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Aging-TCA: a cross-species single-cell transcriptomic atlas for studying testicular aging.
The testis, with the highest number of transcriptionally active genes among human tissues, offers a unique window into how aging affects complex gene regulation. Recent advancements in single-cell RNA sequencing have enabled multi-species studies of aging-related transcriptome changes in testes. However, a comprehensive aging-related cross-species platform for analyzing testicular single-cell transcriptomes remains absent. To address this gap, we developed Aging-TCA (Aging-related Testicular Cell Atlas, available at http://tca.xielab.tech/)— a comprehensive online platform containing aging-related testicular single cell and spatial transcriptome profiles from human, cynomolgus monkey, mouse, and zebrafish across 89 samples and over 460, 000 testicular cells, nuclei or spots. Aging-TCA provides versatile tools for temporal and spatial analyzing testicular aging, including aging-related testicular gene expression visualization, cell-cell communication comparisons, cell-trajectory analysis, gene regulatory network analysis, testicular cell type auto-identification, time-series analysis, and spatial transcriptome comparisons. In summary, we provide a novel platform Aging-TCA for advancing cross-species research into testicular aging, facilitating future investigations in this field.
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Spatial and single-cell RNA sequencing reveals the immune microenvironment of human ascending thoracic aortic aneurysms.
BACKGROUND: Ascending thoracic aortic aneurysm (ATAA) is a fatal vascular disease characterized by immune dysregulation. However, the cellular composition, spatial localization, and functional diversity of immune cells in the ATAA microenvironment remain poorly understood. OBJECTIVE: To construct a high-resolution immune cell atlas of human ATAA and explore the immune-mediated vascular remodelling mechanisms associated with its progression. METHOD: We conduct high-throughput single-cell RNA sequencing (scRNA-seq) of aortic tissues from eight ATAA patients and nine controls, including six from the GEO database. In the ATAA group, CD45+ cell subpopulations are isolated, and the scRNA-seq results are integrated with Visium high-definition spatial transcriptomics analysis to achieve near-single-cell resolution cell localization through deconvolution. Advanced cell segmentation algorithms are applied to generate a high-resolution immune cell atlas of human ATAA. RESULTS: A total of 187 163 high-quality immune cells are identified, encompassing eight major immune cell types. Immune cells are significantly enriched in ATAA tissues compared with the controls. CellChat analysis reveals strong immune cell interactions in ATAA, which may contribute to its occurrence and progression. CONCLUSION: This study presents the first high-resolution immune cell atlas of human ATAA, offering novel insights into its immune-mediated vascular remodelling mechanism. KEY POINTS: What is currently known about this topic? ATAA is a vascular disease characterized by medial degeneration and chronic inflammation. Previous single-cell transcriptomic studies have revealed the cellular heterogeneity of the aortic wall and identified alterations in the populations of smooth muscle, fibroblasts and endothelial cells. However, the immune landscape of ATAA remains unclear. Recent single-cell and spatial analyses of aortic dissections and abdominal aortic aneurysms have revealed dynamic immune remodelling involving macrophage polarization, T-cell activation and cytokine-driven matrix degradation. Nevertheless, the spatial resolution of immune heterogeneity and cross-lineage signalling in human ATAA is poorly understood. What is the key research question? This study aimed to determine the cellular composition, spatial distribution and transcriptional reprogramming of immune cell populations in ATAA and to elucidate how the interactions among immune cells lead to inflammation and pathological remodelling of the aortic wall. What is new? Single-cell RNA sequencing combined with Visium HD spatial transcriptomics can comprehensively characterize the composition, gene expression profiles and spatial localizations of eight types of immune cells in the aortic tissues of the control and ATAA groups at the single-cell level. Targeting of the interferon, AP1, and NF-κB pathways is a novel strategy for treating ATAA. The interactions among immune cells have a synergistic effect during ATAA formation, jointly driving immune remodelling. How might this study influence clinical practice? Elucidation of the dynamic differentiation trajectories and gene expression signatures of distinct immune cell populations in normal and ATAA-affected aortas will enhance our understanding of the immunological mechanisms underlying aneurysm pathogenesis. This knowledge provides a foundation for developing targeted anti-inflammatory therapeutic strategies for ATAA.
