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Metabolic profiling of glioblastoma and identification of G0S2 as a metabolic target.
INTRODUCTION: Metabolic reprogramming is a hallmark of cancer, yet its role in glioma remains poorly understood. Gliomas are characterized by a highly immunosuppressive tumor microenvironment (TME) and poor prognosis. This study systematically explores the relationship between glioma metabolomics, tumor phenotype, and the immune microenvironment. METHODS: Bulk RNA sequencing data were retrieved from the Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA). Single-cell gene set enrichment analysis (ssGSEA) was employed to quantify seven nutrient metabolic pathways and immune infiltration. Consensus clustering was applied to group gliomas based on metabolic gene expression, and survival analysis was performed to evaluate survival differences across these clusters. A predictive model was constructed and validated using our cohort. Finally, we knocked out G0S2 in glioma cells and performed RNA sequencing to investigate differentially activated pathways. Additionally, in vivo experiments were conducted to explore the antitumor effects of G0S2 knockout in combination with PD-1 monoclonal antibody. RESULTS: Significant metabolic differences were identified between low-grade gliomas (LGG) and glioblastomas (GBM), with consistent findings across both databases. We found that LGGs and GBMs exhibit distinct metabolic patterns. Consensus clustering revealed three metabolic subgroups, with the C3 subgroup demonstrating poor survival and enhanced infiltration of immunosuppressive cells. The predictive model showed robust performance in forecasting the survival of glioma patients. Functional analysis identified G0S2 as a key metabolic regulator highly expressed in gliomas. G0S2 knockout activated the type I interferon signaling pathway, enhanced CD8+ T cell functionality, and synergized with anti-PD-1 therapy, resulting in suppressed tumor growth and prolonged survival in vivo. CONCLUSION: These findings provide a comprehensive analysis of glioma metabolic patterns and identify G0S2 as a promising therapeutic target.
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Construction of Metastasis Prediction Models and Screening of Anti-Metastatic Drugs Based on Pan-Cancer Single-Cell EMT Features.
Tumor metastasis is the leading cause of death in cancer patients, with epithelial-mesenchymal transition (EMT) playing a key role. To systematically elucidate the cellular mechanisms and molecular networks through which EMT drives metastasis across cancers, this study integrated transcriptomic data from over 1.2 million single cells across 265 samples representing 12 primary epithelial cancers, constructing a comprehensive pan-cancer single-cell atlas covering diverse stages and metastatic states. By analyzing the metastatic features and interaction networks of malignant epithelial cells and cancer-associated fibroblasts (CAFs), we identified cancer-specific metastasis-related gene sets. Based on these genes, multiple machine learning algorithms were applied to build cancer-specific and cross-cancer metastasis prediction models, leading to the development of the metastasis prediction score (MPS) and global metastasis prediction score (GMPS). Both scores showed excellent predictive performance in independent test and external validation cohorts. MPS exhibited higher cancer specificity, whereas GMPS showed stronger cross-cancer generalization. Moreover, elevated MPS and GMPS reflected immunosuppressive tumor microenvironment features and were significantly associated with poor prognosis across multiple cancer types. Finally, through a drug repositioning framework, we identified several potential anti-metastatic compounds targeting the metastasis network, among which Fostamatinib demonstrated broad-spectrum therapeutic potential against metastasis in multiple cancers.
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Uncovering the prominent role of satellite cells in paravertebral muscle development and aging by single-nucleus RNA sequencing.
To uncover the role of satellite cells (SCs) in paravertebral muscle development and aging, we constructed a single-nucleus transcriptomic atlas of mouse paravertebral muscle across seven timepoints spanning the embryo (day 16.5) to old (month 24) stages. Eight cell types, including SCs, fast muscle cells, and slow muscle cells, were identified. An energy metabolism-related gene set, TCA CYCLE IN SENESCENCE, was enriched in SCs. Forty-two skeletal muscle disease-related genes were highly expressed in SCs and exhibited similar expression patterns. Among them, Pdha1 was the core gene in the TCA CYCLE IN SENESCENCE; Pgam2, Sod1, and Suclg1 are transcription factors closely associated with skeletal muscle energy metabolism. Transcription factor enrichment analysis of the 42 genes revealed that Myod1 and Mef2a were also highly expressed in SCs, which regulated Pdha1 expression and were associated with skeletal muscle development. These findings hint that energy metabolism may be pivotal in SCs development and aging. Three ligand-receptor pairs of extracellular matrix (ECM)-receptor interactions, Lamc1-Dag1, Lama2-Dag1, and Hspg2-Dag1, may play a vital role in SCs interactions with slow/fast muscle cells and SCs self-renewal. Finally, we built the first database of a skeletal muscle single-cell transcriptome, the Musculoskeletal Cell Atlas (http://www.mskca.tech), which lists 630,040 skeletal muscle cells and provides interactive visualization, a useful resource for revealing skeletal muscle cellular heterogeneity during development and aging. Our study could provide new targets and ideas for developing drugs to inhibit skeletal muscle aging and treat skeletal muscle diseases.
