Single-cell Transcriptome Analysis Reveals Dynamic Cell Populations and Differential Gene Expression Patterns in Control and Aneurysmal Human Aortic Tissue
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA649846 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR12358188_1.fastq.gz | Fastqsanger.gz | |||
| SRR12358188_2.fastq.gz | Fastqsanger.gz | |||
| SRR12358189_1.fastq.gz | Fastqsanger.gz | |||
| SRR12358189_2.fastq.gz | Fastqsanger.gz | |||
| SRR12358190_1.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 5 of 22 |
Similar Datasets
Project description:BACKGROUND: Ascending thoracic aortic aneurysm (ATAA) is caused by the progressive weakening and dilatation of the aortic wall and can lead to aortic dissection, rupture, and other life-threatening complications. To improve our understanding of ATAA pathogenesis, we sought to comprehensively characterize the cellular composition of the ascending aortic wall and to identify molecular alterations in each cell population of human ATAA tissues. METHODS: We performed single-cell RNA sequencing (sc-RNAseq) analysis of ascending aortic tissues from 11 study participants, including 8 patients with ATAA (4 women and 4 men) and 3 controls (2 women and 1 man). Cells extracted from aortic tissue were analyzed and categorized by using sc-RNAseq data to perform cluster identification. ATAA-related changes were then examined by comparing the proportions of each cell type and the gene expression profiles between ATAA and control tissues. We also examined which genes may be critical for ATAA by performing the integrative analysis of our sc-RNAseq data with publicly available data from genome-wide association studies (GWAS). RESULTS: We identified 11 major cell types in human ascending aortic tissue; the high-resolution reclustering of these cells further divided them into 40 subtypes. Multiple subtypes were observed for smooth muscle cells, macrophages, and T lymphocytes, suggesting that these cells have multiple functional populations in the aortic wall. Generally, ATAA tissues had fewer nonimmune cells and more immune cells, especially T lymphocytes, than did control tissues. Differential gene expression data suggested the presence of extensive mitochondrial dysfunction in ATAA tissues. In addition, integrative analysis of our sc-RNAseq data with public GWAS data and promoter capture Hi-C data suggested that ERG (ETS [erythroblast transformation-specific] related gene) exerts an important role in maintaining normal aortic wall function. CONCLUSIONS: Our study provides a comprehensive evaluation of the cellular composition of the ascending aortic wall and reveals how the gene expression landscape is altered in human ATAA tissue. The information from this study makes important contributions to our understanding of ATAA formation and progression.
2020-10-01 | GSE155468 | GEO
Project description:We report dynamic temporal and spatial smooth muscle cell phenotype modulation using aortic single cell RNA sequencing in a murine model of Marfan syndrome (Fbn1C1041G/+) and littermate controls. Aortic root/ascending aortic tissue samples from both genotypes were studied at 4 and 24 weeks of age. The non-aneurysmal descending thoracic aorta was also studied at 24 weeks. Finally human aortic tissue from a Marfan syndrome patient undergoing aneurysm repair surgery was studied.
2020-06-30 | GSE153534 | GEO
Project description:Single-molecule level spatial distribution of MERFISH (Multiplexed error-robust fluorescence in situ hybridization) probes targeting 140 genes were analyzed on two control (non-aneurysmal) samples and three TAA samples with Thoracic aortic aneurysm (TAA).
2023-10-14 | GSE241870 | GEO
Project description:Aortic aneurysms are dilations of the aorta that can rupture when left untreated. We used aneurysmal Fibulin-4R/R to further unravel the underlying mechanisms of aneurysm formation. RNA sequencing of 3-month-old Fibulin-4R/R aortas revealed significant upregulation of senescence-associated secretory phenotype (SASP) factors and key senescence factors, indicating involvement of senescence. Analysis of aorta histology and of vascular smooth muscle cells (VSMCs) in vitro confirmed the senescent phenotype of Fibulin-4R/R VSMCs by revealing increased SA-β-gal, p21 and p16 staining, increased IL-6 secretion, increased presence of DNA damage foci and increased nuclei size. Additionally, we found that p21 luminescence was increased in the dilated aorta of Fibulin-4R/R|p21-Luciferase mice. Our studies identify a cellular aging cascade in Fibulin-4 aneurysmal disease, by revealing that Fibulin-4R/R aortic VSMCs have a pronounced SASP and a senescent phenotype that may underlie aortic wall degeneration. Additionally, we demonstrated the therapeutic effect of JAK/STAT and TGF-β pathway inhibition as well as senolytic treatment on Fibulin-4R/R VSMCs in vitro. These findings can contribute to improved therapeutic options for aneurysmal disease aimed at reducing senescent cells.
2024-04-30 | GSE265869 | GEO
Project description:We conducted single-cell RNA sequencing (scRNA-seq) on CD4+ T cells of the aneurysmal aorta and the corresponding splenic cells, in order to unveil the diversity of CD4+ T Cell in Abdominal Aortic Aneurysms.
