scholarly journals SAT0303 SINGLE-CELL DECONVOLUTION OF SKIN FIBROBLAST HETEROGENEITY IN PATIENTS WITH SYSTEMIC SCLEROSIS

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 1097.2-1097
Author(s):  
Q. Yan ◽  
R. Li ◽  
L. Lu
Keyword(s):  
Systemic Sclerosis ◽  
Single Cell ◽  
Skin Fibroblast ◽  
Normal Skin ◽  
Enrichment Analysis ◽  
Cell Types ◽  
Biological Properties ◽  
Gene Set Enrichment Analysis ◽  
Inflammatory Stimuli ◽  
Fibroblast Heterogeneity

Background:Fibroblast heterogeneity and homeostasis has long been recognized in patients with systemic sclerosis (SSc). However, there is no common consensus on fibroblast subtypes, lineages, biological properties, signaling, and plasticity, which severely hampers our understanding of SSc pathogenesis.Objectives:This study is aimed to comprehensively classify skin fibroblast populations from SSc patients.Methods:We applied single-cell RNA sequencing on skin fibroblasts from two SSc patients and two health control (HC) with matched age and sex. Cell clustering were mainly determined by UMAP with batch effect correction. Differently expressed genes in each cell cluster was analyzed by Gene Set Enrichment Analysis (GSEA).Results:With an unbiased approach, single-cell transcriptome analyses showed classified and defined eight fibroblast types in SSc skin and six in normal skin. The cell types seldom overlapped between the patients and HC. Extracellular interaction and collagen production were remarkably stronger in SSc fibroblasts. A subgroup of dramatic cell proliferation and activation was defined only in SSc fibroblast. Two subtypes responding inflammatory stimuli were only found in SSc patients. Furthermore, delineation of their differentiation trajectory was achieved by a machine learning method.Conclusion:This collection of single-cell transcriptomes and the distinct classification of fibroblast subsets provide a new resource for understanding the fibroblast landscape and the roles of fibroblasts in SSc.References:N/ADisclosure of Interests:None declared

Age-Dependent Transcriptional Alterations in Cardiac Endothelial Cells

2021 ◽  
Author(s):  
Uchenna Emechebe ◽  
Jonathan William Nelson ◽  
Nabil J. Alkayed ◽  
Sanjiv Kaul ◽  
Andrew C Adey ◽  
...  
Keyword(s):  
Endothelial Cells ◽  
Endothelial Cell ◽  
Single Cell ◽  
Significant Risk ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Mouse Heart ◽  
Age Related ◽  
Organ Systems ◽  
Transcriptional Alterations

Aging is a significant risk factor for cardiovascular disease. Despite the fact that endothelial cells play critical roles in cardiovascular function and disease, the molecular impact of aging on this cell population in many organ systems remains unknown. In this study, we sought to determine age-associated transcriptional alterations in cardiac endothelial cells. Highly enriched populations of endothelial cells (ECs) isolated from the heart, brain and kidney of young (3 months) and aged (24 months) C57/BL6 mice were profiled for RNA expression via bulk RNA sequencing. Approximately 700 cardiac endothelial transcripts significantly differ by age. Gene set enrichment analysis indicated similar patterns for cellular pathway perturbations. Receptor-ligand comparisons indicated parallel alterations in age-affected circulating factors and cardiac endothelial-expressed receptors. Single-cell RNA-seq analysis identified 9 distinct endothelial cell subtypes in the heart with an age-associated population shift observed for the Aplnr-enriched endothelial cell clusters. Gene and pathway enrichment analyses show that age-related transcriptional response of cardiac endothelial cells is distinct from that of endothelial cells derived from the brain or kidney vascular bed. Furthermore, single-cell analysis identified 9 distinct EC subtypes, and shows that the Aplnr-enriched subtype is reduced with age in mouse heart. Finally, we identify age-dysregulated genes in specific aged cardiac endothelial subtypes.

scholarly journals Single-cell analysis reveals fibroblast heterogeneity and myofibroblasts in systemic sclerosis-associated interstitial lung disease

