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Integrative analysis of single-cell and bulk RNA sequencing unveils the senescence landscape in ischemic stroke.

ABSTRACT: Ischemic stroke (IS) is a fatal neurological disease that occurs when the blood flow to the brain is disrupted, leading to brain tissue damage and functional impairment. Cellular senescence, a vital characteristic of aging, is associated with a poor prognosis for IS. This study explores the potential role of cellular senescence in the pathological process following IS by analyzing transcriptome data from multiple datasets (GSE163654, GSE16561, GSE119121, and GSE174574). By using bioinformatics methods, we identified hub-senescence-related genes such as ANGPTL4, CCL3, CCL7, CXCL16, and TNF and verified them using quantitative reverse transcription polymerase chain reaction. Further analysis of single-cell RNA sequencing data suggests that MG4 microglial is highly correlated with cellular senescence in MCAO, and might play a crucial role in the pathological process after IS. Additionally, we identified retinoic acid as a potential drug for improving the prognosis of IS. This comprehensive investigation of cellular senescence in various brain tissues and peripheral blood cell types provides valuable insights into the underlying mechanisms of the pathology of IS and identifies potential therapeutic targets for improving patient outcomes.

SUBMITTER: Fu L

PROVIDER: S-EPMC10333064 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature

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Integrative analysis of single-cell and bulk RNA sequencing unveils the senescence landscape in ischemic stroke.

Fu Longhui L Yu Beibei B Zhang Yongfeng Y Cao Shuai S Lv Boqiang B Tian Yunze Y Chen Huangtao H Yang Shijie S Hu Yutian Y Hua Jinghua J Ren Pengyu P Li Jianzhong J Gong Shouping S

Aging 20230628 12

Ischemic stroke (IS) is a fatal neurological disease that occurs when the blood flow to the brain is disrupted, leading to brain tissue damage and functional impairment. Cellular senescence, a vital characteristic of aging, is associated with a poor prognosis for IS. This study explores the potential role of cellular senescence in the pathological process following IS by analyzing transcriptome data from multiple datasets (GSE163654, GSE16561, GSE119121, and GSE174574). By using bioinformatics m ...[more]

PMID: 37382646

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Project description:BackgroundThe demographic shift towards an older population presents significant challenges for kidney transplantation (KTx), particularly due to the vulnerability of aged donor kidneys to ischemic damage, delayed graft function, and reduced graft survival. KTx rejection poses a significant threat to allograft function and longevity of the kidney graft. The relationship between senescence and rejection remains elusive and controversial.MethodsGene Expression Omnibus (GEO) provided microarray and single-cell RNA sequencing datasets. After integrating Senescence-Related Genes (SRGs) from multiple established databases, differential expression analysis, weighted gene co-expression network analysis (WGCNA), and machine learning algorithms were applied to identify predictive SRGs (pSRGs). A cluster analysis of rejection samples was conducted using the consensus clustering algorithm. Subsequently, we utilized multiple machine learning methods (RF, SVM, XGB, GLM and LASSO) based on pSRGs to develop the optimal Acute Rejection (AR) diagnostic model and long-term graft survival predictive signatures. Finally, we validated the role of pSRGs and senescence in kidney rejection through the single-cell landscape.ResultsThirteen pSRGs were identified, correlating with rejection. Two rejection clusters were divided (Cluster C1 and C2). GSVA analysis of two clusters underscored a positive correlation between senescence, KTx rejection occurrence and worse graft survival. A non-invasive diagnostic model (AUC = 0.975) and a prognostic model (1- Year AUC = 0.881; 2- Year AUC = 0.880; 3- Year AUC = 0.883) for graft survival were developed, demonstrating significant predictive capabilities to early detect acute rejection and long-term graft outcomes. Single-cell sequencing analysis provided a detailed cellular-level landscape of rejection, supporting the conclusions drawn from above.ConclusionOur comprehensive analysis underscores the pivotal role of senescence in KTx rejection, highlighting the potential of SRGs as biomarkers for diagnosing rejection and predicting graft survival, which may enhance personalized treatment strategies and improve transplant outcomes.

Project description:BackgroundHeart failure (HF) represents one of healthcare's biggest challenges. Although rarely noticed, aging is a crucial risk factor for cardiovascular disease. Our study aims to reveal aging's role in HF by integrating single-cell RNA-sequencing (scRNA-seq) and bulk RNA-sequencing databases.MethodsWe collected HF heart sample data from the Gene Expression Omnibus database and senescence gene data from CellAge. The FindCluster () package was used for cell cluster analysis. Differentially expressed genes (DEG) were identified operating the FindMarkers function. Cell activity score calculation was performed using the AUCell package. UpSetR plotted the intersection between DEGs of active cell types, bulk data DEGs, and genes associated with aging. Using the DGIdb database gene-drug interaction data, we search for potential targeted therapeutics based on common senescence genes.ResultsThe scRNA-seq data revealed myocardial heterogeneity in HF tissues. A series of crucial common senescence genes were found. The senescence gene expression profile hints at an intriguing connection between monocytes and HF. After analyzing the DEGs in the bulk dataset, the DEGs in scRNA-seq, the DEGs in each active cell type, and senescence genes, we identified ten genes as common senescence genes present in HF. Correlation analysis of transcriptomics, proteomics, and ceRNA was performed to provide ideas for future studies individually. Moreover, we discovered that common senescence genes and potential therapeutic drugs interact among different cell types. Further research is needed on the expression pattern of senescence genes and molecular regulation in HF.ConclusionsIn summary, we identified the functional significance of the senescence gene in HF using integrated data. It is possible that this more profound understanding of how senescence contributes to the development of HF will aid in unraveling the mechanisms that promote the disease and provide hints for developing therapeutics.

Dataset Information

Integrative analysis of single-cell and bulk RNA sequencing unveils the senescence landscape in ischemic stroke.

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Integrative analysis of single-cell and bulk RNA sequencing unveils the senescence landscape in ischemic stroke.

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