De Novo Lipogenesis Alters the Phospholipidome of Esophageal Adenocarcinoma.

The incidence of esophageal adenocarcinoma is rising, survival remains poor, and new tools to improve early diagnosis and precise treatment are needed. Cancer phospholipidomes quantified with mass spectrometry imaging (MSI) can support objective diagnosis in minutes using a routine frozen tissue section. However, whether MSI can objectively identify primary esophageal adenocarcinoma is currently unknown and represents a significant challenge, as this microenvironment is complex with phenotypically similar tissue-types. Here, we used desorption electrospray ionization-MSI (DESI-MSI) and bespoke chemometrics to assess the phospholipidomes of esophageal adenocarcinoma and relevant control tissues. Multivariate models derived from phospholipid profiles of 117 patients were highly discriminant for esophageal adenocarcinoma both in discovery (AUC = 0.97) and validation cohorts (AUC = 1). Among many other changes, esophageal adenocarcinoma samples were markedly enriched for polyunsaturated phosphatidylglycerols with longer acyl chains, with stepwise enrichment in premalignant tissues. Expression of fatty acid and glycerophospholipid synthesis genes was significantly upregulated, and characteristics of fatty acid acyls matched glycerophospholipid acyls. Mechanistically, silencing the carbon switch ACLY in esophageal adenocarcinoma cells shortened glycerophospholipid chains, linking de novo lipogenesis to the phospholipidome. Thus, DESI-MSI can objectively identify invasive esophageal adenocarcinoma from a number of premalignant tissues and unveils mechanisms of phospholipidomic reprogramming. SIGNIFICANCE: These results call for accelerated diagnosis studies using DESI-MSI in the upper gastrointestinal endoscopy suite, as well as functional studies to determine how polyunsaturated phosphatidylglycerols contribute to esophageal carcinogenesis.

Enhanced Fatty Acid Scavenging and Glycerophospholipid Metabolism Accompany Melanocyte Neoplasia Progression in Zebrafish.

Alterations in lipid metabolism in cancer cells impact cell structure, signaling, and energy metabolism, making lipid metabolism a potential diagnostic marker and therapeutic target. In this study, we combined PET, desorption electrospray ionization-mass spectrometry (DESI-MS), nonimaging MS, and transcriptomic analyses to interrogate changes in lipid metabolism in a transgenic zebrafish model of oncogenic RAS-driven melanocyte neoplasia progression. Exogenous fatty acid uptake was detected in melanoma tumor nodules by PET using the palmitic acid surrogate tracer 14(R,S)-18F-fluoro-6-thia-heptadecanoic acid ([18F]-FTHA), consistent with upregulation of genes associated with fatty acid uptake found through microarray analysis. DESI-MS imaging revealed that FTHA uptake in tumors was heterogeneous. Transcriptome and lipidome analyses further highlighted dysregulation of glycerophospholipid pathways in melanoma tumor nodules, including increased abundance of phosphatidyl ethanolamine and phosphatidyl choline species, corroborated by DESI-MS, which again revealed heterogeneous phospholipid composition in tumors. Overexpression of the gene encoding lipoprotein lipase (LPL), which was upregulated in zebrafish melanocyte tumor nodules and expressed in the majority of human melanomas, accelerated progression of oncogenic RAS-driven melanocyte neoplasia in zebrafish. Depletion or antagonism of LPL suppressed human melanoma cell growth; this required simultaneous fatty acid synthase (FASN) inhibition when FASN expression was also elevated. Collectively, our findings implicate fatty acid acquisition as a possible therapeutic target in melanoma, and the methods we developed for monitoring fatty acid uptake have potential for diagnosis, patient stratification, and monitoring pharmacologic response. SIGNIFICANCE: These findings demonstrate the translational potential of monitoring fatty acid uptake and identify lipoprotein lipase as a potential therapeutic target in melanoma.

Optimised Desorption Electrospray Ionisation Mass Spectrometry Imaging (DESI-MSI) for the Analysis of Proteins/Peptides Directly from Tissue Sections on a Travelling Wave Ion Mobility Q-ToF.