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Single-Cell RNA-Seq Recognized Key Genes for Metastasis and Macrophage Infiltration in Colorectal Cancer.
Colorectal cancer (CRC) is one of the most common malignancies in the world. However, the main causes of metastasis and immune cell infiltration in CRC are still unclear. This experiment was conducted to identify the key genes of metastasis and macrophage infiltration in CRC according to single-cell sequencing (scRNA-seq) data. By analyzing the data of GSE261012 and GSE234804 in the Gene Expression Omnibus (GEO) database, the key node genes for the stages of tumorigenesis, epithelial-mesenchymal transition, and metastasis of CRC were found. These genes were modeled by lasso regression by The Cancer Genome Atlas (TCGA) database, and ZFAND2A was identified as a key gene for metastasis and macrophage infiltration in CRC. Finally, the specific function of ZFAND2A in cancer cell activity was explored in vitro by qRT-PCR, WB analysis, CCK-8, and transwell assay. The specific function of ZFAND2A in macrophage polarization was explored in vitro by qRT-PCR, ELISA, and flow cytometry. We identified crucial gene expression in the entire process of CRC tumor progression, including tumorigenesis, epithelial-mesenchymal transition, and metastasis. Ten thousand six hundred and thirty-seven genes were determined as genes associated with tumor progression and metastasis. Among them, six genes were identified to be related to CRC prognosis. The results of TCGA data indicated that ZFAND2A showed lower expression in tumors and was related to a good prognosis of CRC. Overexpression of ZFAND2A inhibits the proliferation and migration of CRC cells. Additionally, there was a correlation between ZFAND2A expression and macrophage infiltration. Increasing ZFAND2A promotes M1 polarization in macrophages. Our findings provide new potential biomarkers for the metastatic mechanisms and prognosis of CRC. In addition, ZFAND2A is expected to become a potential therapeutic target for CRC.
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The molecular cytoarchitecture of the adult mouse brain.
The function of the mammalian brain relies upon the specification and spatial positioning of diversely specialized cell types. Yet, the molecular identities of the cell types and their positions within individual anatomical structures remain incompletely known. To construct a comprehensive atlas of cell types in each brain structure, we paired high-throughput single-nucleus RNA sequencing with Slide-seq1,2-a recently developed spatial transcriptomics method with near-cellular resolution-across the entire mouse brain. Integration of these datasets revealed the cell type composition of each neuroanatomical structure. Cell type diversity was found to be remarkably high in the midbrain, hindbrain and hypothalamus, with most clusters requiring a combination of at least three discrete gene expression markers to uniquely define them. Using these data, we developed a framework for genetically accessing each cell type, comprehensively characterized neuropeptide and neurotransmitter signalling, elucidated region-specific specializations in activity-regulated gene expression and ascertained the heritability enrichment of neurological and psychiatric phenotypes. These data, available as an online resource ( www.BrainCellData.org ), should find diverse applications across neuroscience, including the construction of new genetic tools and the prioritization of specific cell types and circuits in the study of brain diseases.
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Single-cell analysis identifies monocyte signatures of disease activity and clinical subtypes in Behçet's disease.
OBJECTIVES: Behçet's disease (BD) is a multisystem inflammatory disorder with diverse phenotypes and incompletely defined immune mechanisms. This study aimed to map immune dysregulation in BD at high resolution, comparing active vs remission states and identifying pathways linked to clinical phenotypes. METHODS: We performed single-cell RNA sequencing on 247,028 peripheral blood mononuclear cells from 34 patients with BD and 12 healthy controls. Transcriptomic profiling, differential gene expression, pathway enrichment analyses, and phenotype-stratified comparisons were used to delineate immune cell alterations associated with disease activity and clinical subtypes. RESULTS: All 3 monocyte subsets were markedly expanded in BD and demonstrated dominant interferon (IFN)-γ-associated activation, robust heat-shock responses, and enhanced antigen-presentation programmes. In active disease, monocytes exhibited pronounced type II IFN signatures, which reversed in remission alongside restoration of regulatory and metabolic pathways. Remission was instead characterised by increased expression of type I IFN-regulated genes and activation of serine protease inhibitor (SERPIN)-associated programmes linked to tissue stabilisation. Clinical phenotype stratification revealed distinct transcriptional signatures in peripheral blood monocytes, including enrichment of heat-shock and stress-response pathways in monocytes from patients with vascular BD and tumour necrosis factor/NF-κB-associated programmes in monocytes from patients with ocular BD. Patients without organ involvement demonstrated an increased type I IFN gene signature. CONCLUSIONS: This study provides a high-resolution immune atlas of BD, identifying monocyte-driven dysregulation as a central feature. Our findings map the immune heterogeneity of BD, identify activity- and phenotype-linked peripheral blood monocyte states, and suggest immune pathways suitable for targeted intervention.
