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: 4 939
Page 331 of 494-
Primordial germ cell specification and early developmental cell states in Pacific oyster.
BACKGROUND: Primordial germ cells (PGCs) are the precursor cells of gametes and pivotal in understanding reproductive and developmental biology. Importantly, having a thorough understanding of PGC specification is leading to critical advances in sterility induction in aquaculture species. In shellfish, however, the ability to develop these approaches is hampered by the lack of information available regarding germ cell specification. The goal of this study was to identify genes uniquely expressed in these earliest germ cells of the economically and ecologically important bivalve mollusc, the Pacific oyster (Crassostrea (Magallana) gigas). RESULTS: To capture specification of the PGCs - which represent a rare cell type - during embryonic development, we analyzed single-cell transcriptomes during cleavage, blastula, and gastrulation stages of C. gigas development. We identified cells in gastrulae that likely represent developing, distinct larval tissue types and organs, including muscles and shell gland, as well as undifferentiated cells. Using expression of the germ cell marker gene vasa, we identified cells in blastulae that likely represent the developing germ cell lineage that had yet to fully differentiate and segregate from somatic cell types. However, by the gastrula stage, vasa expression was limited primarily to a single cluster of cells. Other genes uniquely expressed in these vasa-positive cells include those with functions in transcriptional repression, chromatin architecture, and DNA repair, suggesting these cells represent oyster PGCs. Interestingly, some genes with no known homologies are also uniquely expressed in this cluster, perhaps representing novel PGC-associated genes in bivalves. CONCLUSIONS: We identified a suite of candidate genes that can be explored for their role in oyster PGC specification and advance efforts to develop methods to achieve reproductive sterility via germ cell disruption in cultured shellfish. In addition, this effort produced a transcriptional atlas of early developmental cell states in bivalve embryos, providing a wealth of information on genes contributing to other important developmental processes, such as tissue differentiation and shell production. These data represent the earliest developmental stages examined via single-cell RNA sequencing in a lophotrochozoan.
-
Spatial transcriptomic profiling of developing mouse hearts reveals a spatially patterned signaling environment.
Heart development involves dynamic signaling interactions between cells and their surrounding environment (niche). Single-cell mRNA sequencing (scRNA-seq) has been widely used to profile gene expression in individual cells, but it faces challenges in dissecting niche signals due to the need for cell dissociation. In contrast, spatial transcriptomics can preserve tissue structure and represents a potentially effective approach for this purpose. In this study, we used two spatial transcriptomics platforms, 10x Genomics Visium and Curio Slide-seq (Curio Seeker), to generate a spatial atlas of hearts at embryonic and neonatal stages. Using Visium data, we analyzed the spatial patterns of cell cycle phases, compact and trabecular myocardium signatures, and chamber-specific genes across developmental progression. We discovered that atrial cardiomyocytes exhibit a mature myocardium transcriptional signature. Additionally, we identified the spatial patterns of signaling activities at different stages. Using Slide-seq data, we identified cardiac conduction cells, including cardiac neurons, sinoatrial nodal cells, atrioventricular nodal cells, and Purkinje fiber cells, and further studied their niche signaling. Moreover, by combining lineage tracing and spatial transcriptomics, we identified four types of epicardial cell-derived cells (EPDCs) and analyzed their signaling interactions with niche cells. We then eliminated the EPDCs using a cell ablation system and observed reduced signaling in the ablated hearts through spatial transcriptomics analysis. In summary, we generated a spatial transcriptomic atlas for developing mouse hearts and identified niche signaling for cardiac conduction cells and EPDCs.
-
Spatial single-cell proteomics landscape decodes the tumor microenvironmental ecosystem of intrahepatic cholangiocarcinoma.
