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: 4 905
Page 120 of 491-
scREAD: A Single-Cell RNA-Seq Database for Alzheimer's Disease.
Alzheimer's disease (AD) is a progressive neurodegenerative disorder of the brain and the most common form of dementia among the elderly. The single-cell RNA-sequencing (scRNA-Seq) and single-nucleus RNA-sequencing (snRNA-Seq) techniques are extremely useful for dissecting the function/dysfunction of highly heterogeneous cells in the brain at the single-cell level, and the corresponding data analyses can significantly improve our understanding of why particular cells are vulnerable in AD. We developed an integrated database named scREAD (single-cell RNA-Seq database for Alzheimer's disease), which is as far as we know the first database dedicated to the management of all the existing scRNA-Seq and snRNA-Seq data sets from the human postmortem brain tissue with AD and mouse models with AD pathology. scREAD provides comprehensive analysis results for 73 data sets from 10 brain regions, including control atlas construction, cell-type prediction, identification of differentially expressed genes, and identification of cell-type-specific regulons.
-
Multi-omics analysis of GBP2 as a prognostic and immune-associated biomarker in papillary thyroid carcinoma.
BACKGROUND: Guanylate-binding protein 2 (GBP2) is dysregulated in several malignancies and participates in immune regulation. This study aimed to characterize its expression, prognostic relevance, cellular distribution, immune associations, and biological role in papillary thyroid carcinoma (PTC). METHODS: We integrated multi-omics data from The Cancer Genome Atlas thyroid carcinoma project (TCGA-THCA), Gene Expression Omnibus (GEO), and the Human Protein Atlas (HPA). The TCGA-THCA cohort primarily comprises differentiated thyroid carcinoma (DTC), with PTC as the predominant histologic subtype; single-cell RNA-sequencing (scRNA-seq) data from PTC samples were also analyzed. Kaplan-Meier survival analysis and univariable and multivariable Cox regression were used to evaluate the prognostic association of GBP2. ESTIMATE, CIBERSORT, and TIMER2.0 were used to assess associations between GBP2 and the tumor immune microenvironment and immune-cell infiltration. Immune-checkpoint expression and immunophenoscores (IPSs) from The Cancer Immunome Atlas (TCIA) were used to characterize immune-related phenotypes. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA) were performed to explore potential functions. scRNA-seq data were used to characterize the PTC microenvironment and cell-type-specific GBP2 expression, identify GBP2-associated differentially expressed genes, and infer potential intercellular communication with CellChat. Finally, GBP2 was transiently silenced with small interfering RNA (siRNA) in TPC-1 cells, followed by cell counting kit-8 (CCK-8), wound-healing, Transwell invasion, and flow-cytometric apoptosis assays. RESULTS: GBP2 mRNA and protein expression were higher in PTC than in normal thyroid tissue. Patients with high GBP2 expression had longer overall survival (OS), and multivariable Cox regression showed an adjusted association between GBP2 expression and a lower risk of death [hazard ratio (HR), 0.23; 95% confidence interval (CI): 0.08-0.65; P=0.006]. GBP2 expression was negatively associated with age and clinical stage and positively associated with N stage. Stratified survival analyses and interaction testing did not indicate a significant interaction between GBP2 and N stage. High GBP2 expression was associated with higher immune and stromal scores and with infiltration by several immune-cell populations, including CD8+ T cells, B cells, and dendritic cells. GBP2-related genes were enriched in T-cell activation, antigen processing and presentation, and interferon-response pathways. scRNA-seq analysis showed that GBP2 was detectable in T cells, malignant thyrocytes, and myeloid cells, with the highest average expression in T cells. In malignant thyrocytes, higher GBP2 expression was associated with enrichment of major histocompatibility complex (MHC) and immune-related functions. CellChat suggested a potential HLA-A/B/C/E-CD8A/B communication pattern between GBP2-high malignant thyrocytes and CD8+ T cells; this finding requires experimental validation. In TPC-1 cells, GBP2 knockdown increased CCK-8 signals, wound closure, and invasive capacity and reduced apoptosis. CONCLUSIONS: In predominantly PTC cohorts, high GBP2 expression is associated with longer OS and an immune-inflamed tumor microenvironment (TME). GBP2 may therefore serve as a candidate biomarker for prognostic and immune-phenotype studies in PTC. Its mechanistic role in tumor cells and the immune microenvironment requires further validation.
