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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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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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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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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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Spatiotemporal single-cell analysis of gene expression in the mouse suprachiasmatic nucleus.
Mammalian circadian behaviors are orchestrated by the suprachiasmatic nucleus (SCN) in the ventral hypothalamus, but the number of SCN cell types and their functional roles remain unclear. We have used single-cell RNA-sequencing to identify the basic cell types in the mouse SCN and to characterize their circadian and light-induced gene expression patterns. We identified eight major cell types, with each type displaying a specific pattern of circadian gene expression. Five SCN neuronal subtypes, each with specific combinations of markers, differ in their spatial distribution, circadian rhythmicity and light responsiveness. Through a complete three-dimensional reconstruction of the mouse SCN at single-cell resolution, we obtained a standardized SCN atlas containing the spatial distribution of these subtypes and gene expression. Furthermore, we observed heterogeneous circadian gene expression between SCN neuron subtypes. Such a spatiotemporal pattern of gene regulation within the SCN may have an important function in the circadian pacemaker.
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Single-Cell Omics in Legumes: Research Trends and Applications.
Legumes are important food crops and play a central role in sustainable agriculture through their ability to form symbiosis with rhizobia, soil bacteria that fix atmospheric nitrogen. Recent advances in single-cell and spatial transcriptomics, along with single-cell epigenomics, have enabled high-resolution analysis of gene expression dynamics and the prediction of cell-type-specific regulatory networks. In this review, we highlight recent progress in the use of single-cell omics in legumes, with a particular focus on how genes functioning in distinct cell types contribute to plant development, responses to pathogens, stress-induced plasticity, and the establishment of root nodule symbioses. Case studies in Medicago truncatula, Lotus japonicus, Glycine max, and Arachis hypogaea illustrate the shift from bulk to single-cell multi-omics. We conclude by outlining current limitations and future directions for building integrated legume cell atlases that will support translational research and crop improvement.
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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.
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Succinylation heterogeneity in lung adenocarcinoma: from prognostic model to KLK6-driven tumor microenvironment remodeling.
BACKGROUND: Lung adenocarcinoma (LUAD) is a leading cause of cancer-related mortality. Protein succinylation, a key post-translational modification, is implicated in tumor progression. However, its comprehensive landscape and clinical significance in LUAD remain largely unexplored. METHODS: We integrated multi-omics data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) cohorts. A set of core succinylation-related genes was identified through differential expression and univariable Cox regression analyses. Molecular subtypes based on succinylation were determined by principal component analysis (PCA). A succinylation prognostic model was constructed via least absolute shrinkage and selection operator (LASSO) and multivariable Cox regression. The differences of tumor microenvironment (TME), tumor mutation burden and drug sensitivity in different risk groups were further explored. Single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics revealed effects of succinylation on TME. High-dimensional weighted gene co-expression networks analysis (hdWGCNA) was used to identify potential succinylation-related therapeutic targets. The function of therapeutic targets was further validated through scRNA-seq, spatial transcriptomics, and in vitro experiments. RESULTS: We identified 31 core succinylation-related genes and defined three molecular subtypes with distinct prognostic and TME characteristics. A robust 7-gene succinylation-based prognostic signature was developed and validated across 7 independent GEO cohorts, effectively stratifying patients into high- and low-risk groups with significant differences in survival, demonstrating high predictive accuracy, consistency, and clinical utility. The low-risk group exhibited an immunoreactive TME with enhanced immune cell infiltration and superior response to immunotherapy. scRNA-seq and spatial transcriptomics revealed enhanced succinylation in LUAD. Kallikrein-related peptidase 6 (KLK6) was identified as a potential therapeutic target. KLK6 was significantly upregulated in LUAD, correlated with poor prognosis and therapy resistance. KLK6 promoted global succinylation, proliferation, migration, and invasion of LUAD cells in vitro. Mechanistically, KLK6-positive tumor cells might foster an immunosuppressive TME by driving fibroblast-to-myofibroblast differentiation, enhancing extracellular matrix (ECM) deposition, and inhibiting CD8+ T cell infiltration. CONCLUSION: Our study delineates the succinylation landscape in LUAD, establishes a novel prognostic model for risk stratification and immunotherapy prediction. Meanwhile, we identified KLK6 as a potential promoter of tumor progression and immunosuppression. Targeting the succinylation pathway, particularly KLK6, may represent a promising therapeutic strategy for LUAD.
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In Situ Maturation and Tissue Adaptation of Type 2 Innate Lymphoid Cell Progenitors.
Innate lymphoid cells (ILCs) are generated early during ontogeny and persist predominantly as tissue-resident cells. Here, we examined how ILCs are maintained and renewed within tissues. We generated a single cell atlas of lung ILC2s and found that Il18r1+ ILCs comprise circulating and tissue-resident ILC progenitors (ILCP) and effector-cells with heterogeneous expression of the transcription factors Tcf7 and Zbtb16, and CD103. Our analyses revealed a continuous differentiation trajectory from Il18r1+ ST2- ILCPs to Il18r- ST2+ ILC2s, which was experimentally validated. Upon helminth infection, recruited and BM-derived cells generated the entire spectrum of ILC2s in parabiotic and shield chimeric mice, consistent with their potential role in the renewal of tissue ILC2s. Our findings identify local ILCPs and reveal ILCP in situ differentiation and tissue adaptation as a mechanism of ILC maintenance and phenotypic diversification. Local niches, rather than progenitor origin, or the developmental window during ontogeny, may dominantly imprint ILC phenotypes in adult tissues.
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Untimely TGFβ responses in COVID-19 limit antiviral functions of NK cells.
SARS-CoV-2 is a single-stranded RNA virus that causes COVID-19. Given its acute and often self-limiting course, it is likely that components of the innate immune system play a central part in controlling virus replication and determining clinical outcome. Natural killer (NK) cells are innate lymphocytes with notable activity against a broad range of viruses, including RNA viruses1,2. NK cell function may be altered during COVID-19 despite increased representation of NK cells with an activated and adaptive phenotype3,4. Here we show that a decline in viral load in COVID-19 correlates with NK cell status and that NK cells can control SARS-CoV-2 replication by recognizing infected target cells. In severe COVID-19, NK cells show defects in virus control, cytokine production and cell-mediated cytotoxicity despite high expression of cytotoxic effector molecules. Single-cell RNA sequencing of NK cells over the time course of the COVID-19 disease spectrum reveals a distinct gene expression signature. Transcriptional networks of interferon-driven NK cell activation are superimposed by a dominant transforming growth factor-β (TGFβ) response signature, with reduced expression of genes related to cell-cell adhesion, granule exocytosis and cell-mediated cytotoxicity. In severe COVID-19, serum levels of TGFβ peak during the first two weeks of infection, and serum obtained from these patients severely inhibits NK cell function in a TGFβ-dependent manner. Our data reveal that an untimely production of TGFβ is a hallmark of severe COVID-19 and may inhibit NK cell function and early control of the virus.