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Found: 4 882
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Single-cell profiling defines cellular ecosystems and malignant cell state coupling across pan-cancer tumor microenvironments.
Tumors are embedded within complex microenvironments composed of malignant, immune and stromal cells, yet how coordinated cellular organization shapes tumor biology across cancers remains incompletely understood. Here we integrate single-cell transcriptomic profiles from over two million cells spanning 19 human epithelial cancers to construct a comprehensive pan-cancer atlas of tumor microenvironment (TME) organization. We identify cell subsets selectively enriched in tumors compared with normal tissues and show that their relative abundance is associated with clinical outcomes. By clustering tumors according to their cellular composition, we define eight recurrent cellular ecosystem subtypes that capture coordinated variation in immune and stromal architectures across cancer types. Comparative analyses reveal that these ecosystem states are associated with distinct tumor-intrinsic molecular programs, including differences in signaling pathway activity, transcriptional regulation and copy number alterations. We further uncover subtype-specific patterns of ligand-receptor interactions linking malignant cells with immune and stromal compartments and provide independent spatial support for these interactions using spatial transcriptomic data. Together, this work provides a unified framework for tumor ecosystem stratification and highlights cellular organization as a key dimension of tumor heterogeneity with potential implications for precision cancer therapy.
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Single-cell transcriptomics of Pacific white shrimp (Litopenaeus vannamei) hepatopancreas reveal immune and metabolic responses to AHPND-causing Vibrio parahaemolyticus.
BACKGROUND: The shrimp aquaculture industry is severely impacted by acute hepatopancreatic necrosis disease (AHPND) caused by the bacterium Vibrio parahaemolyticus. The hepatopancreas is a multi-functional organ with roles in digestion, immunity, molting and reproduction. The mechanism by which the pathogen causes disease, and the host immune response is not completely understood. Therefore, we set out to characterize the cells of the hepatopancreas and host response to bacterial infection at single-cell resolution. METHODS: First, hepatopancreas from three healthy Pacific white shrimp (Litopenaeus vannamei) were sampled to produce a single-cell transcriptomic atlas. Then, the hepatopancreas from three V. parahaemolyticus infected and two mocked treated shrimp were sampled for an infection study. Primary cell suspensions were produced, and single-cell libraries were generated using the 10x Genomics Chromium X controller with GEM-X 3' gene expression reagents. Libraries were sequenced and data aligned to the shrimp reference genome using Cell Ranger. Seurat and clusterProfiler were used for downstream analyses. RESULTS: The atlas consists of 11,006 quality cells that were grouped into nine clusters, and represent the hepatopancreas epithelial cells, myocytes and hemocytes. The infection study generated 16,368 quality cells and was integrated with the atlas for 27,374 cells grouped into nine clusters. Cells from the infected shrimp exhibited expression of immune related genes including diverse pathogen recognition receptors and humoral proteins, including hemocyanin, proteases and C-type lectins. We also found that cells expressed factors that the PirA/B toxins present in the infective V. parahaemolyticus may bind to, such as fatty acid binding protein (Fabp). In response to infection, energy metabolism (oxidative phosphorylation) was altered in a cluster-dependent manner which may reflect immune or pathogenic processes. CONCLUSIONS: We characterized the cells types of the hepatopancreas and examined the transcriptomic response to a virulent isolate of V. parahaemolyticus, the causative agent of AHPND. Cells exhibited significant humoral immune responses suggesting the role of these genes in immune responsiveness to the pathogen. The outcomes will inform future functional studies and provide insights toward novel preventative measures or treatments.
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Mapping the Regulatory Landscape of Sheep Pituitary Gland Associated With Puberty at Single-Cell Resolution.
