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Spatial analysis identifies LAMTOR2 overexpression in hepatocellular carcinoma with vessels encapsulating tumor clusters.
BACKGROUND: Vessels encapsulating tumor clusters (VETC) is a distinct angiogenic pattern in hepatocellular carcinoma (HCC). While VETC-positive HCC is associated with poor prognosis, it demonstrates improved responses to vascular-targeted therapies. However, the molecular signatures that define VETC-positive HCC remain elusive. This study employed spatial transcriptomics and single-cell RNA sequencing to characterize the molecular landscape of VETC-positive HCC and identify candidate biomarkers. METHODS: Using GeoMx Digital Spatial Profiling, we analyzed tumor and adjacent liver tissues from 24 HCC patients, focusing on VETC-positive, VETC-negative, or non-neoplastic liver regions. Differential gene expression and pathway enrichment analyses were performed. Candidate genes were further evaluated using immunohistochemistry, The Cancer Genome Atlas (TCGA) dataset, and single-cell RNA sequencing. RESULTS: Spatial transcriptomic analysis identified 39 genes upregulated in VETC-positive regions, with enrichment of angiogenesis, WNT/β-catenin, Hedgehog, and p53 signaling, epithelial-mesenchymal transition, and fatty acid metabolism pathways, and suppression of immune-related pathways. Among these, LAMTOR2, DPP4, ZNHIT1, and MLXIPL were consistently upregulated in VETC-positive regions compared to both VETC-negative and normal liver regions. LAMTOR2 demonstrated tumor-specific localization in TCGA dataset. Immunohistochemistry confirmed that peripheral accentuation pattern of LAMTOR2 expression was restricted to tumor cells and strongly correlated with RNA expression and VETC positivity. Single-cell RNA sequencing further revealed LAMTOR2 upregulation in malignant hepatocytes, particularly within VETC-high subpopulations enriched for lipid degradation, fatty acid oxidation, and detoxification pathways. CONCLUSION: VETC-positive HCC exhibits a distinct transcriptomic and metabolic profile. LAMTOR2, which is consistently upregulated in VETC-positive HCCs, may contribute to angiogenic and metabolic reprogramming, offering potential implications for therapeutic stratification in HCC.
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Cross-species integration of single-cell data reveals conserved pathology-associated cell populations across animal models and human samples.
Single-cell RNA sequencing (scRNA-seq) enables high-resolution profiling of cellular heterogeneity, but integrating data across species remains challenging due to technical variation and complex gene homology. We present TACMAN (transformer-based alignment of cross-species metapath aggregation network), a computational framework for cross-species scRNA-seq integration that combines a metapath-based heterogeneous graph neural network with an encoder-only transformer. TACMAN aligns conserved cell types across species under normal physiological conditions while preserving biological signals. We demonstrate its utility by integrating clinical human and mammalian model scRNA-seq data, revealing conserved cell subtypes in tumor, inflammatory, and infectious diseases. Notably, using our in-house single-cell transcriptomic atlas of an evolutionarily distant Caenorhabditis elegans germline tumor model, TACMAN identifies tumor-related cell populations conserved in human testicular germ cell tumor samples, enabling cross-species comparison under pathological conditions. TACMAN thus offers a powerful tool for comparative single-cell analysis, advancing translational research using animal models.
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Integrated single-cell transcriptomic atlas of human gastric and colorectal tissues across diverse phenotypes.
Gastrointestinal cancer is one of the most burdensome health threats worldwide, accounting for approximately one-quarter of all cancer incidences. Gastric and colorectal cancers are among the most prevalent and lethal malignancies of the gastrointestinal tract globally. Despite steadily rising incidence, the complex cellular landscape comprising the tumor microenvironment and accelerating tumorigenesis remains insufficiently explored. Although single-cell transcriptomics has been employed to investigate this complexity, previous studies are limited by the small number of cells analyzed. In this study, we present a comprehensive single-cell transcriptomic atlas comprising 574,532 cells across 70 cell types from 229 human stomach tissues and 479,629 cells across 70 cell types from 220 human colorectal tissues, spanning diverse phenotypes. Data quality and cell type annotations were rigorous validated utilizing multiple computational tools. Additionally, standardized cell type definitions and annotated clinical information facilitate the usability of this dataset, enabling analysis across clinical subtypes and metastatic states. This resource provides a valuable foundation for meta-analyses of gastric and colorectal cancers at single-cell resolution.
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A longitudinal single-nucleus transcriptomic atlas of bovine placentation reveals dynamic cellular hierarchies and regulatory programs.
