scholarly journals Single-cell RNA Sequencing Reveals Transcriptional Signatures and Cell-cell Crosstalk in Patients With Hypertensive Nephropathy

2021 ◽  
Author(s):  
Rong Tang ◽  
Wei Lin ◽  
Chanjuan Shen ◽  
Ting Meng ◽  
Joshua D Ooi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Signaling Pathways ◽  
Rna Sequencing ◽  
Specific Gene ◽  
Specific Gene Expression ◽  
Distinct Cell ◽  
Single Cell Rna Sequencing ◽  
Cell Crosstalk ◽  
Cell Cell

Abstract BackgroundHypertensive nephropathy (HTN) is one of the leading causes of end-stage renal disease, yet the precise mechanisms and cell-specific gene expression changes are still unknown. This study used single-cell RNA sequencing (scRNA-seq) to explore novel molecular mechanisms and gene targets for HTN for the first time. Methods: The gene expression profiles of renal biopsy samples obtained from HTN patients and healthy living donor controls were determined by scRNA-seq technology. Distinct cell clusters, differential gene expression, cell-cell interaction and potential signaling pathways involved in HTN were determined. Results18 distinct cell clusters were identified in kidney from HTN and control subjects. Endothelial cells overexpressed LRG1 , a pleiotropic factor linked to apoptosis and inflammation, providing a potential novel molecular target. HTN endothelium also overexpressed genes linked to cellular adhesion, extracellular matrix accumulation and inflammation. In HTN patients, mesangial cells highly expressed proliferation related signatures ( MGST1 , TMSB10, EPS8 and IER2 ) not detected in renal diseases before. The upregulated genes in tubules of HTN were mainly participating in inflammatory signatures including IFN-γ signature, IL-17 signaling and TLR signaling. Specific gene expression of kidney-resident CD8 + T cells exhibited a proinflammatory, chemotactic and cytotoxic phenotype. Furthermore, receptor-ligand interaction analysis indicated cell-cell crosstalk in kidney contributes to recruitment and infiltration of inflammatory cells into kidneys, and fibrotic process in hypertensive renal injury. ConclusionsIn summary, our data identifies a distinct cell-specific gene expression profile, pathogenic signaling pathways and potential cell-cell communications in the pathogenesis of HTN. These findings will provide a promising novel landscape for mechanisms and treatment of HTN.

scholarly journals SCDC: bulk gene expression deconvolution by multiple single-cell RNA sequencing references

2020 ◽  
Author(s):  
Meichen Dong ◽  
Aatish Thennavan ◽  
Eugene Urrutia ◽  
Yun Li ◽  
Charles M Perou ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Specific Gene ◽  
Rna Seq ◽  
Cell Type ◽  
Mixed Cell ◽  
Single Cell Rna Sequencing

Abstract Recent advances in single-cell RNA sequencing (scRNA-seq) enable characterization of transcriptomic profiles with single-cell resolution and circumvent averaging artifacts associated with traditional bulk RNA sequencing (RNA-seq) data. Here, we propose SCDC, a deconvolution method for bulk RNA-seq that leverages cell-type specific gene expression profiles from multiple scRNA-seq reference datasets. SCDC adopts an ENSEMBLE method to integrate deconvolution results from different scRNA-seq datasets that are produced in different laboratories and at different times, implicitly addressing the problem of batch-effect confounding. SCDC is benchmarked against existing methods using both in silico generated pseudo-bulk samples and experimentally mixed cell lines, whose known cell-type compositions serve as ground truths. We show that SCDC outperforms existing methods with improved accuracy of cell-type decomposition under both settings. To illustrate how the ENSEMBLE framework performs in complex tissues under different scenarios, we further apply our method to a human pancreatic islet dataset and a mouse mammary gland dataset. SCDC returns results that are more consistent with experimental designs and that reproduce more significant associations between cell-type proportions and measured phenotypes.

scholarly journals Implication of specific retinal cell-type involvement and gene expression changes in AMD progression using integrative analysis of single-cell and bulk RNA-seq profiling

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yafei Lyu ◽  
Randy Zauhar ◽  
Nicholas Dana ◽  
Christianne E. Strang ◽  
Jian Hu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Age Related Macular Degeneration ◽  
Specific Gene ◽  
Cell Type ◽  
Adult Human ◽  
Single Cell Rna Sequencing ◽  
Cell Type Specific ◽  
Cell Data

AbstractAge‐related macular degeneration (AMD) is a blinding eye disease with no unifying theme for its etiology. We used single-cell RNA sequencing to analyze the transcriptomes of ~ 93,000 cells from the macula and peripheral retina from two adult human donors and bulk RNA sequencing from fifteen adult human donors with and without AMD. Analysis of our single-cell data identified 267 cell-type-specific genes. Comparison of macula and peripheral retinal regions found no cell-type differences but did identify 50 differentially expressed genes (DEGs) with about 1/3 expressed in cones. Integration of our single-cell data with bulk RNA sequencing data from normal and AMD donors showed compositional changes more pronounced in macula in rods, microglia, endothelium, Müller glia, and astrocytes in the transition from normal to advanced AMD. KEGG pathway analysis of our normal vs. advanced AMD eyes identified enrichment in complement and coagulation pathways, antigen presentation, tissue remodeling, and signaling pathways including PI3K-Akt, NOD-like, Toll-like, and Rap1. These results showcase the use of single-cell RNA sequencing to infer cell-type compositional and cell-type-specific gene expression changes in intact bulk tissue and provide a foundation for investigating molecular mechanisms of retinal disease that lead to new therapeutic targets.