2019 ◽  
Vol 78 (10) ◽  
pp. 1379-1387 ◽  
Author(s):  
Eleanor Valenzi ◽  
Melissa Bulik ◽  
Tracy Tabib ◽  
Christina Morse ◽  
John Sembrat ◽  
...  
Keyword(s):  
Gene Expression ◽  
Systemic Sclerosis ◽  
Interstitial Lung Disease ◽  
Lung Disease ◽  
Single Cell ◽  
Mesenchymal Cell ◽  
Mesenchymal Cells ◽  
Cell Populations ◽  
Healthy Control ◽  
Fibroblast Heterogeneity

ObjectivesMyofibroblasts are key effector cells in the extracellular matrix remodelling of systemic sclerosis-associated interstitial lung disease (SSc-ILD); however, the diversity of fibroblast populations present in the healthy and SSc-ILD lung is unknown and has prevented the specific study of the myofibroblast transcriptome. We sought to identify and define the transcriptomes of myofibroblasts and other mesenchymal cell populations in human healthy and SSc-ILD lungs to understand how alterations in fibroblast phenotypes lead to SSc-ILD fibrosis.MethodsWe performed droplet-based, single-cell RNA-sequencing with integrated canonical correlation analysis of 13 explanted lung tissue specimens (56 196 cells) from four healthy control and four patients with SSc-ILD, with findings confirmed by cellular indexing of transcriptomes and epitopes by sequencing in additional samples.ResultsExamination of gene expression in mesenchymal cells identified two major, SPINT2hi and MFAP5hi, and one minor, WIF1hi, fibroblast populations in the healthy control lung. Combined analysis of control and SSc-ILD mesenchymal cells identified SPINT2hi, MFAP5hi, few WIF1hi fibroblasts and a new large myofibroblast population with evidence of actively proliferating myofibroblasts. We compared differential gene expression between all SSc-ILD and control mesenchymal cell populations, as well as among the fibroblast subpopulations, showing that myofibroblasts undergo the greatest phenotypic changes in SSc-ILD and strongly upregulate expression of collagens and other profibrotic genes.ConclusionsOur results demonstrate previously unrecognised fibroblast heterogeneity in SSc-ILD and healthy lungs, and define multimodal transcriptome-phenotypes associated with these populations. Our data indicate that myofibroblast differentiation and proliferation are key pathological mechanisms driving fibrosis in SSc-ILD.

scholarly journals Compendium of skin molecular signatures identifies key pathological features associated with fibrosis in systemic sclerosis

2019 ◽  
Vol 78 (6) ◽  
pp. 817-825 ◽  
Author(s):  
Su-Jin Moon ◽  
Jung Min Bae ◽  
Kyung-Su Park ◽  
Ilias Tagkopoulos ◽  
Ki-Jo Kim
Keyword(s):  
Systemic Sclerosis ◽  
Drug Targets ◽  
Leading Edge ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Skin Fibrosis ◽  
Akt Pathway ◽  
Data Sets ◽  
Phosphoinositide 3 Kinase ◽  
Genome Scale

ObjectivesTreatment of patients with systemic sclerosis (SSc) can be challenging because of clinical heterogeneity. Integration of genome-scale transcriptomic profiling for patients with SSc can provide insights on patient categorisation and novel drug targets.MethodsA normalised compendium was created from 344 skin samples of 173 patients with SSc, covering an intersection of 17 424 genes from eight data sets. Differentially expressed genes (DEGs) identified by three independent methods were subjected to functional network analysis, where samples were grouped using non-negative matrix factorisation. Finally, we investigated the pathways and biomarkers associated with skin fibrosis using gene-set enrichment analysis.ResultsWe identified 1089 upregulated DEGs, including 14 known genetic risk factors and five potential drug targets. Pathway-based subgrouping revealed four distinct clusters of patients with SSc with distinct activity signatures for SSc-relevant pathways. The inflammatory subtype was related to significant improvement in skin fibrosis at follow-up. The phosphoinositide-3-kinase-protein kinase B (PI3K-Akt) signalling pathway showed both the closest correlation and temporal pattern to skin fibrosis score. COMP, THBS1, THBS4, FN1, and TNC were leading-edge genes of the PI3K-Akt pathway in skin fibrogenesis.ConclusionsConstruction and analysis of normalised skin transcriptomic compendia can provide useful insights on pathway involvement by SSc subsets and discovering viable biomarkers for a skin fibrosis index. Particularly, the PI3K-Akt pathway and its leading players are promising therapeutic targets.

