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An integrated single-nucleus and spatial transcriptomics atlas reveals the molecular landscape of the human hippocampus.
Cell types in the hippocampus with unique morphology, physiology and connectivity serve specialized functions associated with cognition and mood. These cell types are spatially organized, necessitating molecular profiling strategies that retain cytoarchitectural organization. Here we generated spatially-resolved transcriptomics (SRT) and single-nucleus RNA-sequencing (snRNA-seq) data from anterior human hippocampus in ten adult neurotypical donors. Using non-negative matrix factorization (NMF) and label transfer, we integrated these data by defining gene expression patterns within the snRNA-seq data and then inferring expression in the SRT data. These patterns captured transcriptional variation across neuronal cell types and indicated spatial organization of excitatory and inhibitory postsynaptic specializations. Leveraging the NMF and label transfer approach with rodent datasets, we identified putative patterns of activity-dependent transcription and circuit connectivity in the human SRT dataset. Finally, we characterized the spatial organization of NMF patterns corresponding to pyramidal neurons and identified regionally-specific snRNA-seq clusters of the retrohippocampus, subiculum and presubiculum. To make this molecular atlas widely accessible, raw and processed data are freely available, including through interactive web applications.
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BCAT1 mediates the carcinogenic effects of environmental bisphenol exposure: mechanistic discoveries in osteosarcoma and pan-cancer analysis.
Bisphenols (including BPA, BPF, BPS, BPAF and TBBPA) are ubiquitous environmental endocrine disruptors linked to increased risk of multiple malignancies, yet their carcinogenic mechanisms remain poorly understood. To address this gap, the present study aimed to elucidate how bisphenols drive osteosarcoma initiation and progression, identify core mediating targets, and explore their pan-cancer relevance. We integrated network toxicology with osteosarcoma transcriptome datasets to screen hub genes, validated candidates via independent cohorts and machine learning, and pinpointed the core target through combined SHapley Additive Explanations (SHAP) and Weighted Gene Co-expression Network Analysis (WGCNA). Using data from The Cancer Genome Atlas (TCGA), we analyzed the target's association with osteosarcoma patient survival, while reverse transcription quantitative polymerase chain reaction (RT-qPCR), RNA sequencing (RNA-seq), and Western blotting confirmed its differential expression in osteosarcoma cell lines versus normal osteoblasts. Additionally, we performed simulated BCAT1 gene knockout using scTenifoldKnk to investigate its regulatory effects on downstream gene networks. Molecular docking and all-atom molecular dynamics simulations assessed bisphenol-target binding affinity, complemented by comprehensive pan-cancer analyses of public omics databases. Bioinformatic analyses identified branched-chain amino acid transaminase 1 (BCAT1) as the key regulatory gene, with experimental validation confirming its marked upregulation in osteosarcoma cells. All five bisphenols exhibited high binding affinity for BCAT1 in structural simulations, and pan-cancer analysis revealed BCAT1 overexpression in multiple solid tumors, correlating with unfavorable clinical outcomes. Collectively, these findings suggest that bisphenols may act as potential regulators of BCAT1, with implications for tumor progression, though further experimental validation is required to confirm the actual enzymatic activity modulation and carcinogenic effects.
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DIANA-miTED: a microRNA tissue expression database.
microRNAs (miRNAs) are short (∼23nt) single-stranded non-coding RNAs that act as potent post-transcriptional gene expression regulators. Information about miRNA expression and distribution across cell types and tissues is crucial to the understanding of their function and for their translational use as biomarkers or therapeutic targets. DIANA-miTED is the most comprehensive and systematic collection of miRNA expression values derived from the analysis of 15 183 raw human small RNA-Seq (sRNA-Seq) datasets from the Sequence Read Archive (SRA) and The Cancer Genome Atlas (TCGA). Metadata quality maximizes the utility of expression atlases, therefore we manually curated SRA and TCGA-derived information to deliver a comprehensive and standardized set, incorporating in total 199 tissues, 82 anatomical sublocations, 267 cell lines and 261 diseases. miTED offers rich instant visualizations of the expression and sample distributions of requested data across variables, as well as study-wide diagrams and graphs enabling efficient content exploration. Queries also generate links towards state-of-the-art miRNA functional resources, deeming miTED an ideal starting point for expression retrieval, exploration, comparison, and downstream analysis, without requiring bioinformatics support or expertise. DIANA-miTED is freely available at http://www.microrna.gr/mited.
