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Page 170 of 493-
Implicating Gene and Cell Networks Responsible for Differential COVID-19 Host Responses via an Interactive Single Cell Web Portal.
Numerous studies have provided single-cell transcriptome profiles of host responses to SARS-CoV-2 infection. Critically lacking however is a datamine that allows users to compare and explore cell profiles to gain insights and develop new hypotheses. To accomplish this, we harmonized datasets from COVID-19 and other control condition blood, bronchoalveolar lavage, and tissue samples, and derived a compendium of gene signature modules per cell type, subtype, clinical condition, and compartment. We demonstrate approaches to probe these via a new interactive web portal (http://toppcell.cchmc.org/COVID-19). As examples, we develop three hypotheses: (1) a multicellular signaling cascade among alternatively differentiated monocyte-derived macrophages whose tasks include T cell recruitment and activation; (2) novel platelet subtypes with drastically modulated expression of genes responsible for adhesion, coagulation and thrombosis; and (3) a multilineage cell activator network able to drive extrafollicular B maturation via an ensemble of genes strongly associated with risk for developing post-viral autoimmunity.
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Spatial transcriptional mapping of the human nephrogenic program.
Congenital abnormalities of the kidney and urinary tract are among the most common birth defects, affecting 3% of newborns. The human kidney forms around a million nephrons from a pool of nephron progenitors over a 30-week period of development. To establish a framework for human nephrogenesis, we spatially resolved a stereotypical process by which equipotent nephron progenitors generate a nephron anlage, then applied data-driven approaches to construct three-dimensional protein maps on anatomical models of the nephrogenic program. Single-cell RNA sequencing identified progenitor states, which were spatially mapped to the nephron anatomy, enabling the generation of functional gene networks predicting interactions within and between nephron cell types. Network mining identified known developmental disease genes and predicted targets of interest. The spatially resolved nephrogenic program made available through the Human Nephrogenesis Atlas (https://sckidney.flatironinstitute.org/) will facilitate an understanding of kidney development and disease and enhance efforts to generate new kidney structures.
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Single-cell transcriptomic analysis of chondrocytes in cartilage and pathogenesis of osteoarthritis.
Chondrocyte is considered the only cell type in cartilage. However, the cell heterogeneity of chondrocytes in human articular cartilage is still not well defined, which hinders our understanding of the pathogenesis of osteoarthritis (OA). Here, we constructed a single-cell transcriptomic atlas of chondrocytes in healthy cartilage and identified nine chondrocyte subsets including homeostatic chondrocytes, proliferate fibrochondrocytes, and hypertrophic chondrocytes (HTC). Interestingly, we identified two distinct HTC subpopulations, among which HTC-1 specifically expressed genes associated with apoptosis and programmed cell death. We identified two main trajectories of chondrocytes, one of which differentiates into fibrochondrocytes, while the other terminates in apoptosis. Comparison of chondrocyte subsets between healthy and OA cartilage showed that proliferate fibrochondrocytes and HTC-1 expanded in OA patients, whereas homeostatic chondrocytes decreased. Interestingly, we discovered an OA-specific proliferate fibrochondrocyte subset that may contribute to the development of OA via inflammation. In summary, this study significantly enhanced our understanding of cell heterogeneity of chondrocytes in articular cartilage and provides insight into the pathogenesis of OA.
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Integrated Single-Cell and Spatial Transcriptomics Reveal Cell-Type-Specific Immune Regulatory Networks in Maize Responding to Southern Corn Rust.
Southern corn rust (SCR), caused by Puccinia polysora Underw. (P. polysora), poses a significant threat to maize production, yet the cell-type-specific defense mechanisms remain insufficiently characterized. To address this, we integrated single-nucleus RNA sequencing (snRNA-seq) and spatial transcriptomic sequencing (stRNA-seq) to elucidate the cell-type-specific transcriptional dynamics in maize leaves during early infection with P. polysora. Analyses at 24 and 48 h post infection (hpi) revealed eight major cell types and highlighted key defense responses, which are primarily initiated in the mesophyll and epidermal cells 24 hpi. Notably, the cell-type-specific activation of RLPs/RLKs and jasmonic acid was observed. Functional defense modules were activated in specific cell types at 24 hpi, with pseudotime and cell-cell communication analyses further uncovering immune-related cellular dynamics. Importantly, multi-omics analysis identified core DEGs across critical cell types and time points. Functional validation through virus-induced gene silencing (VIGS) demonstrated that silencing the ZmXET1 gene significantly reduced disease severity and pathogen biomass, while silencing the positive regulator ZmRBG, increased susceptibility. This study provides a high-resolution spatiotemporal atlas of maize defense against P. polysora, identifying ZmXET1 as a key susceptibility factor and ZmRBG as a resistance component, thereby offering valuable targets for disease resistance breeding.
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Cell type diversity in scallop adductor muscles revealed by single-cell RNA-Seq.
