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Characterizing proton beam damage in biological materials is of interest to enable the integration of proton microprobe elemental mapping techniques with other imaging modalities. It is also of relevance to obtain a deeper understanding of mechanical damage to lipids in tissues during proton beam cancer therapy. We have developed a novel strategy to characterize proton beam damage to lipids in biological tissues based on mass spectrometry imaging. This methodology is applied to characterize changes to lipids in tissues ex vivo, irradiated under different conditions designed to mitigate beam damage. This work shows that performing proton beam irradiation at ambient pressure, as well as including the application of an organic matrix prior to irradiation, can reduce damage to lipids in tissues. We also discovered that, irrespective of proton beam irradiation, placing a sample in a vacuum prior to desorption electrospray ionization imaging can enhance lipid signals, a conclusion that may be of future benefit to the mass spectrometry imaging community.

Mass spectrometry imaging (MSI) encompasses a powerful suit of techniques which provide spatially resolved atomic and molecular information from almost any sample type. MSI is now widely used in preclinical research to provide insight into metabolic phenotypes of disease. Typically, fresh-frozen tissue preparations are considered optimal for biological MSI and other traditional preservation methods such as formalin fixation, alone or with paraffin embedding (FFPE), are considered less optimal or even incompatible. Due to the prevalence of FFPE tissue storage, particularly for rare and therefore high-value tissue samples, there is substantial motivation for optimizing MSI methods for analysis of FFPE tissue. Here, we present a novel modality, atmospheric-pressure infrared laser-ablation plasma postionization (AP-IR-LA-PPI), with the first proof-of-concept examples of MSI for FFPE and fresh-frozen tissues, with no post-sectioning sample preparation. We present ion images from FFPE and fresh tissues in positive and negative ion modes. Molecular annotations (via the Metaspace annotation engine) and on-tissue MS/MS provide additional confidence that the detected ions arise from a broad range of metabolite and lipid classes from both FFPE and fresh-frozen tissues.

Gemcitabine (dFdC) is a common treatment for pancreatic cancer; however, it is thought that treatment may fail because tumor stroma prevents drug distribution to tumor cells. Gemcitabine is a pro-drug with active metabolites generated intracellularly; therefore, visualizing the distribution of parent drug as well as its metabolites is important. A multimodal imaging approach was developed using spatially coregistered mass spectrometry imaging (MSI), imaging mass cytometry (IMC), multiplex immunofluorescence microscopy (mIF), and hematoxylin and eosin (H&E) staining to assess the local distribution and metabolism of gemcitabine in tumors from a genetically engineered mouse model of pancreatic cancer (KPC) allowing for comparisons between effects in the tumor tissue and its microenvironment. Mass spectrometry imaging (MSI) enabled the visualization of the distribution of gemcitabine (100 mg/kg), its phosphorylated metabolites dFdCMP, dFdCDP and dFdCTP, and the inactive metabolite dFdU. Distribution was compared to small-molecule ATR inhibitor AZD6738 (25 mg/kg), which was codosed. Gemcitabine metabolites showed heterogeneous distribution within the tumor, which was different from the parent compound. The highest abundance of dFdCMP, dFdCDP, and dFdCTP correlated with distribution of endogenous AMP, ADP, and ATP in viable tumor cell regions, showing that gemcitabine active metabolites are reaching the tumor cell compartment, while AZD6738 was located to nonviable tumor regions. The method revealed that the generation of active, phosphorylated dFdC metabolites as well as treatment-induced DNA damage primarily correlated with sites of high proliferation in KPC PDAC tumor tissue, rather than sites of high parent drug abundance.