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Mature tertiary lymphoid structures are key niches of tumour-specific immune responses in pancreatic ductal adenocarcinomas.
OBJECTIVE: To better understand the immune microenvironment of pancreatic ductal adenocarcinomas (PDACs), here we explored the relevance of T and B cell compartmentalisation into tertiary lymphoid structures (TLSs) for the generation of local antitumour immunity. DESIGN: We characterised the functional states and spatial organisation of PDAC-infiltrating T and B cells using single-cell RNA sequencing (scRNA-seq), flow cytometry, multicolour immunofluorescence, gene expression profiling of microdissected TLSs, as well as in vitro assays. In addition, we performed a pan-cancer analysis of tumour-infiltrating T cells using scRNA-seq and sc T cell receptor sequencing datasets from eight cancer types. To evaluate the clinical relevance of our findings, we used PDAC bulk RNA-seq data from The Cancer Genome Atlas and the PRINCE chemoimmunotherapy trial. RESULTS: We found that a subset of PDACs harbours fully developed TLSs where B cells proliferate and differentiate into plasma cells. These mature TLSs also support T cell activity and are enriched with tumour-reactive T cells. Importantly, we showed that chronically activated, tumour-reactive T cells exposed to fibroblast-derived TGF-β may act as TLS organisers by producing the B cell chemoattractant CXCL13. Identification of highly similar subsets of clonally expanded CXCL13 + tumour-infiltrating T cells across multiple cancer types further indicated a conserved link between tumour-antigen recognition and the allocation of B cells within sheltered hubs in the tumour microenvironment. Finally, we showed that the expression of a gene signature reflecting mature TLSs was enriched in pretreatment biopsies from PDAC patients with longer survival after receiving different chemoimmunotherapy regimens. CONCLUSION: We provided a framework for understanding the biological role of PDAC-associated TLSs and revealed their potential to guide the selection of patients for future immunotherapy trials.
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A human cell atlas of fetal gene expression.
The gene expression program underlying the specification of human cell types is of fundamental interest. We generated human cell atlases of gene expression and chromatin accessibility in fetal tissues. For gene expression, we applied three-level combinatorial indexing to >110 samples representing 15 organs, ultimately profiling ~4 million single cells. We leveraged the literature and other atlases to identify and annotate hundreds of cell types and subtypes, both within and across tissues. Our analyses focused on organ-specific specializations of broadly distributed cell types (such as blood, endothelial, and epithelial), sites of fetal erythropoiesis (which notably included the adrenal gland), and integration with mouse developmental atlases (such as conserved specification of blood cells). These data represent a rich resource for the exploration of in vivo human gene expression in diverse tissues and cell types.
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Topic modeling analysis of the Allen Human Brain Atlas.
The human brain is a complex interconnected structure controlling all elementary and high-level cognitive tasks. It is composed of many regions that exhibit specific distributions of cell types and distinct patterns of functional connections. This complexity is rooted in differential transcription. The constituent cell types of different brain regions express distinctive combinations of genes as they develop and mature, ultimately shaping their functional state in adulthood. How precisely the genetic information of anatomical structures is connected to their underlying biological functions remains an open question in modern neuroscience. A major challenge is the identification of "universal patterns", which do not depend on the particular individual, but are instead basic structural properties shared by all brains. Despite the vast amount of gene expression data available at both the bulk and single-cell levels, this task remains challenging, mainly due to the lack of suitable data mining tools. In this paper, we propose an approach to address this issue based on a hierarchical version of Stochastic Block Modeling. Thanks to its specific choice of priors, the method is particularly effective in identifying these universal features. We use as a laboratory to test our algorithm a dataset obtained from six independent human brains from the Allen Human Brain Atlas. We show that the proposed method is indeed able to identify universal patterns much better than more traditional algorithms such as Latent Dirichlet Allocation or Weighted Correlation Network Analysis. The probabilistic association between genes and samples that we find well represents the known anatomical and functional brain organization. Moreover, leveraging the peculiar "fuzzy" structure of the gene sets obtained with our method, we identify examples of transcriptional and post-transcriptional pathways associated with specific brain regions, highlighting the potential of our approach.
