scholarly journals Single-Cell Profiling Reveals Transcriptional Signatures and Cell-Cell Crosstalk in Anti-PLA2R Positive Idiopathic Membranous Nephropathy Patients

2021 ◽  
Vol 12 ◽  
Author(s):  
Jie Xu ◽  
Chanjuan Shen ◽  
Wei Lin ◽  
Ting Meng ◽  
Joshua D. Ooi ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Membranous Nephropathy ◽  
Expression Patterns ◽  
Mapk Signaling ◽  
Idiopathic Membranous Nephropathy ◽  
Kidney Glomerulus ◽  
Single Cell Rna Sequencing ◽  
Cell Crosstalk ◽  
Cell Cell

Idiopathic membranous nephropathy (IMN) is an organ-specific autoimmune disease of the kidney glomerulus. It may gradually progress to end-stage renal disease (ESRD) characterized by increased proteinuria, which leads to serious consequences. Although substantial advances have been made in the understanding of the molecular bases of IMN in the last 10 years, certain questions remain largely unanswered. To define the transcriptomic landscape at single-cell resolution, we analyzed kidney samples from 6 patients with anti-PLA2R positive IMN and 2 healthy control subjects using single-cell RNA sequencing. We then identified distinct cell clusters through unsupervised clustering analysis of kidney specimens. Identification of the differentially expressed genes (DEGs) and enrichment analysis as well as the interaction between cells were also performed. Based on transcriptional expression patterns, we identified all previously described cell types in the kidney. The DEGs in most kidney parenchymal cells were primarily enriched in genes involved in the regulation of inflammation and immune response including IL-17 signaling, TNF signaling, NOD-like receptor signaling, and MAPK signaling. Moreover, cell-cell crosstalk highlighted the extensive communication of mesangial cells, which infers great importance in IMN. IMN with massive proteinuria displayed elevated expression of genes participating in inflammatory signaling pathways that may be involved in the pathogenesis of the progression of IMN. Overall, we applied single-cell RNA sequencing to IMN to uncover intercellular interactions, elucidate key pathways underlying the pathogenesis, and identify novel therapeutic targets of anti-PLA2R positive IMN.

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 MO124THE SINGLE-CELL TRANSCRIPTOMIC OF HUMAN IDIOPATHIC MEMBRANOUS NEPHROPATHY*

2021 ◽  
Vol 36 (Supplement_1) ◽  
Author(s):  
Yong Zhong ◽  
Qiaoling Zhou
Keyword(s):  
Endothelial Cells ◽  
Single Cell ◽  
Signaling Pathway ◽  
Rna Sequencing ◽  
Membranous Nephropathy ◽  
Mesangial Cells ◽  
Enrichment Analysis ◽  
Idiopathic Membranous Nephropathy ◽  
Control Subjects ◽  
Single Cell Rna Sequencing

Abstract Background and Aims Idiopathic membranous nephropathy (IMN) is an organ-specific autoimmune disease of the kidney glomerulus. Although substantial advances have been made in the understanding of the molecular bases of IMN in the last 10 years, there remain largely unanswered questions. Method To define the transcriptomic landscape at the single-cell resolution, we analyzed kidney samples from 6 patients with idiopathic membranous nephropathy (IMN) and 2 healthy control subjects using single-cell RNA sequencing. Results Based on transcriptional expression patterns, we identified all previously described cell types in the kidney. Also, we identified a novel population of the epithelial cell which is mainly from IMN patients. GO enrichment analysis showed that DEGs were enriched in the regulation of apoptosis and type I interferon signaling pathway in mesangial cells, while DEGs were enriched in the regulation of programmed cell death, and various cytokine-mediated signaling pathway in endothelial cells and the regulation of protein modification in pericytes. KEGG enrichment analysis revealed that DEGs were mainly associated with the IL-17 signaling pathway, TNF signaling pathway, NOD-like receptor signaling pathway as well as MAPK signaling pathway in endothelial cells as well as pericytes DEGs of proximal tubules cells (PT) and PCs between IMN and control subjects were both enriched in IL-17 signaling, TNF signaling, NOD-like receptor signaling. Moreover, the cell-cell crosstalk highlighted the extensive communication of mesangial cells, which infers great importance in IMN. IMN with massive proteinuria displayed elevated genes participating in the inflammatory signaling pathways that may be involved in the pathogenesis of the progression of IMN. Conclusion Overall, we applied single-cell RNA sequencing to IMN to uncover intercellular interactions, elucidate key pathways underlying the pathogenesis, and identify novel therapeutic targets of IMN.

