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: 4 934
Страница 281 из 494-
GeneSurfer enables transcriptome-wide exploration and annotation of gene co-expression modules in 3D spatial transcriptomics data.
Gene co-expression provides crucial insights into biological functions, however, there is a lack of exploratory analysis tools for localized gene co-expression in large-scale datasets. We present GeneSurfer, an interactive interface designed to explore localized transcriptome-wide gene co-expression patterns in the 3D spatial domain. Key features of GeneSurfer include transcriptome-wide gene filtering and gene clustering based on spatial local co-expression within transcriptomically similar cells, multi-slice 3D rendering of average expression of gene clusters, and on-the-fly Gene Ontology term annotation of co-expressed gene sets. Additionally, GeneSurfer offers multiple linked views for investigating individual genes or gene co-expression in the spatial domain at each exploration stage. Demonstrating its utility with both spatially resolved transcriptomics and single-cell RNA sequencing data from the Allen Brain Cell Atlas, GeneSurfer effectively identifies and annotates localized transcriptome-wide co-expression, providing biological insights and facilitating hypothesis generation and validation.
-
Prediction Model for Therapeutic Responses in Ovarian Cancer Patients using Paclitaxel-resistant Immune-related lncRNAs.
BACKGROUND: Ovarian cancer (OC) is the deadliest malignant tumor in women with a poor prognosis due to drug resistance and lack of prediction tools for therapeutic responses to anti- cancer drugs. OBJECTIVE: The objective of this study was to launch a prediction model for therapeutic responses in OC patients. METHODS: The RNA-seq technique was used to identify differentially expressed paclitaxel (PTX)- resistant lncRNAs (DE-lncRNAs). The Cancer Genome Atlas (TCGA)-OV and ImmPort database were used to obtain immune-related lncRNAs (ir-lncRNAs). Univariate, multivariate, and LASSO Cox regression analyses were performed to construct the prediction model. Kaplan- meier plotter, Principal Component Analysis (PCA), nomogram, immune function analysis, and therapeutic response were applied with Genomics of Drug Sensitivity in Cancer (GDSC), CIBERSORT, and TCGA databases. The biological functions were evaluated in the CCLE database and OC cells. RESULTS: The RNA-seq defined 186 DE-lncRNAs between PTX-resistant A2780-PTX and PTXsensitive A2780 cells. Through the analysis of the TCGA-OV database, 225 ir-lncRNAs were identified. Analyzing 186 DE-lncRNAs and 225 ir-lncRNAs using univariate, multivariate, and LASSO Cox regression analyses, 9 PTX-resistant immune-related lncRNAs (DEir-lncRNAs) acted as biomarkers were discovered as potential biomarkers in the prediction model. Single-cell RNA sequencing (scRNA-seq) data of OC confirmed the relevance of DEir-lncRNAs in immune responsiveness. Patients with a low prediction score had a promising prognosis, whereas patients with a high prediction score were more prone to evade immunotherapy and chemotherapy and had poor prognosis. CONCLUSION: The novel prediction model with 9 DEir-lncRNAs is a valuable tool for predicting immunotherapeutic and chemotherapeutic responses and prognosis of patients with OC.
-
Single-Cell Transcriptome Analysis Reveals the Role of Pancreatic Secretome in COVID-19 Associated Multi-organ Dysfunctions.