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From multi-omics to functional validation: the PTMRS stratifies TME and positions PDGFRB in CRC biology.
BACKGROUND: Colorectal cancer (CRC) outcomes remain heterogeneous despite therapeutic advances, posing challenges to precise prognostic stratification. Post-translational modifications (PTMs) critically regulate protein function, tumor microenvironment (TME) crosstalk, and CRC progression, while most existing studies only focus on single PTM types and PTM signals are rarely integrated into CRC risk models. METHODS: We built a post-translational modification-related risk signature (PTMRS) model by screening post-translational modification-related (PTM-related) genes associated with prognosis in The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression project (GTEx)datasets, then benchmarking 101 modeling strategies to select a Cox partial least squares framework. External validation was conducted across multiple independent cohorts and immunotherapy datasets. Single-cell RNA sequencing (scRNA-seq) data were used to calculate cell type-specific scores for the PTMRS and to perform CellChat-based analyses of cell-cell communication. To investigate mechanism, we targeted platelet-derived growth factor receptor-β (PDGFRβ) in human colonic fibroblasts and assessed CRC-cell responses to their conditioned media. RESULTS: PTMRS robustly stratified overall survival across cohorts and aligned with an immune-cold, stroma-enriched phenotype. PTMRS was associated with pathways related to antigen presentation, protein homeostasis, and other processes within the CRC TIME. ScRNA-seq analysis further showed that PTMRS scores influenced intercellular communication between CRC cells and immune cells. PDGFRB was validated as a core node within the PTMRS network: activation of PDGFRβ in human colonic fibroblasts promoted CRC cell proliferation and migration, whereas sunitinib attenuated and reversed these effects. CONCLUSIONS: We established a post-translational modification-related risk signature (PTMRS) that robustly stratifies CRC prognosis and links tumor-intrinsic programs with features of the TIME. Higher PTMRS scores, particularly in tumors with PDGFRB enrichment, were associated with a stroma-rich, immune-cold phenotype. Experimental validation highlighted PDGFRβ in colonic fibroblasts as a stromal hub whose activation promotes CRC cell proliferation and migration. These findings suggest that PTMRS may help identify patients who could benefit from combining immunotherapy with therapies targeting PDGFRβ or other PTM-related pathways. Further validation in in vivo models and prospective clinical cohorts is needed.
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IRAK4 Signaling Drives Resistance to Checkpoint Immunotherapy in Pancreatic Ductal Adenocarcinoma.
BACKGROUND & AIMS: Checkpoint immunotherapy is largely ineffective in pancreatic ductal adenocarcinoma (PDAC). The innate immune nuclear factor (NF)-κB pathway promotes PDAC cell survival and stromal fibrosis, and is driven by Interleukin-1 Receptor Associated Kinase-4 (IRAK4), but its impact on tumor immunity has not been directly investigated. METHODS: We interrogated The Cancer Genome Atlas data to identify the correlation between NF-κB and T cell signature, and a PDAC tissue microarray (TMA) to correlate IRAK4 activity with CD8+ T cell abundance. We performed RNA sequencing (RNA-seq) on IRAK4-deleted PDAC cells, and single-cell RNA-seq on autochthonous KPC (p48-Cre/TP53f/f/LSL-KRASG12D) mice treated with an IRAK4 inhibitor. We generated conditional IRAK4-deleted KPC mice and complementarily used IRAK4 inhibitors to determine the impact of IRAK4 on T cell immunity. RESULTS: We found positive correlation between NF-κB activity, IRAK4 and T cell exhaustion from The Cancer Genome Atlas. We observed inverse correlation between phosphorylated IRAK4 and CD8+ T cell abundance in a PDAC tissue microarray. Loss of IRAK4 abrogates NF-κB activity, several immunosuppressive factors, checkpoint ligands, and hyaluronan synthase 2, all of which drive T cell dysfunction. Accordingly, conditional deletion or pharmacologic inhibition of IRAK4 markedly decreased tumor desmoplasia and increased the abundance and activity of infiltrative CD4+ and CD8+ T cells in KPC tumors. Single-cell RNA-seq showed myeloid and fibroblast reprogramming toward acute inflammatory responses following IRAK4 inhibition. These changes set the stage for successful combination of IRAK4 inhibitors with checkpoint immunotherapy, resulting in excellent tumor control and markedly prolonged survival of KPC mice. CONCLUSION: IRAK4 drives T cell dysfunction in PDAC and is a novel, promising immunotherapeutic target.
