Браузер не даёт сохранять данные (например, приватный режим): закладки пропадут после закрытия вкладки.
Поиск статей PubMed
Ключевые слова, автор, журнал, годы — результаты из PubMed, аннотации с переводом на русский, закладки в браузере.
Найдено: 4 877
Страница 8 из 488-
Single-cell RNA-seq and bulk RNA-seq explore the prognostic value of exhausted T cells in hepatocellular carcinoma.
Hepatocellular carcinoma (HCC) remains a worldwide health problem. Mounting evidence indicates that exhausted T cells play a critical role in the progress and treatment of HCC. Therefore, a detailed characterisation of exhausted T cells and their clinical significance warrants further investigation in HCC. Based on the GSE146115, we presented a comprehensive single-cell Atlas in HCC. Pseudo-time analysis revealed that tumour heterogeneity progressively increased, and the exhausted T cells gradually appeared during tumour progression. Functional enrichment analysis revealed that the evolutionary process of exhausted T cells mainly contained the pathway of cadherin binding, proteasome, cell cycle, and T cell receptor regulation of apoptosis. In the International Cancer Genome Consortium database, we divided patients into three clusters with the T cell evolution-associated genes. We found that the exhausted T cells are significantly related to poor outcomes through immunity and survival analysis. In The Cancer Genome Atlas database, the authors enrolled weighted gene co-expression network analysis, univariate Cox analysis, and Lasso Cox analysis, then screened the 19 core genes in T cells evolution and built a robust prognostic model. This study offers a fresh view on evaluating the patients' outcomes from an exhausted T cells perspective and might help clinicians develop therapeutic systems.
-
TranscriptFormer: A generative cell atlas across 1.5 billion years of evolution.
Single-cell transcriptomics is revolutionizing our understanding of cellular diversity, yet comparing transcriptional programs across the tree of life remains challenging. We developed TranscriptFormer, a family of generative foundation models trained on up to 112 million cells spanning 1.53 billion years of evolution across 12 species. We demonstrate state-of-the-art performance on cell type classification, even for species separated by over 685 million years of evolution, and zero-shot disease state identification in human cells. Developmental trajectories, phylogenetic relationships, and cellular hierarchies emerge naturally in TranscriptFormer's representations without any explicit training on these annotations. This work establishes a powerful framework for quantitative single-cell analysis and comparative cellular biology, thus demonstrating that universal principles of cellular organization can be learned and predicted across the tree of life.
-
Toward universal cell embeddings: integrating single-cell RNA-seq datasets across species with SATURN.
Analysis of single-cell datasets generated from diverse organisms offers unprecedented opportunities to unravel fundamental evolutionary processes of conservation and diversification of cell types. However, interspecies genomic differences limit the joint analysis of cross-species datasets to homologous genes. Here we present SATURN, a deep learning method for learning universal cell embeddings that encodes genes' biological properties using protein language models. By coupling protein embeddings from language models with RNA expression, SATURN integrates datasets profiled from different species regardless of their genomic similarity. SATURN can detect functionally related genes coexpressed across species, redefining differential expression for cross-species analysis. Applying SATURN to three species whole-organism atlases and frog and zebrafish embryogenesis datasets, we show that SATURN can effectively transfer annotations across species, even when they are evolutionarily remote. We also demonstrate that SATURN can be used to find potentially divergent gene functions between glaucoma-associated genes in humans and four other species.
-
Single-cell multiome and enhancer connectome of human retinal pigment epithelium and choroid nominate causal variants in macular degeneration.
Age-related macular degeneration (AMD) is a leading cause of vision loss worldwide. Genome-wide association studies (GWASs) of AMD have identified dozens of risk loci that may house disease targets. However, variants at these loci are largely noncoding, making it difficult to assess their function and whether they are causal. Here, we present a single-cell gene expression and chromatin accessibility atlas of human retinal pigment epithelium (RPE) and choroid to systematically analyze both coding and noncoding variants implicated in AMD. We employ HiChIP and activity-by-contact modeling to map enhancers in these tissues and predict cell and gene targets of risk variants. We further perform allele-specific self-transcribing active regulatory region sequencing (STARR-seq) to functionally test variant activity in RPE cells, including in the context of complement activation. Our work nominates pathogenic variants and mechanisms in AMD and offers a rich and accessible resource for studying diseases of the RPE and choroid.
-
scRNA-seq Can Identify Different Cell Populations in Ovarian Cancer Bulk RNA-seq Experiments.