BACKGROUND: Elucidating cellular hierarchies and regulatory mechanisms of placental development across gestation is critical for understanding pregnancy maintenance and improving reproductive outcomes in mammals, including cattle. However, a comprehensive, temporally resolved single-cell characterization of the bovine placenta has remained lacking. RESULTS: We construct a longitudinal single-nucleus transcriptomic atlas comprising 311,299 placental cells and nuclei across 13 developmental timepoints (E12, E14, E16, E18, E24, E30, E50, E60, E85, E110, E180, E240, and E280). We identify 13 major cell types and 14 trophoblast subtypes, revealing pronounced cellular heterogeneity and stage-specific transcriptional programs. Regulatory analyses highlight HAND1 and DLX5 as candidate key regulators of maternal recognition of pregnancy. Trajectory inference demonstrates that binucleate cells arise from specific uninucleate cell subpopulations around E24, with differentiation governed by genomic imprinting and metabolic reprogramming. Integration with genome-wide association study data identifies eight early trophoblast subtypes significantly associated with gestation length, along with candidate pathways and risk genes, including CYCS, HMGA1, and VDAC1, under strong evolutionary constraint. Additionally, we find that placental macrophages emerge from E30 in cattle and show significant associations with pregnancy loss in both cattle and humans, sharing conserved risk pathways. CONCLUSIONS: This study provides a comprehensive spatiotemporal single-cell atlas of bovine placental development, defining cellular hierarchies, lineage dynamics, and regulatory networks at the maternal-fetal interface. These findings offer a valuable resource and conceptual framework for understanding pregnancy maintenance and for improving reproductive traits in ruminants.
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A single-cell atlas of peripheral immunity suggests immune subset attrition during tuberculosis dissemination.
ObjectiveThis study investigated immune lineage remodeling and intercellular communication to elucidate the mechanisms driving the progression from active tuberculosis to disseminated tuberculosis.MethodsWe integrated single-cell RNA-sequencing datasets of peripheral blood mononuclear cells from healthy controls, patients with active tuberculosis, and patients with disseminated tuberculosis (n = 3 for each group) to perform cellular clustering, functional assessment, and communication network analyses.ResultsAnalysis identified 16 clusters across 5 major lineages. Although global cellular proportions remained statistically stable across patient cohorts, disease progression was characterized by distinct cell-intrinsic transcriptomic shifts and subset-specific remodeling. This landscape was highlighted by the systematic peripheral depletion of homeostatic "guardian" monocytes, alongside functional disruptions in natural killer and B-cell states, and a severe-stage-specific accumulation of exhausted T cells. Consequently, circulating intercellular communication networks were broadly attenuated in infected groups, featuring diminished receptor-ligand interactions between peripheral dendritic cells and naïve CD8+ T cells, driven by cell-intrinsic transcriptional rewiring rather than shifts in underlying cell frequencies.ConclusionsTuberculosis progression involves profound peripheral immune remodeling marked by the systemic attrition of circulating homeostatic populations and altered blood interactome dynamics. This preliminary atlas provides an exploratory framework for potential therapeutic interventions in severe tuberculosis.
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Single-cell transcriptomics reveals diversity and conservation of chicken lymphocytes.
The domestic chicken (Gallus gallus) is a critical model for immunology and a major agricultural species, yet a comprehensive single-cell census of its immune architecture is lacking. Here, we constructed a high-resolution single-cell transcriptome atlas of three primary immune organs (thymus, bursa of Fabricius, and spleen) from chickens. Our analysis identified 24 distinct immune and stromal cell types, mapping their organ-specific distributions: B cells were predominantly localized to the bursa of Fabricius, while T cell subsets were enriched in the thymus. We delineated developmental trajectories and identified key transcription factors governing lineage commitment, including TCF12 for CD4⁺CD8⁺ T cells and SOX13 for T helper 2 cells. Further analysis revealed potential sexually dimorphic immune maturation, where B cells exhibited sex-biased gene expression primarily in early differentiation stages, whereas T cell differences were most pronounced during proliferation. Cross-species comparison demonstrated remarkable conservation of core immunoregulatory circuits, with transcription factors like MEF2C, LEF1, and GATA3 playing conserved roles in avian and mammalian immunity. This resource provides a foundational framework for understanding avian immune defense, advancing poultry disease-resistant breeding, and offers evolutionary insights into vertebrate immunology.
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Plant bZIPs in Root Environmental Adaptation: From Single-Cell Expression Atlas to Functional Insights.
Plant roots interact dynamically with complex environments, and their capacity to adapt is crucial for growth, development, survival, and productivity. Basic leucine zipper (bZIP) transcription factors have emerged as key regulators in managing the root's response to various environmental signals. The shift from bulk tissue analysis to single-cell RNA sequencing (scRNA-seq) has enabled the creation of a highly detailed expression atlas for root bZIPs, significantly enhancing our understanding of their functions. This review first summarizes the classification and structural features of bZIPs in Arabidopsis, and compares representative members with their orthologs in cereal crops. Next, we integrate the expression patterns of various bZIP members in root cells and clarify their roles through single-cell expression profiling. Furthermore, we delineate characterized bZIP regulatory modules that respond to signals spanning light, hormones, nutrients, and stresses, thereby orchestrating transcriptional reprogramming to facilitate plant adaptation. By combining single-cell omics with functional genetics, we reveal how bZIPs control critical processes, including responses to light signals, hormonal interactions, nutrient uptake and balance, and reactions to abiotic stresses. Ultimately, this integrated perspective highlights the potential for targeting bZIP transcription factors in the development of climate-resilient crops with optimized root systems, thereby enabling them to adapt to changing environmental conditions.