Background: Inhibiting SDH (succinate dehydrogenase), with the competitive inhibitor malonate, has shown promise in ameliorating ischemia/reperfusion injury. However, key for translation to the clinic is understanding the mechanism of malonate entry into cells to enable inhibition of SDH, its mitochondrial target, as malonate itself poorly permeates cellular membranes. The possibility of malonate selectively entering the at-risk heart tissue on reperfusion, however, remains unexplored. Methods: C57BL/6J mice, C2C12 and H9c2 myoblasts, and HeLa cells were used to elucidate the mechanism of selective malonate uptake into the ischemic heart upon reperfusion. Cells were treated with malonate while varying pH or together with transport inhibitors. Mouse hearts were either perfused ex vivo (Langendorff) or subjected to in vivo left anterior descending coronary artery ligation as models of ischemia/reperfusion injury. Succinate and malonate levels were assessed by liquid chromatography-tandem mass spectrometry LC-MS/MS, in vivo by mass spectrometry imaging, and infarct size by TTC (2,3,5-triphenyl-2H-tetrazolium chloride) staining. Results: Malonate was robustly protective against cardiac ischemia/reperfusion injury, but only if administered at reperfusion and not when infused before ischemia. The extent of malonate uptake into the heart was proportional to the duration of ischemia. Malonate entry into cardiomyocytes in vivo and in vitro was dramatically increased at the low pH (≈6.5) associated with ischemia. This increased uptake of malonate was blocked by selective inhibition of MCT1 (monocarboxylate transporter 1). Reperfusion of the ischemic heart region with malonate led to selective SDH inhibition in the at-risk region. Acid-formulation greatly enhances the cardioprotective potency of malonate. Conclusions: Cardioprotection by malonate is dependent on its entry into cardiomyocytes. This is facilitated by the local decrease in pH that occurs during ischemia, leading to its selective uptake upon reperfusion into the at-risk tissue, via MCT1. Thus, malonate’s preferential uptake in reperfused tissue means it is an at-risk tissue-selective drug that protects against cardiac ischemia/reperfusion injury.

Liquid extraction surface analysis (LESA) coupled to native mass spectrometry (MS) presents unique analytical opportunities due to its sensitivity, speed, and automation. Here, we examine whether this tool can be used to quantitatively probe protein-ligand interactions through calculation of equilibrium dissociation constants (

Imaging mass cytometry (IMC) offers the opportunity to image metal- and heavy halogen-containing xenobiotics in a highly multiplexed experiment with other immunochemistry-based reagents to distinguish uptake into different tissue structures or cell types. However, in practice, many xenobiotics are not amenable to this analysis, as any compound which is not bound to the tissue matrix will delocalize during aqueous sample-processing steps required for IMC analysis. Here, we present a strategy to perform IMC experiments on a water-soluble polysarcosine-modified dendrimer drug-delivery system (S-Dends). This strategy involves two consecutive imaging acquisitions on the same tissue section using the same instrumental platform, an initial laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MSI) experiment followed by tissue staining and a standard IMC experiment. We demonstrated that settings can be found for the initial ablation step that leave sufficient residual tissue for subsequent antibody staining and visualization. This workflow results in lateral resolution for the S-Dends of 2 μm followed by imaging of metal-tagged antibodies at 1 μm.

Clinical tissue specimens are often unscreened, and preparation of tissue sections for analysis by mass spectrometry imaging (MSI) can cause aerosolization of particles potentially carrying an infectious load. We here present a decontamination approach based on ultraviolet-C (UV-C) light to inactivate clinically relevant pathogens such as herpesviridae, papovaviridae human immunodeficiency virus, or SARS-CoV-2, which may be present in human tissue samples while preserving the biodistributions of analytes within the tissue. High doses of UV-C required for high-level disinfection were found to cause oxidation and photodegradation of endogenous species. Lower UV-C doses maintaining inactivation of clinically relevant pathogens to a level of increased operator safety were found to be less destructive to the tissue metabolome and xenobiotics. These doses caused less alterations of the tissue metabolome and allowed elucidation of the biodistribution of the endogenous metabolites. Additionally, we were able to determine the spatially integrated abundances of the ATR inhibitor ceralasertib from decontaminated human biopsies using desorption electrospray ionization-MSI (DESI-MSI).

Mass spectrometry imaging can produce large amounts of complex spectral and spatial data. Such data sets are often analyzed with unsupervised machine learning approaches, which aim at reducing their complexity and facilitating their interpretation. However, choices made during data processing can impact the overall interpretation of these analyses. This work investigates the impact of the choices made at the peak selection step, which often occurs early in the data processing pipeline. The discussion is done in terms of visualization and interpretation of the results of two commonly used unsupervised approaches: t-distributed stochastic neighbor embedding and k-means clustering, which differ in nature and complexity. Criteria considered for peak selection include those based on hypotheses (exemplified herein in the analysis of metabolic alterations in genetically engineered mouse models of human colorectal cancer), particular molecular classes, and ion intensity. The results suggest that the choices made at the peak selection step have a significant impact in the visual interpretation of the results of either dimensionality reduction or clustering techniques and consequently in any downstream analysis that relies on these. Of particular significance, the results of this work show that while using the most abundant ions can result in interesting structure-related segmentation patterns that correlate well with histological features, using a smaller number of ions specifically selected based on prior knowledge about the biochemistry of the tissues under investigation can result in an easier-to-interpret, potentially more valuable, hypothesis-confirming result. Findings presented will help researchers understand and better utilize unsupervised machine learning approaches to mine high-dimensionality data.