BACKGROUND AND AIMS: The prognoses and therapeutic responses of patients with intrahepatic cholangiocarcinoma (iCCA) depend on spatial interactions among tumor microenvironment (TME) components. However, the spatial TME characteristics of iCCA remain poorly understood. The aim of this study was to generate a comprehensive spatial atlas of iCCA using artificial intelligence-assisted spatial multiomics patterns and to identify spatial features associated with prognosis and immunotherapy. APPROACH AND RESULTS: Spatial multiomics, including imaging mass cytometry (n=155 in-house), spatial proteomics (n=155 in-house), spatial transcriptomics (n=4 in-house), multiplex immunofluorescence (n=20 in-house), single-cell RNA sequencing (scRNA-seq, n=9 in-house and n=34 public), bulk RNA-seq (n=244 public), and bulk proteomics (n=110 in-house and n=214 public), were employed to elucidate the spatial TME of iCCA. More than 1.06 million cells were resolved, and the findings revealed that spatial topology, including cellular deposition patterns, cellular communities, and intercellular communications, profoundly correlates with the prognosis of patients with iCCA. Specifically, CD163 hi M2-like resident-tissue macrophages suppress antitumor immunity by directly interacting with CD8 + T cells, resulting in poorer patient survival. Additionally, 5 spatial subtypes with distinct prognoses were identified, and potential therapeutic options were generated for these subtypes. Furthermore, a spatial TME deep learning system was developed to predict the prognosis of patients with iCCA with high accuracy from a single 1-mm 2 tumor sample. CONCLUSIONS: This study offers preliminary insights into the spatial TME ecosystem of iCCA, providing valuable foundations for precise patient classification and the development of personalized treatment strategies.
-
Characterizing tertiary lymphoid structures associated single-cell atlas in breast cancer patients.
The tertiary lymphoid structure (TLS) is recognized as a potential prognosis factor for breast cancer and is strongly associated with response to immunotherapy. Inducing TLS neogenesis can enhance the immunogenicity of tumors and improve the efficacy of immunotherapy. However, our understanding of TLS associated region at the single-cell level remains limited. Therefore, we employed high-resolution techniques, including single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST), and a TLS-specific signature to investigate TLS associated regions in breast cancer. We identified eighteen cell types within the TLS associated regions and calculated differential expression genes by comparing TLS associated regions with other areas. Notably, macrophages in the TLS associated regions exhibit lineage transformation, shifting from facilitators of immune activation to supporters of tumor cell growth. In terms of cell-cell communication within the TLS associated regions, KRT86+ CD8+ T cells, HISTIH4C+ cycling CD8+ T cells, IFNG+ CD8+ T cells, and IGKV3-20+ B cells demonstrate strong interactions with other cells. Additionally, we found that APOD+ fibroblast and CCL21+ fibroblast primarily recruit T and B cells through the CXCL12-CXCR4 ligand-receptor signaling pathway. We also validate these findings in four independent breast cancer datasets, which include one cell-level resolution dataset from the 10 × Xenium platform and three spot-level datasets from the 10 × Visium platform.
-
Single-Cell RNA Sequencing of a Postmenopausal Normal Breast Tissue Identifies Multiple Cell Types That Contribute to Breast Cancer.
The human breast is composed of diverse cell types. Studies have delineated mammary epithelial cells, but the other cell types in the breast have scarcely been characterized. In order to gain insight into the cellular composition of the tissue, we performed droplet-mediated RNA sequencing of 3193 single cells isolated from a postmenopausal breast tissue without enriching for epithelial cells. Unbiased clustering analysis identified 10 distinct cell clusters, seven of which were nonepithelial devoid of cytokeratin expression. The remaining three cell clusters expressed cytokeratins (CKs), representing breast epithelial cells; Cluster 2 and Cluster 7 cells expressed luminal and basal CKs, respectively, whereas Cluster 9 cells expressed both luminal and basal CKs, as well as other CKs of unknown specificity. To assess which cell type(s) potentially contributes to breast cancer, we used the differential gene expression signature of each cell cluster to derive gene set variation analysis (GSVA) scores and classified breast tumors in The Cancer Gene Atlas (TGGA) dataset (n = 1100) by assigning the highest GSVA scoring cell cluster number for each tumor. The results showed that five clusters (Clusters 2, 3, 7, 8, and 9) could categorize >85% of breast tumors collectively. Notably, Cluster 2 (luminal epithelial) and Cluster 3 (fibroblast) tumors were equally prevalent in the luminal breast cancer subtypes, whereas Cluster 7 (basal epithelial) and Cluster 9 (other epithelial) tumors were present primarily in the triple-negative breast cancer (TNBC) subtype. Cluster 8 (immune) tumors were present in all subtypes, indicating that immune cells may contribute to breast cancer regardless of the subtypes. Cluster 9 tumors were significantly associated with poor patient survival in TNBC, suggesting that this epithelial cell type may give rise to an aggressive TNBC subset.