-
Single-cell RNA sequencing highlights the role of epithelial-immune dual features of proximal tubule cells in BK polyomavirus nephropathy.
Up to 10% of renal allograft failures are caused by BK polyomavirus nephropathy (BKPyVN). However, there is no specific antiviral agent for BKPyVN. The only treatment is to reduce the levels of immunosuppression, which is not always practical and increases the risk of rejection. Since targeting the microenvironment is a promising approach, we performed single-cell RNA sequencing (scRNA-seq) on BKPyVN samples and stable allografts to obtain BKPyVN microenvironmental atlases. Interestingly, we identified a novel subpopulation of proximal tubule cells (annotated as IGKC+ PT) with epithelial-immune dual features that may contribute to the progression of BKPyVN through T-cell exhaustion. Additionally, we determined that the IGKC+ PT subpopulation might serve as a non-invasive diagnostic marker through scRNA-seq of urine samples and co-immunofluorescence staining. These results improve our understanding of the BKPyVN microenvironment and may guide the development of new therapeutic and diagnostic approaches for a wide range of patients.IMPORTANCEBKPyVN severely threatens kidney transplant recipients. Due to the lack of effective drugs against BK polyomavirus (BKPyV), reducing immunosuppressant therapy is the only treatment. Unfortunately, this approach is not always effective and increases the acute rejection risk. A growing body of research suggests that potential therapeutic targets may be identified by studying the disease microenvironment. However, traditional methods have not explained why the large number of infiltrating T cells in the BKPyVN microenvironment does not effectively clear BKPyV. Newly available large-scale scRNA-seq technology can be used to study gene expression at a single-cell resolution, offering a new way to investigate the BKPyVN microenvironment. By combining scRNA-seq with experimental analysis, we found a novel subpopulation of proximal tubule cells (annotated as IGKC+ PT) with epithelial-immune dual features that may contribute to the progression of BKPyVN through T-cell exhaustion.
-
Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram.
Charting an organs' biological atlas requires us to spatially resolve the entire single-cell transcriptome, and to relate such cellular features to the anatomical scale. Single-cell and single-nucleus RNA-seq (sc/snRNA-seq) can profile cells comprehensively, but lose spatial information. Spatial transcriptomics allows for spatial measurements, but at lower resolution and with limited sensitivity. Targeted in situ technologies solve both issues, but are limited in gene throughput. To overcome these limitations we present Tangram, a method that aligns sc/snRNA-seq data to various forms of spatial data collected from the same region, including MERFISH, STARmap, smFISH, Spatial Transcriptomics (Visium) and histological images. Tangram can map any type of sc/snRNA-seq data, including multimodal data such as those from SHARE-seq, which we used to reveal spatial patterns of chromatin accessibility. We demonstrate Tangram on healthy mouse brain tissue, by reconstructing a genome-wide anatomically integrated spatial map at single-cell resolution of the visual and somatomotor areas.
-
Single-cell RNA sequencing of cultured human endometrial CD140b+CD146+ perivascular cells highlights the importance of in vivo microenvironment.
BACKGROUND: Endometrial mesenchymal-like stromal/stem cells (eMSCs) have been proposed as adult stem cells contributing to endometrial regeneration. One set of perivascular markers (CD140b&CD146) has been widely used to enrich eMSCs. Although eMSCs are easily accessible for regenerative medicine and have long been studied, their cellular heterogeneity, relationship to primary counterpart, remains largely unclear. METHODS: In this study, we applied 10X genomics single-cell RNA sequencing (scRNA-seq) to cultured human CD140b+CD146+ endometrial perivascular cells (ePCs) from menstrual and secretory endometrium. We also analyzed publicly available scRNA-seq data of primary endometrium and performed transcriptome comparison between cultured ePCs and primary ePCs at single-cell level. RESULTS: Transcriptomic expression-based clustering revealed limited heterogeneity within cultured menstrual and secretory ePCs. A main subpopulation and a small stress-induced subpopulation were identified in secretory and menstrual ePCs. Cell identity analysis demonstrated the similar cellular composition in secretory and menstrual ePCs. Marker gene expression analysis showed that the main subpopulations identified from cultured secretory and menstrual ePCs simultaneously expressed genes marking mesenchymal stem cell (MSC), perivascular cell, smooth muscle cell, and stromal fibroblast. GO enrichment analysis revealed that genes upregulated in the main subpopulation enriched in actin filament organization, cellular division, etc., while genes upregulated in the small subpopulation enriched in extracellular matrix disassembly, stress response, etc. By comparing subpopulations of cultured ePCs to the publicly available primary endometrial cells, it was found that the main subpopulation identified from cultured ePCs was culture-unique which was unlike primary ePCs or primary endometrial stromal fibroblast cells. CONCLUSION: In summary, these data for the first time provides a single-cell atlas of the cultured human CD140b+CD146+ ePCs. The identification of culture-unique relatively homogenous cell population of CD140b+CD146+ ePCs underscores the importance of in vivo microenvironment in maintaining cellular identity.
