
Researchers at Baylor College of Medicine, Yale University and other institutions have identified previously unrecognized changes in gene expression and cellular interactions in distinct cell populations in chronic obstructive pulmonary disease (COPD). The findings highlight the complexity and diversity of cellular injury and inflammation in COPD, providing a better understanding of the factors and processes involved in the disease.
Published in Nature Communications, the study used single-cell RNA sequencing to analyze gene expression profiles of lung tissue obtained from patients with COPD and from mice exposed to cigarette smoke. The researchers separated all the cells within the lung and measured the gene expression profile of each individual cell. Then, they organized this information in a cell atlas that is available to researchers interested in exploring gene expression in individual cells in lungs with the disease.
“Our analysis identified novel changes in gene expression and cellular interactions in three distinct cell populations commonly implicated in COPD: epithelial (in the lungs), endothelial (in blood vessels) and macrophage cells (part of the immune system),” said Dr. Ivan Rosas, senior author of the study and section chief of pulmonary, critical care and sleep medicine in the Department of Medicine at Baylor.
Profiling of cell types in COPD using scRNAseq data

A Overview of project design. Tissue from 17 lungs with advanced COPD and 15 control donor lungs were dissociated into single-cell suspensions. Individual cells were barcoded and sequenced for analysis. Similarly, lung tissue from 2 male (M) and 2 female (F) mice exposed to 10 months of cigarette smoke (CS) and 2 M and 2 F mice exposed only to room air (RA) were dissociated into single-cell suspensions, barcoded, and sequenced for analysis. B Uniform Manifold Approximation and Projection (UMAP) representation of 111,540 single cells grouped into 37 distinct cell types (left) with identification of COPD and control cells (right). Violin plots of normalized expression values for canonical cell-specific marker genes for C epithelial, D endothelial, and E stromal cells. AT1 alveolar epithelial type I cells, AT2 alveolar epithelial type II cells, PNEC pulmonary neuroendocrine cells, SMC smooth muscle cells, gCap general capillary, cMonocyte classical monocytes, ncMonocyte non-classical monocyte, Macs. macrophages, DC dendritic cells, cDC conventional dendritic cells, pDC plasmacytoid dendritic cells, NK natural killer cells, ILC innate lymphoid cells.
The researchers identified a subpopulation of epithelial cells in lungs with COPD that has abnormal expression of genes involved in metabolic, antioxidant and cellular stress responses, when compared to controls.
“This highly innovative translational study is a continuation of a successful long-standing collaboration with Dr. Naftali Kaminski and colleagues at Yale University, which has led to the description of multiple lung cell atlases in health and disease that are now publicly available. In this study, Drs. Maor Sauler and John McDonough of Yale University, the corresponding authors, demonstrate that endothelial cells in capillary blood vessels in COPD lungs are inflamed and that a subpopulation of macrophages expressing high levels of metallothioneins, proteins that regulate the balance of certain metals in the body, seems to contribute to the disease,” Rosas said.
Future studies evaluating outcomes of specific alterations identified in this study will provide further insights into mechanisms that contribute to COPD and having a large and accessible data set of COPD cells, such as the cell atlas, is an essential tool to begin developing therapies to target the condition.
The research team continues to focus on developing innovative genomic datasets to better understand the pathologic underpinnings of chronic lung disease. They are currently in the process of completing the largest COVID-19 lung fibrosis cell atlas.
Source – Baylor College of Medicine
Sauler M, McDonough JE, Adams TS, Kothapalli N, Barnthaler T, Werder RB, Schupp JC, Nouws J, Robertson MJ, Coarfa C, Yang T, Chioccioli M, Omote N, Cosme C Jr, Poli S, Ayaub EA, Chu SG, Jensen KH, Gomez JL, Britto CJ, Raredon MSB, Niklason LE, Wilson AA, Timshel PN, Kaminski N, Rosas IO. (2022) Characterization of the COPD alveolar niche using single-cell RNA sequencing. Nat Commun 13(1):494. [article]
Researchers at Baylor College of Medicine, Yale University and other institutions have identified previously unrecognized changes in gene expression and cellular interactions in distinct cell populations in chronic obstructive pulmonary disease (COPD). The findings highlight the complexity and diversity of cellular injury and inflammation in COPD, providing a better understanding of the factors and processes involved in the disease.
Published in Nature Communications, the study used single-cell RNA sequencing to analyze gene expression profiles of lung tissue obtained from patients with COPD and from mice exposed to cigarette smoke. The researchers separated all the cells within the lung and measured the gene expression profile of each individual cell. Then, they organized this information in a cell atlas that is available to researchers interested in exploring gene expression in individual cells in lungs with the disease.
