Your browser does not allow saving data (e.g. private mode): bookmarks will be lost when the tab closes.
Search PubMed
Keywords, author, journal, years — results from PubMed, abstracts with Russian translation, bookmarks in your browser.
Found: 4 895
Page 78 of 490-
MARS: discovering novel cell types across heterogeneous single-cell experiments.
Although tremendous effort has been put into cell-type annotation, identification of previously uncharacterized cell types in heterogeneous single-cell RNA-seq data remains a challenge. Here we present MARS, a meta-learning approach for identifying and annotating known as well as new cell types. MARS overcomes the heterogeneity of cell types by transferring latent cell representations across multiple datasets. MARS uses deep learning to learn a cell embedding function as well as a set of landmarks in the cell embedding space. The method has a unique ability to discover cell types that have never been seen before and annotate experiments that are as yet unannotated. We apply MARS to a large mouse cell atlas and show its ability to accurately identify cell types, even when it has never seen them before. Further, MARS automatically generates interpretable names for new cell types by probabilistically defining a cell type in the embedding space.
-
Integrating single-cell RNA-seq and bulk RNA-seq to explore prognostic value and immune landscapes of methionine metabolism-related signature in breast cancer.
BACKGROUND: Neoadjuvant, endocrine, and targeted therapies have significantly improved the prognosis of breast cancer (BC). However, due to the high heterogeneity of cancer, some patients cannot benefit from existing treatments. Increasing evidence suggests that amino acids and their metabolites can alter the tumor malignant behavior through reshaping tumor microenvironment and regulation of immune cell function. Breast cancer cell lines have been identified as methionine-dependent, and methionine restriction has been proposed as a potential cancer treatment strategy. METHODS: We integrated transcriptomic and single-cell RNA sequencing (ScRNA-seq) analyses based on The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) datasets. Then we applied weighted gene co-expression network analysis (WGCNA) and Cox regression to evaluate methionine metabolism-related genes (MRGs) in BC, constructing and validating a prognostic model for BC patients. Immune landscapes and immunotherapy were further explored. Finally, in vitro experiments were conducted to assess the expression and function of key genes APOC1. RESULTS: In this study, we established and validated a prognostic signature based on eight methionine-related genes to predict overall survival (OS) in BC patients. Patients were further stratified into high-risk and low-risk groups according to prognostic risk score. Further analysis revealed significant differences between two groups in terms of pathway alterations, immune microenvironment characteristics, and immune checkpoint expression. Our study shed light on the relationship between methionine metabolism and immune infiltration in BC. APOC1, a key gene in the prognostic signature, was found to be upregulated in BC and closely associated with immune cell infiltration. Notably, APOC1 was primarily expressed in macrophages. Subsequent in vitro experiments demonstrated that silencing APOC1 reduced the generation of tumor-associated macrophages (TAMs) with an M2 phenotype while significantly decreasing the proliferation, invasion, and migration of MDA-MB-231 and MDA-MB-468 breast cancer cell lines. CONCLUSION: We established a prognostic risk score consisting of genes associated with methionine metabolism, which helps predict prognosis and response to treatment in BC. The function of APOC1 in regulating macrophage polarization was explored.
-
Developmental Heterogeneity of Microglia and Brain Myeloid Cells Revealed by Deep Single-Cell RNA Sequencing.
Microglia are increasingly recognized for their major contributions during brain development and neurodegenerative disease. It is currently unknown whether these functions are carried out by subsets of microglia during different stages of development and adulthood or within specific brain regions. Here, we performed deep single-cell RNA sequencing (scRNA-seq) of microglia and related myeloid cells sorted from various regions of embryonic, early postnatal, and adult mouse brains. We found that the majority of adult microglia expressing homeostatic genes are remarkably similar in transcriptomes, regardless of brain region. By contrast, early postnatal microglia are more heterogeneous. We discovered a proliferative-region-associated microglia (PAM) subset, mainly found in developing white matter, that shares a characteristic gene signature with degenerative disease-associated microglia (DAM). Such PAM have amoeboid morphology, are metabolically active, and phagocytose newly formed oligodendrocytes. This scRNA-seq atlas will be a valuable resource for dissecting innate immune functions in health and disease.