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Integrated Analysis of Single-cell and Bulk-RNA Sequencing Data to Identify Redox-related Genes as Prognostic Biomarkers and Therapeutic Targets of Lung Adenocarcinoma With Osimertinib Resistance.
BACKGROUND: Acquired resistance limits the therapeutic efficacy of osimertinib in lung adenocarcinoma (LUAD). Redox homeostasis is crucial for LUAD progression. However, how redox imbalance interacts with the tumor microenvironment (TME) to drive osimertinib resistance (OR) remains unclear. METHODS: The single-cell RNA sequencing (scRNA-seq) data GSE243562 were combined with the Cancer Genome Atlas (TCGA)-LUAD transcriptomes to map the TME cell population heterogeneity in osimertinib-resistant LUAD. Through univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regularization, a prognostic signature founded on redox-related genes (RRGs) was built. Therapeutic compounds targeting these signature genes were prioritized by molecular docking. Their expression patterns were subsequently validated in vitro. RESULTS: Cancer-associated fibroblasts (CAFs) were central hubs in the TME of osimertinib-resistant LUAD, exhibiting enhanced intercellular communication. Computational profiling identified 10 differentially expressed RRGs, predominantly enriched in CAFs. Using a six-gene signature comprising AGER, CYP2J2, FMO2, HSPA1B, SOD3, and VASN, we categorized LUAD patients into separate risk categories. High-risk patients showed significantly reduced survival, an immunosuppressive status, and a higher tumor mutation burden (p < 0.05). The overexpression of these six genes was confirmed in OR cells. Critically, inhibiting SOD3 restored osimertinib sensitivity in vitro (p < 0.05). Clinically, SOD3 expression was lower in patients sensitive to third-generation epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) than in those with resistant disease. CONCLUSIONS: Targeting CAFs represents a promising strategy to overcome osimertinib resistance. Our six-gene redox signature offers a clinical framework for patient risk stratification and novel therapeutic strategy design. Future work will explore these targets to develop new treatments for LUAD.
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Integrative single-cell RNA-seq and ATAC-seq analysis reveals the key role of inflammatory cell activation in pulmonary arterial hypertension.
BACKGROUND: The cellular heterogeneity and molecular complexity of pulmonary arterial hypertension (PAH) have not been fully elucidated. METHODS: Here, we constructed an integrative transcriptome and chromatin accessibility atlas of PAH mice by using single-cell RNA sequencing and single-cell ATAC sequencing. RESULTS: In PAH mice, the numbers of granulocytes and monocytes/macrophages in the lung tissues were increased, and the hypoxia-inducing factor pathway was specifically activated in these inflammatory cells. Furthermore, monocyte/macrophage subcluster analysis revealed an increase of chemokine C-C-motif receptor 2 (CCR2)+ proinflammatory macrophages but a decrease of M2-like macrophages. Notably, S100a9 expression was significantly upregulated in both granulocytes and CCR2+ proinflammatory macrophages, accompanied by increased chromatin accessibility at the promoter region specifically in CCR2+ macrophages. Given its restricted upregulation in these two key proinflammatory cell populations, we generated S100a9 global knockout mice to investigate its role in PAH. S100a9 deletion alleviated the pulmonary arterial remodeling and right ventricular dysfunction in PAH mice. CONCLUSION: In conclusion, this study established a comprehensive transcriptome and chromatin accessibility atlas for PAH mice and further indicated that activation of S100a9-expressing inflammatory cell might be associated with the development of PAH. Further researches are warranted to investigate the underlying mechanisms.