scholarly journals A review of digital cytometry methods: estimating the relative abundance of cell types in a bulk of cells

2020 ◽  
Author(s):  
Trang Le ◽  
Rachel A Aronow ◽  
Arkadz Kirshtein ◽  
Leili Shahriyari
Keyword(s):  
Gene Expression ◽  
Gene Expression Profile ◽  
Expression Profile ◽  
Single Cells ◽  
Enrichment Analysis ◽  
Cell Types ◽  
Gene Set Enrichment Analysis ◽  
Single Sample ◽  
Batch Correction ◽  
Signature Matrix

Abstract Due to the high cost of flow and mass cytometry, there has been a recent surge in the development of computational methods for estimating the relative distributions of cell types from the gene expression profile of a bulk of cells. Here, we review the five common ‘digital cytometry’ methods: deconvolution of RNA-Seq, cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT), CIBERSORTx, single sample gene set enrichment analysis and single-sample scoring of molecular phenotypes deconvolution method. The results show that CIBERSORTx B-mode, which uses batch correction to adjust the gene expression profile of the bulk of cells (‘mixture data’) to eliminate possible cross-platform variations between the mixture data and the gene expression data of single cells (‘signature matrix’), outperforms other methods, especially when signature matrix and mixture data come from different platforms. However, in our tests, CIBERSORTx S-mode, which uses batch correction for adjusting the signature matrix instead of mixture data, did not perform better than the original CIBERSORT method, which does not use any batch correction method. This result suggests the need for further investigations into how to utilize batch correction in deconvolution methods.

scholarly journals Comprehensive analysis of ceRNA networks reveals prognostic lncRNAs related to immune infiltration in colorectal cancer

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jingyi Chen ◽  
Yuxuan Song ◽  
Mei Li ◽  
Yu Zhang ◽  
Tingru Lin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
Target Genes ◽  
Immune Cell ◽  
Pearson Correlation ◽  
Enrichment Analysis ◽  
Cell Types ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Cerna Network

Abstract Background Competing endogenous RNA (ceRNA) represents a class of RNAs (e.g., long noncoding RNAs [lncRNAs]) with microRNA (miRNA) binding sites, which can competitively bind miRNA and inhibit its regulation of target genes. Increasing evidence has underscored the involvement of dysregulated ceRNA networks in the occurrence and progression of colorectal cancer (CRC). The purpose of this study was to construct a ceRNA network related to the prognosis of CRC and further explore the potential mechanisms that affect this prognosis. Methods RNA-Seq and miRNA-Seq data from The Cancer Genome Atlas (TCGA) were used to identify differentially expressed lncRNAs (DElncRNAs), microRNAs (DEmiRNAs), and mRNAs (DEmRNAs), and a prognosis-related ceRNA network was constructed based on DElncRNA survival analysis. Subsequently, pathway enrichment, Pearson correlation, and Gene Set Enrichment Analysis (GSEA) were performed to determine the function of the genes in the ceRNA network. Gene Expression Profiling Interactive Analysis (GEPIA) and immunohistochemistry (IHC) were also used to validate differential gene expression. Finally, the correlation between lncRNA and immune cell infiltration in the tumor microenvironment was evaluated based on the CIBERSORT algorithm. Results A prognostic ceRNA network was constructed with eleven key survival-related DElncRNAs (MIR4435-2HG, NKILA, AFAP1-AS1, ELFN1-AS1, AC005520.2, AC245884.8, AL354836.1, AL355987.4, AL591845.1, LINC02038, and AC104823.1), 54 DEmiRNAs, and 308 DEmRNAs. The MIR4435-2HG- and ELFN1-AS1-associated ceRNA subnetworks affected and regulated the expression of the COL5A2, LOX, OSBPL3, PLAU, VCAN, SRM, and E2F1 target genes and were found to be related to prognosis and tumor-infiltrating immune cell types. Conclusions MIR4435-2HG and ELFN1-AS1 are associated with prognosis and tumor-infiltrating immune cell types and could represent potential prognostic biomarkers or therapeutic targets in colorectal carcinoma.

