Gain-of-function mutations in isocitrate dehydrogenase (IDH) in human cancers result in the production of d -2-hydroxyglutarate ( d -2HG), an oncometabolite that promotes tumorigenesis through epigenetic alterations. The cancer cell–intrinsic effects of d -2HG are well understood, but its tumor cell–nonautonomous roles remain poorly explored. We compared the oncometabolite d -2HG with its enantiomer, l -2HG, and found that tumor-derived d -2HG was taken up by CD8 + T cells and altered their metabolism and antitumor functions in an acute and reversible fashion. We identified the glycolytic enzyme lactate dehydrogenase (LDH) as a molecular target of d -2HG. d -2HG and inhibition of LDH drive a metabolic program and immune CD8 + T cell signature marked by decreased cytotoxicity and impaired interferon-γ signaling that was recapitulated in clinical samples from human patients with IDH1 mutant gliomas.

Mass spectrometry imaging (MSI) has become a widespread analytical technique to perform nonlabeled spatial molecular identification. The Achilles' heel of MSI is the annotation and identification of molecular species due to intrinsic limitations of the technique (lack of chromatographic separation and the difficulty to apply tandem MS). Successful strategies to perform annotation and identification combine extra analytical steps, like using orthogonal analytical techniques to identify compounds; with algorithms that integrate the spectral and spatial information. In this review, we discuss different experimental strategies and bioinformatics tools to annotate and identify compounds in MSI experiments. We target strategies and tools for small molecule applications, such as lipidomics and metabolomics. First, we explain how sample preparation and the acquisition process influences annotation and identification, from sample preservation to the use of orthogonal techniques. Then, we review twelve software tools for annotation and identification in MSI. Finally, we offer perspectives on two current needs of the MSI community: the adaptation of guidelines for communicating confidence levels in identifications; and the creation of a standard format to store and exchange annotations and identifications in MSI.

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Imaging techniques based on mass spectrometry or spectroscopy methods inform in situ about the chemical composition of biological tissues or organisms, but they are sometimes limited by their specificity, sensitivity, or spatial resolution. Multimodal imaging addresses these limitations by combining several imaging modalities; however, measuring the same sample with the same preparation using multiple imaging techniques is still uncommon due to the incompatibility between substrates, sample preparation protocols, and data formats. We present a multimodal imaging approach that employs a gold-coated nanostructured silicon substrate to couple surface-assisted laser desorption/ionization mass spectrometry (SALDI-MS) and surface-enhanced Raman spectroscopy (SERS). Our approach integrates both imaging modalities by using the same substrate, sample preparation, and data analysis software on the same sample, allowing the coregistration of both images. We transferred molecules from clean fingertips and fingertips covered with plasticine modeling clay onto our nanostructure and analyzed their chemical composition and distribution by SALDI-MS and SERS. Multimodal analysis located the traces of plasticine on fingermarks and provided chemical information on the composition of the clay. Our multimodal approach effectively combines the advantages of mass spectrometry and vibrational spectroscopy with the signal enhancing abilities of our nanostructured substrate.

How the glioma immune microenvironment fosters tumorigenesis remains incompletely defined. Here, we use single-cell RNA-sequencing and multiplexed tissue-imaging to characterize the composition, spatial organization, and clinical significance of extracellular purinergic signaling in glioma. We show that microglia are the predominant source of CD39, while tumor cells principally express CD73. In glioblastoma, CD73 is associated with EGFR amplification, astrocyte-like differentiation, and increased adenosine, and is linked to hypoxia. Glioblastomas enriched for CD73 exhibit inflammatory microenvironments, suggesting that purinergic signaling regulates immune adaptation. Spatially-resolved single-cell analyses demonstrate a strong spatial correlation between tumor-CD73 and microglial-CD39, with proximity associated with poor outcomes. Similar spatial organization is present in pediatric high-grade gliomas including H3K27M-mutant diffuse midline glioma. These data reveal that purinergic signaling in gliomas is shaped by genotype, lineage, and functional state, and that core enzymes expressed by tumor and myeloid cells are organized to promote adenosine-rich microenvironments potentially amenable to therapeutic targeting.

Mass spectrometry imaging (MSI) has become a mature, widespread analytical technique to perform non-targeted spatial metabolomics. However, the compounds used to promote desorption and ionization of the analyte during acquisition cause spectral interferences in the low mass range that hinder downstream data processing in metabolomics applications. Thus, it is advisable to annotate and remove matrix-related peaks to reduce the number of redundant and non-biologically-relevant variables in the dataset. We have developed rMSIcleanup, an open-source R package to annotate and remove signals from the matrix, according to the matrix chemical composition and the spatial distribution of its ions. To validate the annotation method, rMSIcleanup was challenged with several images acquired using silver-assisted laser desorption ionization MSI (AgLDI MSI). The algorithm was able to correctly classify m/z signals related to silver clusters. Visual exploration of the data using Principal Component Analysis (PCA) demonstrated that annotation and removal of matrix-related signals improved spectral data post-processing. The results highlight the need for including matrix-related peak annotation tools such as rMSIcleanup in MSI workflows.

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14Mass spectrometry imaging (MSI) has become a mature, widespread analytical technique to 15 perform non-targeted spatial metabolomics. However, the compounds used to promote 16 desorption and ionization of the analyte during acquisition cause spectral interferences in the 17 low mass range that hinder downstream data processing in metabolomics applications. Thus, it 18 is advisable to annotate and remove matrix-related peaks to reduce the number of redundant 19and non-biologically-relevant variables in the dataset. We have developed rMSIcleanup, an 20 open-source R package to annotate and remove matrix-related signals based on its chemical 21 formula and the spatial distribution of its ions. To validate the annotation method, rMSIcleanup 22was challenged with several images acquired using silver-assisted laser desorption ionization 23 MSI (AgLDI MSI). The algorithm was able to correctly classify m/z signals related to silver clusters. 24Visual exploration of the data using Principal Component Analysis (PCA) demonstrated that 25 annotation and removal of matrix-related signals improved spectral data post-processing. The 26 results highlight the need for including matrix-related peak annotation tools such as 27 rMSIcleanup in MSI workflows. 28