Studies on cell atlas in marine invertebrates provide a better understanding of cell types, stem cell maintenance, and lineages of cell differentiation. To investigate the molecular features of various cell types in molluscan muscles, we performed single-cell RNA sequencing (scRNA-seq) to map cell types in scallop adductor muscles. We uncovered the cell type-specific features of 20 cell clusters defined by the expression of multiple specific molecular markers. These cell clusters are mainly classified into four broad classes, including mesenchymal stem cells, muscle cells, neurons, and haemolymph cells. In particular, we identified a diverse repertoire of neurons in the striated adductor muscle, but not in the smooth muscle. We further reconstructed the cell differentiation events using all the cell clusters by single-cell pseudotemporal trajectories. By integrating dual BrdU-PCNA immunodetection, neuron-specific staining and electron microscopy observation, we showed the spatial distribution of mesenchymal stem cells and neurons in striated adductor muscle of scallops. The present findings will not only be useful to address the cell type-specific gene expression profiles in scallop muscles, but also provide valuable resources for cross-species comparison of marine organisms.
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Expression characteristics, molecular mechanisms, and clinical significance of DICER1 in breast cancer.
OBJECTIVE: This study aims to investigate the expression patterns, molecular mechanisms, and clinical significance of DICER1 in breast cancer (BRCA), providing new biomarkers and therapeutic targets for prognosis assessment and personalized treatment of breast cancer. METHODS: By integrating RNA-seq data, clinical data, and tumor mutation burden (TMB) data from The Cancer Genome Atlas (TCGA) database, as well as single-cell transcriptomic data from the Gene Expression Omnibus (GEO) database, we analyzed the expression characteristics of DICER1 in breast cancer. Weighted gene co-expression network analysis (WGCNA) was used to identify gene modules associated with the breast cancer phenotype, and gene set enrichment analysis (GSEA) was performed to explore their biological functions. Cellular experiments were conducted to verify the effects of DICER1 on the proliferation, migration, and invasion of breast cancer cells. A nomogram model was constructed based on clinical data to evaluate its prognostic value. Additionally, the effects of DICER1 expression levels on drug sensitivity and the tumor immune microenvironment were analyzed. RESULTS: The expression of DICER1 in breast cancer tissues was significantly lower than that in normal tissues, and was significantly correlated with tumor stage, T stage, and TMB levels. The expression level of DICER1 was an independent prognostic factor for breast cancer patients. The nomogram model based on DICER1 expression and clinical features demonstrated good discriminative ability in predicting patient survival probability. Drug sensitivity analysis revealed that the high-expression group of DICER1 exhibited higher sensitivity to multiple drugs. Immune microenvironment analysis indicated that the low-expression group of DICER1 had higher immune-suppressive features and immune exclusion scores, suggesting potential resistance to immunotherapy. Single-cell transcriptomic analysis revealed heterogeneous expression of DICER1 in breast cancer cell populations and its potential role in cell-cell communication. CONCLUSION: DICER1 plays an important regulatory role in breast cancer, with its expression level closely related to tumor progression, the immune microenvironment, and drug sensitivity. DICER1 has the potential to become an important biomarker for prognosis assessment in breast cancer and may provide new targets for future immunotherapy and targeted therapy.
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Integrative Multi-Omics Analysis Identifies Thrombosis-Associated Molecular Features Linked to Germline Susceptibility and Immune Cell Communication in Gastric Cancer.
Emerging evidence indicates that coagulation-related molecular programs are associated with thrombosis, tumor progression, and molecular dysregulation in gastric cancer (GC). However, thrombosis-associated molecular features in GC and their potential links to inherited susceptibility remain insufficiently understood. Integrated analyses of transcriptomic data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets were performed to identify thrombosis-associated genes and establish a machine learning-based prognostic signature. Genome-wide association study (GWAS), expression quantitative trait loci (eQTL), transcriptome-wide association study (TWAS), and Mendelian randomization (MR) analyses were conducted to investigate susceptibility-associated transcriptional programs in GC. Functional assays were used to evaluate candidate genes associated with malignant phenotypes. Single-cell RNA sequencing (scRNA-seq) and cell-cell communication analyses were further performed to characterize cell-type-specific expression patterns and potential intercellular interactions. A total of 22 differentially expressed thrombosis-associated genes were identified, and a prognostic signature comprising 14 genes was established. The signature stratified patients into high- and low-risk groups and showed prognostic performance in both the training and validation cohorts. Integrative GWAS, eQTL, and TWAS analyses identified susceptibility-associated transcriptional programs that were positively correlated with the thrombosis-associated risk score. Silencing ACTN2 and CRYAB significantly reduced GC cell migration and invasion. scRNA-seq analysis revealed relatively high CRYAB expression in neutrophils, and CellChat analysis suggested potential neutrophil-B cell interactions involving COLLAGEN-related signaling. This integrative multi-omics study identified a thrombosis-associated molecular signature linked to prognosis and germline susceptibility-associated transcriptional programs in GC. ACTN2 and CRYAB may represent candidate genes associated with GC cell migration and invasion, while single-cell analysis suggested potential immune-related communication features.
