Project description:This dataset comprises spatial multi-omic profiling of malignant pleural mesothelioma (MPM) tissue, enabling simultaneous measurement of transcriptomic and protein expression at spatial resolution. To address the lack of spatially resolved proteomic measurements in standard spatial transcriptomics (ST) technologies, we generated this in-house MPM spatial transcriptomics and proteomics dataset and used it to train and evaluate DGAT (Dual-Graph Attention Network), a deep learning framework for imputing protein expression from ST data. DGAT integrates transcriptomic, proteomic, and spatial information using graph attention networks and jointly reconstructs mRNA and protein profiles through multi-task learning. This dataset serves both as a biological resource for understanding the tumor immune microenvironment in MPM and as a benchmark for spatial protein inference. DGAT’s predictions on this dataset enabled improved identification of immune phenotypes and tumor–stroma spatial organization, with implications for biomarker discovery and therapeutic targeting in mesothelioma. Spatial transcriptomics (ST) technologies provide genome-wide mRNA profiles in tissue context but lack direct protein-level measurements, which are critical for interpreting cellular function and microenvironmental organization. We present DGAT (Dual-Graph Attention Network), a deep learning framework that imputes spatial protein expression from transcriptomics-only ST data by learning RNA–protein relationships from spatial CITE-seq datasets. DGAT constructs heterogeneous graphs integrating transcriptomic, proteomic, and spatial information, encoded using graph attention networks. Task-specific decoders reconstruct mRNA and predict protein abundance from a shared latent representation.
Project description:Deciphering the metabolome is essential for a better understanding of the cellular metabolism as a system. Typical metabolomics data show a few but significant correlations among metabolite levels when data sampling is repeated across individuals grown under strictly controlled conditions. Although several studies have assessed topologies in metabolomic correlation networks, it remains unclear whether highly connected metabolites in these networks have specific functions in known tissue- and/or genotype-dependent biochemical pathways. In our study of metabolite profiles we subjected root tissues to gas chromatography-time-of-flight/mass spectrometry (GC-TOF/MS) and used published information on the aerial parts of 3 Arabidopsis genotypes, Col-0 wild-type, methionine over-accumulation 1 (mto1), and transparent testa4 (tt4) to compare systematically the metabolomic correlations in samples of roots and aerial parts. We then applied graph clustering to the constructed correlation networks to extract densely connected metabolites and evaluated the clusters by biochemical-pathway enrichment analysis. We found that the number of significant correlations varied by tissue and genotype and that the obtained clusters were significantly enriched for metabolites included in biochemical pathways. We demonstrate that the graph-clustering approach identifies tissue- and/or genotype-dependent metabolomic clusters related to the biochemical pathway. Metabolomic correlations complement information about changes in mean metabolite levels and may help to elucidate the organization of metabolically functional modules.
Project description:Ceramides contribute to the lipotoxicity that underlies diabetes, hepatic steatosis, and heart disease. By genetically engineering mice, we deleted the enzyme dihydroceramide desaturase-1 (DES1) which inserts a conserved double bond into the backbone of ceramides and other predominant sphingolipids. Ablation of DES1 from whole animals, or tissue-specific deletion in the liver, and/or adipose tissue resolved hepatic steatosis and insulin resistance in mice caused by leptin deficiency or obesogenic diets. Mechanistic studies revealed new ceramide actions that promoted lipid uptake and storage and impaired glucose utilization, none of which could be recapitulated by (dihydro)ceramides that lacked the critical double bond. These studies suggest that inhibition of DES1 may provide a means of treating hepatic steatosis and cardiometabolic disorders.
Project description:T cells are often weakly responsive to tumor self-antigens because of central tolerance, which constrains their ability to eliminate tumors. Affinity-matured T cell receptors can exhibit enhanced tumor killing properties but in therapeutic settings have been accompanied by off-target cross-reactivity and toxicity, because high-affinity TCRs antigen specificity is altered compared to naturally selected TCRs. Here, we exploited the physiological biophysical mechanism of TCR activation through mechanical force, by engineering to a weakly reactive TCR specific for a non-mutated human prostate tumor associated antigen (TAA), Prostatic Acid Phosphatase (PAP). We isolated a catch bonding “hotspot” whose mutation enhanced T cell activity by increasing TCR-pMHC bond lifetime, whilst maintaining physiological affinities and antigen fine-specificities. T cells expressing these engineered TCRs showed vastly superior expansion and tumor killing properties in vitro and in vivo, as well as enhanced effector phenotypes and proliferation in the tumor, as measured by single-cell RNA-seq. High resolution structures and molecular dynamics simulations of the TCR-pMHC complexes reveal the structural hotspot in TCR CDR1 is primed for peptide interaction in the catch bond engineered TCR. These studies establish catch bond engineering as a viable biophysically-based strategy to convert tolerized anti-tumor T cells into potent TCR-T killers.