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A multi-omic atlas in the African turquoise killifish reveals increased glucocorticoid signaling as a hallmark of brain aging.
Aging is the leading risk factor for cognitive impairment and neurodegeneration, yet molecular changes that unfold in the brain over time, and how they drive this vulnerability, remain unclear. The naturally short-lived African turquoise killifish (Nothobranchius furzeri) offers a powerful model to understand brain aging on an accelerated timescale and test the impact of potential interventions. Here, we present a multi-omic atlas of brain aging of female and male African turquoise killifish from 2 independent genetic strains of different captive lifespans, encompassing single-nuclei RNA-seq, single nuclei ATAC-seq, and bulk ATAC-seq to capture transcriptional and regulatory changes. Interestingly, our atlas indicates that aging leads to a significant expansion of microglia numbers, regardless of sex or strain, which we independently validate using in-situ hybridization. In addition, we identify robust and conserved gene regulation changes, that are consistent with activation of glucocorticoid signalling as a hallmark (and potential driver) of vertebrate brain aging. Furthermore, pharmacological inhibition of glucocorticoid receptor activity starting at middle-age led to significant rescue of key molecular and cellular aging phenotypes. Thus, our study provides a powerful resource and framework to leverage the African turquoise killifish and rapidly uncover actionable pathways driving brain aging.
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Unified Mouse and Human Kidney Single-Cell Expression Atlas Reveal Commonalities and Differences in Disease States.
SIGNIFICANCE STATEMENT: Mouse models have been widely used to understand kidney disease pathomechanisms and play an important role in drug discovery. However, these models have not been systematically analyzed and compared. The authors characterized 18 different mouse kidney disease models at both bulk and single-cell gene expression levels and compared single-cell gene expression data from diabetic kidney disease (DKD) mice and from patients with DKD. Although single cell-level gene expression changes were mostly model-specific, different disease models showed similar changes when compared at a pathway level. The authors also found that changes in fractions of cell types are major drivers of bulk gene expression differences. Although the authors found only a small overlap of single cell-level gene expression changes between the mouse DKD model and patients, they observed consistent pathway-level changes. BACKGROUND: Mouse models have been widely used to understand kidney disease pathomechanisms and play an important role in drug discovery. However, these models have not been systematically analyzed and compared. METHODS: We analyzed single-cell RNA sequencing data (36 samples) and bulk gene expression data (42 samples) from 18 commonly used mouse kidney disease models. We compared single-nucleus RNA sequencing data from a mouse diabetic kidney disease model with data from patients with diabetic kidney disease and healthy controls. RESULTS: We generated a uniformly processed mouse single-cell atlas containing information for nearly 300,000 cells, identifying all major kidney cell types and states. Our analysis revealed that changes in fractions of cell types are major drivers of differences in bulk gene expression. Although gene expression changes at the single-cell level were mostly model-specific, different disease models showed similar changes when compared at a pathway level. Tensor decomposition analysis highlighted the important changes in proximal tubule cells in disease states. Specifically, we identified important alterations in expression of metabolic and inflammation-associated pathways. The mouse diabetic kidney disease model and patients with diabetic kidney disease shared only a small number of conserved cell type-specific differentially expressed genes, but we observed pathway-level activation patterns conserved between mouse and human diabetic kidney disease samples. CONCLUSIONS: This study provides a comprehensive mouse kidney single-cell atlas and defines gene expression commonalities and differences in disease states in mice. The results highlight the key role of cell heterogeneity in driving changes in bulk gene expression and the limited overlap of single-cell gene expression changes between animal models and patients, but they also reveal consistent pathway-level changes.
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SCNS: a graphical tool for reconstructing executable regulatory networks from single-cell genomic data.
BACKGROUND: Reconstruction of executable mechanistic models from single-cell gene expression data represents a powerful approach to understanding developmental and disease processes. New ambitious efforts like the Human Cell Atlas will soon lead to an explosion of data with potential for uncovering and understanding the regulatory networks which underlie the behaviour of all human cells. In order to take advantage of this data, however, there is a need for general-purpose, user-friendly and efficient computational tools that can be readily used by biologists who do not have specialist computer science knowledge. RESULTS: The Single Cell Network Synthesis toolkit (SCNS) is a general-purpose computational tool for the reconstruction and analysis of executable models from single-cell gene expression data. Through a graphical user interface, SCNS takes single-cell qPCR or RNA-sequencing data taken across a time course, and searches for logical rules that drive transitions from early cell states towards late cell states. Because the resulting reconstructed models are executable, they can be used to make predictions about the effect of specific gene perturbations on the generation of specific lineages. CONCLUSIONS: SCNS should be of broad interest to the growing number of researchers working in single-cell genomics and will help further facilitate the generation of valuable mechanistic insights into developmental, homeostatic and disease processes.