-
TCEP flame retardant linked to prostate cancer progression: Integrative analysis and novel biomarker discovery.
Tris (2-chloroethyl) phosphate (TCEP) is a widely used organophosphate flame retardant found in consumer products and human tissues. It is listed as a chemical known to cause cancer, but its effects on prostate cancer (PCa) progression remain unexplored. This study investigates the impact of TCEP on prostate tumor aggressiveness through comprehensive in vitro, in vivo, and multi-omics approaches. PCa cell lines (DU-145 and LNCaP) were treated with 1 or 10 μM TCEP to assess changes in migration, invasion, and proliferation. Male mice bearing PCa xenografts received daily intraperitoneal TCEP (100 μg/kg) to evaluate tumor growth and metastatic spread. Proteomic analysis of TCEP-exposed cells was performed to identify dysregulated pathways, followed by integration with patient transcriptomic data (The Cancer Genome Atlas-Prostate Adenocarcinoma, TCGA-PRAD) to derive a prognostic gene signature. Single-cell RNA-seq data (GSE176031) were analyzed to localize the expression of signature genes in tumor microenvironment compartments. Molecular docking simulations were conducted to assess the predicted structural compatibility between TCEP and candidate target proteins. TCEP exposure significantly increased PCa cell migration, invasion, and proliferation in vitro, and accelerated tumor growth and metastasis in vivo compared to controls. Proteomic profiling revealed that TCEP dysregulates pathways involved in cholesterol metabolism, lysosome function, and PPAR signaling. Mapping TCEP-altered proteins to TCGA data identified a four-gene signature (APOE, SPC25, TSPAN1, VGF) associated with shorter biochemical recurrence-free survival (P < 0.05). Single-cell analysis indicated that these genes are predominantly expressed in immune and stromal cells of the tumor microenvironment. Molecular docking simulations suggested potential structural compatibility between TCEP and the four protein targets, supporting possible molecular interactions that require further experimental validation. This study provides the first evidence that exposure to TCEP can drive PCa progression and metastasis. Our multi-faceted findings reveal novel mechanisms linking an environmental contaminant to PCa aggressiveness and uncover a four-gene prognostic biomarker signature, underscoring the impact of environmental toxicants on cancer progression.
-
Single-cell atlas of axial skeletal immune-matrix interaction in spondylitis.
Axial skeletal involvement is central to ankylosing spondylitis, yet the in situ interplay between immune effectors (especially cytotoxic T lymphocytes) and the bone matrix microenvironment remains poorly resolved. Prior single-cell studies in peripheral compartments revealed exhaustion-resistant CTLs and dysregulated transcriptional regulators (e.g. NFKB, FOS, JUN) in AS patients, but lacked spatial and stromal context. We obtained axial skeletal tissue (vertebral bone marrow, enthesial fibrocartilage, adjacent endplate stromal tissue) from 24 AS patients with active disease and 12 age-matched controls undergoing spine surgery (total 108,752 single cells). We performed joint single-cell RNA-seq and ATAC-seq on matched samples, followed by integrative clustering, differential gene expression, chromatin accessibility analysis, pseudotime trajectories, and ligand-receptor network inference. Correlations with clinical indices (BASDAI, MRI inflammation scores) and imaging structural progression were assessed. We identified a discrete CTL subset in axial tissues with high expression of checkpoint molecules (PD-1, TIM-3), cytotoxic genes (GZMB, PRF1), and chromatin accessibility favoring NFKB/FOS motif enrichment, consistent with resistance to exhaustion. This subset increased by about 2.8 times in AS compared to controls (p < 0.001). Simultaneously, enthesial stromal fibroblasts displayed the upregulation of matrix remodeling genes (MMP9, COL1A1, COL3A1) and osteogenic drivers (RUNX2, BMP2). Ligand-receptor modeling demonstrated robust interactions between CTLs and stromal fibroblasts via TNF, TGFβ, integrin, and Notch signaling pathways. It is important to note that CTL-stromal interaction scores were related to MRI inflammation grade (r = 0.62, p = 0.003) and structural progression (r = 0.54, p = 0.01). This integrated single-cell atlas identifies a pathogenic CTL-stromal axis in the axial skeleton of AS patients, linking immune cytotoxic mechanisms with matrix remodeling and osteogenesis. These findings delineate actionable molecular crosstalk nodes that could inform precision therapeutic strategies aimed at immune-matrix interactions in situ.Clinical trial numberNot applicable.