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A high-resolution route map reveals distinct stages of chondrocyte dedifferentiation for cartilage regeneration.
Articular cartilage damage is a universal health problem. Despite recent progress, chondrocyte dedifferentiation has severely compromised the clinical outcomes of cell-based cartilage regeneration. Loss-of-function changes are frequently observed in chondrocyte expansion and other pathological conditions, but the characteristics and intermediate molecular mechanisms remain unclear. In this study, we demonstrate a time-lapse atlas of chondrocyte dedifferentiation to provide molecular details and informative biomarkers associated with clinical chondrocyte evaluation. We performed various assays, such as single-cell RNA sequencing (scRNA-seq), live-cell metabolic assays, and assays for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq), to develop a biphasic dedifferentiation model consisting of early and late dedifferentiation stages. Early-stage chondrocytes exhibited a glycolytic phenotype with increased expression of genes involved in metabolism and antioxidation, whereas late-stage chondrocytes exhibited ultrastructural changes involving mitochondrial damage and stress-associated chromatin remodeling. Using the chemical inhibitor BTB06584, we revealed that early and late dedifferentiated chondrocytes possessed distinct recovery potentials from functional phenotype loss. Notably, this two-stage transition was also validated in human chondrocytes. An image-based approach was established for clinical use to efficiently predict chondrocyte plasticity using stage-specific biomarkers. Overall, this study lays a foundation to improve the quality of chondrocytes in clinical use and provides deep insights into chondrocyte dedifferentiation.
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Spatial transcriptomics reveals distinct and conserved tumor core and edge architectures that predict survival and targeted therapy response.
The spatial organization of the tumor microenvironment has a profound impact on biology and therapy response. Here, we perform an integrative single-cell and spatial transcriptomic analysis on HPV-negative oral squamous cell carcinoma (OSCC) to comprehensively characterize malignant cells in tumor core (TC) and leading edge (LE) transcriptional architectures. We show that the TC and LE are characterized by unique transcriptional profiles, neighboring cellular compositions, and ligand-receptor interactions. We demonstrate that the gene expression profile associated with the LE is conserved across different cancers while the TC is tissue specific, highlighting common mechanisms underlying tumor progression and invasion. Additionally, we find our LE gene signature is associated with worse clinical outcomes while TC gene signature is associated with improved prognosis across multiple cancer types. Finally, using an in silico modeling approach, we describe spatially-regulated patterns of cell development in OSCC that are predictably associated with drug response. Our work provides pan-cancer insights into TC and LE biology and interactive spatial atlases ( http://www.pboselab.ca/spatial_OSCC/ ; http://www.pboselab.ca/dynamo_OSCC/ ) that can be foundational for developing novel targeted therapies.
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A spatially-resolved transcriptional atlas of the murine dorsal pons at single-cell resolution.
The "dorsal pons", or "dorsal pontine tegmentum" (dPnTg), is part of the brainstem. It is a complex, densely packed region whose nuclei are involved in regulating many vital functions. Notable among them are the parabrachial nucleus, the Kölliker Fuse, the Barrington nucleus, the locus coeruleus, and the dorsal, laterodorsal, and ventral tegmental nuclei. In this study, we applied single-nucleus RNA-seq (snRNA-seq) to resolve neuronal subtypes based on their unique transcriptional profiles and then used multiplexed error robust fluorescence in situ hybridization (MERFISH) to map them spatially. We sampled ~1 million cells across the dPnTg and defined the spatial distribution of over 120 neuronal subtypes. Our analysis identified an unpredicted high transcriptional diversity in this region and pinpointed many neuronal subtypes' unique marker genes. We also demonstrated that many neuronal subtypes are transcriptionally similar between humans and mice, enhancing this study's translational value. Finally, we developed a freely accessible, GPU and CPU-powered dashboard (http://harvard.heavy.ai:6273/) that combines interactive visual analytics and hardware-accelerated SQL into a data science framework to allow the scientific community to query and gain insights into the data.
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A single-cell atlas to map sex-specific gene-expression changes in blood upon neurodegeneration.