Purpose: Osimertinib is a potent and selective EGFR tyrosine kinase inhibitor (EGFR-TKI) of both sensitizing and T790M resistance mutations. To treat metastatic brain disease, blood–brain barrier (BBB) permeability is considered desirable for increasing clinical efficacy. Experimental Design: We examined the level of brain penetration for 16 irreversible and reversible EGFR-TKIs using multiple in vitro and in vivo BBB preclinical models. Results: In vitro osimertinib was the weakest substrate for human BBB efflux transporters (efflux ratio 3.2). In vivo rat free brain to free plasma ratios (Kpuu) show osimertinib has the most BBB penetrance (0.21), compared with the other TKIs (Kpuu ≤ 0.12). PET imaging in Cynomolgus macaques demonstrated osimertinib was the only TKI among those tested to achieve significant brain penetrance (Cmax %ID 1.5, brain/blood Kp 2.6). Desorption electrospray ionization mass spectroscopy images of brains from mouse PC9 macrometastases models showed osimertinib readily distributes across both healthy brain and tumor tissue. Comparison of osimertinib with the poorly BBB penetrant afatinib in a mouse PC9 model of subclinical brain metastases showed only osimertinib has a significant effect on rate of brain tumor growth. Conclusions: These preclinical studies indicate that osimertinib can achieve significant exposure in the brain compared with the other EGFR-TKIs tested and supports the ongoing clinical evaluation of osimertinib for the treatment of EGFR-mutant brain metastasis. This work also demonstrates the link between low in vitro transporter efflux ratios and increased brain penetrance in vivo supporting the use of in vitro transporter assays as an early screen in drug discovery.

Oncogenic KRAS mutations and inactivation of the APC tumor suppressor co-occur in colorectal cancer (CRC). Despite efforts to target mutant KRAS directly, most therapeutic approaches focus on downstream pathways, albeit with limited efficacy. Moreover, mutant KRAS alters the basal metabolism of cancer cells, increasing glutamine utilization to support proliferation. We show that concomitant mutation of Apc and Kras in the mouse intestinal epithelium profoundly rewires metabolism, increasing glutamine consumption. Furthermore, SLC7A5, a glutamine antiporter, is critical for colorectal tumorigenesis in models of both early- and late-stage metastatic disease. Mechanistically, SLC7A5 maintains intracellular amino acid levels following KRAS activation through transcriptional and metabolic reprogramming. This supports the increased demand for bulk protein synthesis that underpins the enhanced proliferation of KRAS-mutant cells. Moreover, targeting protein synthesis, via inhibition of the mTORC1 regulator, together with Slc7a5 deletion abrogates the growth of established Kras-mutant tumors. Together, these data suggest SLC7A5 as an attractive target for therapy-resistant KRAS-mutant CRC.

Here, we demonstrate detection by mass spectrometry of an intact protein-drug complex directly from liver tissue from rats that had been orally dosed with the drug. The protein-drug complex comprised fatty acid binding protein 1, FABP1, non-covalently bound to the small molecule therapeutic bezafibrate. Moreover, we demonstrate spatial mapping of the [FABP1+bezafibrate] complex across a thin section of liver by targeted mass spectrometry imaging. This work is the first demonstration of in situ mass spectrometry analysis of a non-covalent protein-drug complex formed in vivo and has implications for early stage drug discovery by providing a route to target-drug characterization directly from the physiological environment.

