scholarly journals Defining the Cell Types That Drive Idiopathic Pulmonary Fibrosis Using Single-Cell RNA Sequencing

2019 ◽  
Vol 199 (12) ◽  
pp. 1454-1456 ◽  
Author(s):  
Joanna M. Poczobutt ◽  
Oliver Eickelberg
Keyword(s):  
Idiopathic Pulmonary Fibrosis ◽  
Pulmonary Fibrosis ◽  
Single Cell ◽  
Rna Sequencing ◽  
Cell Types ◽  
Single Cell Rna Sequencing

scholarly journals Single-cell RNA-sequencing reveals profibrotic roles of distinct epithelial and mesenchymal lineages in pulmonary fibrosis

2019 ◽  
Author(s):  
Arun C. Habermann ◽  
Austin J. Gutierrez ◽  
Linh T. Bui ◽  
Stephanie L. Yahn ◽  
Nichelle I. Winters ◽  
...  
Keyword(s):  
Epithelial Cell ◽  
Pulmonary Fibrosis ◽  
Single Cell ◽  
Rna Sequencing ◽  
Cell Types ◽  
Distinct Cell ◽  
Single Cell Rna Sequencing ◽  
Epithelial Remodeling ◽  
Discrete Manner ◽  
Cell Phenotypes

AbstractPulmonary fibrosis is a form of chronic lung disease characterized by pathologic epithelial remodeling and accumulation of extracellular matrix. In order to comprehensively define the cell types, mechanisms and mediators driving fibrotic remodeling in lungs with pulmonary fibrosis, we performed single-cell RNA-sequencing of single-cell suspensions from 10 non-fibrotic control and 20 PF lungs. Analysis of 114,396 cells identified 31 distinct cell types. We report a remarkable shift in epithelial cell phenotypes occurs in the peripheral lung in PF, and identify several previously unrecognized epithelial cell phenotypes including a KRT5−/KRT17+, pathologic ECM-producing epithelial cell population that was highly enriched in PF lungs. Multiple fibroblast subtypes were observed to contribute to ECM expansion in a spatially-discrete manner. Together these data provide high-resolution insights into the complexity and plasticity of the distal lung epithelium in human disease, and indicate a diversity of epithelial and mesenchymal cells contribute to pathologic lung fibrosis.One Sentence SummarySingle-cell RNA-sequencing provides new insights into pathologic epithelial and mesenchymal remodeling in the human lung.

scholarly journals Single cell RNA Sequencing Identifies G-protein Coupled Receptor 87 as a Novel Basal Cell Marker of Distal Honeycomb Cysts in Idiopathic Pulmonary Fibrosis

2021 ◽  
Author(s):  
Katharina Heinzelmann ◽  
Qianjiang Hu ◽  
Yan Hu ◽  
Evgenia Dobrinskikh ◽  
Henrik M. Ulke ◽  
...  
Keyword(s):  
Idiopathic Pulmonary Fibrosis ◽  
Pulmonary Fibrosis ◽  
Single Cell ◽  
Rna Sequencing ◽  
G Protein ◽  
Potential Contribution ◽  
G Protein Coupled Receptor ◽  
Basal Progenitor ◽  
Single Cell Rna Sequencing ◽  
G Protein Coupled

AbstractIdiopathic Pulmonary Fibrosis (IPF) is a progressive and fatal lung disease with limited therapeutic options. Epithelial reprogramming and honeycomb cysts are key pathological features of IPF, however, the IPF distal bronchiole cell subtypes and their potential contribution to IPF development and progression still remain poorly characterized. Here, we utilized single-cell RNA sequencing on enriched EpCAM+ cells of the distal IPF and Donor lung. Using the 10x Genomics platform, we generated a dataset of 47,881 cells and found distinct cell clusters, including rare cell types, such as suprabasal cells recently reported in the healthy lung. We identified G-protein coupled receptor (GPR) 87 as a novel surface marker of distal Keratin (KRT)5+ basal cells. GPR87 expression was localized to distal bronchioles and honeycomb cysts in IPF in situ by RNA Scope and immunolabeling. Modulation of GPR87 in primary human bronchial epithelial cells cultures resulted in impaired airway differentiation and ciliogenesis. Thus, GPR87 is a novel marker and potentially druggable target of KRT5+ basal progenitor cells likely contributing to bronchiole remodeling and honeycomb cyst development in IPF.

scholarly journals Single-cell RNA sequencing reveals profibrotic roles of distinct epithelial and mesenchymal lineages in pulmonary fibrosis

2020 ◽  
Vol 6 (28) ◽  
pp. eaba1972 ◽  
Author(s):  
Arun C. Habermann ◽  
Austin J. Gutierrez ◽  
Linh T. Bui ◽  
Stephanie L. Yahn ◽  
Nichelle I. Winters ◽  
...  
Keyword(s):  
Epithelial Cell ◽  
Pulmonary Fibrosis ◽  
Single Cell ◽  
Rna Sequencing ◽  
Cell Types ◽  
Distinct Cell ◽  
Single Cell Rna Sequencing ◽  
Epithelial Remodeling ◽  
Discrete Manner ◽  
Cell Phenotypes

Pulmonary fibrosis (PF) is a form of chronic lung disease characterized by pathologic epithelial remodeling and accumulation of extracellular matrix (ECM). To comprehensively define the cell types, mechanisms, and mediators driving fibrotic remodeling in lungs with PF, we performed single-cell RNA sequencing of single-cell suspensions from 10 nonfibrotic control and 20 PF lungs. Analysis of 114,396 cells identified 31 distinct cell subsets/states. We report that a remarkable shift in epithelial cell phenotypes occurs in the peripheral lung in PF and identify several previously unrecognized epithelial cell phenotypes, including a KRT5−/KRT17+ pathologic, ECM-producing epithelial cell population that was highly enriched in PF lungs. Multiple fibroblast subtypes were observed to contribute to ECM expansion in a spatially discrete manner. Together, these data provide high-resolution insights into the complexity and plasticity of the distal lung epithelium in human disease and indicate a diversity of epithelial and mesenchymal cells contribute to pathologic lung fibrosis.

scholarly journals Single-cell data clustering based on sparse optimization and low-rank matrix factorization

2021 ◽  
Author(s):  
Yinlei Hu ◽  
Bin Li ◽  
Falai Chen ◽  
Kun Qu
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Matrix Factorization ◽  
Data Clustering ◽  
Cell Types ◽  
Low Rank ◽  
Sequencing Data ◽  
Rank Matrix ◽  
Single Cell Rna Sequencing ◽  
Low Rank Matrix

Abstract Unsupervised clustering is a fundamental step of single-cell RNA sequencing data analysis. This issue has inspired several clustering methods to classify cells in single-cell RNA sequencing data. However, accurate prediction of the cell clusters remains a substantial challenge. In this study, we propose a new algorithm for single-cell RNA sequencing data clustering based on Sparse Optimization and low-rank matrix factorization (scSO). We applied our scSO algorithm to analyze multiple benchmark datasets and showed that the cluster number predicted by scSO was close to the number of reference cell types and that most cells were correctly classified. Our scSO algorithm is available at https://github.com/QuKunLab/scSO. Overall, this study demonstrates a potent cell clustering approach that can help researchers distinguish cell types in single-cell RNA sequencing data.

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