-
Single cell transcriptomics of the developing zebrafish lens and identification of putative controllers of lens development.
The vertebrate lens is a valuable model system for investigating the gene expression changes that coordinate tissue differentiation due to its inclusion of two spatially separated cell types, the outer epithelial cells and the deeper denucleated fiber cells that they support. Zebrafish are a useful model system for studying lens development given the organ's rapid development in the first several days of life in an accessible, transparent embryo. While we have strong foundational knowledge of the diverse lens crystallin proteins and the basic gene regulatory networks controlling lens development, no study has detailed gene expression in a vertebrate lens at single cell resolution. Here we report an atlas of lens gene expression in zebrafish embryos and larvae at single cell resolution through five days of development, identifying a number of novel putative regulators of lens development. Our data address open questions about the temperospatial expression of α-crystallins during lens development that will support future studies of their function and provide the first detailed view of β- and γ-crystallin expression in and outside the lens. We describe divergent expression in transcription factor genes that occur as paralog pairs in the zebrafish. Finally, we examine the expression dynamics of cytoskeletal, membrane associated, RNA-binding, and transcription factor genes, identifying a number of novel patterns. Overall these data provide a foundation for identifying and characterizing lens developmental regulatory mechanisms and revealing targets for future functional studies with potential therapeutic impact.
-
Advantages of cell proliferation and immune regulation in CD146+NESTIN+ HUMSCs: insights from single-cell RNA sequencing.
The heterogeneity of stem cells is a significant factor inhibiting their clinical application, as different cell subpopulations may exhibit substantial differences in biological functions. We performed single-cell sequencing on human umbilical cord mesenchymal stem cells (HUMSCs) from 3 donors of different gestational ages (22 + 5, 28, and 39 weeks). We also compared the data with single-cell sequencing data from BMSCs from 2 public databases. The content of CD146+Nestin+ MSCs in preterm HUMSCs (22 + 5W: 30.2%, 28W: 25.8%) was higher than that in full-term HUMSCs (39W: 0.5%) and BMSCs (BMSC1: 0, BMSC2: 0.9%). Cell cycle analysis indicated a higher proportion of cells in the proliferative G2M phase in CD146+Nestin+ MSCs (40.8%) compared to CD146+Nestin- MSCs (20%) and CD146-Nestin- MSCs (12.5%). The degree of differentiation assessment suggested that CD146+Nestin+ MSCs exhibited lower differentiation than other cell subpopulations. Differential gene analysis revealed that CD146+Nestin+ MSCs overexpressed immune regulation-related factors. GO and KEGG enrichment analysis of modules identified by weighted gene co-expression network analysis suggested enrichment in pathways related to cellular immune regulation, antimicrobial activity, and proliferation. Immune-related gene analysis indicated that CD146+Nestin+ MSCs exhibited expression of multiple immune-related genes associated with "antimicrobials," "cytokines," and "cytokine receptors." Gene regulatory network analysis revealed high expression of immune-related regulators RELB, GAPB1, and EHF in CD146+Nestin+ MSCs. Our study provides a single-cell atlas of preterm HUMSCs, demonstrating the expression of CD146+Nestin+ MSCs across different tissues and confirming their advantages in cellular proliferation, antimicrobial activity, immune regulation, and low differentiation at the RNA level. This contributes valuable insights for the clinical application of HUMSCs.
-
Identification of MAGE-A10 specific T cell receptor promising in immunotherapy of hepatocellular carcinoma.
Due to the limitations of current treatment strategies, hepatocellular carcinoma (HCC) continues to impose a severe burden on people's health. In the process of exploring novel therapies, T cell receptor-engineered T cell (TCR-T) therapy has been extensively developed in HCC immunotherapy. Melanoma-associated antigen family A member 10 (MAGE-A10) is a cancer-testis antigen (CTA), specifically expressed on HCC cells. However, the identification of TCR-T targeting MAGE-A10 in HCC remains rarely discussed. In this study, single-cell RNA sequencing (scRNA-seq) and TCR sequencing (scTCR-seq) were performed on samples from HCC patients. The cellular landscape of HCC was illustrated through a single-cell atlas. Reactive T cells were defined based on the matched T cells. Additionally, most reactive T cells were enriched in CD4_CD69_Th, CD4_FOXP3_Treg, CD4_CXCL13_TEX, and CD8_CXCL13_TEX. GLIPH2 was utilized to cluster TCRs from reactive T cells, enabling the identification of reactive TCRs. TCRMatch predicted MAGE-A10 as a specific antigen recognized by one of the reactive TCRs. Furthermore, the affinity assessments between human leukocyte antigen (HLA), epitope of MAGE-A10, and the identified TCR were performed with NetMHCpan and DLpTCR. Finally, cytotoxicity assays indicated the specific recognition and killing of MAGE-A10-TCR-T cells against HCC cells, paving the way for TCR-T immunotherapy in HCC.