The SARS-CoV-2 infection affects the lungs, heart, kidney, intestine, olfactory epithelia, liver, and pancreas and brings forward multi-organ dysfunctions (MODs). However, mechanistic details of SARS-CoV-2-induced MODs are unclear. Here, we have investigated the role of pancreatic secretory proteins to mechanistically link COVID-19 with MODs using single-cell transcriptome analysis. Secretory proteins were identified using the Human Protein Atlas. Gene ontology, pathway, and disease enrichment analyses were used to highlight the role of upregulated pancreatic secretory proteins (secretome). We show that SARS-CoV-2 infection shifts the expression profile of pancreatic endocrine cells to acinar and ductal cell-specific profiles, resulting in increased expression of acinar and ductal cell-specific genes. Among all the secretory proteins, the upregulated expression of IL1B, AGT, ALB, SPP1, CRP, SERPINA1, C3, TFRC, TNFSF10, and MIF was mainly associated with disease of diverse organs. Extensive literature and experimental evidence are used to validate the association of the upregulated pancreatic secretome with the coagulation cascade, complement activation, renin-angiotensinogen system dysregulation, endothelial cell injury and thrombosis, immune system dysregulation, and fibrosis. Our finding suggests the influence of an upregulated secretome on multi-organ systems such as nervous, cardiovascular, immune, digestive, and urogenital systems. Our study provides evidence that an upregulated pancreatic secretome is a possible cause of SARS-CoV-2-induced MODs. This finding may have a significant impact on the clinical setting regarding the prevention of SARS-CoV-2-induced MODs.
-
A Single-Cell Atlas of the Murine Pancreatic Ductal Tree Identifies Novel Cell Populations With Potential Implications in Pancreas Regeneration and Exocrine Pathogenesis.
BACKGROUND & AIMS: Pancreatic ducts form an intricate network of tubules that secrete bicarbonate and drive acinar secretions into the duodenum. This network is formed by centroacinar cells, terminal, intercalated, intracalated ducts, and the main pancreatic duct. Ductal heterogeneity at the single-cell level has been poorly characterized; therefore, our understanding of the role of ductal cells in pancreas regeneration and exocrine pathogenesis has been hampered by the limited knowledge and unexplained diversity within the ductal network. METHODS: We used single cell RNA sequencing to comprehensively characterize mouse ductal heterogeneity at single-cell resolution of the entire ductal epithelium from centroacinar cells to the main duct. Moreover, we used organoid cultures, injury models, and pancreatic tumor samples to interrogate the role of novel ductal populations in pancreas regeneration and exocrine pathogenesis. RESULTS: We have identified the coexistence of 15 ductal populations within the healthy pancreas and characterized their organoid formation capacity and endocrine differentiation potential. Cluster isolation and subsequent culturing let us identify ductal cell populations with high organoid formation capacity and endocrine and exocrine differentiation potential in vitro, including a Wnt-responsive population, a ciliated population, and Flrt3+ cells. Moreover, we have characterized the location of these novel ductal populations in healthy pancreas, chronic pancreatitis, and tumor samples. The expression of Wnt-responsive, interferon-responsive, and epithelial-to-mesenchymal transition population markers increases in chronic pancreatitis and tumor samples. CONCLUSIONS: In light of our discovery of previously unidentified ductal populations, we unmask potential roles of specific ductal populations in pancreas regeneration and exocrine pathogenesis. Thus, novel lineage-tracing models are needed to investigate ductal-specific populations in vivo.
-
Dynamic single-cell transcriptomic reveals the cellular heterogeneity and a novel fibroblast subpopulation in laryngotracheal stenosis.
BACKGROUND: Laryngotracheal stenosis (LTS), a pathological narrowing of the upper airway caused by excessive extracellular matrix (ECM) deposition, often leads to dyspnea and even respiratory failure. However, systematic studies addressing the specific subpopulations and their contribution to LTS development still remain underexplored. RESULTS: We collected laryngotracheal tissue at multiple time points of LTS rat model, established by injuring their laryngotracheal lining, and performed dynamic single-cell RNA sequencing (scRNA-seq) to elucidate the transcriptomic atlas of LTS development. The results showed, from the inflammatory state to the repair/fibrotic state, infiltration of immune cells such as monocyte macrophages decreased and fibroblast increased. We delineated the markers and functional status of different fibroblasts subsets and identified that fibrotic fibroblasts may originate from multiple fibroblast subpopulations, including a new subpopulation characterized by the expression of chondrogenic markers such as Ucma and Col2a1, we designated this subcluster as chondrocyte injury-related fibroblasts (CIRF). Furthermore, we categorized monocytes/macrophages into several subtypes and identified that SPP1 high macrophages represented the largest macrophage subpopulation in LTS, providing evidence to clarify the importance of SPP1 macrophages in fibrosis disease. Our findings also revealed the interactions among these cells to explore the molecular mechanism associated with LTS pathogenesis. CONCLUSIONS: Our study, for the first time, conducted dynamic scRNA-seq on LTS, revealing the cellular heterogeneity and providing a valuable resource for exploring the intricate molecular landscape of LTS. We propose CIRF may represent a tissue-specific fibroblast lineage in LTS and potentially originate from cells in the perichondrium of the trachea and transform into fibrotic fibroblasts. Integration of our study with those of other respiratory fibrotic diseases will allow for a comprehensive understanding of airway remodeling in respiratory diseases and exploring potential new therapeutic targets for their treatment.