Background: For the treatment of respiratory disease, inhaled drug delivery aims to provide direct access to pharmacological target sites while minimizing systemic exposure. Despite this long-held tenet of inhaled therapeutic advantage, there are limited data of regional drug localization in the lungs after inhalation. The aim of this study was to investigate the distribution and retention of different chemotypes typifying available inhaled drugs [slowly dissolving neutral fluticasone propionate (FP) and soluble bases salmeterol and salbutamol] using mass spectrometry imaging (MSI). Methods: Salmeterol, salbutamol, and FP were simultaneously delivered by inhaled nebulization to rats. In the same animals, salmeterol-d3, salbutamol-d3, and FP-d3 were delivered by intravenous (IV) injection. Samples of lung tissue were obtained at 2- and 30-minute postdosing, and high-resolution MSI was used to study drug distribution and retention. Results: IV delivery resulted in homogeneous lung distribution for all molecules. In comparison, while inhalation also gave rise to drug presence in the entire lung, there were regional chemotype-dependent areas of higher abundance. At the 30-minute time point, inhaled salmeterol and salbutamol were preferentially retained in bronchiolar tissue, whereas FP was retained in all regions of the lungs. Conclusion: This study clearly demonstrates that inhaled small molecule chemotypes are differentially distributed in lung tissue after inhalation, and that high-resolution MSI can be applied to study these retention patterns.

A more complete and holistic view on host-microbe interactions is needed to understand the physiological and cellular barriers that affect the efficacy of drug treatments and allow the discovery and development of new therapeutics. Here, we developed a multimodal imaging approach combining histopathology with mass spectrometry imaging (MSI) and same section imaging mass cytometry (IMC) to study the effects of

A more complete and holistic view on host-microbe interactions is needed to understand the physiological and cellular barriers that affect the efficacy of drug treatments and allow the discovery and development of new therapeutics. Here, we developed a multimodal imaging approach combining histopathology with mass spectrometry imaging (MSI) and same section imaging mass cytometry (IMC) to study the effects of Salmonella Typhimurium infection in the liver of a mouse model using the S. Typhimurium strains SL3261 and SL1344. This approach enables correlation of tissue morphology and specific cell phenotypes with molecular images of tissue metabolism. IMC revealed a marked increase in immune cell markers and localization in immune aggregates in infected tissues. A correlative computational method (network analysis) was deployed to find metabolic features associated with infection and revealed metabolic clusters of acetyl carnitines, as well as phosphatidylcholine and phosphatidylethanolamine plasmalogen species, which could be associated with pro-inflammatory immune cell types. By developing an IMC marker for the detection of Salmonella LPS, we were further able to identify and characterize those cell types which contained S. Typhimurium.

Extracellular pH (pHe) is an important chemical factor in many cellular processes and disease pathologies. The routine sampling of pHe in vitro could lead to innovative advances in therapeutics. To this end, we have fabricated a novel gold-coated polymer mesh, which facilitates the real-time measurement of pHe via surface-enhanced Raman scattering (SERS). In this proof of concept study, we apply our SERS sensor to measure metabolically induced changes in the pHe of carcinoma-derived cell line HepG2/C3A. We demonstrate that gold-coated polyurethane electrospun nanofibers (AuNF) have strong and reproducible SERS spectra of surface-adsorbed analytes. By functionalizing AuNF with pH-responsive reporter 4-mercaptobenzoic acid (MBA), we have developed an accurate pH SERS sensor for the extracellular microenvironment. We cultured HepG2/C3A on the surface of MBA-AuNF and measured an acidic shift in pHe at the cell-fiber interface. Following exposure to staurosporine, an apoptosis-inducing drug, we observed changes in the HepG2/C3A cellular morphology indicative of controlled cell death, and detected an increase in the pHe of HepG2/C3A. These results demonstrate how subtle changes in pHe, induced by the metabolic activity of cells, can be measured with our novel SERS sensor MBA-AuNF. The excellent pH measurement performance of MBA-AuNF provides a unique platform to study extracellular pH on the microscale and will help to deepen our understanding of pHe in disease pathology.

Elemental and molecular imaging play a crucial role in understanding disease pathogenesis. To accurately correlate elemental and molecular markers, it is desirable to perform sequential elemental and molecular imaging on a single-tissue section. However, very little is known about the impact of performing these measurements in sequence. In this work, we highlight some of the challenges and successes associated with performing elemental mapping in sequence with mass spectrometry imaging. Specifically, the feasibility of molecular mapping using the mass spectrometry imaging (MSI) techniques matrix-assisted laser desorption ionization (MALDI) and desorption electrospray ionization (DESI) in sequence with the elemental mapping technique particle-induced X-ray emission (PIXE) is explored. Challenges for integration include substrate compatibility, as well as delocalization and spectral changes. We demonstrate that while sequential imaging comes with some compromises, sequential DESI-PIXE imaging is sufficient to correlate sulfur, iron, and lipid markers in a single tissue section at the 50 μm scale.