-
Construction of a Matrix Cancer-Associated Fibroblast Signature Gene-Based Risk Prognostic Signature for Directing Immunotherapy in Patients with Breast Cancer Using Single-Cell Analysis and Machine Learning.
Cancer-associated fibroblasts (CAFs) are heterogeneous constituents of the tumor microenvironment involved in the tumorigenesis, progression, and therapeutic responses of tumors. This study identified four distinct CAF subtypes of breast cancer (BRCA) using single-cell RNA sequencing (RNA-seq) data. Of these, matrix CAFs (mCAFs) were significantly associated with tumor matrix remodeling and strongly correlated with the transforming growth factor (TGF)-β signaling pathway. Consensus clustering of The Cancer Genome Atlas (TCGA) BRCA dataset using mCAF single-cell characteristic gene signatures segregated samples into high-fibrotic and low-fibrotic groups. Patients in the high-fibrotic group exhibited a significantly poor prognosis. A weighted gene co-expression network analysis and univariate Cox analysis of bulk RNA-seq data revealed 17 differential genes with prognostic values. The mCAF risk prognosis signature (mRPS) was developed using 10 machine learning algorithms. The clinical outcome predictive accuracy of the mRPS was higher than that of the conventional TNM staging system. mRPS was correlated with the infiltration level of anti-tumor effector immune cells. Based on consensus prognostic genes, BRCA samples were classified into the following two subtypes using six machine learning algorithms (accuracy > 90%): interferon (IFN)-γ-dominant (immune C2) and TGF-β-dominant (immune C6) subtypes. Patients with mRPS downregulation were associated with improved prognosis, suggesting that they can potentially benefit from immunotherapy. Thus, the mRPS model can stably predict BRCA prognosis, reflect the local immune status of the tumor, and aid clinical decisions on tumor immunotherapy.
-
Deciphering the regulatory landscape of venom gene expression in a parasitoid wasp at single-cell resolution.
The venom gland is a remarkable organ of the parasitoid wasps, which produces venom that controls the host and is important for the survival and reproduction of the parasitoid. But little is known about how venom gland cells precisely regulate venom synthesis. Here, we used single-nucleus RNA sequencing to construct the cell atlases of the venom gland and ovary of the Pteromalus puparum, a natural enemy of vegetable pests. The cells in the venom gland were clustered into six subsets, and the main cell types in the ovary were follicle cells, germline cells and nurse cells. The cellular function of venom gland subsets named VgC1 to VgC6 was enriched in the production and secretion of venom. In addition, the expression pattern of identified venom genes that 77 genes expressed across all six VgCs and 46 genes in five VgCs, demonstrated that most venom genes expressed widely in VgCs. Finally, hdWGCNA analysis of venom gland cells identified a set of co-expressed venom and non-venom genes that include putative regulators of venom production in P. puparum. Our analysis of transcription factor (TF) binding sites within the venom modules revealed that TFs such as AP-1 and EHF are potential regulators of venom genes. This work reveals the cellular heterogeneity, venom gene expression patterns, and transcriptional regulatory networks in the venom gland of P. puparum at single-cell resolution. These findings provide fundamental insights into the biology of venom-producing organs and establish a molecular foundation for developing novel biopesticides based on venom proteins.
-
A multi-omics integration of bulk and single-cell transcriptomics identifies and validates a 16-gene epithelial-mesenchymal transition prognostic signature in tongue squamous cell carcinoma.