-
Hierarchical and automated cell-type annotation and inference of cancer cell of origin with Census.
MOTIVATION: Cell-type annotation is a time-consuming yet critical first step in the analysis of single-cell RNA-seq data, especially when multiple similar cell subtypes with overlapping marker genes are present. Existing automated annotation methods have a number of limitations, including requiring large reference datasets, high computation time, shallow annotation resolution, and difficulty in identifying cancer cells or their most likely cell of origin. RESULTS: We developed Census, a biologically intuitive and fully automated cell-type identification method for single-cell RNA-seq data that can deeply annotate normal cells in mammalian tissues and identify malignant cells and their likely cell of origin. Motivated by the inherently stratified developmental programs of cellular differentiation, Census infers hierarchical cell-type relationships and uses gradient-boosted \decision trees that capitalize on nodal cell-type relationships to achieve high prediction speed and accuracy. When benchmarked on 44 atlas-scale normal and cancer, human and mouse tissues, Census significantly outperforms state-of-the-art methods across multiple metrics and naturally predicts the cell-of-origin of different cancers. Census is pretrained on the Tabula Sapiens to classify 175 cell-types from 24 organs; however, users can seamlessly train their own models for customized applications. AVAILABILITY AND IMPLEMENTATION: Census is available at Zenodo https://zenodo.org/records/7017103 and on our Github https://github.com/sjdlabgroup/Census.
-
Single-cell spatial transcriptomic atlas of the whole mouse brain.
A comprehensive atlas of genes, cell types, and their spatial distribution across a whole mammalian brain is fundamental for understanding the function of the brain. Here, using single-nucleus RNA sequencing (snRNA-seq) and Stereo-seq techniques, we generated a mouse brain atlas with spatial information for 308 cell clusters at single-cell resolution, involving over 4 million cells, as well as for 29,655 genes. We have identified cell clusters exhibiting preference for cortical subregions and explored their associations with brain-related diseases. Additionally, we pinpointed 155 genes with distinct regional expression patterns within the brainstem and unveiled 513 long non-coding RNAs showing region-enriched expression in the adult brain. Parcellation of brain regions based on spatial transcriptomic information revealed fine structure for several brain areas. Furthermore, we have uncovered 411 transcription factor regulons showing distinct spatiotemporal dynamics during neurodevelopment. Thus, we have constructed a single-cell-resolution spatial transcriptomic atlas of the mouse brain with genome-wide coverage.
-
Cross-species single-cell transcriptomic analyses reveal evolutionary conservation and diversification of ovarian tissues.
BACKGROUND: The ovary is a central female reproductive organ responsible for producing oocytes and secreting steroid hormones. To investigate the molecular similarities and potential evolutionary origins of the ovary across vertebrates, we integrated publicly available single-cell and single-nucleus transcriptomic data from nine species, including oviparous animals (fish and chicken) and viviparous mammals (mouse, rat, sheep, goat, yak, monkey, and human). RESULTS: We generated a multi-species ovarian atlas comprising 186,748 cells and identified nine cell types. Cross-species comparative analysis identified conserved and species-specific features in granulosa cells (GCs) and stromal cells (SCs), including gene expression patterns, signaling pathways, and cell-cell communication networks. For example, proliferation-associated genes such as CKAP5, MCM3, and GINS1 were shared across vertebrates in GC-3, highlighting the evolutionary conservation of the molecular machinery underlying granulosa cell division. A comparison of granulosa cell subtypes further revealed species-specific gene co-expression patterns in oviparous and viviparous species, indicating adaptations for their different reproductive strategies. Stromal cells showed strong evolutionary conservation, as core extracellular matrix genes (e.g., COL6A1, FSTL1, ACTN1) were co-expressed across species in SC-1. Moreover, the conserved ligand-receptor pairs such as COL4A1-CD44, COL1A1-SDC4, LAMB1-ITGA6-ITGB1, and VEGFA-NRP2 constituted fundamental signaling axes of follicle development, while interactions such as PPIA-BSG in mouse and MDK-NCL in human were higher in these species compared with others, reflecting species-specific ovarian cell communication during evolution. CONCLUSIONS: These findings provide comprehensive insights into the molecular evolution and cellular diversity of the ovary, offering a valuable resource for understanding ovarian biology and evolutionary mechanisms.