scholarly journals Learning interpretable cellular and gene signature embeddings from single-cell transcriptomic data

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Yifan Zhao ◽  
Huiyu Cai ◽  
Zuobai Zhang ◽  
Jian Tang ◽  
Yue Li
Keyword(s):  
Single Cell ◽  
Large Scale ◽  
Topic Model ◽  
Enrichment Analysis ◽  
Gene Signature ◽  
Gene Set Enrichment Analysis ◽  
Learning Performance ◽  
Cell Type ◽  
Gene Set Enrichment ◽  
Gene Sets

AbstractThe advent of single-cell RNA sequencing (scRNA-seq) technologies has revolutionized transcriptomic studies. However, large-scale integrative analysis of scRNA-seq data remains a challenge largely due to unwanted batch effects and the limited transferabilty, interpretability, and scalability of the existing computational methods. We present single-cell Embedded Topic Model (scETM). Our key contribution is the utilization of a transferable neural-network-based encoder while having an interpretable linear decoder via a matrix tri-factorization. In particular, scETM simultaneously learns an encoder network to infer cell type mixture and a set of highly interpretable gene embeddings, topic embeddings, and batch-effect linear intercepts from multiple scRNA-seq datasets. scETM is scalable to over 106 cells and confers remarkable cross-tissue and cross-species zero-shot transfer-learning performance. Using gene set enrichment analysis, we find that scETM-learned topics are enriched in biologically meaningful and disease-related pathways. Lastly, scETM enables the incorporation of known gene sets into the gene embeddings, thereby directly learning the associations between pathways and topics via the topic embeddings.

scholarly journals NDRindex: a method for the quality assessment of single-cell RNA-Seq preprocessing data

2020 ◽  
Vol 21 (S16) ◽  
Author(s):  
Ruiyu Xiao ◽  
Guoshan Lu ◽  
Wanqian Guo ◽  
Shuilin Jin
Keyword(s):  
Dimensionality Reduction ◽  
Data Quality ◽  
Single Cell ◽  
Enrichment Analysis ◽  
Cell Types ◽  
Size Reduction ◽  
Rna Seq ◽  
Reduction Methods ◽  
The Many

Abstract Background Single-cell RNA sequencing can be used to fairly determine cell types, which is beneficial to the medical field, especially the many recent studies on COVID-19. Generally, single-cell RNA data analysis pipelines include data normalization, size reduction, and unsupervised clustering. However, different normalization and size reduction methods will significantly affect the results of clustering and cell type enrichment analysis. Choices of preprocessing paths is crucial in scRNA-Seq data mining, because a proper preprocessing path can extract more important information from complex raw data and lead to more accurate clustering results. Results We proposed a method called NDRindex (Normalization and Dimensionality Reduction index) to evaluate data quality of outcomes of normalization and dimensionality reduction methods. The method includes a function to calculate the degree of data aggregation, which is the key to measuring data quality before clustering. For the five single-cell RNA sequence datasets we tested, the results proved the efficacy and accuracy of our index. Conclusions This method we introduce focuses on filling the blanks in the selection of preprocessing paths, and the result proves its effectiveness and accuracy. Our research provides useful indicators for the evaluation of RNA-Seq data.

scholarly journals Modeling Intestinal Epithelial Response to Interferon-γ in Induced Pluripotent Stem Cell-Derived Human Intestinal Organoids