Desorption electrospray ionisation mass spectrometry imaging (DESI-MSI) is typically known for the ionisation of small molecules such as lipids and metabolites, in singly charged form. Here we present a method that allows the direct detection of proteins and peptides in multiply charged forms directly from tissue sections by DESI. Utilising a heated mass spectrometer inlet capillary, combined with ion mobility separation (IMS), the conditions with regard to solvent composition, nebulising gas flow, and solvent flow rate have been explored and optimised. Without the use of ion mobility separation prior to mass spectrometry analysis, only the most abundant charge series were observed. In addition to the dominant haemoglobin subunit(s) related trend line in the m/z vs drift time (DT) 2D plot, trend lines were found relating to background solvent peaks, residual lipids and, more importantly, small proteins/large peptides of lower abundance. These small proteins/peptides were observed with charge states from 1+ to 12+, the majority of which could only be resolved from the background when using IMS. By extracting charge series from the 2D m/z vs DT plot, a number of proteins could be tentatively assigned by accurate mass. Tissue images were acquired with a pixel size of 150 µm showing a marked improvement in protein image resolution compared to other liquid-based ambient imaging techniques such as liquid extraction surface analysis (LESA) and continuous-flow liquid microjunction surface sampling probe (LMJ-SSP) imaging. Graphical Abstract ᅟ.

Faster, More Reproducible DESI-MS for Biological Tissue Imaging.

A new, more robust sprayer for desorption electrospray ionization (DESI) mass spectrometry imaging is presented. The main source of variability in DESI is thought to be the uncontrolled variability of various geometric parameters of the sprayer, primarily the position of the solvent capillary, or more specifically, its positioning within the gas capillary or nozzle. If the solvent capillary is off-center, the sprayer becomes asymmetrical, making the geometry difficult to control and compromising reproducibility. If the stiffness, tip quality, and positioning of the capillary are improved, sprayer reproducibility can be improved by an order of magnitude. The quality of the improved sprayer and its potential for high spatial resolution imaging are demonstrated on human colorectal tissue samples by acquisition of images at pixel sizes of 100, 50, and 20 µm, which corresponds to a lateral resolution of 40-60 µm, similar to the best values published in the literature. The high sensitivity of the sprayer also allows combination with a fast scanning quadrupole time-of-flight mass spectrometer. This provides up to 30 times faster DESI acquisition, reducing the overall acquisition time for a 10 mm × 10 mm rat brain sample to approximately 1 h. Although some spectral information is lost with increasing analysis speed, the resulting data can still be used to classify tissue types on the basis of a previously constructed model. This is particularly interesting for clinical applications, where fast, reliable diagnosis is required. Graphical Abstract ᅟ.

Imaging of Esophageal Lymph Node Metastases by Desorption Electrospray Ionization Mass Spectrometry.

Histopathological assessment of lymph node metastases (LNM) depends on subjective analysis of cellular morphology with inter-/intraobserver variability. In this study, LNM from esophageal adenocarcinoma was objectively detected using desorption electrospray ionization-mass spectrometry imaging (DESI-MSI). Ninety lymph nodes (LN) and their primary tumor biopsies from 11 esophago-gastrectomy specimens were examined and analyzed by DESI-MSI. Images from mass spectrometry and corresponding histology were coregistered and analyzed using multivariate statistical tools. The MSIs revealed consistent lipidomic profiles of individual tissue types found within LNs. Spatial mapping of the profiles showed identical distribution patterns as per the tissue types in matched IHC images. Lipidomic profile comparisons of LNM versus the primary tumor revealed a close association in contrast to benign LN tissue types. This similarity was used for the objective prediction of LNM in mass spectrometry images utilizing the average lipidomic profile of esophageal adenocarcinoma. The multivariate statistical algorithm developed for LNM identification demonstrated a sensitivity, specificity, positive predictive value, and negative predictive value of 89.5%, 100%, 100%, and 97.2%, respectively, when compared with gold-standard IHC. DESI-MSI has the potential to be a diagnostic tool for perioperative identification of LNM and compares favorably with techniques currently used by histopathology experts. Cancer Res; 76(19); 5647-56. ©2016 AACR.

Investigation of the Impact of Desorption Electrospray Ionization Sprayer Geometry on Its Performance in Imaging of Biological Tissue.

In this study, the impact of sprayer design and geometry on performance in desorption electrospray ionization mass spectrometry (DESI-MS) is assessed, as the sprayer is thought to be a major source of variability. Absolute intensity repeatability, spectral composition, and classification accuracy for biological tissues are considered. Marked differences in tissue analysis performance are seen between the commercially available and a lab-built sprayer. These are thought to be associated with the geometry of the solvent capillary and the resulting shape of the primary electrospray. Experiments with a sprayer with a fixed solvent capillary position show that capillary orientation has a crucial impact on tissue complex lipid signal and can lead to an almost complete loss of signal. Absolute intensity repeatability is compared for five lab-built sprayers using pork liver sections. Repeatability ranges from 1 to 224% for individual sprayers and peaks of different spectral abundance. Between sprayers, repeatability is 16%, 9%, 23%, and 34% for high, medium, low, and very low abundance peaks, respectively. To assess the impact of sprayer variability on tissue classification using multivariate statistical tools, nine human colorectal adenocarcinoma sections are analyzed with three lab-built sprayers, and classification accuracy for adenocarcinoma versus the surrounding stroma is assessed. It ranges from 80.7 to 94.5% between the three sprayers and is 86.5% overall. The presented results confirm that the sprayer setup needs to be closely controlled to obtain reliable data, and a new sprayer setup with a fixed solvent capillary geometry should be developed.