Discovery of novel bacterial metabolites reveals an unprecedented role for the microbiome in gut-brain axis communication., Alterations to the gut microbiome are associated with various neurological diseases, yet evidence of causality and identity of microbiome-derived compounds that mediate gut-brain axis interaction remain elusive. Here, we identify two previously unknown bacterial metabolites 3-methyl-4-(trimethylammonio)butanoate and 4-(trimethylammonio)pentanoate, structural analogs of carnitine that are present in both gut and brain of specific pathogen–free mice but absent in germ-free mice. We demonstrate that these compounds are produced by anaerobic commensal bacteria from the family Lachnospiraceae (Clostridiales) family, colocalize with carnitine in brain white matter, and inhibit carnitine-mediated fatty acid oxidation in a murine cell culture model of central nervous system white matter. This is the first description of direct molecular inter-kingdom exchange between gut prokaryotes and mammalian brain cells, leading to inhibition of brain cell function.

The brain is a major sanctuary site for metastatic cancer cells that evade systemic therapies. Through pre-clinical pharmacological, biological, and molecular studies, we characterize the functional link between drug resistance and central nervous system (CNS) relapse in Epidermal Growth Factor Receptor- (EGFR-) mutant non-small cell lung cancer, which can progress in the brain when treated with the CNS-penetrant EGFR inhibitor osimertinib. Despite widespread osimertinib distribution in vivo, the brain microvascular tumor microenvironment (TME) is associated with the persistence of malignant cell sub-populations, which are poised to proliferate in the brain as osimertinib-resistant lesions over time. Cellular and molecular features of this poised state are regulated through a Ras homolog family member A (RhoA) and Serum Responsive Factor (SRF) gene expression program. RhoA potentiates the outgrowth of disseminated tumor cells on osimertinib treatment, preferentially in response to extracellular laminin and in the brain. Thus, we identify pre-existing and adaptive features of metastatic and drug-resistant cancer cells, which are enhanced by RhoA/SRF signaling and the brain TME during the evolution of osimertinib-resistant disease.

Osteosarcoma (OS) is the most common primary bone malignancy and largely effects adolescents and young adults, with 60% of patients under the age of 25. There are multiple cell models of OS described in vitro that express the specific genetic alterations of the sarcoma. In the work reported here, multiple mass spectrometry imaging (MSI) modalities were employed to characterise two aggregated cellular models of OS models formed using the MG63 and SAOS-2 cell lines. Phenotyping of the metabolite activity within the two OS aggregoid models was achieved and a comparison of the metabolite data with OS human tissue samples revealed relevant fatty acid and phospholipid markers. Although, annotations of these species require MS/MS analysis for confident identification of the metabolites. From the putative assignments however, it was suggested that the MG63 aggregoids are an aggressive tumour model that exhibited metastatic-like potential. Alternatively, the SAOS-2 aggregoids are more mature osteoblast-like phenotype that expressed characteristics of cellular differentiation and bone development. It was determined the two OS aggregoid models shared similarities of metabolic behaviour with different regions of OS human tissues, specifically of the higher metastatic grade.

Understanding the biodistribution, metabolism and accumulation of drugs in the body is a fundamental of pharmaceutical research and development. Mass Spectrometry imaging (MSI) has been proven to be a powerful tool to image the unlabelled spatial distribution of exogenous drugs and endogenous metabolites from the surface of tissue sections or small clinical biopsies, aiding the delivery of safe and effective medicines to the market and ultimately benefiting patients. Here we review the current advancements in MSI sample preparation, qualitative and quantitative MSI methodology and drug discovery and development applications. MSI is shown to be supporting R&D from early target identification through to the clinic. In conclusion, we discuss future directions of the technology and the hurdles that need addressing to strengthen its status in multimodal imaging.

