The dentate gyrus differentially metabolizes glucose and alternative fuels during rest and stimulation.

The metabolic demands of neuronal activity are both temporally and spatially dynamic, and neurons are particularly sensitive to disruptions in fuel and oxygen supply. Glucose is considered an obligate fuel for supporting brain metabolism. Although alternative fuels are often available, the extent of their contribution to central carbon metabolism remains debated. Differential fuel metabolism likely depends on cell type, location, and activity state, complicating its study. While biosensors provide excellent spatial and temporal information, they are limited to observations of only a few metabolites. On the other hand, mass spectrometry is rich in chemical information, but traditionally relies on cell culture or homogenized tissue samples. Here, we use mass spectrometry imaging (MALDI-MSI) to focus on the fuel metabolism of the dentate granule cell (DGC) layer in murine hippocampal slices. Using stable isotopes, we explore labeling dynamics at baseline, as well as in response to brief stimulation or fuel competition. We find that at rest, glucose is the predominant fuel metabolized through glycolysis, with little to no measurable contribution from glycerol or fructose. However, lactate/pyruvate, ß-hydroxybutyrate (ßHB), octanoate, and glutamine can contribute to TCA metabolism to varying degrees. In response to brief depolarization with 50 mM KCl, glucose metabolism was preferentially increased relative to the metabolism of alternative fuels. With an increased supply of alternative fuels, both lactate/pyruvate and ßHB can outcompete glucose for TCA cycle entry. While lactate/pyruvate modestly reduced glucose contribution to glycolysis, ßHB caused little change in glycolysis. This approach achieves broad metabolite coverage from a spatially defined region of physiological tissue, in which metabolic states are rapidly preserved following experimental manipulation. Using this powerful methodology, we investigated metabolism within the dentate gyrus not only at rest, but also in response to the energetic demand of activation, and in states of fuel competition.

High-Throughput f-LAESI-IMS-MS for Mapping Biological Nitrogen Fixation One Cell at a Time.

For the characterization of the metabolic heterogeneity of cell populations, high-throughput single-cell analysis platforms are needed. In this study, we utilized mass spectrometry (MS) enhanced with ion mobility separation (IMS) and coupled with an automated sampling platform, fiber-based laser ablation electrospray ionization (f-LAESI), for in situ high-throughput single-cell metabolomics in soybean (Glycine max) root nodules. By fully automating the in situ sampling platform, an overall sampling rate of 804 cells/h was achieved for high numbers (>500) of tissue-embedded plant cells. This is an improvement by a factor of 13 compared to the previous f-LAESI-MS configuration. By introducing IMS, the molecular coverage improved, and structural isomers were separated on a millisecond time scale. The enhanced f-LAESI-IMS-MS platform produced 259 sample-related peaks/cell, almost twice as much as the 131 sample-related peaks/cell produced by f-LAESI-MS without IMS. Using the upgraded system, two types of metabolic heterogeneity characterization methods became possible. For unimodal metabolite abundance distributions, the metabolic noise reported on the metabolite level variations within the cell population. For bimodal distributions, the presence of metabolically distinct subpopulations was established. Discovering these latent cellular phenotypes could be linked to the presence of different cell states, e.g., proliferating bacteria in partially occupied plant cells and quiescent bacteroids in fully occupied cells in biological nitrogen fixation, or spatial heterogeneity due to altered local environments.

Chemical QuantArray: A Quantitative Tool for Mass Spectrometry Imaging.

Matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) is a powerful analytical technique that provides spatially preserved detection and quantification of analytes in tissue specimens. However, clinical translation still requires improved throughput, precision, and accuracy. To accomplish this, we created "Chemical QuantArray", a gelatin tissue microarray (TMA) mold filled with serial dilutions of isotopically labeled endogenous metabolite standards. The mold is then cryo-sectioned onto a tissue homogenate to produce calibration curves. To improve precision and accuracy, we automatically remove pixels outside of each TMA well and investigated several intensity normalizations, including the utilization of a second stable isotope internal standard (IS). Chemical QuantArray enables the quantification of several endogenous metabolites over a wide dynamic range and significantly improve over current approaches. The technique reduces the space needed on the MALDI slides for calibration standards by approximately 80%. Furthermore, removal of empty pixels and normalization to an internal standard or matrix peak provided precision (<20% RSD) and accuracy (<20% DEV). Finally, we demonstrate the applicability of Chemical QuantArray by quantifying multiple purine metabolites in 14 clinical tumor specimens using a single MALDI slide. Chemical QuantArray improves the analytical characteristics and practical feasibility of MALDI-MSI metabolite quantification in clinical and translational applications.

massNet: integrated processing and classification of spatially resolved mass spectrometry data using deep learning for rapid tumor delineation.