A Comparison of DESI-MS and LC-MS for the Lipidomic Profiling of Human Cancer Tissue.

In this study, we make a direct comparison between desorption electrospray ionization-mass spectrometry (DESI-MS) and ultraperformance liquid chromatography-electrospray ionization-mass spectrometry (UPLC-ESI-MS) platforms for the profiling of glycerophospholipid (GPL) species in esophageal cancer tissue. In particular, we studied the similarities and differences in the range of GPLs detected and the congruency of their relative abundances as detected by each analytical platform. The main differences between mass spectra of the two modalities were found to be associated with the variance in adduct formation of common GPLs, rather than the presence of different GPL species. Phosphatidylcholines as formate adducts in UPLC-ESI-MS accounted for the majority of differences in negative ion mode and alkali metal adducts of phosphatidylcholines in DESI-MS for positive ion mode. Comparison of the relative abundance of GPLs, normalized to a common peak, revealed a correlation coefficient of 0.70 (P < 0.001). The GPL profile detected by DESI-MS is congruent to UPLC-ESI-MS, which reaffirms the role of DESI-MS for lipidomic profiling and a potential premise for quantification.

Rapid evaporative ionization mass spectrometry imaging platform for direct mapping from bulk tissue and bacterial growth media.

Rapid evaporative ionization mass spectrometry (REIMS) technology allows real time intraoperative tissue classification and the characterization and identification of microorganisms. In order to create spectral libraries for training the classification models, reference data need to be acquired in large quantities as classification accuracy generally improves as a function of number of training samples. In this study, we present an automated high-throughput method for collecting REIMS data from heterogeneous organic tissue. The underlying instrumentation consists of a 2D stage with an additional high-precision z-axis actuator that is equipped with an electrosurgical diathermy-based sampling probe. The approach was validated using samples of human liver with metastases and bacterial strains, cultured on solid medium, belonging to the species P. aeruginosa, B. subtilis, and S. aureus. For both sample types, spatially resolved spectral information was obtained that resulted in clearly distinguishable multivariate clustering between the healthy/cancerous liver tissues and between the bacterial species.

Towards imaging metabolic pathways in tissues.

Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging using 9-aminoacridine as the matrix leads to the detection of low mass metabolites and lipids directly from cancer tissues. These included lactate and pyruvate for studying the Warburg effect, as well as succinate and fumarate, metabolites whose accumulation is associated with specific syndromes. By using the pathway information present in the human metabolome database, it was possible to identify regions within tumor tissue samples with distinct metabolic signatures that were consistent with known tumor biology. We present a data analysis workflow for assessing metabolic pathways in their histopathological context.

Multicenter matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) identifies proteomic differences in breast-cancer-associated stroma.

MALDI mass spectrometry imaging (MSI) has rapidly established itself as a powerful biomarker discovery tool. To date, no formal investigation has assessed the center-to-center comparability of MALDI MSI experiments, an essential step for it to develop into a new diagnostic method. To test such capabilities, we have performed a multicenter study focused on biomarkers of stromal activation in breast cancer. MALDI MSI experiments were performed in two centers using independent tissue banks, infrastructure, methods, and practitioners. One of the data sets was used for discovery and the other for validation. Areas of intra- and extratumoral stroma were selected, and their protein signals were compared. Four protein signals were found to be significantly associated with tumor-associated stroma in the discovery data set measured in Munich. Three of these peaks were also detected in the independent validation data set measured in Leiden, all of which were also significantly associated with intratumoral stroma. Hierarchical clustering displayed 100% accuracy in the Munich MSI data set and 80.9% accuracy in the Leiden MSI data set. The association of one of the identified mass signals (PA28) with stromal activation was confirmed with immunohistochemistry performed on 20 breast tumors. Independent and international MALDI MSI investigations could identify validated biomarkers of stromal activation.

Chemo-informatic strategy for imaging mass spectrometry-based hyperspectral profiling of lipid signatures in colorectal cancer.