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Single-cell transcriptomics reveals that air-liquid interface culture promotes goblet cell differentiation and inhibits glycolysis in organoid cell monolayers.
Faithfully recapitulating the cellular heterogeneity of the intestinal epithelium is essential when using organoid models. Air-liquid interface (ALI) culture has been shown to promote secretory cell differentiation, but its impact on gene expression in each epithelial cell type remains unclear. In this study, we used single-cell RNA sequencing (scRNA-seq) to characterize the cellular heterogeneity of rabbit cecum-derived organoid monolayers grown under immerged or ALI conditions. We then compared these organoid cell type-specific gene expression profiles to a scRNA-seq atlas of the rabbit cecal epithelium in vivo. We selected the rabbit model notably because, unlike mice, it possesses BEST4+ epithelial cells, a newly discovered subset of mature absorptive cells. Our analysis revealed a high degree of transcriptomic similarity between in vivo and organoid-derived stem and transit-amplifying cells. ALI culture markedly enhanced the differentiation of the secretory lineage, especially goblet cells, whose transcriptome closely resembled that of in vivo goblet cells. Furthermore, ALI was the only condition allowing the detection of enteroendocrine cells. BEST4+ cells, however, were absent from organoids in immerged or ALI conditions despite their presence in vivo. In addition, ALI culture led to a consistent downregulation of hypoxia and glycolysis-associated genes across all cell types, which suggests a metabolic shift likely driven by increased oxygen availability in ALI conditions. Cell-cell communication analyses further indicated that ALI more closely mirrored in vivo patterns than immerged condition. Altogether, these results demonstrate that ALI culture allows for better recapitulation of the in vivo cellular heterogeneity and molecular signatures of the intestinal epithelium.NEW & NOTEWORTHY Using single-cell RNA sequencing, this study shows that air-liquid interface (ALI) culture enhances secretory lineage differentiation of intestinal organoid cell monolayers and improves transcriptomic similarity to the native epithelium. ALI reduced hypoxia-associated gene expression and better recapitulates in vivo-like cell-cell interactions, supporting its value for modeling intestinal epithelial heterogeneity in organoids.
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Kinesin family member 4 a drives Cancer stem cell characteristics in breast Cancer: Insights from scRNA-Seq and experimental validation.
This study identifies cancer stem cell marker genes in breast cancer through an integrated analysis of single cell RNA sequencing and bulk RNA sequencing data. The breast cancer-related single-cell RNA sequencing dataset GSE180286 was analyzed to identify cancer stem cells populations and their marker genes, which were further validated using data from The Cancer Genome Atlas breast cancer dataset. Weighted gene co-expression network analysis revealed seven co-expression modules, with the Turquoise module showing a strong association with messenger RNA stemness indices. Venn diagram analysis identified 19 cancer stem cell marker genes, among which cyclin B1, cell division cycle associated 8, and kinesin family member 4 A were highlighted as core genes. Both in vitro and in vivo experiments demonstrated that silencing kinesin family member 4 A in breast cancer cell lines significantly reduced tumor sphere formation, proliferation, migration, and invasion. Additionally, the downregulation of kinesin family member 4 A in a nude mouse model markedly suppressed tumor growth. These findings suggest that cyclin B1, cell division cycle associated 8, and particularly kinesin family member 4 A are critical regulators of cancer stem cell properties in breast cancer and represent promising therapeutic targets.
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SCAN: Spatiotemporal Cloud Atlas for Neural cells.
The nervous system is one of the most complicated and enigmatic systems within the animal kingdom. Recently, the emergence and development of spatial transcriptomics (ST) and single-cell RNA sequencing (scRNA-seq) technologies have provided an unprecedented ability to systematically decipher the cellular heterogeneity and spatial locations of the nervous system from multiple unbiased aspects. However, efficiently integrating, presenting and analyzing massive multiomic data remains a huge challenge. Here, we manually collected and comprehensively analyzed high-quality scRNA-seq and ST data from the nervous system, covering 10 679 684 cells. In addition, multi-omic datasets from more than 900 species were included for extensive data mining from an evolutionary perspective. Furthermore, over 100 neurological diseases (e.g. Alzheimer's disease, Parkinson's disease, Down syndrome) were systematically analyzed for high-throughput screening of putative biomarkers. Differential expression patterns across developmental time points, cell types and ST spots were discerned and subsequently subjected to extensive interpretation. To provide researchers with efficient data exploration, we created a new database with interactive interfaces and integrated functions called the Spatiotemporal Cloud Atlas for Neural cells (SCAN), freely accessible at http://47.98.139.124:8799 or http://scanatlas.net. SCAN will benefit the neuroscience research community to better exploit the spatiotemporal atlas of the neural system and promote the development of diagnostic strategies for various neurological disorders.