MOTIVATION: Mass spectrometry imaging (MSI) provides rich biochemical information in a label-free manner and therefore holds promise to substantially impact current practice in disease diagnosis. However, the complex nature of MSI data poses computational challenges in its analysis. The complexity of the data arises from its large size, high-dimensionality and spectral nonlinearity. Preprocessing, including peak picking, has been used to reduce raw data complexity; however, peak picking is sensitive to parameter selection that, perhaps prematurely, shapes the downstream analysis for tissue classification and ensuing biological interpretation. RESULTS: We propose a deep learning model, massNet, that provides the desired qualities of scalability, nonlinearity and speed in MSI data analysis. This deep learning model was used, without prior preprocessing and peak picking, to classify MSI data from a mouse brain harboring a patient-derived tumor. The massNet architecture established automatically learning of predictive features, and automated methods were incorporated to identify peaks with potential for tumor delineation. The model's performance was assessed using cross-validation, and the results demonstrate higher accuracy and a substantial gain in speed compared to the established classical machine learning method, support vector machine. AVAILABILITY AND IMPLEMENTATION: https://github.com/wabdelmoula/massNet. The data underlying this article are available in the NIH Common Fund's National Metabolomics Data Repository (NMDR) Metabolomics Workbench under project id (PR001292) with http://dx.doi.org/10.21228/M8Q70T. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Chronic convection-enhanced delivery of topotecan for patients with recurrent glioblastoma: a first-in-patient, single-centre, single-arm, phase 1b trial.

BACKGROUND: Topotecan is cytotoxic to glioma cells but is clinically ineffective because of drug delivery limitations. Systemic delivery is limited by toxicity and insufficient brain penetrance, and, to date, convection-enhanced delivery (CED) has been restricted to a single treatment of restricted duration. To address this problem, we engineered a subcutaneously implanted catheter-pump system capable of repeated, chronic (prolonged, pulsatile) CED of topotecan into the brain and tested its safety and biological effects in patients with recurrent glioblastoma. METHODS: We did a single-centre, open-label, single-arm, phase 1b clinical trial at Columbia University Irving Medical Center (New York, NY, USA). Eligible patients were at least 18 years of age with solitary, histologically confirmed recurrent glioblastoma showing radiographic progression after surgery, radiotherapy, and chemotherapy, and a Karnofsky Performance Status of at least 70. Five patients had catheters stereotactically implanted into the glioma-infiltrated peritumoural brain and connected to subcutaneously implanted pumps that infused 146 µM topotecan 200 µL/h for 48 h, followed by a 5-7-day washout period before the next infusion, with four total infusions. After the fourth infusion, the pump was removed and the tumour was resected. The primary endpoint of the study was safety of the treatment regimen as defined by presence of serious adverse events. Analyses were done in all treated patients. The trial is closed, and is registered with ClinicalTrials.gov, NCT03154996. FINDINGS: Between Jan 22, 2018, and July 8, 2019, chronic CED of topotecan was successfully completed safely in all five patients, and was well tolerated without substantial complications. The only grade 3 adverse event related to treatment was intraoperative supplemental motor area syndrome (one [20%] of five patients in the treatment group), and there were no grade 4 adverse events. Other serious adverse events were related to surgical resection and not the study treatment. Median follow-up was 12 months (IQR 10-17) from pump explant. Post-treatment tissue analysis showed that topotecan significantly reduced proliferating tumour cells in all five patients. INTERPRETATION: In this small patient cohort, we showed that chronic CED of topotecan is a potentially safe and active therapy for recurrent glioblastoma. Our analysis provided a unique tissue-based assessment of treatment response without the need for large patient numbers. This novel delivery of topotecan overcomes limitations in delivery and treatment response assessment for patients with glioblastoma and could be applicable for other anti-glioma drugs or other CNS diseases. Further studies are warranted to determine the effect of this drug delivery approach on clinical outcomes. FUNDING: US National Institutes of Health, The William Rhodes and Louise Tilzer Rhodes Center for Glioblastoma, the Michael Weiner Glioblastoma Research Into Treatment Fund, the Gary and Yael Fegel Foundation, and The Khatib Foundation.