High-grade serous ovarian cancer (HGSC) is a heterogeneous disease. RNA sequencing (RNAseq) of bulk solid tissue is of limited use in these populations due to heterogeneity. Single-cell RNA-seq (scRNA-seq) allows for the identification of diverse genetic compositions of heterogeneous cell populations. New computational methodologies are now available that use scRNAseq results to estimate cell type proportions in bulk RNAseq data. We performed bulk RNA-seq gene expression analysis on 112 HGSC specimens and 12 benign fallopian tube (FT) controls. We identified several publicly available scRNAseq datasets for use as annotation and reference datasets. Deconvolution was performed with MUlti-Subject SIngle Cell Deconvolution (MuSiC) to estimate cell type proportions in the bulk RNA-seq data. Datasets from the Cancer Genome Atlas (TCGA). HGSC repositories were also evaluated. Clinical variables and percentages of cell types were compared for differences in clinical outcomes and treatment results. Pathway enrichment analysis was also performed. Different annotations for referenced scRNA-seq datasets used for deconvolution of bulk RNA-seq data revealed different cellular proportions that were significantly associated with clinical outcomes; for example, higher proportions of macrophages were associated with a better response to primary chemotherapy. Our deconvolution study of bulk RNAseq HGSC samples identified cell populations within the tumor that may be associated with some of the observed clinical outcomes.
-
Spatial atlas of the mouse central nervous system at molecular resolution.
Spatially charting molecular cell types at single-cell resolution across the 3D volume is critical for illustrating the molecular basis of brain anatomy and functions. Single-cell RNA sequencing has profiled molecular cell types in the mouse brain1,2, but cannot capture their spatial organization. Here we used an in situ sequencing method, STARmap PLUS3,4, to profile 1,022 genes in 3D at a voxel size of 194 × 194 × 345 nm3, mapping 1.09 million high-quality cells across the adult mouse brain and spinal cord. We developed computational pipelines to segment, cluster and annotate 230 molecular cell types by single-cell gene expression and 106 molecular tissue regions by spatial niche gene expression. Joint analysis of molecular cell types and molecular tissue regions enabled a systematic molecular spatial cell-type nomenclature and identification of tissue architectures that were undefined in established brain anatomy. To create a transcriptome-wide spatial atlas, we integrated STARmap PLUS measurements with a published single-cell RNA-sequencing atlas1, imputing single-cell expression profiles of 11,844 genes. Finally, we delineated viral tropisms of a brain-wide transgene delivery tool, AAV-PHP.eB5,6. Together, this annotated dataset provides a single-cell resource that integrates the molecular spatial atlas, brain anatomy and the accessibility to genetic manipulation of the mammalian central nervous system.
-
Scope+: an open source generalizable architecture for single-cell RNA-seq atlases at sample and cell levels.
SUMMARY: With the recent advancement in single-cell RNA-sequencing technologies and the increased availability of integrative tools, challenges arise in easy and fast access to large collections of cell atlas. Existing cell atlas portals rarely are open sourced and adaptable, and do not support meta-analysis at cell level. Here, we present an open source, highly optimized and scalable architecture, named Scope+, to allow quick access, meta-analysis and cell-level selection of the atlas data. We applied this architecture to our well-curated 5 million COVID-19 blood and immune cells, as a portal called Covidscope. We achieved efficient access to atlas-scale data via three strategies, such as cell-as-unit data modelling, novel database optimization techniques and innovative software architectural design. Scope+ serves as an open source architecture for researchers to build on with their own atlas. AVAILABILITY AND IMPLEMENTATION: The COVID-19 web portal, data and meta-analysis are available on Covidscope (https://covidsc.d24h.hk/). User tutorials on how to implement Scope+ architecture with their atlases can be found at https://hiyin.github.io/scopeplus-user-tutorial/. Scope+ source code can be found at https://doi.org/10.5281/zenodo.14174632 and https://github.com/hiyin/scopeplus.
-
A single-cell and spatial transcriptomic atlas of human tuberculous constrictive pericardium.
