scholarly journals Single-cell RNA-seq of the stromal vascular fraction of adipose tissue reveals lineage-specific changes in cancer-related lymphedema

2020 ◽  
Author(s):  
Xuanyu Liu ◽  
Meng Yuan ◽  
Qinqin Xiang ◽  
Wen Chen ◽  
Zhujun Li ◽  
...  
Keyword(s):  
Adipose Tissue ◽  
Single Cell ◽  
Stromal Vascular Fraction ◽  
Gene Set Enrichment Analysis ◽  
Receptor Interaction ◽  
Rna Seq ◽  
Communication Analysis ◽  
Adipose Tissues ◽  
Medical Therapies ◽  
Cell Subpopulations

AbstractLymphedema is a chronic tissue edema that frequently occurs following lymph node resection for cancer treatment, and is characterized by progressive swelling, chronic inflammation, excessive fibrosis and adipose deposition in the affected limbs. We still lack targeted medical therapies for this disease due to the incomplete understanding of the mechanism underlying the pathogenesis. Here, we performed single-cell RNA-seq of 70,209 cells of the stromal vascular fraction (SVF) of subcutaneous adipose tissue from patients with cancer-related lymphedema and healthy donors. Unbiased clustering revealed 21 cell clusters, which were assigned to 10 cell lineages. One of the four ASC subpopulations, c3, was significantly expanded in lymphedema, which may be related to the fibrosis and pathologic mineralization of adipose tissues in lymphedema. Dysregulated pathways and genes of ASCs in lymphedema were identified through gene set enrichment analysis and differential regulatory network analysis, which reflect the pathophysiological changes in ASCs in lymphedema: enhanced fibrosis, mineralization and proliferation as well as compromised immunosuppression capacity. In addition, we characterized the three subpopulations of macrophages, and found that the adipose tissue of lymphedema displayed immunological dysfunction characterized by a striking depletion of anti-inflammatory macrophages, i.e., LYVE+ resident-like macrophages. Cell-cell communication analysis revealed a perivascular ligand-receptor interaction module among ASCs, macrophages and vascular endothelial cells in adipose tissue. Communication changes for ASCs in lymphedema were identified. For example, PDGFD-PDGFR complex interactions were significantly enhanced between a number of lineages and ASCs, reflecting the role of PDGFD signaling in the pathophysiological changes in ASCs. Finally, we mapped the previously reported candidate genes predisposing to cancer-related lymphedema to cell subpopulations in the SVF, and found that GJC2, the most likely causal gene was highly expressed in the lymphedema-associated ASC subpopulation c3. In summary, we provided the first comprehensive analysis of cellular heterogeneity, lineage-specific regulatory changes and intercellular communication alterations of the SVF in adipose tissues from cancer-related lymphedema at a single-cell resolution. The lymphedema-associated cell subpopulations and dysregulated pathways may serve as potential targets for medical therapies. Our large-scale dataset constitutes a valuable resource for further investigations of the mechanism of cancer-related lymphedema.

scholarly journals Neuromuscular junction-specific genes screening by deep RNA-seq analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tiankun Hui ◽  
Hongyang Jing ◽  
Xinsheng Lai
Keyword(s):  
Ion Channel ◽  
Neuromuscular Junctions ◽  
Gene Set Enrichment Analysis ◽  
Differentially Expressed ◽  
Muscle Membrane ◽  
Receptor Interaction ◽  
Specific Gene ◽  
Rna Seq ◽  
Channel Activity ◽  
Ppi Networks