Metabolomic profiling of wild-type and mutant soybean root nodules using laser-ablation electrospray ionization mass spectrometry reveals altered metabolism.

The establishment of the nitrogen-fixing symbiosis between soybean and Bradyrhizobium japonicum is a complex process. To document the changes in plant metabolism as a result of symbiosis, we utilized laser ablation electrospray ionization-mass spectrometry (LAESI-MS) for in situ metabolic profiling of wild-type nodules, nodules infected with a B. japonicum nifH mutant unable to fix nitrogen, nodules doubly infected by both strains, and nodules formed on plants mutated in the stearoyl-acyl carrier protein desaturase (sacpd-c) gene, which were previously shown to have an altered nodule ultrastructure. The results showed that the relative abundance of fatty acids, purines, and lipids was significantly changed in response to the symbiosis. The nifH mutant nodules had elevated levels of jasmonic acid, correlating with signs of nitrogen deprivation. Nodules resulting from the mixed inoculant displayed similar, overlapping metabolic distributions within the sectors of effective (fix+ ) and ineffective (nifH mutant, fix- ) endosymbionts. These data are inconsistent with the notion that plant sanctioning is cell autonomous. Nodules lacking sacpd-c displayed an elevation of soyasaponins and organic acids in the central necrotic regions. The present study demonstrates the utility of LAESI-MS for high-throughput screening of plant phenotypes. Overall, nodules disrupted in the symbiosis were elevated in metabolites related to plant defense.

High-Throughput Analysis of Tissue-Embedded Single Cells by Mass Spectrometry with Bimodal Imaging and Object Recognition.

In biological tissues, cell-to-cell variations stem from the stochastic and modulated expression of genes and the varying abundances of corresponding proteins. These variations are then propagated to downstream metabolite products and result in cellular heterogeneity. Mass spectrometry imaging (MSI) is a promising tool to simultaneously provide spatial distributions for hundreds of biomolecules without the need for labels or stains. Technological advances in MSI instrumentation for the direct analysis of tissue-embedded single cells are dominated by improvements in sensitivity, sample pretreatment, and increased spatial resolution but are limited by low throughput. Herein, we introduce a bimodal microscopy imaging system combined with fiber-based laser ablation electrospray ionization (f-LAESI) MSI with improved throughput ambient analysis of tissue-embedded single cells (n > 1000) to provide insight into cellular heterogeneity. Based on automated image analysis, accurate single-cell sampling is achieved by f-LAESI leading to the discovery of cellular phenotypes characterized by differing metabolite levels.

In-Situ Metabolomic Analysis of Setaria viridis Roots Colonized by Beneficial Endophytic Bacteria.

Over the past decades, crop yields have risen in parallel with increasing use of fossil fuel-derived nitrogen (N) fertilizers but with concomitant negative impacts on climate and water resources. There is a need for more sustainable agricultural practices, and biological nitrogen fixation (BNF) could be part of the solution. A variety of nitrogen-fixing, epiphytic, and endophytic plant growth-promoting bacteria (PGPB) are known to stimulate plant growth. However, compared with the rhizobium-legume symbiosis, little mechanistic information is available as to how PGPB affect plant metabolism. Therefore, we investigated the metabolic changes in roots of the model grass species Setaria viridis upon endophytic colonization by Herbaspirillum seropedicae SmR1 (fix+) or a fix- mutant strain (SmR54) compared with uninoculated roots. Endophytic colonization of the root is highly localized and, hence, analysis of whole-root segments dilutes the metabolic signature of those few cells impacted by the bacteria. Therefore, we utilized in-situ laser ablation electrospray ionization mass spectrometry to sample only those root segments at or adjacent to the sites of bacterial colonization. Metabolites involved in purine, zeatin, and riboflavin pathways were significantly more abundant in inoculated plants, while metabolites indicative of nitrogen, starch, and sucrose metabolism were reduced in roots inoculated with the fix- strain or uninoculated, presumably due to N limitation. Interestingly, compounds, involved in indole-alkaloid biosynthesis were more abundant in the roots colonized by the fix- strain, perhaps reflecting a plant defense response.