Mass spectrometry imaging (MSI) provides the opportunity to investigate tumor biology from an entirely novel biochemical perspective and could lead to the identification of a new pool of cancer biomarkers. Effective clinical translation of histology-driven MSI in systems oncology requires precise colocalization of morphological and biochemical features as well as advanced methods for data treatment and interrogation. Currently proposed MSI workflows are subject to several limitations, including nonoptimized raw data preprocessing, imprecise image coregistration, and limited pattern recognition capabilities. Here we outline a comprehensive strategy for histology-driven MSI, using desorption electrospray ionization that covers (i) optimized data preprocessing for improved information recovery; (ii) precise image coregistration; and (iii) efficient extraction of tissue-specific molecular ion signatures for enhanced biochemical distinction of different tissue types. The proposed workflow has been used to investigate region-specific lipid signatures in colorectal cancer tissue. Unique lipid patterns were observed using this approach according to tissue type, and a tissue recognition system using multivariate molecular ion patterns allowed highly accurate (>98%) identification of pixels according to morphology (cancer, healthy mucosa, smooth muscle, and microvasculature). This strategy offers unique insights into tumor microenvironmental biochemistry and should facilitate compilation of a large-scale tissue morphology-specific MSI spectral database with which to pursue next-generation, fully automated histological approaches.

Imaging mass spectrometry-based molecular histology differentiates microscopically identical and heterogeneous tumors.

Many tumors display significant cellular heterogeneity as well as molecular heterogeneity. Sensitive biomarkers that differentiate between diagnostically challenging tumors must contend with this heterogeneity. Mass spectrometry-based molecular histology of a patient series of heterogeneous, microscopically identical bone tumors highlighted the tumor cell types that could be characterized by a single profile and led to the identification of specific peptides that differentiate between the tumors.

Imaging mass spectrometry to visualize biomolecule distributions in mouse brain tissue following hemispheric cortical spreading depression.

MALDI mass spectrometry can simultaneously measure hundreds of biomolecules directly from tissue. Using essentially the same technique but different sample preparation strategies, metabolites, lipids, peptides and proteins can be analyzed. Spatially correlated analysis, imaging MS, enables the distributions of these biomolecular ions to be simultaneously measured in tissues. A key advantage of imaging MS is that it can annotate tissues based on their MS profiles and thereby distinguish biomolecularly distinct regions even if they were unexpected or are not distinct using established histological and histochemical methods e.g. neuropeptide and metabolite changes following transient electrophysiological events such as cortical spreading depression (CSD), which are spreading events of massive neuronal and glial depolarisations that occur in one hemisphere of the brain and do not pass to the other hemisphere , enabling the contralateral hemisphere to act as an internal control. A proof-of-principle imaging MS study, including 2D and 3D datasets, revealed substantial metabolite and neuropeptide changes immediately following CSD events which were absent in the protein imaging datasets. The large high dimensionality 3D datasets make even rudimentary contralateral comparisons difficult to visualize. Instead non-negative matrix factorization (NNMF), a multivariate factorization tool that is adept at highlighting latent features, such as MS signatures associated with CSD events, was applied to the 3D datasets. NNMF confirmed that the protein dataset did not contain substantial contralateral differences, while these were present in the neuropeptide dataset.

Multiple statistical analysis techniques corroborate intratumor heterogeneity in imaging mass spectrometry datasets of myxofibrosarcoma.

MALDI mass spectrometry can generate profiles that contain hundreds of biomolecular ions directly from tissue. Spatially-correlated analysis, MALDI imaging MS, can simultaneously reveal how each of these biomolecular ions varies in clinical tissue samples. The use of statistical data analysis tools to identify regions containing correlated mass spectrometry profiles is referred to as imaging MS-based molecular histology because of its ability to annotate tissues solely on the basis of the imaging MS data. Several reports have indicated that imaging MS-based molecular histology may be able to complement established histological and histochemical techniques by distinguishing between pathologies with overlapping/identical morphologies and revealing biomolecular intratumor heterogeneity. A data analysis pipeline that identifies regions of imaging MS datasets with correlated mass spectrometry profiles could lead to the development of novel methods for improved diagnosis (differentiating subgroups within distinct histological groups) and annotating the spatio-chemical makeup of tumors. Here it is demonstrated that highlighting the regions within imaging MS datasets whose mass spectrometry profiles were found to be correlated by five independent multivariate methods provides a consistently accurate summary of the spatio-chemical heterogeneity. The corroboration provided by using multiple multivariate methods, efficiently applied in an automated routine, provides assurance that the identified regions are indeed characterized by distinct mass spectrometry profiles, a crucial requirement for its development as a complementary histological tool. When simultaneously applied to imaging MS datasets from multiple patient samples of intermediate-grade myxofibrosarcoma, a heterogeneous soft tissue sarcoma, nodules with mass spectrometry profiles found to be distinct by five different multivariate methods were detected within morphologically identical regions of all patient tissue samples. To aid the further development of imaging MS based molecular histology as a complementary histological tool the Matlab code of the agreement analysis, instructions and a reduced dataset are included as supporting information.