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Developmental single-cell atlas of coronary vessel growth and cardiomyocyte interaction in zebrafish.
Cardiac morphogenesis requires the intricate coordination of different cell types and molecular cues. Coronary vessel formation is essential for heart development, yet how coronary vessels grow and contribute to ventricular wall formation remains poorly understood. Here, we combine high-resolution imaging and new genetic tools to systematically analyze coronary vasculature development in zebrafish at each millimeter increment from 7 to 30 mm of body length and identify cellular and molecular milestones defining four distinct coronary network developmental stages. Our data show how coronary vessels expand, pattern, specify and act as vascular scaffolds to guide cardiomyocyte growth throughout development. Manipulating coronary network growth by Vegfa signaling perturbation through gain- and loss-of-function approaches disrupts coronary vessel-cardiomyocyte interactions and cardiomyocyte expansion. To gain further resolution into these processes, we build a single-cell RNA sequencing atlas by profiling 37,554 ventricular cells across the different developmental stages and identify new coronary markers, dynamic cellular interactions, and stage-specific endothelial-cardiomyocyte crosstalk. Overall, we present the first comprehensive roadmap of coronary vessel formation and coordinated cellular and molecular interactions with the developing cardiac muscle in zebrafish.
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Comprehensive analysis of the role of immune-related PANoptosis lncRNA model in renal clear cell carcinoma based on RNA transcriptome and single-cell sequencing.
The high immune infiltration and heterogeneity of the microenvironment in clear cell renal cell carcinoma (ccRCC) result in the variability of prognosis and clinical response. While PANoptosis has strong immunogenicity and is worthy of further study. In this study, data from The Cancer Genome Atlas database was used to obtain immune-related PANoptosis lncRNAs with prognostic value. Subsequently, the role of these lncRNAs in cancer immunity, progression and the therapeutic response was analyzed, and a new prediction model was constructed. Additionally, we further explored the biological value of PANoptosis-related lncRNAs using single-cell data from the Gene Expression Omnibus database. PANoptosis-associated lncRNAs were significantly associated with clinical outcome, immune infiltration, antigen presentation and treatment response in ccRCC. Notably, the risk model, which is based on these immune-related PANoptosis lncRNAs, showed good predictive performance. Subsequent studies on LINC00944 and LINC02611 revealed their high expression in ccRCC and significant correlation with cancer cell migration and invasion. Single-cell sequencing further validated these results and revealed the potential association between LINC00944 and T-cell infiltration and programmed cell death. In conclusion, this study identified the role of immune-related PANoptosis lncRNAs in ccRCC and provided a new risk stratification approach. Furthermore, it highlights the potential of LINC00944 as a prognostic biomarker.
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Integrated Epigenetic Mapping of Human and Mouse Salivary Gene Regulation.
Significant effort has been applied to identify the genome-wide gene expression profiles associated with salivary gland development and pathophysiology. However, relatively little is known about the regulators that control salivary gland gene expression. We integrated data from DNase1 digital genomic footprinting, RNA-seq, and gene expression microarrays to comprehensively characterize the cis- and trans-regulatory components controlling gene expression of the healthy submandibular salivary gland. Analysis of 32 human tissues and 87 mouse tissues was performed to identify the highly expressed and tissue-enriched transcription factors driving salivary gland gene expression. Following RNA analysis, protein expression levels and subcellular localization of 39 salivary transcription factors were confirmed by immunohistochemistry. These expression analyses revealed that the salivary gland highly expresses transcription factors associated with endoplasmic reticulum stress, human T-cell lymphotrophic virus 1 expression, and Epstein-Barr virus reactivation. DNase1 digital genomic footprinting to a depth of 333,426,353 reads was performed and utilized to generate a salivary gland gene regulatory network describing the genome-wide chromatin accessibility and transcription factor binding of the salivary gland at a single-nucleotide resolution. Analysis of the DNase1 gene regulatory network identified dense interconnectivity among PLAG1, MYB, and 13 other transcription factors associated with balanced chromosomal translocations and salivary gland tumors. Collectively, these analyses provide a comprehensive atlas of the cis- and trans-regulators of the salivary gland and highlight known aberrantly regulated pathways of diseases affecting the salivary glands.