scholarly journals SAVER: Gene expression recovery for UMI-based single cell RNA sequencing

2017 ◽  
Author(s):  
Mo Huang ◽  
Jingshu Wang ◽  
Eduardo Torre ◽  
Hannah Dueck ◽  
Sydney Shaffer ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Single Cell Analysis ◽  
Specific Gene ◽  
Recovery Method ◽  
Single Cell Rna Sequencing ◽  
Cell Gene Expression ◽  
Single Cell Profiling ◽  
Downstream Analysis

AbstractRapid advances in massively parallel single cell RNA sequencing (scRNA-seq) is paving the way for high-resolution single cell profiling of biological samples. In most scRNA-seq studies, only a small fraction of the transcripts present in each cell are sequenced. The efficiency, that is, the proportion of transcripts in the cell that are sequenced, can be especially low in highly parallelized experiments where the number of reads allocated for each cell is small. This leads to unreliable quantification of lowly and moderately expressed genes, resulting in extremely sparse data and hindering downstream analysis. To address this challenge, we introduce SAVER (Single-cell Analysis Via Expression Recovery), an expression recovery method for scRNA-seq that borrows information across genes and cells to impute the zeros as well as to improve the expression estimates for all genes. We show, by comparison to RNA fluorescence in situ hybridization (FISH) and by data down-sampling experiments, that SAVER reliably recovers cell-specific gene expression concentrations, cross-cell gene expression distributions, and gene-to-gene and cell-to-cell correlations. This improves the power and accuracy of any downstream analysis involving genes with low to moderate expression.

scholarly journals The in vivo endothelial cell translatome is highly heterogeneous across vascular beds

2019 ◽  
Author(s):  
Audrey C.A. Cleuren ◽  
Martijn A. van der Ent ◽  
Hui Jiang ◽  
Kristina L. Hunker ◽  
Andrew Yee ◽  
...  
Keyword(s):  
Gene Expression ◽  
Endothelial Cells ◽  
Single Cell ◽  
Blood Vessels ◽  
Rna Sequencing ◽  
Specific Gene ◽  
Sequencing Analysis ◽  
Single Cell Rna Sequencing ◽  
Vascular Beds

AbstractEndothelial cells (ECs) are highly specialized across vascular beds. However, given their interspersed anatomic distribution, comprehensive characterization of the molecular basis for this heterogeneity in vivo has been limited. By applying endothelial-specific translating ribosome affinity purification (EC-TRAP) combined with high-throughput RNA sequencing analysis, we identified pan EC-enriched genes and tissue-specific EC transcripts, which include both established markers and genes previously unappreciated for their presence in ECs. In addition, EC-TRAP limits changes in gene expression following EC isolation and in vitro expansion, as well as rapid vascular bed-specific shifts in EC gene expression profiles as a result of the enzymatic tissue dissociation required to generate single cell suspensions for fluorescence-activated cell sorting (FACS) or single cell RNA sequencing analysis. Comparison of our EC-TRAP to published single cell RNA sequencing data further demonstrates considerably greater sensitivity of EC-TRAP for the detection of low abundant transcripts. Application of EC-TRAP to examine the in vivo host response to lipopolysaccharide (LPS) revealed the induction of gene expression programs associated with a native defense response, with marked differences across vascular beds. Furthermore, comparative analysis of whole tissue and TRAP-selected mRNAs identified LPS-induced differences that would not have been detected by whole tissue analysis alone. Together, these data provide a resource for the analysis of EC-specific gene expression programs across heterogeneous vascular beds under both physiologic and pathologic conditions.SignificanceEndothelial cells (ECs), which line all vertebrate blood vessels, are highly heterogeneous across different tissues. The present study uses a genetic approach to specifically tag mRNAs within ECs of the mouse, thereby allowing recovery and sequence analysis to evaluate the EC-specific gene expression program directly from intact organs. Our findings demonstrate marked heterogeneity in EC gene expression across different vascular beds under both normal and disease conditions, with a more accurate picture than can be achieved using other methods. The data generated in these studies advance our understanding of EC function in different blood vessels and provide a valuable resource for future studies.

scholarly journals SCDC: Bulk Gene Expression Deconvolution by Multiple Single-Cell RNA Sequencing References

2019 ◽  
Author(s):  
Meichen Dong ◽  
Aatish Thennavan ◽  
Eugene Urrutia ◽  
Yun Li ◽  
Charles M. Perou ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Specific Gene ◽  
Rna Seq ◽  
Cell Type ◽  
Mixed Cell ◽  
Single Cell Rna Sequencing

AbstractRecent advances in single-cell RNA sequencing (scRNA-seq) enable characterization of transcriptomic profiles with single-cell resolution and circumvent averaging artifacts associated with traditional bulk RNA sequencing (RNA-seq) data. Here, we propose SCDC, a deconvolution method for bulk RNA-seq that leverages cell-type specific gene expression profiles from multiple scRNA-seq reference datasets. SCDC adopts an ENSEMBLE method to integrate deconvolution results from different scRNA-seq datasets that are produced in different laboratories and at different times, implicitly addressing the problem of batch-effect confounding. SCDC is benchmarked against existing methods using both in silico generated pseudo-bulk samples and experimentally mixed cell lines, whose known cell-type compositions serve as ground truths. We show that SCDC outperforms existing methods with improved accuracy of cell-type decomposition under both settings. To illustrate how the ENSEMBLE framework performs in complex tissues under different scenarios, we further apply our method to a human pancreatic islet dataset and a mouse mammary gland dataset. SCDC returns results that are more consistent with experimental designs and that reproduce more significant associations between cell-type proportions and measured phenotypes.

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