2020 ◽  
Vol 22 (1) ◽  
pp. 288
Author(s):  
Michael J. Workman ◽  
Elissa Troisi ◽  
Stephan R. Targan ◽  
Clive N. Svendsen ◽  
Robert J. Barrett
Keyword(s):  
Interferon Gamma ◽  
Adherens Junction ◽  
Induced Pluripotent Stem Cell ◽  
Enrichment Analysis ◽  
Cell Types ◽  
Gene Set Enrichment Analysis ◽  
Intestinal Organoids ◽  
Ifn Γ ◽  
Induced Pluripotent

Human intestinal organoids (HIOs) are increasingly being used to model intestinal responses to various stimuli, yet few studies have confirmed the fidelity of this modeling system. Given that the interferon-gamma (IFN-γ) response has been well characterized in various other cell types, our goal was to characterize the response to IFN-γ in HIOs derived from induced pluripotent stem cells (iPSCs). To achieve this, iPSCs were directed to form HIOs and subsequently treated with IFN-γ. Our results demonstrate that IFN-γ phosphorylates STAT1 but has little effect on the expression or localization of tight and adherens junction proteins in HIOs. However, transcriptomic profiling by microarray revealed numerous upregulated genes such as IDO1, GBP1, CXCL9, CXCL10 and CXCL11, which have previously been shown to be upregulated in other cell types in response to IFN-γ. Notably, “Response to Interferon Gamma” was determined to be one of the most significantly upregulated gene sets in IFN-γ-treated HIOs using gene set enrichment analysis. Interestingly, similar genes and pathways were upregulated in publicly available datasets contrasting the gene expression of in vivo biopsy tissue from patients with IBD against healthy controls. These data confirm that the iPSC-derived HIO modeling system represents an appropriate platform to evaluate the effects of various stimuli and specific environmental factors responsible for the alterations in the intestinal epithelium seen in various gastrointestinal conditions such as inflammatory bowel disease.

scholarly journals Impact of IL-27 on hepatocyte antiviral gene expression and function

2016 ◽  
Vol 1 ◽  
pp. 17 ◽  
Author(s):  
Narayan Ramamurthy ◽  
Sara Boninsegna ◽  
Rebecca Adams ◽  
Natasha Sahgal ◽  
Helen Lockstone ◽  
...  
Keyword(s):  
Cell Culture ◽  
Antigen Presentation ◽  
Microarray Analysis ◽  
Enrichment Analysis ◽  
Cell Types ◽  
Antiviral Effect ◽  
Gene Set Enrichment Analysis ◽  
And Function ◽  
The Impact

Background: Interleukin (IL)-27 is a member of the IL-6/IL-12 family of cytokines. It is a potent cytokine, with potential antiviral impact, and has been shown to play a role in modulating functions of diverse cell types, including Th1, Th2, and NK and B cells, demonstrating both pro- and anti-inflammatory roles.  In hepatocytes, it is capable of inducing signal transducer and activator of transcription (STAT)1, STAT3 and interferon-stimulated genes. Methods: To address its role in viral hepatitis, the antiviral activity of IL-27 against hepatitis C virus (HCV) and hepatitis B virus (HBV) was tested in vitro using cell-culture-derived infectious HCV (HCVcc) cell culture system and the HepaRG HBV cell culture model. To further investigate the impact of IL-27 on hepatocytes, Huh7.5 cells were treated with IL-27 to analyse the differentially expressed genes by microarray analysis. Furthermore, by quantitative PCR, we analyzed the up-regulation of chemokine (CXCL)-10 in response to IL-27. Results: In both HCV and HBV infection models, we observed only a modest direct antiviral effect. Microarray analysis showed that the up-regulated genes mostly belonged to antigen presentation and DNA replication pathways, and involved strong up-regulation of CXCL-10, a gene associated with liver inflammation. Overall, gene set enrichment analysis showed a striking correlation of these genes with those up-regulated in response to related cytokines in diverse cell populations. Conclusion: Our data indicate that IL-27 can have a significant pro-inflammatory impact in vitro, although the direct antiviral effect is modest. It may have a potential impact on hepatocyte function, especially chemokine expression and antigen presentation.

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