BACKGROUND: Tongue squamous cell carcinoma (TSCC) is a highly aggressive malignancy associated with unfavorable clinical outcomes, highlighting the critical need for dependable prognostic indicators. The epithelial-mesenchymal transition (EMT) is a fundamental biological process driving cancer progression. This study aimed to develop and independently validate an EMT-associated gene expression signature for predicting prognosis in TSCC through a multi-omics strategy. METHODS: We analyzed bulk RNA-sequencing (RNA-seq) data from The Cancer Genome Atlas-TSCC (TCGA-TSCC, n=138) and the Gene Expression Omnibus (GEO, GSE41613, n=97). EMT-related differentially expressed genes (DEGs) were identified by intersecting DEGs from TSCC samples with a curated EMT gene set. Prognostic genes were subsequently selected using univariate Cox proportional hazards regression followed by least absolute shrinkage and selection operator (LASSO)-Cox regression to construct a risk score model. The predictive performance of this model was assessed in the independent GEO validation cohort. Further analyses included functional enrichment, immune cell infiltration profiling (using CIBERSORT), and tumor mutation burden (TMB) evaluation. Single-cell RNA-sequencing (scRNA-seq) data from 12 oral cancer samples were analyzed to validate gene expression patterns at the cellular level and to investigate intercellular communication networks (using CellChat). RESULTS: A prognostic signature comprising 16 EMT-related genes (including MMP13, HOXA1, ANO1, DKK1, PLK1, among others) was successfully established. Patients stratified into high- and low-risk groups based on this signature exhibited significantly divergent overall survival (P<0.05). The risk score demonstrated superior predictive accuracy [area under the curve (AUC) =0.844] compared to traditional clinical staging (AUC =0.630). A nomogram integrating the risk score and clinical stage was developed for clinical utility. High-risk patients showed enrichment in biological pathways such as "DNA replication licensing" and displayed an immunosuppressive tumor microenvironment characterized by increased infiltration of activated dendritic cells. Patients with concurrent high-risk scores and high TMB experienced the poorest prognosis. scRNA-seq analysis confirmed the expression of signature genes within specific cell clusters, revealed elevated EMT activity primarily in epithelial and tissue stem cell populations, and identified the COLLAGEN signaling pathway as the predominant mediator of intercellular communication. CONCLUSIONS: Utilizing an integrated bulk and single-cell transcriptomics approach, we have constructed and validated a robust 16-gene EMT-related prognostic signature for TSCC. This signature offers a promising tool for patient risk stratification and sheds light on underlying biological mechanisms involving metabolic reprogramming, immune evasion, and extracellular matrix remodeling.
-
Mapping the Mouse Cell Atlas by Microwell-Seq.
Single-cell RNA sequencing (scRNA-seq) technologies are poised to reshape the current cell-type classification system. However, a transcriptome-based single-cell atlas has not been achieved for complex mammalian systems. Here, we developed Microwell-seq, a high-throughput and low-cost scRNA-seq platform using simple, inexpensive devices. Using Microwell-seq, we analyzed more than 400,000 single cells covering all of the major mouse organs and constructed a basic scheme for a mouse cell atlas (MCA). We reveal a single-cell hierarchy for many tissues that have not been well characterized previously. We built a web-based "single-cell MCA analysis" pipeline that accurately defines cell types based on single-cell digital expression. Our study demonstrates the wide applicability of the Microwell-seq technology and MCA resource.
-
HieRFIT: a hierarchical cell type classification tool for projections from complex single-cell atlas datasets.
MOTIVATION: The emergence of single-cell RNA sequencing (scRNA-seq) has led to an explosion in novel methods to study biological variation among individual cells, and to classify cells into functional and biologically meaningful categories. RESULTS: Here, we present a new cell type projection tool, Hierarchical Random Forest for Information Transfer (HieRFIT), based on hierarchical random forests. HieRFIT uses a priori information about cell type relationships to improve classification accuracy, taking as input a hierarchical tree structure representing the class relationships, along with the reference data. We use an ensemble approach combining multiple random forest models, organized in a hierarchical decision tree structure. We show that our hierarchical classification approach improves accuracy and reduces incorrect predictions especially for inter-dataset tasks which reflect real-life applications. We use a scoring scheme that adjusts probability distributions for candidate class labels and resolves uncertainties while avoiding the assignment of cells to incorrect types by labeling cells at internal nodes of the hierarchy when necessary. AVAILABILITY AND IMPLEMENTATION: HieRFIT is implemented as an R package, and it is available at (https://github.com/yasinkaymaz/HieRFIT/releases/tag/v1.0.0). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.