scholarly journals Cryopreservation of microglia enables single-cell RNA sequencing with minimal effects on disease-related gene expression patterns

iScience ◽  
2021 ◽  
Vol 24 (4) ◽  
pp. 102357
Author(s):  
Brenda Morsey ◽  
Meng Niu ◽  
Shetty Ravi Dyavar ◽  
Courtney V. Fletcher ◽  
Benjamin G. Lamberty ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Related Gene ◽  
Single Cell Rna Sequencing ◽  
Disease Related Gene

scholarly journals Single-Cell RNA Sequencing Unveils Unique Transcriptomic Signatures of Organ-Specific Endothelial Cells

Circulation ◽  
2020 ◽  
Vol 142 (19) ◽  
pp. 1848-1862 ◽  
Author(s):  
David T. Paik ◽  
Lei Tian ◽  
Ian M. Williams ◽  
Siyeon Rhee ◽  
Hao Zhang ◽  
...  
Keyword(s):  
Endothelial Cells ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Patterns ◽  
Receptor Expression ◽  
Transcriptional Networks ◽  
Sequencing Data ◽  
Tissue Specific ◽  
Functional Relationships ◽  
Single Cell Rna Sequencing

Background: Endothelial cells (ECs) display considerable functional heterogeneity depending on the vessel and tissue in which they are located. Whereas these functional differences are presumably imprinted in the transcriptome, the pathways and networks that sustain EC heterogeneity have not been fully delineated. Methods: To investigate the transcriptomic basis of EC specificity, we analyzed single-cell RNA sequencing data from tissue-specific mouse ECs generated by the Tabula Muris consortium. We used a number of bioinformatics tools to uncover markers and sources of EC heterogeneity from single-cell RNA sequencing data. Results: We found a strong correlation between tissue-specific EC transcriptomic measurements generated by either single-cell RNA sequencing or bulk RNA sequencing, thus validating the approach. Using a graph-based clustering algorithm, we found that certain tissue-specific ECs cluster strongly by tissue (eg, liver, brain), whereas others (ie, adipose, heart) have considerable transcriptomic overlap with ECs from other tissues. We identified novel markers of tissue-specific ECs and signaling pathways that may be involved in maintaining their identity. Sex was a considerable source of heterogeneity in the endothelial transcriptome and we discovered Lars2 to be a gene that is highly enriched in ECs from male mice. We found that markers of heart and lung ECs in mice were conserved in human fetal heart and lung ECs. We identified potential angiocrine interactions between tissue-specific ECs and other cell types by analyzing ligand and receptor expression patterns. Conclusions: We used single-cell RNA sequencing data generated by the Tabula Muris consortium to uncover transcriptional networks that maintain tissue-specific EC identity and to identify novel angiocrine and functional relationships between tissue-specific ECs.

scholarly journals PRIME: a probabilistic imputation method to reduce dropout effects in single-cell RNA sequencing

2020 ◽  
Vol 36 (13) ◽  
pp. 4021-4029
Author(s):  
Hyundoo Jeong ◽  
Zhandong Liu
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Imputation Method ◽  
Supplementary Information ◽  
Single Cell Sequencing ◽  
Depth Analysis ◽  
Single Cell Rna Sequencing

Abstract Summary Single-cell RNA sequencing technology provides a novel means to analyze the transcriptomic profiles of individual cells. The technique is vulnerable, however, to a type of noise called dropout effects, which lead to zero-inflated distributions in the transcriptome profile and reduce the reliability of the results. Single-cell RNA sequencing data, therefore, need to be carefully processed before in-depth analysis. Here, we describe a novel imputation method that reduces dropout effects in single-cell sequencing. We construct a cell correspondence network and adjust gene expression estimates based on transcriptome profiles for the local subnetwork of cells of the same type. We comprehensively evaluated this method, called PRIME (PRobabilistic IMputation to reduce dropout effects in Expression profiles of single-cell sequencing), on synthetic and eight real single-cell sequencing datasets and verified that it improves the quality of visualization and accuracy of clustering analysis and can discover gene expression patterns hidden by noise. Availability and implementation The source code for the proposed method is freely available at https://github.com/hyundoo/PRIME. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Combined transient ablation and single cell RNA sequencing reveals the development of medullary thymic epithelial cells