The pituitary gland plays a pivotal role in regulating puberty and reproductive physiology; however, the precise cellular and molecular mechanisms driving the pubertal transition in large animal, such as ewes, remain poorly understood. Here, we generated a comprehensive single-cell transcriptomic atlas of the ovine anterior pituitary, specifically comparing the pre-pubertal (3 month) and post-pubertal (6 month) stages. We identified 30 335 cells classified into ten distinct clusters. Comparative analysis revealed a global transcriptional reprogramming during puberty, characterized by a marked upregulation of genes associated with ribosome biogenesis, unfolded protein response, and hormone secretion across endocrine cells, reflecting an expanded biosynthetic capacity. Specifically, we identified SCG2 as a critical regulator of gonadotroph maturation. Functional validation demonstrated that SCG2 facilitates the biogenesis of secretory granules, thereby promoting FSH synthesis and secretion. Furthermore, intercellular communication analysis uncovered a distinct shift in the pituitary microenvironment: the 6 month pituitary exhibited enhanced regulatory networks, including IGF signaling mediated by non-endocrine cells and NT signaling (e.g., BDNF-NTRK2) driven by multiple cell types. These findings suggest that the onset of puberty relies on a coordinated "endocrine-to-endocrine" and "non-endocrine-to-endocrine" crosstalk. This study provides a high-resolution molecular blueprint of the pubertal transition, highlighting the key roles of biosynthetic machinery upgrades and microenvironmental remodeling in establishing the high reproductive performance of Hu sheep.
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OLFM4 orchestrates the immunosuppressive microenvironment and drives gallbladder cancer progression.
PURPOSE: Gallbladder cancer (GBC) is the most common biliary tract malignancy, characterized by complex microenvironment and poor prognosis. Olfactomedin-4 (OLFM4) has been identified as a key regulator of GBC progression. This study aims to comprehensively elucidate the molecular profiling, tumor microenvironment (TME) and underlying mechanisms through which OLFM4 drives GBC progression. METHODS: Single-cell RNA sequencing (scRNA-seq) was performed on tumor tissues and adjacent non-tumor tissues (ANT) from clinical GBC samples. We analyzed scRNA-seq data to characterize the landscape of heterogeneous TME and intricate intercellular communication networks of the single cells within GBC. Findings were validated through in vitro experiments. RESULTS: Compared with ANT, GBC exhibited a distinct remodeling of the tumor microenvironment. Specifically, epithelial cells were markedly enriched in GBC (18.2% vs. 11.6% in ANT), accompanied by a pronounced reduction in endothelial cells (2.4% vs. 9.7%). In parallel, myeloid cells decreased from 16.8% in ANT to 11.5% in GBC, while fibroblasts remained comparably abundant in both conditions (21.1%). Within the OLFM4+epithelium, there was a significant enrichment of genes and pathways related to malignant progression, including signatures driving cell proliferation, stemness maintenance (WNT/β-catenin), and metastasis, as well as crucial molecules involved in TME remodeling (SPP1, TGF-β). Pseudotemporal trajectory analysis revealed an upregulation of OLFM4 expression correlating with disease progression, suggesting its close association with malignant transition. Cell-cell communication analysis identified OLFM4+epithelial cells as central signaling hubs, primarily communicating with stromal and immune cells via the SPP1-CD44 and SPP1-Integrin axes. Importantly, OLFM4 knockdown inhibited cell proliferation, migration, invasion, and gemcitabine chemoresistance in vitro, underscoring its central role in GBC progression. CONCLUSION: These findings offer a comprehensive insight into the atlas of molecular signatures and TME within GBC, identifying OLFM4 as a potential biomarker and therapeutic target for GBC.
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Integrating and mapping single-cell transcriptomics across the entire gene expression space.
The exponential growth of single-cell transcriptomics datasets has made it essential to integrate heterogeneous datasets for constructing large-scale single-cell reference atlases and mapping query datasets onto these references. However, this integration process is significantly hampered by batch effects, which introduce systematic biases and mask the true biological signals. Moreover, most existing integration methods are mainly limited to the latent space of highly variable genes, restricting their capacity to comprehensively correct the entire transcriptomic landscape and potentially overlooking crucial biological information encoded in genes with lower variability. We introduce scGES, a novel deep learning framework designed to effectively correct batch effects across the entire gene expression space, which leverages information from both highly and lowly variable genes. scGES consists of two main models: scGESI for data integration and scGESM for query mapping. Comprehensive analyses of real data demonstrate that scGES outperforms state-of-the-art methods in batch effect correction and biological variation conservation, thereby enhancing downstream analyses and offering broader biological insights by utilizing information from all genes.