The clinical course and treatment of neurodegenerative disease are complicated by immune-system interference and chronic inflammatory processes, which remain incompletely understood. Mapping immune signatures in larger human cohorts through single-cell gene expression profiling supports our understanding of observed peripheral changes in neurodegeneration. Here, we employ single-cell gene expression profiling of over 909k peripheral blood mononuclear cells (PBMCs) from 121 healthy individuals, 48 patients with mild cognitive impairment (MCI), 46 with Parkinson's disease (PD), 27 with Alzheimer's disease (AD), and 15 with both PD and MCI. The dataset is interactively accessible through a freely available website ( https://www.ccb.uni-saarland.de/adrcsc ). In this work, we identify disease-associated changes in blood cell type composition and the gene expression in a sex-specific manner, offering insights into peripheral and solid tissue signatures in AD and PD.
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Identification and verification of immune-related gene prognostic signature based on ssGSEA for breast cancer.
INTRODUCTION: Breast cancer (BC) is the most common cancer in women worldwide and has a high mortality rate. The fact that the tumor microenvironment affects clinical outcomes of all types of cancers underlines the involvement of various immune-related genes (IRGs). Therefore, this study aimed to establish an IRGs-based signature for the prognosis of BC patients. MATERIAL AND METHODS: In this study, 12 immune cell infiltrating degrees in 1,102 BC cases from The Cancer Genome Atlas (TCGA) database were assessed, and RNA-sequencing (RNA-seq) data of these samples were analyzed by single-sample gene set enrichment analysis (ssGSEA). Based on the results, high, low, and middle immune infiltrating clusters were constructed. A total of 138 overlapped differentially expressed genes (DEGs) were identified in the high and low infiltrating clusters, as well as in normal and BC samples. Univariate Cox regression and LASSO analyses were also performed. Furthermore, GSEA suggested some highly enriched pathways in the different immune infiltrating clusters, leading to a better understanding of potential mechanisms of immune infiltration in BC. RESULTS: Finally, 19 immune-related genes were identified that could be utilized as a potential prognostic biomarker for BC. Kaplan-Meier plot and ROC curve, univariate as well as multivariate Cox analyses were carried out, which suggested that the 19-IRG-based signature is a significant prognosis factor independent of clinical features. Based on the analysis of protein-protein interactions (PPI), the three hub genes were identified. CONCLUSIONS: These results provide a new method to predict the prognosis and survival of BC based on the three genes' features.
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Nidogen-2 (NID2) is a Key Factor in Collagen Causing Poor Response to Immunotherapy in Melanoma.
BACKGROUND: The incidence of cutaneous melanoma continues to rise rapidly and has an extremely poor prognosis. Immunotherapy strategies are the most effective approach for patients who have developed metastases, but not all cases have been successful due to the complex and variable mechanisms of melanoma response to immune checkpoint inhibition. METHODS: We synthesized collagen-coding gene expression data (second-generation and single-cell sequencing) from public Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Bioinformatics analysis was performed using R software and several database resources such as Metascape database, Gene Set Cancer Analysis (GSCA) database, and Cytoscape software, etc., to investigate the biological mechanisms that may be related with collagens. Immunofluorescence and immunohistochemical staining were used to validate the expression and localization of Nidogen-2 (NID2). RESULTS: Melanoma patients can be divided into two collagen clusters. Patients with high collagen levels (C1) had a shorter survival than those with low collagen levels (C2) and were less likely to benefit from immunotherapy. We demonstrated that NID2 is a potential key factor in the collagen phenotype, is involved in fibroblast activation in melanoma, and forms a barrier to limit the proximity of CD8+ T cells to tumor cells. CONCLUSION: We clarified the adverse effects of collagen on melanoma patients and identified NID2 as a potential therapeutic target.
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Comparative single-cell multiome identifies evolutionary changes in neural progenitor cells during primate brain development.
Understanding the cellular and genetic mechanisms driving human-specific features of cortical development remains a challenge. We generated a cell-type resolved atlas of transcriptome and chromatin accessibility in the developing macaque and mouse prefrontal cortex (PFC). Comparing with published human data, our findings demonstrate that although the cortex cellular composition is overall conserved across species, progenitor cells show significant evolutionary divergence in cellular properties. Specifically, human neural progenitors exhibit extensive transcriptional rewiring in growth factor and extracellular matrix (ECM) pathways. Expression of the human-specific progenitor marker ITGA2 in the fetal mouse cortex increases the progenitor proliferation and the proportion of upper-layer neurons. These transcriptional divergences are primarily driven by altered activity in the distal regulatory elements. The chromatin regions with human-gained accessibility are enriched with human-specific sequence changes and polymorphisms linked to intelligence and neuropsychiatric disorders. Our results identify evolutionary changes in neural progenitors and putative gene regulatory mechanisms shaping primate brain evolution.