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Development of a relapse-related RiskScore model to predict the drug sensitivity and prognosis for patients with ovarian cancer.
BACKGROUND: Ovarian cancer (OC) is a highly aggressive malignancy in the reproductive system of women, with a high recurrence rate. The present research was designed to establish a relapse-based RiskScore model to assess the drug sensitivity and prognosis for patients with OC. METHODS: Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases were accessed to obtain relevant sample data. The single-cell atlas of primary and relapse OC was characterized using the "Seurat" package. Differentially expressed genes (DEGs) between primary and relapse samples were identified by FindMarkers function. Subsequently, univariate Cox, least absolute shrinkage and selection operator (LASSO) and stepwise regression analysis were employed to determine independent prognostic genes related to relapse in OC to establish a RiskScore model. Applying "timeROC" package, the predictive performance of RiskScore model was assessed. Drug sensitivity of different risk groups was evaluated using "pRRophetic" package. The effects of relapse-related prognostic genes on OC cells were detected with in vitro assays. RESULTS: The single-cell atlas revealed that compared to primary OC, fibroblasts were reduced but epithelial cells were increased in relapse OC. Five prognostic genes (LDHA, NOP58, NMU, KRT19, and RPS23) independently linked to relapse in OC were identified to construct a RiskScore model, which showed high robustness in the prognostic prediction for OC patients. High-risk group tended to have worse outcomes in terms of different clinical features than the low-risk group. Further, six drugs (Vinorelbine, GW-2580, S-Trityl-L-cysteine, BI-2536, CP466722, NSC-87877) were found to be correlated with the RiskScore. While the high-risk group had higher IC50 values to these drugs, the low-risk group was more sensitive to the six drugs. In addition, KRT19 silencing markedly inhibited the invasion and migration of OC cells. CONCLUSION: This study established a relapse-related RiskScore model based on five prognostic genes (LDHA, NOP58, NMU, KRT19, and RPS23), offering novel insights into the recurrence mechanisms in OC and contributing to the development of individualized treatment strategies.
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[Single-cell transcriptome analysis of multigrade glioma heterogeneity and immune microenvironment revealed potential prognostic biomarkers].
Glioma, the most common intrinsic tumor of the central nervous system, is characterized by its high incidence and poor prognosis. The aim of this study was to identify differentially expressed genes (DEGs) between glioblastoma multiforme (GBM) and low-grade glioma (LGG) to explore prognostic factors of different grades of gliomas. Single-cell transcriptome sequencing data of gliomas were collected from the NCBI Gene Expression Omnibus (GEO), which included a total of 29 097 cell samples from three datasets. For the analysis of human gliomas of different grades, 21 071 cells were obtained by filtering, and 70 genes were screened from differentially expressed genes by gene ontology (GO) analysis, Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis, from which the gene DLL3 was focused by reviewing the literature. The TCGA-based gene expression profiling interactive analysis (GEPIA) database was used to explore the survival curves of genes in LGG and GBM, and the gene expression profiling interactive analysis and tumor immune estimation resource (TIMER) database was used to study the expression of key genes in gliomas of different grades, predicting biomarkers that were closely related to immunotherapy. The cBioPortal database was used to explore the relationship between DLL3 expression and 25 immune checkpoints. Gene set enrichment analysis (GSEA) further identified pathways associated with central genes. Finally, the efficacy of biomarkers in prognosis and prediction was validated in the Chinese glioma genome atlas (CGGA). These results demonstrated that prognostic genes are associated with tumor proliferation and progression. Analysis of biological information and survival suggested that these genes might serve as a promising prognostic biomarker and as new targets for selecting therapeutic strategies.
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Mechanism and endoscopic-treatment-induced evolution of biliary non-anastomotic stricture after liver transplantation revealed by single-cell RNA sequencing.