Abstract Background Neuromuscular junctions (NMJs) are chemical synapses formed between motor neurons and skeletal muscle fibers and are essential for controlling muscle contraction. NMJ dysfunction causes motor disorders, muscle wasting, and even breathing difficulties. Increasing evidence suggests that many NMJ disorders are closely related to alterations in specific gene products that are highly concentrated in the synaptic region of the muscle. However, many of these proteins are still undiscovered. Thus, screening for NMJ-specific proteins is essential for studying NMJ and the pathogenesis of NMJ diseases. Results In this study, synaptic regions (SRs) and nonsynaptic regions (NSRs) of diaphragm samples from newborn (P0) and adult (3-month-old) mice were used for RNA-seq. A total of 92 and 182 genes were identified as differentially expressed between the SR and NSR in newborn and adult mice, respectively. Meanwhile, a total of 1563 genes were identified as differentially expressed between the newborn SR and adult SR. Gene Ontology (GO) enrichment analyses, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and gene set enrichment analysis (GSEA) of the DEGs were performed. Protein–protein interaction (PPI) networks were constructed using STRING and Cytoscape. Further analysis identified some novel proteins and pathways that may be important for NMJ development, maintenance and maturation. Specifically, Sv2b, Ptgir, Gabrb3, P2rx3, Dlgap1 and Rims1 may play roles in NMJ development. Hcn1 may localize to the muscle membrane to regulate NMJ maintenance. Trim63, Fbxo32 and several Asb family proteins may regulate muscle developmental-related processes. Conclusion Here, we present a complete dataset describing the spatiotemporal transcriptome changes in synaptic genes and important synaptic pathways. The neuronal projection-related pathway, ion channel activity and neuroactive ligand-receptor interaction pathway are important for NMJ development. The myelination and voltage-gated ion channel activity pathway may be important for NMJ maintenance. These data will facilitate the understanding of the molecular mechanisms underlying the development and maintenance of NMJ and the pathogenesis of NMJ disorders.

scholarly journals CHARTS: A web application for characterizing and comparing tumor subpopulations in publicly available single-cell RNA-seq datasets

2020 ◽  
Author(s):  
Matthew N. Bernstein ◽  
Zijian Ni ◽  
Michael Collins ◽  
Mark E. Burkard ◽  
Christina Kendziorski ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Web Application ◽  
Cellular Heterogeneity ◽  
Rna Seq ◽  
Individual Gene ◽  
Genome Wide ◽  
Progression Of Disease ◽  
Cell Subpopulations ◽  
Available Information

AbstractBackgroundSingle-cell RNA-seq (scRNA-seq) enables the profiling of genome-wide gene expression at the single-cell level and in so doing facilitates insight into and information about cellular heterogeneity within a tissue. Perhaps nowhere is this more important than in cancer, where tumor and tumor microenvironment heterogeneity directly impact development, maintenance, and progression of disease. While publicly available scRNA-seq cancer datasets offer unprecedented opportunity to better understand the mechanisms underlying tumor progression, metastasis, drug resistance, and immune evasion, much of the available information has been underutilized, in part, due to the lack of tools available for aggregating and analysing these data.ResultsWe present CHARacterizing Tumor Subpopulations (CHARTS), a computational pipeline and web application for analyzing, characterizing, and integrating publicly available scRNA-seq cancer datasets. CHARTS enables the exploration of individual gene expression, cell type, malignancy-status, differentially expressed genes, and gene set enrichment results in subpopulations of cells across multiple tumors and datasets.ConclusionCHARTS is an easy to use, comprehensive platform for exploring single-cell subpopulations within tumors across the ever-growing collection of public scRNA-seq cancer datasets. CHARTS is freely available at charts.morgridge.org.

scholarly journals Beyondcell: targeting cancer therapeutic heterogeneity in single-cell RNA-seq data

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Coral Fustero-Torre ◽  
María José Jiménez-Santos ◽  
Santiago García-Martín ◽  
Carlos Carretero-Puche ◽  
Luis García-Jimeno ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Lines ◽  
Tumour Cell ◽  
Enrichment Score ◽  
Cell Populations ◽  
Drug Selection ◽  
Malignant Cells ◽  
Rna Seq ◽  
Computational Methodology ◽  
Cell Subpopulations

AbstractWe present Beyondcell, a computational methodology for identifying tumour cell subpopulations with distinct drug responses in single-cell RNA-seq data and proposing cancer-specific treatments. Our method calculates an enrichment score in a collection of drug signatures, delineating therapeutic clusters (TCs) within cellular populations. Additionally, Beyondcell determines the therapeutic differences among cell populations and generates a prioritised sensitivity-based ranking in order to guide drug selection. We performed Beyondcell analysis in five single-cell datasets and demonstrated that TCs can be exploited to target malignant cells both in cancer cell lines and tumour patients. Beyondcell is available at: https://gitlab.com/bu_cnio/beyondcell.