-
A single-cell transcriptome atlas of Lueyang black-bone chicken skin.
As the largest organ of the body, the skin participates in various physiological activities, such as barrier function, sensory function, and temperature regulation, thereby maintaining the balance between the body and the natural environment. To date, compositional and transcriptional profiles in chicken skin cells have not been reported. Here, we report detailed transcriptome analyses of cell populations present in the skin of a black-feather chicken and a white-feather chicken using single-cell RNA sequencing (scRNA-seq). By analyzing cluster-specific gene expression profiles, we identified 12 cell clusters, and their corresponding cell types were also characterized. Subsequently, we characterized the subpopulations of keratinocytes, myocytes, mesenchymal cells, fibroblasts, and melanocytes. It is worth noting that we have identified a subpopulation of keratinocytes involved in pigment granule capture and a subpopulation of melanocytes involved in pigment granule deposition, both of which have a higher cell abundance in black-feather chicken compared to white-feather chicken. Meanwhile, we also compared the cellular heterogeneity features of Lueyang black-bone chicken skin with different feather colors. In addition, we also screened out 12 genes those could be potential markers of melanocytes. Finally, we validated the specific expression of SGK1, WNT5A, CTSC, TYR, and LAPTM5 in black-feather chicken, which may be the key candidate genes determining the feather color differentiation of Lueyang black-bone chicken. In summary, this study first revealed the transcriptome characteristics of chicken skin cells via scRNA-seq technology. These datasets provide valuable information for the study of avian skin characteristics and have important implications for future poultry breeding.
-
Single-cell transcriptomic atlas throughout anti-BCMA CAR-T therapy in patients with multiple myeloma.
INTRODUCTION: The emergence of chimeric antigen receptor (CAR)-T therapy targeting B cell maturation antigen (BCMA) has improved the prognosis of patients with multiple myeloma (MM); however, the majority of patients eventually experience relapse. METHODS: In this study, employing the latest single-cell RNA sequencing technology, we examined 24 bone marrow or peripheral blood samples collected throughout the course of anti-BCMA CAR-T therapy, analyzing a total of 59,725 bone marrow cells and 72,479 peripheral blood cells. RESULTS: Our findings reveal that tumor cells in relapsed patient exhibit higher expression levels of HSP90B1 and HSPA5, and demonstrate significantly enriched pathways regarding endoplasmic reticulum stress and unfolded protein response. In the analysis of T cells, we observed that patient with impaired effector function and increased expression of immune checkpoints in endogenous T cell are more susceptible to relapse. Notably, T cells from both the bone marrow microenvironment and peripheral blood share highly similar biological characteristics. DISCUSSION: Overall, this study provides a comprehensive atlas of endogenous immune cells, particularly in the relatively long term, after CAR-T therapy. It offers clinical evidence for a deeper understanding of the internal environment post CAR-T treatment and for identifying mechanisms underlying relapse.
-
Accessible chromatin maps of inflammatory bowel disease intestine nominate cell-type mediators of genetic disease risk.