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Single-cell transcriptomics reveal mechanisms of skeletal muscle differentiation across duck embryonic development.
Skeletal muscle development is cornerstone of vertebrate locomotion, relies on the functionally distinct muscle fiber-type. Although the cellular dynamics in myogenesis have been extensively studied, the developmental origins and pathways governing fiber-type diversification remain unresolved. Furthermore, the evolutionary conservation of these mechanisms across vertebrates is poorly understood. Thus, we generate a comprehensive single-cell transcriptomic atlas of duck skeletal muscle across embryonic development to explore the trajectory from myogenic progenitors to myofiber. We identified a differentiation mechanism whereby slow-twitch type could transdifferentiate into the fast-twitch type, a process mediated by LEF1+(I) subtype. Comparative analysis of datasets across vertebrates (avian and mammalian) reveals that this fiber-type conversion program is phylogenetically conserved, suggesting homology in muscle adaptation mechanisms. Our study provides the transcription factors roadmap of vertebrate myofiber development, bridging gaps in developmental and evolutionary biology. These insights advance fundamental knowledge of tissue patterning and hold translational potential for regenerative medicine and agricultural biotechnology.
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Single-cell and spatial transcriptomics reveal the regulatory continuum from initial substrates to storage compounds during wheat grain development.
Wheat grain development determines yield and quality but involves complex spatial organization across genetically distinct maternal and filial tissues. Dissecting the regulatory architecture of this composite organ requires approaches that resolve both cellular identity and spatial context. Here, we integrate single-nucleus RNA sequencing with spatial transcriptomics to construct a high-resolution spatiotemporal atlas of wheat grain development at the milk stage (25 days after flowering, 25 DAF). This integrated framework resolves 13 transcriptionally and spatially distinct cell populations spanning maternal tissues, endosperm, aleurone layer, and embryo. By combining cell-resolved and in situ expression profiles, we uncover pervasive subgenome-biased transcription, with pronounced B subgenome dominance in grain tissues. Spatially informed regulatory and hormone signaling analyses reveal stage-specific maternal-filial communication and coordinated auxin, abscisic acid, and gibberellin crosstalk. Co-expression network integration further identifies NAC transcription factors as central regulators linking storage metabolism, programmed cell death, and grain maturation. Our study demonstrates the power of integrating single-nucleus and spatial transcriptomics to resolve regulatory complexity in polyploid crop organs and provides a foundational resource for dissecting wheat grain development and improving yield and quality.
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Single‑cell and spatial transcriptomics in chronic lung disease: cellular heterogeneity and molecular mechanisms in IPF and COPD.
Single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) have advanced the study of complex lung diseases by resolving cellular heterogeneity, tissue architecture, and intercellular communication. These technologies are particularly informative in chronic respiratory disorders, where pathological remodeling involves coordinated alterations across epithelial, stromal, immune, and endothelial compartments. Idiopathic pulmonary fibrosis (IPF), a rare fatal fibrotic disorder, and chronic obstructive pulmonary disease (COPD), a prevalent heterogeneous respiratory condition, represent distinct chronic lung diseases shaped by persistent injury, maladaptive repair, and progressive tissue remodeling. However, current understanding of their pathogenesis remains incomplete, and conventional approaches lack the resolution to define disease-associated cell states, spatially restricted niches, or compartment-specific interactions. This review synthesizes recent scRNA-seq and ST studies in IPF and COPD, emphasizing how high-resolution atlases refine established pathobiological frameworks and reveal shared and disease-specific cellular programs. We highlight epithelial dysfunction and impaired progenitor repair as shared mechanistic features of IPF and COPD, while emphasizing alveolar regenerative failure, fibroblast activation, and profibrotic niche formation as central features of IPF, and compartment-specific airway remodeling, inflammatory epithelial states, and emphysema-associated parenchymal injury as prominent features of COPD. We further discuss stromal and immune remodeling, hypoxia-associated vascular remodeling, and the translational implications of these findings for molecular stratification, target prioritization, and mechanism-informed therapeutic development, while noting limitations related to end-stage tissue sampling, platform constraints, and mouse-human correspondence. This review provides an integrated framework for understanding the cellular and spatial mechanisms underlying the pathogenesis and translational relevance of IPF and COPD.