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Mapping Genetic Susceptibility to Stenosis in the Proximal Airway.
OBJECTIVES: Recent translational scientific efforts in subglottic stenosis (SGS) support a disease model where epithelial alterations facilitate microbiome displacement, dysregulated immune activation, and localized fibrosis. Yet despite recent advances, the genetic basis of SGS remains poorly understood. We sought to identify candidate risk genes associated with an SGS phenotype, investigate their biological function, and identify the cell types enriched for their expression. METHODS: The Online Mendelian Inheritance in Man (OMIM) database was queried for single gene variants associated with an SGS phenotype. The functional intersections and molecular roles of the identified genes were explored using pathway enrichment analysis (PEA) computational methods. Cellular localization of the candidate risk genes was measured via transcriptional quantification in an established single cell RNA sequencing (scRNA-seq) atlas of the proximal airway. RESULTS: Twenty genes associated with SGS phenotype were identified. PEA resulted in 24 significantly enriched terms including "cellular response to TGF-β," "epithelial-to-mesenchymal transition," and "adherens junctions." Mapping the 20 candidate risk genes to the scRNA-seq atlas found 3 (15%) genes were enriched in epithelial cells, 3 (15%) in fibroblasts, and 3 (15%) in endothelial cells. 11 (55%) genes were expressed ubiquitously among tissue types. Interestingly, immune cells were not significantly enriched for candidate risk genes. CONCLUSION: We identify and provide biologic context for 20 genes associated with fibrotic disease of the proximal airway and form the foundation for future detailed genetic study. LEVEL OF EVIDENCE: N/A Laryngoscope, 133:3049-3056, 2023.
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ZNF556 serves as an immunological and prognostic biomarker in colon adenocacinoma.
OBJECTIVES: Colon adenocarcinoma (COAD) is one of the most common and deadliest digestive tract malignancies, but its diagnostic and prognostic markers still need further exploration as targets for risk stratification and treatment response. METHODS: COAD cohorts were retrieved from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. Differences in gene expression were analyzed using relevant R packages, followed by gene function enrichment analysis and Weighted Gene Co-expression Network Analysis (WGCNA). Next, the Kaplan-Meier method and LASSO regression analysis were employed to screen for prognostic signature genes associated with the survival of COAD patients. Furthermore, comprehensive analyses were conducted to assess the role of this signature gene in immunomodulation and genetic mutation. Single-cell RNA sequencing (scRNA-seq) analyses were performed using the GSE166555 dataset. Additionally, CCK-8, colony formation, wound healing and Transwell assays were used to evaluate the biological functions of the signature gene in COAD cells. RESULTS: Differential expression analysis and WGCNA led to the identification of 156 candidate genes, which were primarily enriched in functional pathways including the cell cycle, P53 signaling pathway, and cellular senescence. Subsequently, LASSO regression combined with survival analysis screened out ZNF556 as a characteristic prognostic gene for COAD. The expression level of ZNF556 correlates with the infiltration of plasma cells, mast cells, and monocytes, and is further associated with the expression of multiple immune checkpoint genes. Additionally, gene mutation analysis revealed a significant correlation between mutant TP53 and ZNF556 expression. ScRNA-seq analysis further indicated that, in the context of COAD, ZNF556 may exert a key regulatory role in the differentiation of epithelial cells, thereby facilitating tumor initiation and progression. Furthermore, subsequent experiments confirmed the cancer-promoting effect of ZNF556, whose expression was positively regulated by mutant TP53. CONCLUSION: ZNF556 was highly up-regulated in COAD tissues, with high expression correlating with poor prognosis and difference in TIICs. It functions as a diagnostic, immunological and prognostic biomarker for COAD, and a potential therapeutic target.
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Cella: 3D data visualization for plant single-cell transcriptomics in Blender.
AIMS: Recent advancements in single-cell transcriptomics have facilitated the possibility of acquiring vast amounts of data at single-cell resolution. This development has provided a broader and more comprehensive understanding of complex biological processes. The growing datasets require a visualization tool that transforms complex data into an intuitive representation. To address this challenge, we have utilized an open-source 3D software Blender to design Cella, a cell atlas visualization tool, which transforms data into 3D heatmaps that can be rendered into image libraries. Our tool is designed to support especially research on plant development. DATA RESOURCES GENERATED: To validate our method, we have created a 3D model representing the Arabidopsis thaliana root meristem and mapped an existing single-cell RNA-seq dataset into the 3D model. This provided a user-friendly visual representation of the expression profiles of 21,489 genes from two perspectives (42,978 images). UTILITY OF THE RESOURCE: This approach is not limited to single-cell RNA-seq data of the Arabidopsis root meristem. We provide detailed step-by-step instructions to generate 3D models and a script that can be customized to project data onto different tissues. KEY RESULTS: Our tool provides a proof-of-concept method for how increasingly complex single-cell RNA-seq datasets can be visualized in a simple and cohesive manner.