scholarly journals Single cell atlas of beige remodeling of white adipose tissue reveals a myeloid to lymphoid shift during cold exposure compared to beta 3 adrenergic stimulation

2020 ◽  
Author(s):  
Nabil Rabhi ◽  
Anna C. Belkina ◽  
Kathleen Desevin ◽  
Briana Noel Cortez ◽  
Stephen R. Farmer
Keyword(s):  
Adipose Tissue ◽  
Single Cell ◽  
White Adipose Tissue ◽  
Cold Exposure ◽  
Immune Cells ◽  
Drug Targets ◽  
Stromal Vascular Fraction ◽  
Adrenergic Stimulation ◽  
Cellular Dynamics ◽  
Metabolic Complications

SUMMARYWhite adipose tissue (WAT) is a dynamic tissue, which responds to environmental stimuli and dietary cues by changing its morphology and metabolic capacity. The ability of WAT to undergo a beige remodeling has become an appealing strategy to combat obesity and its related metabolic complications. Within the cell mixture that constitutes the stromal vascular fraction (SVF), WAT beiging is initiated through expansion and differentiation of adipocytes progenitor cells, however, the extent of the SVF cellular changes is still poorly understood. Additionally, direct beta 3 adrenergic receptor (Adrb3) stimulation has been extensively used to mimic physiological cold- induced beiging, yet it is still unknown whether Adrb3 activation induces the same WAT remodeling as cold exposure. Here, by using single cell RNA sequencing, we provide a comprehensive atlas of the cellular dynamics during beige remodeling within white adipose tissue. We reveal drastic changes both in the overall cellular composition and transcriptional states of individual cell subtypes between Adrb3- and cold-induced beiging. Moreover, we demonstrate that cold exposure induces a myeloid to lymphoid shift of the immune compartment compared to Adrb3 activation. Further analysis, showed that Adrb3 stimulation leads to activation of the interferon/Stat1 pathways favoring infiltration of myeloid immune cells, while repression of this pathway by cold promotes lymphoid immune cells recruitment. These findings provide new insight into the cellular dynamics during WAT beige remodeling and could ultimately lead to novel strategies to identify translationally-relevant drug targets to counteract obesity and T2D.

scholarly journals Cross-Tissue Characterization of Heterogeneities of Mesenchymal Stem Cells and Their Differentiation Potentials

2021 ◽  
Vol 9 ◽  
Author(s):  
Wenhong Hou ◽  
Li Duan ◽  
Changyuan Huang ◽  
Xingfu Li ◽  
Xiao Xu ◽  
...  
Keyword(s):  
Single Cell ◽  
Umbilical Cord ◽  
Stromal Cells ◽  
Tissue Characterization ◽  
Single Cell Level ◽  
Rna Seq ◽  
Cell Level ◽  
Cell Sources ◽  
Cell Subpopulations

Mesenchymal stem/stromal cells (MSCs) are promising cell sources for regenerative medicine and the treatment of autoimmune disorders. Comparing MSCs from different tissues at the single-cell level is fundamental for optimizing clinical applications. Here we analyzed single-cell RNA-seq data of MSCs from four tissues, namely umbilical cord, bone marrow, synovial tissue, and adipose tissue. We identified three major cell subpopulations, namely osteo-MSCs, chondro-MSCs, and adipo/myo-MSCs, across all MSC samples. MSCs from the umbilical cord exhibited the highest immunosuppression, potentially indicating it is the best immune modulator for autoimmune diseases. MSC subpopulations, with different subtypes and tissue sources, showed pronounced differences in differentiation potentials. After we compared the cell subpopulations and cell status pre-and-post chondrogenesis induction, osteogenesis induction, and adipogenesis induction, respectively, we found MSC subpopulations expanded and differentiated when their subtypes consist with induction directions, while the other subpopulations shrank. We identified the genes and transcription factors underlying each induction at the single-cell level and subpopulation level, providing better targets for improving induction efficiency.