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Mapping neutrophil fate and function in ischemic stroke: A single-cell roadmap for translational insights.
BACKGROUND: Ischemic stroke (IS) accounts for 71 % of all strokes, whose diagnosis and prognosis require further exploration. Neutrophil extracellular traps (NETs) are produced by neutrophils, and there is already evidence that NETs play a role in IS, but further studies about crosstalk between immune cells, pathways and NETs are still needed. MATERIALS AND METHODS: To assess the expression of neutrophil extracellular traps (NETs), we utilized single sample Gene Set Enrichment Analysis. Stroke-associated NETs genes (SN genes) were identified through differential expression analysis combined with Weighted Correlation Network Analysis. Based on these SN genes, we developed a sophisticated diagnostic model incorporating machine learning techniques. Furthermore, we constructed a single-cell atlas of neutrophil transitions in post-stroke mice. Validation of our findings was conducted both in vitro and in vivo. In vitro, we employed oxygen-glucose deprivation (OGD) experiments to simulate ischemic conditions, facilitating the assessment of NETs formation and monitoring alterations in SN genes expression within neutrophils. In vivo, validation involved tracking changes in peripheral blood levels of these genes in a mouse model of transient middle cerebral artery occlusion (tMCAO) post-cerebral ischemia. RESULTS: A detailed single-cell landscape depicting the dynamic transitions of neutrophils within the cerebral microenvironment post-stroke has been elaborately constructed. NETs displayed significant differential expression between IS and control groups in peripheral blood, correlating strongly with the activities of neutrophils and macrophages. Pathways pertinent to IS and NETs were delineated. A diagnostic model incorporating two SN genes was developed, demonstrating an AUC greater than 0.98, effectively pinpointing the hyperacute phase of IS. Additionally, the ceRNA networks concerning IS and NETs were mapped out. In vitro validation with oxygen-glucose deprivation (OGD) experiments revealed marked changes in NET formation and SN genes expression in neutrophils, corroborating our computational predictions. In vivo validation using a mouse transient middle cerebral artery occlusion (tMCAO) model confirmed significant changes in peripheral blood levels of F12 and PLXDC2 after cerebral ischemia, proving the excellent predictive value of these markers for IS. CONCLUSION: This study elucidates the complex roles and dynamic changes of neutrophils within the cerebral microenvironment of mice from 3 h to 3 days following stroke onset. We have identified key genes, immune cells, signaling pathways, and ceRNA networks implicated in the formation of NETs in IS. Our study constructed a robust diagnostic model capable of detecting the hyperacute phase of IS, with an AUC value greater than 0.98. The inclusion of experimental validation for the SN genes F12 and PLXDC2 not only corroborates our model's predictive accuracy but also underscores its potential utility in clinical settings. These findings offer promising avenues for improving early diagnosis and potentially guiding therapeutic strategies in IS.
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LncCE: Landscape of Cellularly-elevated lncRNAs in Single Cells Across Normal and Cancer Tissues.
Long non-coding RNAs (lncRNAs) have emerged as significant players in maintaining the morphology and function of tissues and cells. The precise regulatory effectiveness of lncRNAs is closely associated with their spatial expression patterns across tissues and cells. Here, we propose the Cellularly-Elevated LncRNA (LncCE) resource to systematically explore cellularly-elevated (CE) lncRNAs across normal and cancer tissues at single-cell resolution. LncCE encompasses 87,946 entries of CE lncRNAs of 149 cell types by analyzing 181 single-cell RNA sequencing datasets, involving 20 fetal normal tissues, 59 adult normal tissues, 32 adult cancer types, and 5 pediatric cancer types. Two main search options are provided via a given lncRNA name or cell type. The results emphasize both qualitative and quantitative expression features of lncRNAs across different cell types, their co-expression with protein-coding genes, and their involvement in biological functions. In particular, LncCE provides quantitative visualizations of lncRNA expression changes in cancers compared to control samples, as well as clinical associations with patients' overall survival. Together, LncCE offers an extensive, quantitative, and user-friendly interface to create a CE expression atlas for lncRNAs across normal and cancer tissues at the single-cell level. The LncCE database is available at http://bio-bigdata.hrbmu.edu.cn/LncCE.