Single-Cell Metabolic Profiling: Metabolite Formulas from Isotopic Fine Structures in Heterogeneous Plant Cell Populations.

Characterization of the metabolic heterogeneity in cell populations requires the analysis of single cells. Most current methods in single-cell analysis rely on cell manipulation, potentially altering the abundance of metabolites in individual cells. A small sample volume and the chemical diversity of metabolites are additional challenges in single-cell metabolomics. Here, we describe the combination of fiber-based laser ablation electrospray ionization (f-LAESI) with 21 T Fourier transform ion cyclotron resonance mass spectrometry (21TFTICR-MS) for in situ single-cell metabolic profiling in plant tissue. Single plant cells infected by bacteria were selected and sampled directly from the tissue without cell manipulation through mid-infrared ablation with a fine optical fiber tip for ionization by f-LAESI. Ultrahigh performance 21T-FTICR-MS enabled the simultaneous capture of isotopic fine structures (IFSs) for 47 known and 11 unknown compounds, thus elucidating their elemental compositions from single cells and providing information on metabolic heterogeneity in the cell population.

Ambient Metabolic Profiling and Imaging of Biological Samples with Ultrahigh Molecular Resolution Using Laser Ablation Electrospray Ionization 21 Tesla FTICR Mass Spectrometry.

Mass spectrometry (MS) is an indispensable analytical tool to capture the array of metabolites within complex biological systems. However, conventional MS-based metabolomic workflows require extensive sample processing and separation resulting in limited throughput and potential alteration of the native molecular states in these systems. Ambient ionization methods, capable of sampling directly from tissues, circumvent some of these issues but require high-performance MS to resolve the molecular complexity within these samples. Here, we demonstrate a unique combination of laser ablation electrospray ionization (LAESI) coupled with a 21 tesla Fourier transform ion cyclotron resonance (21T-FTICR) for direct MS analysis and imaging applications. This analytical platform provides isotopic fine structure information directly from biological tissues, enabling the rapid assignment of molecular formulas and delivering a higher degree of confidence for molecular identification.

Laser-ablation electrospray ionization mass spectrometry with ion mobility separation reveals metabolites in the symbiotic interactions of soybean roots and rhizobia.

Technologies enabling in situ metabolic profiling of living plant systems are invaluable for understanding physiological processes and could be used for rapid phenotypic screening (e.g., to produce plants with superior biological nitrogen-fixing ability). The symbiotic interaction between legumes and nitrogen-fixing soil bacteria results in a specialized plant organ (i.e., root nodule) where the exchange of nutrients between host and endosymbiont occurs. Laser-ablation electrospray ionization mass spectrometry (LAESI-MS) is a method that can be performed under ambient conditions requiring minimal sample preparation. Here, we employed LAESI-MS to explore the well characterized symbiosis between soybean (Glycine max L. Merr.) and its compatible symbiont, Bradyrhizobium japonicum. The utilization of ion mobility separation (IMS) improved the molecular coverage, selectivity, and identification of the detected biomolecules. Specifically, incorporation of IMS resulted in an increase of 153 differentially abundant spectral features in the nodule samples. The data presented demonstrate the advantages of using LAESI-IMS-MS for the rapid analysis of intact root nodules, uninfected root segments, and free-living rhizobia. Untargeted pathway analysis revealed several metabolic processes within the nodule (e.g., zeatin, riboflavin, and purine synthesis). Compounds specific to the uninfected root and bacteria were also detected. Lastly, we performed depth profiling of intact nodules to reveal the location of metabolites to the cortex and inside the infected region, and lateral profiling of sectioned nodules confirmed these molecular distributions. Our results established the feasibility of LAESI-IMS-MS for the analysis and spatial mapping of plant tissues, with its specific demonstration to improve our understanding of the soybean-rhizobial symbiosis.

Large-Scale Metabolite Analysis of Standards and Human Serum by Laser Desorption Ionization Mass Spectrometry from Silicon Nanopost Arrays.