scholarly journals Genome-wide mRNA profiling identifies the NRF2-regulated lymphocyte oxidative stress status in patients with silicosis

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Yingzheng Zhao ◽  
Guangcui Xu ◽  
Haibin Li ◽  
Meiyu Chang ◽  
Cheng Xiong ◽  
...  
Keyword(s):  
Oxidative Stress ◽  
Stress State ◽  
Transforming Growth Factor ◽  
Quantitative Polymerase Chain Reaction ◽  
Gene Set Enrichment Analysis ◽  
Receptor Interaction ◽  
Related Factor ◽  
Healthy Controls ◽  
Rna Seq ◽  
Antioxidant Genes

Abstract Background The immunomodulatory abnormalities of silicosis are related to the lymphocyte oxidative stress state. The potential effect of antioxidant therapy on silicosis may depend on the variation in nuclear factor erythroid 2-related factor 2 (NRF2)-regulated antioxidant genes in peripheral blood mononuclear cells (PBMCs). As NRF2 is a redox-sensitive transcription factor, its possible roles and underlying mechanism in the treatment of silicosis need to be clarified. Methods Ninety-two male patients with silicosis and 87 male healthy volunteers were randomly selected. PBMCs were isolated from fresh blood from patients with silicosis and healthy controls. The lymphocyte oxidative stress state was investigated by evaluating NRF2 expression and NRF2-dependent antioxidative genes in PBMCs from patients with silicosis. Key differentially expressed genes (DEGs) and signaling pathways were identified utilizing RNA sequencing (RNA-Seq) and bioinformatics technology. Gene set enrichment analysis was used to identify the differences in NRF2 signaling networks between patients with silicosis and healthy controls. Results The number of monocytes was significantly higher in patients with silicosis than that of healthy controls. Furthermore, RNA-Seq findings were confirmed using quantitative polymerase chain reaction and revealed that NRF2-regulated DEGs were associated with glutathione metabolism, transforming growth factor-β, and the extracellular matrix receptor interaction signaling pathway in PBMCs from patients with silicosis. The top 10 hub genes were identified by PPI analysis: SMAD2, MAPK3, THBS1, SMAD3, ITGB3, integrin alpha-V (ITGAV), von Willebrand factor (VWF), BMP4, CD44, and SMAD7. Conclusions These findings suggest that NRF2 signaling regulates the lymphocyte oxidative stress state and may contribute to fibrogenic responses in human PBMCs. Therefore, NRF2 might serve as a novel preventive and therapeutic candidate for silicosis.

scholarly journals Accurate denoising of single-cell RNA-Seq data using unbiased principal component analysis

2019 ◽  
Author(s):  
Florian Wagner ◽  
Dalia Barkley ◽  
Itai Yanai
Keyword(s):  
Principal Component Analysis ◽  
Single Cell ◽  
Simulated Data ◽  
Principal Component ◽  
Cell Aggregation ◽  
Component Analysis ◽  
Rna Seq ◽  
Highly Expressed Genes ◽  
Cell Subpopulations ◽  
Aggregation Step

AbstractSingle-cell RNA-Seq measurements are commonly affected by high levels of technical noise, posing challenges for data analysis and visualization. A diverse array of methods has been proposed to computationally remove noise by sharing information across similar cells or genes, however their respective accuracies have been difficult to establish. Here, we propose a simple denoising strategy based on principal component analysis (PCA). We show that while PCA performed on raw data is biased towards highly expressed genes, this bias can be mitigated with a cell aggregation step, allowing the recovery of denoised expression values for both highly and lowly expressed genes. We benchmark our resulting ENHANCE algorithm and three previously described methods on simulated data that closely mimic real datasets, showing that ENHANCE provides the best overall denoising accuracy, recovering modules of co-expressed genes and cell subpopulations. Implementations of our algorithm are available at https://github.com/yanailab/enhance.

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