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Identification of Breast Cancer Subtypes by Integrating Genomic Analysis with the Immune Microenvironment.
Objectives: We aim to identify the breast cancer (BC) subtype clusters and the crucial gene classifier prognostic signatures by integrating genomic analysis with the tumor immune microenvironment (TME). Methods: Data sets of BC were derived from the Cancer Genome Atlas (TCGA), METABRIC, and Gene Expression Omnibus (GEO) databases. Unsupervised consensus clustering was carried out to obtain the subtype clusters of BC patients. Weighted gene coexpression network analysis (WGCNA), least absolute shrinkage and selection operator (LASSO), and univariate and multivariate regression analysis were employed to obtain the gene classifier signatures and their biological functions, which were validated by the BC dataset from the METABRIC database. Additionally, to evaluate the overall survival rates of BC patients, Kaplan-Meier survival analysis was carried out. Moreover, to assess how BC subtype clusters are related to the TME, single-cell analysis was performed. Finally, the drug sensitivity and the immune cell infiltration for different phenotypes of BC patients were also calculated by the CIBERSORT and ESTIMATE algorithms. Results : TCGA-BC samples were divided into three subtype clusters, S1, S2, and S3, among which the prognosis of S2 was poor and that of S1 and S3 were better. Three key pathways and 10 crucial prognostic-related gene signatures are screened. Finally, single-cell analysis suggests that S1 samples have the most types of immune cells, S2 with more sensitivity to tumor treatment drugs are enriched with more neutrophils, and more multilymphoid progenitor cells are involved in subtype cluster S3. Conclusions: Our novelty was to identify the BC subtype clusters and the gene classifier signatures employing a large-amount dataset combined with multiple bioinformatics methods. All of the results provide a basis for clinical precision treatment of BC.
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As a potential predictor of pan-cancer, UBE2S is related to tumor-associated macrophage infiltration.
Background: At the pan-cancer level, exploring the expression and prognostic significance of a gene, such as UBE2S, will help to gain insight into the role of the gene and its feasibility for cancer screening, prognosis assessment and even gene therapy. Methods: The Cancer Genome Atlas, Human Protein Atlas, Kaplan-Meier, Tumor Immunology Estimation Resource and other databases were used to analyze the expression of UBE2S at the pan-cancer level, its prognosis and the role of the immune microenvironment. Immunohistochemistry samples of tumor tissue collected in our clinic were taken as verification. Results: UBE2S is significantly overexpressed in pan-cancer and is closely associated with malignant clinical features, poor prognosis and tumor-associated macrophages. Conclusion: UBE2S may be a potential diagnostic and prognostic marker for pan-cancer and is associated with tumor-associated macrophages.
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Single-Cell Sequencing to Identify Six Heat Shock Protein (HSP) Genes-Mediated Progression Subtypes of Clear Cell Renal Cell Carcinoma.
BACKGROUND: Heat shock proteins (HSPs) are widely involved in tumor occurrence and development and are prognostic markers for multiple tumors. However, the role of HSPs in clear cell renal cell carcinoma (ccRCC) remains unclear. METHODS: We used Cytoscape to identify hub genes in the ccRCC single-cell sequencing data set from the Gene Expression Omnibus (GEO) data repository. We identified subtypes, C1 and C2, of The Cancer Genome Atlas (TCGA) patients based on the expression of hub genes using unsupervised consensus clustering. Principal component analysis (PCA) was used to verify the clustering differences, and Kaplan-Meier (K-M) estimate was used to verify the survival differences between C1 and C2 patients. We used TIMER 2.0 and CIBERSORT to evaluate the immune cell infiltration of HSP genes and C1 and C2 patients. The R package "pRRophetic" was used to evaluate the sensitivity in C1 and C2 patients to the four first-line treatment drugs. RESULTS: We identified six hub genes (HSP90AA1, HSPH1, HSPA1B, HSPA8, and HSPA1A) encoding HSP, five of which were significantly downregulated in TCGA group, and four had a protective effect on prognosis (p <0.05). Survival analysis showed that C1 patients had a better overall survival (p <0.001). TIMER 2.0 analysis showed that three HSP genes were significantly correlated with the infiltration of CD4+ T cells and CD4+ Th1 cells (|cor|>0.5, p<0.001). CIBERSORT showed significant differences in multiple infiltrating immune cells between C1 and C2 patients. Meanwhile, the expression of PD1 was significantly lower in C1 patients than in C2 patients, and the expression of PDL1 is the another way around. Drug sensitivity analysis showed that C1 patients were more sensitive to sorafenib, pazopanib, and axitinib (p <0.001). CONCLUSION: Our research revealed two molecular subtypes of ccRCC based on 6 HSP genes, and revealed significant differences between the two subtypes in terms of clinical prognosis, immune infiltration, and drug sensitivity.