BACKGROUND: Biliary complications, especially non-anastomotic stricture (NAS), are the main complications after liver transplantation. Insufficient sampling and no recognized animal models obstruct the investigation. Thus, the mechanisms and alterations that occur during endoscopic treatment (ET) of NAS remain unclear. METHODS: Samples were obtained with endoscopic forceps from the hilar bile ducts of NAS patients receiving continuous biliary stent implantation after diagnosis. Retrospective analysis of multiple studies indicated that the duration of ET for NAS was approximately 1-2 years. Thus, we divided the patients into short-term treatment (STT) and long-term treatment (LTT) groups based on durations of less or more than 1 year. Samples were subjected to single-cell RNA sequencing. Transcriptomic differences between STT and normal groups were defined as the NAS mechanism. Similarly, alterations from STT to LTT groups were regarded as endoscopic-treatment-induced evolution. RESULTS: In NAS, inflammation and immune-related pathways were upregulated in different cell types, with nonimmune cells showing hypoxia pathway upregulation and immune cells showing ATP metabolism pathway upregulation, indicating heterogeneity. We confirmed a reduction in bile acid metabolism-related SPP1+ epithelial cells in NAS. Increases in proinflammatory and profibrotic fibroblast subclusters indicated fibrotic progression in NAS. Furthermore, immune disorders in NAS were exacerbated by an increase in plasma cells and dysfunction of NK and NKT cells. ET downregulated multicellular immune and inflammatory responses and restored epithelial and endothelial cell proportions. CONCLUSIONS: This study reveals the pathophysiological and genetic mechanisms and evolution of NAS induced by ET, thereby providing preventive and therapeutic insights into NAS. HIGHLIGHTS: For the first time, single-cell transcriptome sequencing was performed on the bile ducts of patients with biliary complications. scRNA-seq analysis revealed distinct changes in the proportion and phenotype of multiple cell types during Nonanastomotic stricture (NAS) and endoscopic treatment. A reduction in bile acid metabolism-related SPP1+ epithelial cells and VEGFA+ endothelial cells, along with explosive infiltration of plasma cells and dysfunction of T and NK cells in NAS patients. SPP1+ macrophages and BST2+ T cells might serve as a surrogate marker for predicting endoscopic treatment.
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VirusCircBase: a database of virus circular RNAs.
Circular RNAs (circRNAs) are covalently closed long noncoding RNAs critical in diverse cellular activities and multiple human diseases. Several cancer-related viral circRNAs have been identified in double-stranded DNA viruses (dsDNA), yet no systematic study about the viral circRNAs has been reported. Herein, we have performed a systematic survey of 11 924 circRNAs from 23 viral species by computational prediction of viral circRNAs from viral-infection-related RNA sequencing data. Besides the dsDNA viruses, our study has also revealed lots of circRNAs in single-stranded RNA viruses and retro-transcribing viruses, such as the Zika virus, the Influenza A virus, the Zaire ebolavirus, and the Human immunodeficiency virus 1. Most viral circRNAs had reverse complementary sequences or repeated sequences at the flanking sequences of the back-splice sites. Most viral circRNAs only expressed in a specific cell line or tissue in a specific species. Functional enrichment analysis indicated that the viral circRNAs from dsDNA viruses were involved in KEGG pathways associated with cancer. All viral circRNAs presented in the current study were stored and organized in VirusCircBase, which is freely available at http://www.computationalbiology.cn/ViruscircBase/home.html and is the first virus circRNA database. VirusCircBase forms the fundamental atlas for the further exploration and investigation of viral circRNAs in the context of public health.
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SNAC: A Single-Nuclei Atlas of Capsid Distribution in Nonhuman Primate Eye.