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Telomerase-Associated Genes as Prognostic Markers in High-Grade Serous Ovarian Cancer.
BACKGROUND High-grade serous ovarian cancer (HGSOC) is an aggressive malignancy with poor prognosis. This study investigates telomerase-associated genes (TAGs) and their cellular interactions in HGSOC to identify novel prognostic markers. MATERIAL AND METHODS Single-cell RNA sequencing and bulk RNA sequencing data were obtained from the Gene Expression Omnibus and the Cancer Genome Atlas databases. We performed cell type identification, pseudotime trajectory analysis, and CellChat analysis for intercellular communication. A prognostic model was developed using the intersection of TAGs and HGSOC differentially expressed genes (DEGs) through univariate Cox and least absolute shrinkage and selection operator analyses. Survival prognosis, immune infiltration, and drug sensitivity analyses were conducted. RESULTS Thirty cell clusters, 12 cell types, and 2089 TAGs were identified. Pseudotime trajectory analysis suggested 3 distinct cell states during HGSOC progression. CellChat analysis indicated intercellular communication via NAMPT-INSR and SPP1 (ITGAV+ITGB1) signaling pathways. In total, 1204 DEGs were identified between the transcriptomically inferred telomerase-active and -inactive cell populations, along with 1925 DEGs between HGSOC and normal tissue samples. Intersection of these gene sets yielded 768 key TAGs. The prognostic risk model categorized patients into high-risk and low-risk groups, with significant survival differences. Immune infiltration analysis displayed differential abundance of 12 immune cell types between the groups. Drug sensitivity analysis suggested a potential association between the low-risk group and increased sensitivity to certain therapeutic agents. Mutations in TP53 and TTN were frequently observed across both risk groups. CONCLUSIONS This study generates the hypothesis that TAGs are linked to HGSOC progression and yields a candidate prognostic model.
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Cross-modal mapping of cancer stem-like cell plasticity using deep learning.
Cancer stem-like cells (CSCs) play a pivotal role in driving tumor heterogeneity, therapeutic resistance, and disease progression. Despite the power of single-cell RNA sequencing (scRNA-seq) to resolve intratumoral hierarchies, there remains a need for robust, scalable tools to consistently profile CSCs across both single-cell and bulk transcriptomic data. To address this, we developed ACSCeND-a unified, machine learning-based framework that enables high-resolution CSC state classification and tissue-level deconvolution. ACSCeND comprises (i) a supervised classifier trained on curated scRNA-seq datasets to assign cells into pluripotent-like, multipotent-like, or unipotent-like states, and (ii) an attention-guided autoencoder that deconvolves CSC subtype proportions from bulk RNA sequencing data. Compared to existing tissue deconvolution tools, ACSCeND achieves superior performance, with higher accuracy across synthetic and real-world samples. Applied to over 25 000 tumor profiles from The Cancer Genome Atlas (TCGA), PREdiction of Clinical Outcomes from Genomics (PRECOG), tumor-relapse, and checkpoint inhibitor studies, ACSCeND reveals that CSC abundance strongly correlates with poor disease-free survival and reduced immunotherapy efficacy. Moreover, it uncovers distinct CSC-state-specific molecular programs, offering insights into CSC-driven heterogeneity and tumor evolution. The model also recapitulates known developmental hierarchies in noncancerous tissues, supporting its broader biological relevance. By integrating single-cell precision with bulk-level applicability, ACSCeND offers a robust, interpretable approach to profiling CSC dynamics and establishes CSC state as a clinically meaningful, pan-cancer biomarker for guiding stemness-informed therapies. ACSCeND is available as a python package (through pip) at https://pypi.org/project/ACSCeND/.