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Benchmarking cross-species single-cell RNA-seq data integration methods: towards a cell type tree of life.
Cross-species single-cell RNA-seq data hold immense potential for unraveling cell type evolution and transferring knowledge between well-explored and less-studied species. However, challenges arise from interspecific genetic variation, batch effects stemming from experimental discrepancies and inherent individual biological differences. Here, we benchmarked nine data-integration methods across 20 species, encompassing 4.7 million cells, spanning eight phyla and the entire animal taxonomic hierarchy. Our evaluation reveals notable differences between the methods in removing batch effects and preserving biological variance across taxonomic distances. Methods that effectively leverage gene sequence information capture underlying biological variances, while generative model-based approaches excel in batch effect removal. SATURN demonstrates robust performance across diverse taxonomic levels, from cross-genus to cross-phylum, emphasizing its versatility. SAMap excels in integrating species beyond the cross-family level, especially for atlas-level cross-species integration, while scGen shines within or below the cross-class hierarchy. As a result, our analysis offers recommendations and guidelines for selecting suitable integration methods, enhancing cross-species single-cell RNA-seq analyses and advancing algorithm development.
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Spatially resolved single-cell analyses of human meningioma identify novel cell states influencing tumor microenvironment and progression.
Recent advances in our understanding of the molecular landscape of meningioma have generated new insights into the biology and heterogeneity of this disease, with demonstrated clinical value. However, there remains a need to understand tumor-intrinsic heterogeneity at single-cell resolution to inform potential therapeutic avenues. In this study, we examined the breadth of cell types and states in meningioma using a large cohort profiled with single-nuclear RNA sequencing and high-resolution spatial transcriptomics, as well as bulk DNA methylation and RNA sequencing (n = 712), bulk proteomics (n = 88) and plasma methylation (n = 59). We demonstrated that myeloid cell states differ across molecular groups of meningiomas and evolve meaningfully from dura to tumor. Myeloid cell states were also associated with unique myeloid-neoplastic interactions and neoplastic gene programs, suggesting a role in shaping the microenvironment. Finally, multiple non-neoplastic cell states refined outcome prediction beyond molecular group, suggesting a role in meningioma progression.
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The human infertility single-cell testis atlas (HISTA): an interactive molecular scRNA-Seq reference of the human testis.
BACKGROUND: Single-cell RNA-seq (scRNA-Seq) has been widely adopted to study gene expression of the human testis. Several datasets of scRNA-Seq from human testis have been generated from different groups processed with different informatics pipelines. An integrated atlas of scRNA-Seq expression constructed from multiple donors, developmental ages, and fertility states would be widely useful for the testis research community. OBJECTIVE: To describe the generation and use of the human infertility single-cell testis atlas (HISTA), an interactive web tool for understanding human spermatogenesis through scRNA-Seq analysis. METHODS: We obtained scRNA-Seq datasets derived from 12 donors, including healthy adult controls, juveniles, and several infertility cases, and reprocessed these data using methods to remove batch effects. Using Shiny, an open-source environment for data visualization, we created numerous interactive tools for exploring the data, some of which support simple statistical hypothesis testing. We used the resulting HISTA browser and its underlying data to demonstrate HISTA's value for testis researchers. RESULTS: A primary application of HISTA is to search by a single gene or a set of genes; thus, we present various analyses that quantify and visualize gene expression across the testis cells and pathology. HISTA also contains machine-learning-derived gene modules ("components") that capture the entire transcriptional landscape of the testis tissue. We show how the use of these components can simplify the highly complex data in HISTA and assist with the interpretation of genes with unknown functions. Finally, we demonstrate the diverse ways HISTA can be used for new data analysis, including hypothesis testing. DISCUSSION AND CONCLUSIONS: HISTA is a research environment that can help scientists organize and understand the high-dimensional transcriptional landscape of the human testis. HISTA has already contributed to published testis research and can be updated as needed with input from the research community or downloaded and modified for individual needs.