-
Exploring the prognostic significance and therapeutic potential of SUCLG2 in prostate cancer.
BACKGROUND: Prostate cancer (PCa), a highly heterogeneous cancer with a complex molecular pathogenesis, is a leading cause of cancer-related mortality among men globally. The present study presents a lipid metabolism-based risk model for PCa and explores the role of succinyl-CoA ligase GDP-forming subunit beta (SUCLG2), a potential marker and therapeutic target in PCa involved in lipid metabolism and cancer progression, from the perspective of developing effective diagnostic and therapeutic strategies. METHODS: High-throughput RNA sequencing and single-cell RNA sequencing were used to investigate the expression and functional relevance of SUCLG2 in PCa. We analyzed 497 PCa samples from The Cancer Genome Atlas and conducted a comprehensive bioinformatics analysis, including univariate Cox proportional hazards regression, least absolute shrinkage and selection operator regression, and gene set enrichment analysis. Furthermore, quantitative real-time polymerase chain reaction and immunofluorescence assays were performed to validate SUCLG2 expression in clinical samples and the prostate carcinoma epithelial cell line 22Rv1. RESULTS: Our findings revealed that lipid metabolism-related genes, including SUCLG2, have significant prognostic value, based on a 16-gene risk model constructed that accurately predicted PCa prognosis. In particular, SUCLG2 was significantly enriched in luminal and basal/intermediate cell subsets, highlighting its potential role in tumor progression and therapy resistance. Drug sensitivity analysis indicated that SUCLG2 expression is correlated with the efficacy of several chemotherapeutic agents, based on which strategies for personalized therapy in PCa treatment could be devised. CONCLUSION: SUCLG2 plays a pivotal role in the metabolic reprogramming of PCa, thus offering new insights into its progression and potential therapeutic targets. Our study underscores the importance of metabolic pathways in PCa pathogenesis and paves the way for the development of targeted therapies, thus contributing to personalized medicine in PCa management.
-
Prognostic Biomarkers Identification in Esophageal Cancer Based on WGCNA and Single-Cell Analysis.
BACKGROUND: Esophageal cancer is one of the most common cancers worldwide. Dysregulation of genes plays an important role in cancer. In this study, we aimed to investigate the prognostic biomarkers in esophageal cancer based on comprehensive bioinformatics analysis including WGCNA and single cell analysis. METHODS: RNA sequencing data of esophageal cancer was downloaded from GSE75241 dataset in the GEO database. We also selected esophageal cancer patients from public databases (Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA)). WGCNA was used to construct a scale-free coexpression network of genes. Multifactor Cox analysis model was constructed as the prognostic model in esophageal cancer. Furthermore, single-cell gene analysis was used to discover the mechanism of hub genes in esophageal cancer. RESULTS: WGCNA discovered 182 genes for further analysis. Among 182 genes, four genes including ANGPT2, VCAN, MS4A4A, and FOS had significant prognostic value in esophageal cancer. In single cell analysis, seven types of cells subsets were distinguished including T cells, B cells, NK cells, monocytes, macrophages, DCs, neutrophils. The expression of four hub genes (ANGPT2, VCAN, MS4A4A, and FOS) in inflammatory cell subsets was evaluated, respectively. Hub genes were correlated with inflammatory cells in esophageal cancer. In addition, the subgroups of specific inflammatory cells such as macrophages, monocytes, and DCs were analyzed to identify the function of hub genes, either. Hub genes were correlated with differentiation of inflammatory cells including monocytes, macrophages, and DCs in tumor environment. CONCLUSIONS: We identified specific hub genes correlated with prognosis of esophageal cancer. These hub genes play critical roles by regulating inflammatory cells status in esophageal cancer.