An ever-increasing array of imaging technologies are being used in the study of complex biological samples, each of which provides complementary, occasionally overlapping information at different length scales and spatial resolutions. It is important to understand the information provided by one technique in the context of the other to achieve a more holistic overview of such complex samples. One way to achieve this is to use annotations from one modality to investigate additional modalities. For microscopy-based techniques, these annotations could be manually generated using digital pathology software or automatically generated by machine learning (including deep learning) methods. Here, we present a generic method for using annotations from one microscopy modality to extract information from complementary modalities. We also present a fast, general, multimodal registration workflow [evaluated on multiple mass spectrometry imaging (MSI) modalities, matrix-assisted laser desorption/ionization, desorption electrospray ionization, and rapid evaporative ionization mass spectrometry] for automatic alignment of complex data sets, demonstrating an order of magnitude speed-up compared to previously published work. To demonstrate the power of the annotation transfer and multimodal registration workflows, we combine MSI, histological staining (such as hematoxylin and eosin), and deep learning (automatic annotation of histology images) to investigate a pancreatic cancer mouse model. Neoplastic pancreatic tissue regions, which were histologically indistinguishable from one another, were observed to be metabolically different. We demonstrate the use of the proposed methods to better understand tumor heterogeneity and the tumor microenvironment by transferring machine learning results freely between the two modalities.

International audience; PURPOSE: To compare carbon-13 ((13) C) MRSI of hyperpolarized [1-(13) C]pyruvate metabolism in a murine tumor model with mass spectrometric (MS) imaging of the corresponding tumor sections in order to cross validate these metabolic imaging techniques and to investigate the effects of pyruvate delivery and tumor lactate concentration on lactate labeling.METHODS: [1-(13) C]lactate images were obtained from tumor-bearing mice, following injection of hyperpolarized [1-(13) C]pyruvate, using a single-shot 3D (13) C spectroscopic imaging sequence in vivo and using desorption electrospray ionization MS imaging of the corresponding rapidly frozen tumor sections ex vivo. The images were coregistered, and levels of association were determined by means of Spearman rank correlation and Cohen kappa coefficients as well as linear mixed models. The correlation between [1-(13) C]pyruvate and [1-(13) C]lactate in the MRS images and between [(12) C] and [1-(13) C]lactate in the MS images were determined by means of Pearson correlation coefficients.RESULTS: [1-(13) C]lactate images generated by MS imaging were significantly correlated with the corresponding MRS images. The correlation coefficient between [1-(13) C]lactate and [1-(13) C]pyruvate in the MRS images was higher than between [1-(13) C]lactate and [(12) C]lactate in the MS images.CONCLUSION: The inhomogeneous distribution of labeled lactate observed in the MRS images was confirmed by MS imaging of the corresponding tumor sections. The images acquired using both techniques show that the rate of (13) C label exchange between the injected pyruvate and endogenous tumor lactate pool is more correlated with the rate of pyruvate delivery to the tumor cells and is less affected by the endogenous lactate concentration.

High dimensionality omics and hyperspectral imaging datasets present difficult challenges for feature extraction and data mining due to huge numbers of features that cannot be simultaneously examined. The sample numbers and variables of these methods are constantly growing as new technologies are developed, and computational analysis needs to evolve to keep up with growing demand. Current state of the art algorithms can handle some routine datasets but struggle when datasets grow above a certain size. We present a training deep learning via neural networks on non-linear dimensionality reduction, in particular t-distributed stochastic neighbour embedding (t-SNE), to overcome prior limitations of these methods.One Sentence SummaryAnalysis of prohibitively large datasets by combining deep learning via neural networks with non-linear dimensionality reduction.

Diabetes is a global health problem that was estimated to be the 7th leading cause of death worldwide in 2016. Type 2 diabetes mellitus (T2DM) is classically associated with genetic and environmental factors, however recent studies have demonstrated that the gut microbiome, which is altered in T2DM patients, is also likely to play a significant role in disease development. Despite this, the identity of microbiome-derived metabolites that influence T2DM onset and/or progression remain elusive. Here we demonstrate that a serum biomarker for T2DM, previously of unknown structure and origin, is actually two microbiome-derived metabolites, 3-methyl-4-(trimethylammonio)butanoate (3M-4-TMAB) and 4-(trimethylammonio)pentanoate (4-TMAP). These metabolites are produced by the Lachnospiraceae family of bacteria, which are highly prevalent in the gut microbiome of T2DM patients and are associated with high dietary fat intake. Treatment of human liver cells with 3M-4-TMAB and 4-TMAP results in a distinct change in the acylcarnitine profile in these cells and significantly reduced their insulin sensitivity; both indicators of T2DM. These results provide evidence of a mechanistic link between gut microbiome derived metabolites and T2DM.