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Single-cell transcriptomes reveal heterogeneity of high-grade serous ovarian carcinoma.
BACKGROUND: High-grade serous ovarian carcinoma (HGSOC) is the most common and aggressive histotype of epithelial ovarian cancer. The heterogeneity and molecular basis of this disease remain incompletely understood. METHODS: To address this question, we have performed a single-cell transcriptomics analysis of matched primary and metastatic HGSOC samples. RESULTS: A total of 13 571 cells are categorized into six distinct cell types, including epithelial cells, fibroblast cells, T cells, B cells, macrophages, and endothelial cells. A subset of aggressive epithelial cells with hyperproliferative and drug-resistant potentials is identified. Several new markers that are highly expressed in epithelial cells are characterized, and their roles in ovarian cancer cell growth and migration are further confirmed. Dysregulation of multiple signaling pathways, including the translational machinery, is associated with ovarian cancer metastasis through the trajectory analysis. Moreover, single-cell regulatory network inference and clustering (SCENIC) analysis reveals the gene regulatory networks and suggests the JUN signaling pathway as a potential therapeutic target for treatment of ovarian cancer, which is validated using the JUN/AP-1 inhibitor T-5224. Finally, our study depicts the epithelial-fibroblast cell communication atlas and identifies several important receptor-ligand complexes in ovarian cancer development. CONCLUSIONS: This study uncovers new molecular features and the potential therapeutic target of HGSOC, which would advance the understanding and treatment of the disease.
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Identifying NDUFB2 as a prognostic biomarker for glioblastoma through an exploratory analysis of an anesthesia-related gene signature.
BACKGROUND: Glioblastoma (GBM) is the most aggressive primary brain tumor with poor prognosis despite multimodal therapy. Understanding the molecular and genetic characteristics of GBM and the influence of anesthesia drugs on tumor behavior is crucial for developing new therapeutic strategies. METHODS: We analyzed RNA-seq data from The Cancer Genome Atlas (TCGA)-GBM cohort and a Gene Expression Omnibus (GEO) dataset (GSE179004) of GBM samples treated with propofol and sevoflurane. Differential expression analysis identified anesthesia-related genes (ARGs), and their prognostic relevance was assessed using Cox regression. Consensus clustering stratified GBM patients into subgroups with distinct survival outcomes, immune cell infiltration, and pathway activities. An ARGs-based prognostic model was developed using Lasso-Cox regression and validated across cohorts. The hub gene NDUFB2 was identified and validated using single-cell sequencing, drug sensitivity assessment, and spatial transcriptome analysis. NDUFB2 expression levels were experimentally verified in our GBM samples. RESULTS: ARGs were significantly differentially expressed between GBM and normal tissues, with NDUFB2 identified as a hub gene associated with poor prognosis. Consensus clustering divided GBM patients into two subgroups with significant survival differences. An 11-ARGs-based prognostic model was established, demonstrating strong correlation with overall survival. NDUFB2 was predominantly expressed in malignant cells and associated with decreased survival and drug sensitivity. CONCLUSIONS: Our study, based on an initial exploratory analysis of anesthesia-related genes (ARGs), highlights their potential prognostic significance in GBM. We propose NDUFB2 as a robust biomarker for prognosis and therapeutic response, supported by extensive validation. These findings offer insights into the molecular classification of GBM and suggest a possible impact of anesthesia drugs on tumor progression, warranting further validation in larger cohorts.