Inflammatory Bowel Disease ( IBD ) is a chronic and often debilitating autoinflammatory condition, with an increasing incidence in children. Standard-of-care therapies lead to sustained transmural healing and clinical remission in fewer than one-third of patients. For children, TNFα inhibition remains the only FDA-approved biologic therapy, providing an even greater urgency to understanding mechanisms of response. Genome-wide association studies ( GWAS ) have identified 418 independent genetic risk loci contributing to IBD, yet the majority are noncoding and their mechanisms of action are difficult to decipher. If causal, they likely alter transcription factor ( TF ) binding and downstream gene expression in particular cell types and contexts. To bridge this knowledge gap, we built a novel resource: multiome-seq (tandem single-nuclei ( sn )RNA-seq and chromatin accessibility ( snATAC )-seq) of intestinal tissue from pediatric IBD patients, where anti-TNF response was defined by endoscopic healing. From the snATAC-seq data, we generated a first-time atlas of chromatin accessibility (putative regulatory elements) for diverse intestinal cell types in the context of IBD. For cell types/contexts mediating genetic risk, we reasoned that accessible chromatin will co-localize with genetic disease risk loci. We systematically tested for significant co-localization of our chromatin accessibility maps and risk variants for 758 GWAS traits. Globally, genetic risk variants for IBD, autoimmune and inflammatory diseases are enriched in accessible chromatin of immune populations, while other traits (e.g., colorectal cancer, metabolic) are enriched in epithelial and stromal populations. This resource opens new avenues to uncover the complex molecular and cellular mechanisms mediating genetic disease risk.
-
Single cell RNA sequencing approaches to cardiac development and congenital heart disease.
The development of single cell RNA sequencing technologies has accelerated the ability of scientists to understand healthy and disease states of the cardiovascular system. Congenital heart defects occur in approximately 40,000 births each year and 1 out of 4 children are born with critical congenital heart disease requiring surgical interventions and a lifetime of monitoring. An understanding of how the normal heart develops and how each cell contributes to normal and pathological anatomy is an important goal in pediatric cardiovascular research. Single cell sequencing has provided the tools to increase the ability to discover rare cell types and novel genes involved in normal cardiac development. Knowledge of gene expression of single cells within cardiac tissue has contributed to the understanding of how each cell type contributes to the anatomic structures of the heart. In this review, we summarize how single cell RNA sequencing has been utilized to understand cardiac developmental processes and congenital heart disease. We discuss the advantages and disadvantages of whole cell versus single nuclei RNA sequencing and describe the approaches to analyze the interactomes, transcriptomes, and differentiation trajectory from single cell data. We summarize the currently available single cell RNA sequencing technologies and technical aspects of performing single cell analysis and how to overcome common obstacles. We also review data from the recently published human and mouse fetal heart atlases and advancements that have occurred within the field due to the application of these single cell tools. Finally we highlight the potential for single cell technologies to uncover novel mechanisms of disease pathogenesis by leveraging findings from genome wide association studies.
-
Time-Varying Gene Expression Network Analysis Reveals Conserved Transition States in Hematopoietic Differentiation between Human and Mouse.
(1) Background: analyses of gene networks can elucidate hematopoietic differentiation from single-cell gene expression data, but most algorithms generate only a single, static network. Because gene interactions change over time, it is biologically meaningful to examine time-varying structures and to capture dynamic, even transient states, and cell-cell relationships. (2) Methods: a transcriptomic atlas of hematopoietic stem and progenitor cells was used for network analysis. After pseudo-time ordering with Monocle 2, LOGGLE was used to infer time-varying networks and to explore changes of differentiation gene networks over time. A range of network analysis tools were used to examine properties and genes in the inferred networks. (3) Results: shared characteristics of attributes during the evolution of differentiation gene networks showed a "U" shape of network density over time for all three branches for human and mouse. Differentiation appeared as a continuous process, originating from stem cells, through a brief transition state marked by fewer gene interactions, before stabilizing in a progenitor state. Human and mouse shared hub genes in evolutionary networks. (4) Conclusions: the conservation of network dynamics in the hematopoietic systems of mouse and human was reflected by shared hub genes and network topological changes during differentiation.