Mass spectrometry imaging (MSI) is an established analytical tool capable of defining and understanding complex tissues by determining the spatial distribution of biological molecules. Three-dimensional (3D) cell culture models mimic the pathophysiological environment of in vivo tumors and are rapidly emerging as a valuable research tool. Here, multimodal MSI techniques were employed to characterize a novel aggregated 3D lung adenocarcinoma model, developed by the group to mimic the in vivo tissue. Regions of tumor heterogeneity and the hypoxic microenvironment were observed based on the spatial distribution of a variety of endogenous molecules. Desorption electrospray ionization (DESI)-MSI defined regions of a hypoxic core and a proliferative outer layer from metabolite distribution. Targeted metabolites (e.g., lactate, glutamine, and citrate) were mapped to pathways of glycolysis and the TCA cycle demonstrating tumor metabolic behavior. The first application of imaging mass cytometry (IMC) with 3D cell culture enabled single-cell phenotyping at 1 μm spatial resolution. Protein markers of proliferation (Ki-67) and hypoxia (glucose transporter 1) defined metabolic signaling in the aggregoid model, which complemented the metabolite data. Laser ablation inductively coupled plasma (LA-ICP)-MSI analysis localized endogenous elements including magnesium and copper, further differentiating the hypoxia gradient and validating the protein expression. Obtaining a large amount of molecular information on a complementary nature enabled an in-depth understanding of the biological processes within the novel tumor model. Combining powerful imaging techniques to characterize the aggregated 3D culture highlighted a future methodology with potential applications in cancer research and drug development.

International audience; During the past decade, mass spectrometry imaging (MSI) has become a robust and versatile methodology to support modern pharmaceutical research and development. The technologies provide data on the biodistribution, metabolism, and delivery of drugs in tissues, while also providing molecular maps of endogenous metabolites, lipids, and proteins. This allows researchers to make both pharmacokinetic and pharmacodynamic measurements at cellular resolution in tissue sections or clinical biopsies. Despite drug imaging within samples now playing a vital role within research and development (R&D) in leading pharmaceutical companies, however, the challenges in turning compounds into medicines continue to evolve as rapidly as the technologies used to discover them. The increasing cost of development of new and emerging therapeutic modalities, along with the associated risks of late-stage program attrition, means there is still an unmet need in our ability to address an increasing array of challenging bioanalytical questions within drug discovery. We require new capabilities and strategies of integrated imaging to provide context for fundamental disease-related biological questions that can also offer insights into specific project challenges. Integrated molecular imaging and advanced image analysis have the opportunity to provide a world-class capability that can be deployed on projects in which we cannot answer the question with our battery of established assays. Therefore, here we will provide an updated concise review of the use of MSI for drug discovery; we will also critically consider what is required to embed MSI into a wider evolving R&D landscape and allow long-lasting impact in the industry.

A new tissue sample embedding and processing method is presented that provides downstream compatibility with numerous different histological, molecular biology, and analytical techniques. The methodology is based on the low temperature embedding of fresh frozen specimens into a hydrogel matrix composed of hydroxypropyl methylcellulose (HPMC) and polyvinylpyrrolidone (PVP) and sectioning using a cryomicrotome. The hydrogel was expected not to interfere with standard tissue characterization methods, histologically or analytically. We assessed the compatibility of this protocol with various mass spectrometric imaging methods including matrix-assisted laser desorption ionization (MALDI), desorption electrospray ionization (DESI) and secondary ion mass spectrometry (SIMS). We also demonstrated the suitability of the universal protocol for extraction based molecular biology techniques such as rt-PCR. The integration of multiple analytical modalities through this universal sample preparation protocol offers the ability to study tissues at a systems biology level and directly linking results to tissue morphology and cellular phenotype.