2020 ◽  
Author(s):  
Kristen L. Wells ◽  
Corey N. Miller ◽  
Andreas R. Gschwind ◽  
Wu Wei ◽  
Jonah D. Phipps ◽  
...  
Keyword(s):  
Epithelial Cells ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Patterns ◽  
Critical Role ◽  
Lineage Tracing ◽  
Thymic Epithelial Cells ◽  
Single Cell Rna Sequencing ◽  
Medullary Thymic Epithelial Cells

AbstractMedullary thymic epithelial cells (mTECs) play a critical role in central immune tolerance by mediating negative selection of autoreactive T cells through the collective expression of the peripheral self-antigen compartment, including tissue-specific antigens (TSAs). Recent work has shown that gene expression patterns within the mTEC compartment are remarkably heterogenous and include multiple differentiated cell states. To further define mTEC development and medullary epithelial lineage relationships, we combined lineage tracing and recovery from transient in vivo mTEC ablation with single cell RNA-sequencing. The combination of bioinformatic and experimental approaches revealed a non-stem transit-amplifying population of cycling mTECs that preceded Aire expression. Based on our findings, we propose a branching model of mTEC development wherein a heterogeneous pool of transit-amplifying cells gives rise to Aire- and Ccl21a-expressing mTEC subsets. We further use experimental techniques to show that within the Aire-expressing developmental branch, TSA expression peaked as Aire expression decreased, implying Aire expression must be established before TSA expression can occur. Collectively, these data provide a higher order roadmap of mTEC development and demonstrate the power of combinatorial approaches leveraging both in vivo models and high-dimensional datasets.

scholarly journals Aging-associated alterations in the mammary gland revealed by single-cell RNA sequencing

2019 ◽  
Author(s):  
Carman Man-Chung Li ◽  
Hana Shapiro ◽  
Christina Tsiobikas ◽  
Laura Selfors ◽  
Huidong Chen ◽  
...  
Keyword(s):  
Mammary Gland ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Patterns ◽  
Cell Types ◽  
Single Cell Rna Sequencing ◽  
Numerous Cell ◽  
Luminal Cells ◽  
Increased Susceptibility ◽  
Mammary Tissues

AbstractAging of the mammary gland is closely associated with increased susceptibility to diseases such as cancer, but there have been limited systematic studies of aging-induced alterations within this organ. We performed high-throughput single-cell RNA-sequencing (scRNA-seq) profiling of mammary tissues from young and old nulliparous mice, including both epithelial and stromal cell types. Our analysis identified altered proportions and distinct gene expression patterns in numerous cell populations as a consequence of the aging process, independent of parity and lactation. In addition, we detected a subset of luminal cells that express both hormone-sensing and alveolar markers and decrease in relative abundance with age. These data provide a high-resolution landscape of aging mammary tissues, with potential implications for normal tissue functions and cancer predisposition.

scholarly journals Probabilistic index models for testing differential expression in single cell RNA sequencing data

2019 ◽  
Author(s):  
Alemu Takele Assefa ◽  
Jo Vandesompele ◽  
Olivier Thas
Keyword(s):  
Data Analysis ◽  
Single Cell ◽  
Differential Expression ◽  
Rna Sequencing ◽  
Effect Size ◽  
Expression Patterns ◽  
Simulation Studies ◽  
Sequencing Data ◽  
Rank Tests ◽  
Single Cell Rna Sequencing

AbstractSingle-cell RNA sequencing (scRNA-seq) technologies profile gene expression patterns in individual cells. It is often of interest to test for differential expression (DE) between conditions, e.g. treatment vs control or between cell types. Simulation studies have shown that non-parametric tests, such as the Wilcoxon-rank sum test, can robustly detect significant DE, with better performance than many parametric tools specifically developed for scRNA-seq data analysis. However, these rank tests cannot be used for complex experimental designs involving multiple groups, multiple factors and confounding variables. Further, rank based tests do not provide an interpretable measure of the effect size. We propose a semi-parametric approach based on probabilistic index models (PIM) that form a flexible class of models that generalize classical rank tests. Our method does not rely on strong distributional assumptions and it allows accounting for confounding factors. Moreover, it allows for the estimation of the effect size in terms of a probabilistic index. Real data analysis demonstrate that PIM is capable of identifying biologically meaningful DE. Our simulation studies also show that DE tests succeed well in controlling the false discovery rate at its nominal level, while maintaining good sensitivity as compared to competing methods.

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