-
Machine Learning Uncovers Vascular Endothelial Cell Identity Genes by Expression Regulation Features in Single Cells.
Deciphering cell identity genes is pivotal to understanding cell differentiation, development, and many diseases involving cell identity dysregulation. Here, we introduce SCIG, a machine-learning method to uncover cell identity genes in single cells. In alignment with recent reports that cell identity genes are regulated with unique epigenetic signatures, we found cell identity genes exhibit distinctive genetic sequence signatures, e.g., unique enrichment patterns of cis-regulatory elements. Using these genetic sequence signatures, along with gene expression information from single-cell RNA-seq data, enables SCIG to uncover the identity genes of a cell without a need for comparison to other cells. Cell identity gene score defined by SCIG surpassed expression value in network analysis to uncover master transcription factors regulating cell identity. Applying SCIG to the human endothelial cell atlas revealed that the tissue microenvironment is a critical supplement to master transcription factors for cell identity refinement. SCIG is publicly available at https://github.com/kaifuchenlab/SCIG , offering a valuable tool for advancing cell differentiation, development, and regenerative medicine research.
-
Integration of Single-Cell RNA Sequencing and Bulk RNA Sequencing Data to Establish and Validate a Prognostic Model for Patients With Lung Adenocarcinoma.
Background: Lung adenocarcinoma (LUAD) remains a lethal disease worldwide, with numerous studies exploring its potential prognostic markers using traditional RNA sequencing (RNA-seq) data. However, it cannot detect the exact cellular and molecular changes in tumor cells. This study aimed to construct a prognostic model for LUAD using single-cell RNA-seq (scRNA-seq) and traditional RNA-seq data. Methods: Bulk RNA-seq data were downloaded from The Cancer Genome Atlas (TCGA) database. LUAD scRNA-seq data were acquired from Gene Expression Omnibus (GEO) database. The uniform manifold approximation and projection (UMAP) was used for dimensionality reduction and cluster identification. Weighted Gene Correlation Network Analysis (WGCNA) was utilized to identify key modules and differentially expressed genes (DEGs). The non-negative Matrix Factorization (NMF) algorithm was used to identify different subtypes based on DEGs. The Cox regression analysis was used to develop the prognostic model. The characteristics of mutation landscape, immune status, and immune checkpoint inhibitors (ICIs) related genes between different risk groups were also investigated. Results: scRNA-seq data of four samples were integrated to identify 13 clusters and 9cell types. After applying differential analysis, NK cells, bladder epithelial cells, and bronchial epithelial cells were identified as significant cell types. Overall, 329 DEGs were selected for prognostic model construction through differential analysis and WGCNA. Besides, NMF identified two clusters based on DEGs in the TCGA cohort, with distinct prognosis and immune characteristics being observed. We developed a prognostic model based on the expression levels of six DEGs. A higher risk score was significantly correlated with poor survival outcomes but was associated with a more frequent TP53 mutation rate, higher tumor mutation burden (TMB), and up-regulation of PD-L1. Two independent external validation cohorts were also adopted to verify our results, with consistent results observed in them. Conclusion: This study constructed and validated a prognostic model for LUAD by integrating 10× scRNA-seq and bulk RNA-seq data. Besides, we observed two distinct subtypes in this population, with different prognosis and immune characteristics.