BACKGROUND: Tuberculous pericarditis (TP) can progress to tuberculous constrictive pericarditis (TB-CP), a life-threatening fibrotic syndrome. However, the cellular architecture and intercellular circuits that link granulomatous inflammation to pericardial fibrosis remain poorly defined. METHODS: We performed single-cell RNA sequencing (scRNA-seq) on 81,634 cells from surgically resected pericardium of six patients with TB-CP and three normal controls. Ligand-receptor and trajectory analyses were integrated with spatial transcriptomics, histology, immunohistochemistry, and multiplex immunofluorescence to map cell states, signalling pathways, and immune-stromal niches in situ. FINDINGS: We generated an integrated single-cell and spatial atlas of the human pericardium in TB-CP, delineating 14 major cell types and heterogeneous transcriptional programmes associated with granuloma formation, vascular remodelling, and fibrotic activation. Spatial mapping revealed MMP9+ macrophage-rich granulomatous cores surrounded by infiltrating CCL19+ fibroblasts and T cells. In parallel, S100A4+ endothelial cells displayed endothelial-to-mesenchymal transition-like programmes and trajectories converging on ACTA2+ myofibroblasts, supported by immune cell-derived TGF-β and VEGF signalling. Ligand-receptor analysis and spatial co-localisation suggested macrophages may act as hubs that couple immune activation to endothelial reprogramming, fibroblast activation, and T-cell recruitment within fibrotic lesions. INTERPRETATION: This human atlas defines the cellular landscape and key intercellular circuits underlying the immunopathogenesis of TB-CP. Our findings show that a spatially organised immune-endothelial-fibroblast network is associated with pericardial fibrosis and nominate VEGF/TGF-β pathways and associated cell states as potential biomarkers and therapeutic targets. FUNDING: National Natural Science Foundation of China (82270486 to F.X.); Science and Technology Bureau of Sichuan Province (2021YFS0051, 2024YFFK0209 to Y.W.; 2024NSFC0646 to F.X.).
-
Comparative Single-Cell Transcriptomic Atlas Reveals the Genetic Regulation of Reproductive Traits.
Reproduction is a fundamental biological process regulated by complex cellular and molecular networks across the neuroendocrine and reproductive systems. To explore conserved and species-specific mechanisms of fertility regulation, we constructed a high-resolution single-cell transcriptomic atlas of 15 reproductive and central nervous system (CNS) tissues from sheep and integrated it with human single-cell datasets from 13 matched tissues. This comparative atlas comprises over 1.09 million cells and identifies 76 major cell types across species. Cross-species integration based on 15 748 orthologous genes revealed that 54 cell types (71.1%) are shared between sheep and humans, showing strong conservation in transcriptional programs, cell lineage trajectories, and regulatory networks. Integrating genome-wide association studies (GWAS) for sheep lifetime average litter size with the single-cell atlas identified crucial fertility-associated genes and signaling pathways. Cell-cell communication analysis revealed UNC5-SLIT-BMP signaling cascades coordinating neuroendocrine regulation of fertility along the hypothalamus-pituitary-ovary (HPO) axis. Trait-cell type enrichment analyses for 41 human complex traits further demonstrated that conserved reproductive and CNS cell types in sheep recapitulate key human GWAS associations. Together, this cross-species single-cell atlas (https://csca.njau.edu.cn/) provides a valuable resource for understanding how conserved cellular programs and inter-organ signaling networks regulate fertility and other complex traits.
-
Integrated Single-cell RNA-seq and Bulk RNA-seq Identify Diagnostic Biomarkers for Postmenopausal Osteoporosis.
AIM: We aimed to explore diagnostic biomarkers of postmenopausal osteoporosis (PMOP). BACKGROUND: PMOP brings enormous physical and economic burden to elderly women. OBJECTIVES: This study aims to screen new biomarkers for osteoporosis, providing insights for early diagnosis and therapeutic targets of osteoporosis. METHODS: Weighted gene co-expression network analysis (WGCNA) was applied to identify osteoporosis-related hub genes. Single-cell transcriptomic atlas of osteoporosis was depicted and the heterogeneity of monocytes was analyzed, based on which the biomarkers for osteoporosis were screened. Gene set enrichment analysis (GSEA) was conducted on the biomarkers. The diagnostic model (nomogram) was established and evaluated based on the expression levels of biomarkers. Additionally, the transcription factor (TF) regulatory network was constructed to predict the potential TF and targeted miRNA of biomarkers. The drugs with significant correlation with biomarkers were identified by Spearman correlation analysis. RESULTS: We obtained 30 osteoporosis-associated hub genes. 9 cell types were identified, and the monocytes were subdivided to 4 subtypes. Three biomarkers, DHX29, LSM5, and UBE2V2, were screened. DHX29 and UBE2V2 were highly expressed in non-classical monocytes, while LSM5 exhibited the highest expression in other monocytes, followed by non-classical monocytes. GSEA indicated that osteoporosis may be correlated with vascular calcification and the biomarkers may be involved in the formation of immune cells. Then, nomogram was constructed and exhibited good robustness. In addition, MYC and SETDB1 were the shared IF in three biomarkers, which may play critical regulatory roles in the progression of osteoporosis. Moreover, 41, 49, and 68 drugs appeared significant correlations with DHX29, LSM5, and UBE2V2, respectively. CONCLUSION: This study provided a basis for early diagnosis and targeted treatment of osteoporosis.