Adeno-associated virus (AAV) vectors have emerged as the leading gene therapy vehicle due to their favorable safety profile and sustained payload expression. Approved therapies such as voretigene neparvovec (Luxturna) and omnasemnogene abeparvovec (Zolgensma) rely on the tropism of natural AAV variants. The majority of discovered natural AAVs and engineered AAV capsids have not been comprehensively profiled for their biodistribution, especially at single-cell resolution. Recent advances in single nuclei sequencing can enable further refinement of AAV cell-type specificity and reduce off-target effects. However, low levels of transduction and muted sensitivity of current single-cell detection methods make screening pooled capsids at single-cell resolution challenging. Here, we develop SNAC (Single-Nuclei Atlas of Capsid distribution), an improved method for single-nuclei profiling of AAV transduction at multiplex scale. We provide proof of concept using the nonhuman primate eye as a model system, showing that we can accurately identify and quantify vector expression in all major retinal cell types. Furthermore, the ranking of capsids by SNAC agrees with that from pre-established tissue sampling protocols. Our method promises to reduce the time, effort, and cost of accurate cell-type-specific profiling of AAV capsids.
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Integrated Single-Cell Transcriptomic Atlas of Human Kidney Endothelial Cells.
KEY POINTS: We created a comprehensive reference atlas of normal human kidney endothelial cells. We confirmed that endothelial cell types in the human kidney were also highly conserved in the mouse kidney. BACKGROUND: Kidney endothelial cells are exposed to different microenvironmental conditions that support specific physiologic processes. However, the heterogeneity of human kidney endothelial cells has not yet been systematically described. METHODS: We reprocessed and integrated seven human kidney control single-cell/single-nucleus RNA sequencing datasets of >200,000 kidney cells in the same process. RESULTS: We identified five major cell types, 29,992 of which were endothelial cells. Endothelial cell reclustering identified seven subgroups that differed in molecular characteristics and physiologic functions. Mapping new data to a normal kidney endothelial cell atlas allows rapid data annotation and analysis. We confirmed that endothelial cell types in the human kidney were also highly conserved in the mouse kidney and identified endothelial marker genes that were conserved in humans and mice, as well as differentially expressed genes between corresponding subpopulations. Furthermore, combined analysis of single-cell transcriptome data with public genome-wide association study data showed a significant enrichment of endothelial cells, especially arterial endothelial cells, in BP heritability. Finally, we identified M1 and M12 from coexpression networks in endothelial cells that may be deeply involved in BP regulation. CONCLUSIONS: We created a comprehensive reference atlas of normal human kidney endothelial cells that provides the molecular foundation for understanding how the identity and function of kidney endothelial cells are altered in disease, aging, and between species. Finally, we provide a publicly accessible online tool to explore the datasets described in this work (https://vascularmap.jiangnan.edu.cn).
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Single-cell analysis reveals the regulatory role of ECT2 in HPV-driven cervical carcinogenesis process.
BACKGROUND: This study attempted to investigate possible mechanisms of human papillomavirus (HPV) infection from high-grade squamous intraepithelial lesions (HSIL) to cervical cancer (CESC) by combining bulk RNA-seq and scRNA-seq data of CESC. METHODS: scRNA-seq (GSE208653) and bulk RNA-seq (GSE63514) of CESC were obtained from GEO database. scRNA-seq and clinical data were obtained from the The Cancer Genome Atlas (TCGA) database. mRNA sequencing was performed on cervical samples from HPV-infected patients with histologically normal inflammation (IA), HSIL patients, and CESC patients. Cell-cell communication and hdWGCNA analysis were performed on GSE208653 data. Differentially upregulated genes in HSIL.vs.IA and CESC.vs.HSIL were identified. The intersection of them with the module genes were obtained by hdWGCNA to obtain intersection genes. Immunological analysis was conducted using CIBERSORT in TCGA data. Using MsigDB database and literature research, key signaling pathways were determined, and the relationship between ECT2 and glycolysis metabolism was analyzed. RESULTS: scRNA-seq data analysis identified 13 distinct cell clusters, including B cells, epithelial cells, and cancer stem cells, etc. Cell-cell communication analysis revealed that changes in epithelial cells may affect CESC progression. Key gene ECT2 was obtained through hdWGCNA analysis and clinical sequencing analysis. Validation of multiple datasets revealed that ECT2 may participate in epithelial cell repair during the early inflammatory response to HPV infection through glycolysis, and later glycolysis became a pro-cancer condition, promoting tumor stemness and inhibiting immune activity through lactate accumulation. CONCLUSION: This study probed into relationships among HPV infection, HSIL, and CESC, and identified potential oncogene ECT2 in epithelial cells and its possible regulatory mechanisms. These findings provided new insights into understanding HPV infection and CESC progression.