The unique challenges presented by metabolomics have driven the development of new mass spectrometry (MS)-based techniques for small molecule analysis. We have previously demonstrated silicon nanopost arrays (NAPA) to be an effective substrate for laser desorption ionization (LDI) of small molecules for MS. However, the utility of NAPA-LDI-MS for a wide range of metabolite classes has not been investigated. Here we apply NAPA-LDI-MS to the large-scale acquisition of high-resolution mass spectra and tandem mass spectra from a collection of metabolite standards covering a range of compound classes including amino acids, nucleotides, carbohydrates, xenobiotics, lipids, and other classes. In untargeted analysis of metabolite standard mixtures, detection was achieved for 374 compounds and useful MS/MS spectra were obtained for 287 compounds, without individual optimization of ionization or fragmentation conditions. Metabolite detection was evaluated in the context of 31 metabolic pathways, and NAPA-LDI-MS was found to provide detection for 63% of investigated pathway metabolites. Individual, targeted analysis of the 20 common amino acids provided detection of 100% of the investigated compounds, demonstrating that improved coverage is possible through optimization and targeting of individual analytes or analyte classes. In direct analysis of aqueous and organic extracts from human serum samples, spectral features were assigned to a total of 108 small metabolites and lipids. Glucose and amino acids were quantitated within their physiological concentration ranges. The broad coverage demonstrated by this large-scale screening experiment opens the door for use of NAPA-LDI-MS in numerous metabolite analysis applications.

Molecular Imaging of Biological Samples on Nanophotonic Laser Desorption Ionization Platforms.

Mass spectrometry imaging (MSI) is a comprehensive tool for the analysis of a wide range of biomolecules. The mainstream method for molecular MSI is matrix-assisted laser desorption ionization, however, the presence of a matrix results in spectral interferences and the suppression of some analyte ions. Herein we demonstrate a new matrix-free MSI technique using nanophotonic ionization based on laser desorption ionization (LDI) from a highly uniform silicon nanopost array (NAPA). In mouse brain and kidney tissue sections, the distributions of over 80 putatively annotated molecular species are determined with 40 µm spatial resolution. Furthermore, NAPA-LDI-MS is used to selectively analyze metabolites and lipids from sparsely distributed algal cells and the lamellipodia of human hepatocytes. Our results open the door for matrix-free MSI of tissue sections and small cell populations by nanophotonic ionization.

Metabolic transformation of microalgae due to light acclimation and genetic modifications followed by laser ablation electrospray ionization mass spectrometry with ion mobility separation.

Metabolic profiling of various microalga species and their genetic variants, grown under varied environmental conditions, has become critical to accelerate the exploration of phytoplankton biodiversity and biology. The accumulation of valuable metabolites, such as glycerolipids, is also sought in microalgae for biotechnological applications ranging from food, feed, medicine, cosmetics to bioenergy and green chemistry. In this report we describe the direct analysis of metabolites and lipids in small cell populations of the green alga Chlamydomonas reinhardtii, using laser ablation electrospray ionization (LAESI) mass spectrometry (MS) coupled with ion mobility separation (IMS). These microorganisms are capable of redirecting energy storage pathways from starch to neutral lipids depending on environmental conditions and nutrient availability. Metabolite and lipid productions were monitored in wild type (WT), and genetically modified C. reinhardtii strains with an impaired starch pathway. Lipids, such as triacylglycerols (TAG) and diacylglyceryl-N,N,N-trimethylhomoserine (DGTS), were monitored over time under altered light conditions. More than 200 ions related to metabolites, e.g., arginine, cysteine, serine, palmitate, chlorophyll a, chlorophyll b, etc., were detected. The lipid profiles at different light intensities for strains with impaired starch pathway (Sta1 and Sta6) contained 26 glycerolipids, such as DGTS, monogalactosyldiacylglycerol (MGDG) and digalactosyldiacylglycerol (DGDG), as well as 33 TAG species. Results were obtained over a 72 hour time period under high and low light conditions for the WT species and the two mutants. Our results indicate that LAESI-IMS-MS can be utilized for the rapid analysis of increased TAG production at elevated light intensities. Compared to WT, the Sta6 strain showed 2.5 times higher lipid production at 72 hours under high light conditions. The results demonstrate our ability to rapidly observe numerous changes in metabolite and lipid levels in microalgal population. These capabilities are expected to facilitate the exploration of genetically altered microalgal strains for biofuel production.