2024-05-06 | GSE253247 | GEO
Project description:BackgroundAscending thoracic aortic aneurysm (ATAA) is caused by the progressive weakening and dilatation of the aortic wall and can lead to aortic dissection, rupture, and other life-threatening complications. To improve our understanding of ATAA pathogenesis, we aimed to comprehensively characterize the cellular composition of the ascending aortic wall and to identify molecular alterations in each cell population of human ATAA tissues.MethodsWe performed single-cell RNA sequencing analysis of ascending aortic tissues from 11 study participants, including 8 patients with ATAA (4 women and 4 men) and 3 control subjects (2 women and 1 man). Cells extracted from aortic tissue were analyzed and categorized with single-cell RNA sequencing data to perform cluster identification. ATAA-related changes were then examined by comparing the proportions of each cell type and the gene expression profiles between ATAA and control tissues. We also examined which genes may be critical for ATAA by performing the integrative analysis of our single-cell RNA sequencing data with publicly available data from genome-wide association studies.ResultsWe identified 11 major cell types in human ascending aortic tissue; the high-resolution reclustering of these cells further divided them into 40 subtypes. Multiple subtypes were observed for smooth muscle cells, macrophages, and T lymphocytes, suggesting that these cells have multiple functional populations in the aortic wall. In general, ATAA tissues had fewer nonimmune cells and more immune cells, especially T lymphocytes, than control tissues did. Differential gene expression data suggested the presence of extensive mitochondrial dysfunction in ATAA tissues. In addition, integrative analysis of our single-cell RNA sequencing data with public genome-wide association study data and promoter capture Hi-C data suggested that the erythroblast transformation-specific related gene(ERG) exerts an important role in maintaining normal aortic wall function.ConclusionsOur study provides a comprehensive evaluation of the cellular composition of the ascending aortic wall and reveals how the gene expression landscape is altered in human ATAA tissue. The information from this study makes important contributions to our understanding of ATAA formation and progression.
| S-EPMC7539140 | biostudies-literature
Project description:In current models of ascending aortic disease, mural cells undergo phenotypic changes that promote extracellular matrix destruction and structural weakening. To explore the intersection of this biology with quantitative trait GWAS loci we analyzed the genetic features of thoracic aortic aneurysm tissue. Single nuclear RNA sequencing was performed on 13 samples from human donors, 6 with thoracic aortic aneurysm and 7 without aneurysm. Individual transcriptomes were then clustered based on transcriptional profiles. Clusters were used for between-disease differential gene expression analyses, subcluster analysis, and analyzed for intersection with genetic aortic trait data. In total, we sequenced 71,689 nuclei from human thoracic aortas without aneurysm, and with sporadic aneurysm. We identified 14 clusters, aligning with 11 cell types, predominantly VSMCs, consistent with existing histologic data, but contrary to the majority of human aortic single cell studies to-date. With unbiased methodology, we found 7 VSMC and 6 fibroblast subclusters. Differentially expressed genes (DEGs) analysis revealed a VSMC group accounting for the majority of differential gene expression. Fibroblast populations in aneurysm exhibit distinct behavior with almost complete disappearance of quiescent fibroblasts. DEGs were used to prioritize genes at aortic diameter and distensibility GWAS loci highlighting the genes JUN, LTBP4, and IL34 in fibroblasts, ENTPD1, PDLIM5, ACTN4, and GLRX in VSMCs, as well as LRP1 in macrophage populations. In conclusion, using nuclear RNA sequencing we describe the cellular diversity of healthy and aneurysmal human ascending aorta. Sporadic aortic aneurysm is characterized by differential gene expression within known cellular classes rather than by the appearance of novel cellular forms. Single nuclear RNA sequencing of aortic tissue can be used to prioritize genes at aortic trait loci.
2022-10-03 | GSE207784 | GEO
Project description:This study aims to identify and characterize miRNA expression in aneurysmal smooth muscle cells (SMCs) and M1 and M2 macrophages isolated by laser microdissection from human AAA biopsy samples. The aim of this study was to profile (with microarray technology) miRNAs in M1 and M2 macrophages and Smooth muscle cells (SMCs), isolated by laser capture microdissection (LCM). SMCs isolated from control non-aneurysmal aortas were the control group according to which data were normalized. Areas enriched in M1 macrophages, M2 macrophages and smooth muscle cells were microdissected (9.8 [4.7-16.2] mm2) from two different biopsy samples of human abdominal aortic aneurysm. An area enriched in smooth muscle cells was microdissected from two biopsy samples of non aneurysmal abdominal aortas. Each microdissected samples were analyzed independently on microarray. M1 and M2 macrophages and smooth muscle cells expressed in human abdominal aneurysmal aortas were each analyzed in duplicate (each isolated from different human donors of abdominal aortic aneurysm). Analysis of replicated samples of cells were performed on a second microarray. Data were normalized with smooth muscle cells isolated from human abdominal non aneurysmal aortas.
2014-10-09 | E-GEOD-62179 | biostudies-arrayexpress
Project description:High throughput miRNA microarray screening approach, we compared the miRNA expression pattern in ruptured aneurysm tissues obtained during surgery from patients with aneurysmal subarachnoid hemorrhage (aSAH) with control tissues. Aim was to determine miRNA signature in aneurysmal tissues.
2020-11-21 | GSE161870 | GEO
Project description:Patients with bicuspid aortic valve (BAV) have increased risk of thoracic ascending aortic aneurysm (AscAA) and dissection compared to those with a normal tricuspid aortic valve (TAV). The present study was undertaken to evaluate whether differences in gene expression exist in aortas from BAV and TAV patients with AscAA. Experiment Overall Design: Aneurysmal tissue of ascending aorta was collected from 13 patients with bicuspid aortic valve (BAV) and 12 patients with tricuspid aortic valve (TAV). Patients were selected on the basis of aortic diameter, age and other disease conditions. Patients with giant cell aortitis, cardiovascular abnormalities, inherited connective tissue disorders such as Marfan and Ehlers-Danlos syndrome were excluded from the study. RNA was extracted using Invitrogen RNA extraction kit and shown to be of adequate quality before application to Affymetrix microarray U133A gene chips probing for over 16,000 genes per chip. Two different methods of data analysis were performed: a linear model and GeneSpring.
2009-02-07 | E-GEOD-5180 | biostudies-arrayexpress