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Functional KCC2 expression marks an evolutionarily conserved population of early-maturing interneurons in the perinatal cortex.
The developmental shift from depolarizing to hyperpolarizing GABA responses is a pivotal step in the maturation of GABAergic transmission and cortical circuits; classically documented in principal neurons during the first postnatal week in the mouse cortex. Surprisingly, whether maturation of GABA-mediated responses follows the same temporal pattern in cortical interneurons (INs) remains unresolved. Leveraging an array of methods, a high-resolution cortical development mouse atlas and single-cell RNA sequencing, we identify and comprehensively characterize a population of early-maturing cortical INs in mice, distinguished by KCC2 expression at embryonic stages and concomitant hyperpolarizing GABAA responses at birth. These early KCC2-expressing INs exhibit precocious intrinsic excitability, synaptic integration, and dendritic complexity at birth, contrasting delayed maturation in principal neurons and other INs. Spatial transcriptomics and differential gene expression (DGE) analyses reveal early KCC2-expressing INs localize predominantly to layer 5, express somatostatin, and show upregulation of synaptogenic genes, consistent with the recorded elevated synaptic activity. Crucially, evolutionary conservation of early KCC2-expressing INs in humans was demonstrated with analogous genetic profiles enriched for signaling and synaptic maturation pathways. This work resolves a critical gap in developmental neurobiology, demonstrating heterogenous GABAergic functional maturation within IN subpopulations and establishing KCC2 as a marker of early-maturing INs.
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Telomere-based Risk Model for Prognosis Prediction in Clear Cell Renal Cell Carcinoma.
INTRODUCTION: Telomeres have become extensively studied in renal cell carcinoma (RCC), and this study aims to identify relevant diagnostic biomarkers in the predominant RCC subtype, clear cell RCC (ccRCC). MATERIALS AND METHODS: This study retrieved telomere-related genes from the TelNet database and integrated data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) to calculate telomere enrichment scores using single-sample gene set enrichment analysis (ssGSEA). Weighted gene co-expression network analysis (WGCNA) and differential expression analysis were then applied to identify candidate genes, which were further refined through protein-protein interaction (PPI) network construction and two machine learning methods: least absolute shrinkage and selection operator (LASSO) regression and support vector machine-recursive feature elimination (SVM-RFE). The associations between the identified feature genes and immune cell infiltration were subsequently evaluated using CIBERSORT and ESTIMATE. Furthermore, single-cell analysis was employed to determine the highly expressed genes in different cell clusters. Finally, using a ccRCC cell line, quantitative real-time PCR, wound healing, and Transwell assays were performed to validate the expression and potential biological functions of the selected key genes. RESULTS: A higher telomere score was observed in ccRCC. The common genes from the DEGs and the gene modules were mainly enriched in cell division- and senescence-related pathways. Moreover, six genes (ASPM, CENPF, CEP55, MELK, BUB1, and EXO1) were identified as feature genes with satisfactory diagnostic efficacy and high expression in ccRCC; they were positively correlated with most immune cells and highly expressed in T cells. Notably, CEP55 knockdown suppressed the migration and invasion of ccRCC cells. DISCUSSION: Our present study, based on the data from the public databases, unraveled 6 genes with diagnostic efficacy in ccRCC, which may aid the development of a relevant future diagnostic method in ccRCC. CONCLUSION: This study identified six telomere-related genes with high expression and strong diagnostic value in ccRCC, highlighting their association with immune infiltration and potential as diagnostic and therapeutic targets.