A Benzarone Derivative Inhibits EYA to Suppress Tumor Growth in SHH Medulloblastoma.

Medulloblastoma is one of the most common malignant brain tumors of children, and 30% of medulloblastomas are driven by gain-of-function genetic lesions in the Sonic Hedgehog (SHH) signaling pathway. EYA1, a haloacid dehalogenase phosphatase and transcription factor, is critical for tumorigenesis and proliferation of SHH medulloblastoma (SHH-MB). Benzarone and benzbromarone have been identified as allosteric inhibitors of EYA proteins. Using benzarone as a point of departure, we developed a panel of 35 derivatives and tested them in SHH-MB. Among these compounds, DS-1-38 functioned as an EYA antagonist and opposed SHH signaling. DS-1-38 inhibited SHH-MB growth in vitro and in vivo, showed excellent brain penetrance, and increased the lifespan of genetically engineered mice predisposed to fatal SHH-MB. These data suggest that EYA inhibitors represent promising therapies for pediatric SHH-MB. SIGNIFICANCE: Development of a benzarone derivative that inhibits EYA1 and impedes the growth of SHH medulloblastoma provides an avenue for improving treatment of this malignant pediatric brain cancer.

Deep Learning on Multimodal Chemical and Whole Slide Imaging Data for Predicting Prostate Cancer Directly from Tissue Images.

Prostate cancer is one of the most common cancers globally and is the second most common cancer in the male population in the US. Here we develop a study based on correlating the hematoxylin and eosin (H&E)-stained biopsy data with MALDI mass-spectrometric imaging data of the corresponding tissue to determine the cancerous regions and their unique chemical signatures and variations of the predicted regions with original pathological annotations. We obtain features from high-resolution optical micrographs of whole slide H&E stained data through deep learning and spatially register them with mass spectrometry imaging (MSI) data to correlate the chemical signature with the tissue anatomy of the data. We then use the learned correlation to predict prostate cancer from observed H&E images using trained coregistered MSI data. This multimodal approach can predict cancerous regions with ∼80% accuracy, which indicates a correlation between optical H&E features and chemical information found in MSI. We show that such paired multimodal data can be used for training feature extraction networks on H&E data which bypasses the need to acquire expensive MSI data and eliminates the need for manual annotation saving valuable time. Two chemical biomarkers were also found to be predicting the ground truth cancerous regions. This study shows promise in generating improved patient treatment trajectories by predicting prostate cancer directly from readily available H&E-stained biopsy images aided by coregistered MSI data.

Spatially resolved metabolomics and isotope tracing reveal dynamic metabolic responses of dentate granule neurons with acute stimulation.

Neuronal activity creates an intense energy demand that must be met by rapid metabolic responses. To investigate metabolic adaptations in the neuron-enriched dentate granule cell (DGC) layer within its native tissue environment, we employed murine acute hippocampal brain slices, coupled with fast metabolite preservation and followed by mass spectrometry (MS) imaging, to generate spatially resolved metabolomics and isotope-tracing data. Here we show that membrane depolarization induces broad metabolic changes, including increased glycolytic activity in DGCs. Increased glucose metabolism in response to stimulation is accompanied by mobilization of endogenous inosine into pentose phosphates via the action of purine nucleotide phosphorylase (PNP). The PNP reaction is an integral part of the neuronal response to stimulation, because inhibition of PNP leaves DGCs energetically impaired during recovery from strong activation. Performing MS imaging on brain slices bridges the gap between live-cell physiology and the deep chemical analysis enabled by MS.

WSD-0922, a novel brain-penetrant inhibitor of epidermal growth factor receptor, promotes survival in glioblastoma mouse models.