BackgroundThe gut microbiota is known to influence virtually all facets of human health. Recent work has highlighted a potential role for the gut microbiota in neurological health through the microbiome-gut-brain axis. Microbes can influence the brain both directly and indirectly; through neurotransmitter production, induction of host immunomodulators, or through the release or induction of other microbial or host molecules.MethodsHere we used mass spectrometry imaging (MSI), a label-free imaging tool, to map the molecular changes that occur in the murine gut and brain in germ-free, antibiotic-treated and control mice.ResultsWe determined the spatial distribution and relative quantification of neurotransmitters and their precursors across brain and gut sections in response to the microbiome. Using untargeted MSI of small molecules, we detected a significant change in the levels of four identified metabolites in the brains of germ-free animals compared to controls; vitamin B5, 3-hydroxy-3-methylglutaric acid, 3-methyl-4-(trimethylammonio)butanoate and 4-(trimethylammonio)pentanoate. However, antibiotic treatment induced no significant changes in these metabolites in the brain after one week of treatment.ConclusionsThis work exemplifies the utility of MSI as a tool in determining the spatial distribution and quantification of bacterial and host metabolites in the gut and brain whilst also offering the potential for discovery of novel mediators of microbiome-gut-brain axis communication.

Oncogenic KRAS mutations and inactivation of the APC tumor suppressor co-occur in colorectal cancer (CRC). Despite efforts to target mutant KRAS directly, most therapeutic approaches focus on downstream pathways, albeit with limited efficacy. Moreover, mutant KRAS alters the basal metabolism of cancer cells, increasing glutamine utilization to support proliferation. We show that concomitant mutation of Apc and Kras in the mouse intestinal epithelium profoundly rewires metabolism, increasing glutamine consumption. Furthermore, SLC7A5, a glutamine antiporter, is critical for colorectal tumorigenesis in models of both early- and late-stage metastatic disease. Mechanistically, SLC7A5 maintains intracellular amino acid levels following KRAS activation through transcriptional and metabolic reprogramming. This supports the increased demand for bulk protein synthesis that underpins the enhanced proliferation of KRAS-mutant cells. Moreover, targeting protein synthesis, via inhibition of the mTORC1 regulator, together with Slc7a5 deletion abrogates the growth of established Kras-mutant tumors. Together, these data suggest SLC7A5 as an attractive target for therapy-resistant KRAS-mutant CRC.

Mass spectrometry imaging using matrix-assisted laser desorption/ionization and desorption electrospray ionization has recently been employed to investigate the distribution of neurotransmitters, including biogenic amines and amino acids, directly in brain tissue sections. Ionization is facilitated by charge-tagging through pyrylium derivatization of primary amine containing neurotransmitters directly in tissue sections, significantly improving the limit of detection. Since the derivatization adds carbon and hydrogen to the target compounds, the resulting isotopic patterns of the products are not distinctive from those of the nonderivatized species. Here, we describe an approach for chemically modifying the reactive pyrylium ion to introduce the distinct isotopic signature of bromine in mass spectra of chemically derivatized substances in tissue sections. The method enables monoamine compounds to be distinguished directly in tissue sections, facilitating their identification.

We have previously demonstrated liquid extraction surface analysis (LESA) high field asymmetric waveform ion mobility spectrometry (FAIMS) mass spectrometry imaging of proteins in thin tissue sections of brain and liver. Here, we present an improved approach that makes use of multiple static FAIMS parameters at each sampled location and allows a significant improvement in the number of proteins, lipids, and drugs that can be imaged simultaneously. The approach is applied to the mass spectrometry imaging of control and cassette-dosed rat kidneys. Mass spectrometry imaging of kidneys typically requires washing to remove excess hemoglobin; however, that is not necessary with this approach. Multistep static FAIMS mass spectrometry resulted in a 6- to 16-fold increase in the number of proteins detected in the absence of FAIMS, in addition to smaller increases over single step static FAIMS (chosen for optimum transmission of total protein ions). The benefits of multistep static FAIMS mass spectrometry for protein detection are also shown for sections of testes. The numbers of proteins detected following multistep FAIMS increased between 2- and 3-fold over single step FAIMS and between 2- and 14-fold over LESA alone. Finally, to date, LESA mass spectrometry of proteins in tissue has been undertaken solely on fresh frozen samples. In this work, we demonstrate that heat-preserved tissues are also suitable for these analyses. Heat preservation of tissue improved the number of proteins detected by LESA MS for both kidney and testes tissue (by between 2- and 4-fold). For both tissue types, the majority of the proteins additionally detected in the heat-treated samples were subsequently detected in the frozen samples when FAIMS was incorporated. Improvements in the numbers of proteins detected were observed for LESA FAIMS MS for the kidney tissue; for testes tissue, fewer total proteins were detected following heat preservation; however, approximately one-third were unique to the heat-preserved samples.