Here, we investigated whether there is a correspondence between lipid cancer features observed by desorption electrospray ionization (DESI)-MSI in tissue and those detected by LC-MS in plasma samples. The study included 28 tissues and 20 plasma samples from 24 women with ductal breast carcinomas of both special and no special type (NST) along with 22 plasma samples from healthy women. The comparison of plasma and tissue lipid signatures revealed that each one of the studied matrices (i.e., blood or tumor) has its own specific molecular signature and the full interposition of their discriminant ions is not possible. This comparison also revealed that the molecular indicators of tissue injury, characteristic of the breast cancer tissue profile obtained by DESI-MSI, do not persist as cancer discriminators in peripheral blood even though some of them could be found in plasma samples.

The histological and molecular subtypes of breast cancer demand distinct therapeutic approaches. Invasive ductal carcinoma (IDC) is subtyped according to estrogen-receptor (ER), progesterone-receptor (PR), and HER2 status, among other markers. Desorption-electrospray-ionization-mass-spectrometry imaging (DESI-MSI) is an ambient-ionization MS technique that has been previously used to diagnose IDC. Aiming to investigate the robustness of ambient-ionization MS for IDC diagnosis and subtyping over diverse patient populations and interlaboratory use, we report a multicenter study using DESI-MSI to analyze samples from 103 patients independently analyzed in the United States and Brazil. The lipid profiles of IDC and normal breast tissues were consistent across different patient races and were unrelated to country of sample collection. Similar experimental parameters used in both laboratories yielded consistent mass-spectral data in mass-to-charge ratios ( m/ z) above 700, where complex lipids are observed. Statistical classifiers built using data acquired in the United States yielded 97.6% sensitivity, 96.7% specificity, and 97.6% accuracy for cancer diagnosis. Equivalent performance was observed for the intralaboratory validation set (99.2% accuracy) and, most remarkably, for the interlaboratory validation set independently acquired in Brazil (95.3% accuracy). Separate classification models built for ER and PR statuses as well as the status of their combined hormone receptor (HR) provided predictive accuracies (89.0%), although low classification accuracies were achieved for HER2 status. Altogether, our multicenter study demonstrates that DESI-MSI is a robust and reproducible technology for rapid breast-cancer-tissue diagnosis and therefore is of value for clinical use.

Desorption electrospray ionization (DESI) mass spectrometry imaging has become a powerful strategy for analysis of tissue sections, enabling differentiation of normal and diseased tissue based on changes in the lipid profiles. The most common DESI workflow involves collection of MS1 spectra as the DESI spray is rastered over a tissue section. Relying on MS1 spectra inherently limits the ability to differentiate isobaric and isomeric species or evaluate variations in the relative abundances of key isomeric lipids, such as double-bond positional isomers which may distinguish normal and diseased tissues. Here, 193 nm ultraviolet photodissociation (UVPD), a technique capable of differentiating double-bond positional isomers, is coupled with DESI to map differences in the double-bond isomer composition in tissue sections in a fast, high throughput manner compatible with imaging applications.

The standard treatment for high-grade serous ovarian cancer is primary debulking surgery followed by chemotherapy. The extent of metastasis and invasive potential of lesions can influence the outcome of these primary surgeries. Here, we explored the underlying mechanisms that could increase metastatic potential in ovarian cancer. We discovered that FABP4 (fatty acid binding protein) can substantially increase the metastatic potential of cancer cells. We also found that miR-409-3p regulates FABP4 in ovarian cancer cells and that hypoxia decreases miR-409-3p levels. Treatment with DOPC nanoliposomes containing either miR-409-3p mimic or FABP4 siRNA inhibited tumor progression in mouse models. With RPPA and metabolite arrays, we found that FABP4 regulates pathways associated with metastasis and affects metabolic pathways in ovarian cancer cells. Collectively, these findings demonstrate that FABP4 is functionally responsible for aggressive patterns of disease that likely contribute to poor prognosis in ovarian cancer., In ovarian cancer, metastatic phenotype may impact surgical outcomes. Here, the authors show miR-409-3p regulates FABP4 which can increase metastatic potential of ovarian cancer, and treatment with DOPC nanoliposomes containing either miR-409--3p mimic or FABP4 siRNA inhibits tumor progression in mice.

Tissues present complex biochemical and morphological composition associated with their various cell types and physiological functions. Mass spectrometry (MS) imaging technologies are powerful tools to investigate the molecular information from biological tissue samples and visualize their complex spatial distributions. Coupling of gas-phase ion mobility spectrometry (IMS) technologies to MS imaging has been increasingly explored to improve performance for biological tissue imaging. This approach allows improved detection of low abundance ions and separation of isobaric molecular species, thus resulting in more accurate determination of the spatial distribution of molecular ions. In this review, we highlight recent advances in the field focusing on promising applications of these technologies for metabolite, lipid and protein tissue imaging.

• Technical advances are motivating translational applications of MS-based imaging. • Tissue imaging by MS enables increased objectivity in disease classification. • MS-based imaging is useful for toxicological assessment of pharmaceuticals. • Recent technologies enable interrogation of tissue by MS in the surgical theater.