Background: Although the epidermal growth factor receptor (EGFR) is a frequent oncogenic driver in glioblastoma (GBM), efforts to therapeutically target this protein have been largely unsuccessful. The present preclinical study evaluated the novel EGFR inhibitor WSD-0922. Methods: We employed flank and orthotopic patient-derived xenograft models to characterize WSD-0922 and compare its efficacy to erlotinib, a potent EGFR inhibitor that failed to provide benefit for GBM patients. We performed long-term survival studies and collected short-term tumor, plasma, and whole-brain samples from mice treated with each drug. We utilized mass spectrometry to measure drug concentrations and spatial distribution and to assess the impact of each drug on receptor activity and cellular signaling networks. Results: WSD-0922 inhibited EGFR signaling as effectively as erlotinib in in vitro and in vivo models. While WSD-0922 was more CNS penetrant than erlotinib in terms of total concentration, comparable concentrations of both drugs were measured at the tumor site in orthotopic models, and the concentration of free WSD-0922 in the brain was significantly less than the concentration of free erlotinib. WSD-0922 treatment provided a clear survival advantage compared to erlotinib in the GBM39 model, with marked suppression of tumor growth and most mice surviving until the end of the study. WSD-0922 treatment preferentially inhibited phosphorylation of several proteins, including those associated with EGFR inhibitor resistance and cell metabolism. Conclusions: WSD-0922 is a highly potent inhibitor of EGFR in GBM, and warrants further evaluation in clinical studies.

Rewiring of cortical glucose metabolism fuels human brain cancer growth.

The brain avidly consumes glucose to fuel neurophysiology. Cancers of the brain, such as glioblastoma (GBM), lose aspects of normal biology and gain the ability to proliferate and invade healthy tissue. How brain cancers rewire glucose utilization to fuel these processes is poorly understood. Here we perform infusions of 13 C-labeled glucose into patients and mice with brain cancer to define the metabolic fates of glucose-derived carbon in tumor and cortex. By combining these measurements with quantitative metabolic flux analysis, we find that human cortex funnels glucose-derived carbons towards physiologic processes including TCA cycle oxidation and neurotransmitter synthesis. In contrast, brain cancers downregulate these physiologic processes, scavenge alternative carbon sources from the environment, and instead use glucose-derived carbons to produce molecules needed for proliferation and invasion. Targeting this metabolic rewiring in mice through dietary modulation selectively alters GBM metabolism and slows tumor growth. Significance: This study is the first to directly measure biosynthetic flux in both glioma and cortical tissue in human brain cancer patients. Brain tumors rewire glucose carbon utilization away from oxidation and neurotransmitter production towards biosynthesis to fuel growth. Blocking these metabolic adaptations with dietary interventions slows brain cancer growth with minimal effects on cortical metabolism.

Investigative needle core biopsies for multi-omics in Glioblastoma.

Glioblastoma (GBM) is a primary brain cancer with an abysmal prognosis and few effective therapies. The ability to investigate the tumor microenvironment before and during treatment would greatly enhance both understanding of disease response and progression, as well as the delivery and impact of therapeutics. Stereotactic biopsies are a routine surgical procedure performed primarily for diagnostic histopathologic purposes. The role of investigative biopsies - tissue sampling for the purpose of understanding tumor microenvironmental responses to treatment using integrated multi-modal molecular analyses ('Multi-omics") has yet to be defined. Secondly, it is unknown whether comparatively small tissue samples from brain biopsies can yield sufficient information with such methods. Here we adapt stereotactic needle core biopsy tissue in two separate patients. In the first patient with recurrent GBM we performed highly resolved multi-omics analysis methods including single cell RNA sequencing, spatial-transcriptomics, metabolomics, proteomics, phosphoproteomics, T-cell clonotype analysis, and MHC Class I immunopeptidomics from biopsy tissue that was obtained from a single procedure. In a second patient we analyzed multi-regional core biopsies to decipher spatial and genomic variance. We also investigated the utility of stereotactic biopsies as a method for generating patient derived xenograft models in a separate patient cohort. Dataset integration across modalities showed good correspondence between spatial modalities, highlighted immune cell associated metabolic pathways and revealed poor correlation between RNA expression and the tumor MHC Class I immunopeptidome. In conclusion, stereotactic needle biopsy cores are of sufficient quality to generate multi-omics data, provide data rich insight into a patient's disease process and tumor immune microenvironment and can be of value in evaluating treatment responses. One sentence summary: Integrative multi-omics analysis of stereotactic needle core biopsies in glioblastoma.