Profiling of cell types in COPD using scRNAseq data
A Overview of project design. Tissue from 17 lungs with advanced COPD and 15 control donor lungs were dissociated into single-cell suspensions. Individual cells were barcoded and sequenced for analysis. Similarly, lung tissue from 2 male (M) and 2 female (F) mice exposed to 10 months of cigarette smoke (CS) and 2 M and 2 F mice exposed only to room air (RA) were dissociated into single-cell suspensions, barcoded, and sequenced for analysis. B Uniform Manifold Approximation and Projection (UMAP) representation of 111,540 single cells grouped into 37 distinct cell types (left) with identification of COPD and control cells (right). Violin plots of normalized expression values for canonical cell-specific marker genes for C epithelial, D endothelial, and E stromal cells. AT1 alveolar epithelial type I cells, AT2 alveolar epithelial type II cells, PNEC pulmonary neuroendocrine cells, SMC smooth muscle cells, gCap general capillary, cMonocyte classical monocytes, ncMonocyte non-classical monocyte, Macs. macrophages, DC dendritic cells, cDC conventional dendritic cells, pDC plasmacytoid dendritic cells, NK natural killer cells, ILC innate lymphoid cells.
The researchers identified a subpopulation of epithelial cells in lungs with COPD that has abnormal expression of genes involved in metabolic, antioxidant and cellular stress responses, when compared to controls.
Future studies evaluating outcomes of specific alterations identified in this study will provide further insights into mechanisms that contribute to COPD and having a large and accessible data set of COPD cells, such as the cell atlas, is an essential tool to begin developing therapies to target the condition.
The research team continues to focus on developing innovative genomic datasets to better understand the pathologic underpinnings of chronic lung disease. They are currently in the process of completing the largest COVID-19 lung fibrosis cell atlas.
Source – Baylor College of Medicine
Sauler M, McDonough JE, Adams TS, Kothapalli N, Barnthaler T, Werder RB, Schupp JC, Nouws J, Robertson MJ, Coarfa C, Yang T, Chioccioli M, Omote N, Cosme C Jr, Poli S, Ayaub EA, Chu SG, Jensen KH, Gomez JL, Britto CJ, Raredon MSB, Niklason LE, Wilson AA, Timshel PN, Kaminski N, Rosas IO. (2022) Characterization of the COPD alveolar niche using single-cell RNA sequencing. Nat Commun 13(1):494. [article]
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Researchers at Baylor College of Medicine, Yale University and other institutions have identified previously unrecognized changes in gene expression and cellular interactions in distinct cell populations in chronic obstructive pulmonary disease (COPD). The findings highlight the complexity and diversity of cellular injury and inflammation in COPD, providing a better understanding of the factors and processes involved in the disease.
Published in Nature Communications, the study used single-cell RNA sequencing to analyze gene expression profiles of lung tissue obtained from patients with COPD and from mice exposed to cigarette smoke. The researchers separated all the cells within the lung and measured the gene expression profile of each individual cell. Then, they organized this information in a cell atlas that is available to researchers interested in exploring gene expression in individual cells in lungs with the disease.
Profiling of cell types in COPD using scRNAseq data
A Overview of project design. Tissue from 17 lungs with advanced COPD and 15 control donor lungs were dissociated into single-cell suspensions. Individual cells were barcoded and sequenced for analysis. Similarly, lung tissue from 2 male (M) and 2 female (F) mice exposed to 10 months of cigarette smoke (CS) and 2 M and 2 F mice exposed only to room air (RA) were dissociated into single-cell suspensions, barcoded, and sequenced for analysis. B Uniform Manifold Approximation and Projection (UMAP) representation of 111,540 single cells grouped into 37 distinct cell types (left) with identification of COPD and control cells (right). Violin plots of normalized expression values for canonical cell-specific marker genes for C epithelial, D endothelial, and E stromal cells. AT1 alveolar epithelial type I cells, AT2 alveolar epithelial type II cells, PNEC pulmonary neuroendocrine cells, SMC smooth muscle cells, gCap general capillary, cMonocyte classical monocytes, ncMonocyte non-classical monocyte, Macs. macrophages, DC dendritic cells, cDC conventional dendritic cells, pDC plasmacytoid dendritic cells, NK natural killer cells, ILC innate lymphoid cells.
The researchers identified a subpopulation of epithelial cells in lungs with COPD that has abnormal expression of genes involved in metabolic, antioxidant and cellular stress responses, when compared to controls.
Future studies evaluating outcomes of specific alterations identified in this study will provide further insights into mechanisms that contribute to COPD and having a large and accessible data set of COPD cells, such as the cell atlas, is an essential tool to begin developing therapies to target the condition.
The research team continues to focus on developing innovative genomic datasets to better understand the pathologic underpinnings of chronic lung disease. They are currently in the process of completing the largest COVID-19 lung fibrosis cell atlas.
Source – Baylor College of Medicine
Sauler M, McDonough JE, Adams TS, Kothapalli N, Barnthaler T, Werder RB, Schupp JC, Nouws J, Robertson MJ, Coarfa C, Yang T, Chioccioli M, Omote N, Cosme C Jr, Poli S, Ayaub EA, Chu SG, Jensen KH, Gomez JL, Britto CJ, Raredon MSB, Niklason LE, Wilson AA, Timshel PN, Kaminski N, Rosas IO. (2022) Characterization of the COPD alveolar niche using single-cell RNA sequencing. Nat Commun 13(1):494. [article]
Related Posts
Single-cell and single-embryo RNA sequencing
RNA sequencing reveals functional chimeric mRNAs in mammalian immunity
Deep learning improves microRNA target prediction from sequence
Atlas of the brain’s striatum could guide researchers to new drug treatments
scLS – a computationally efficient differentially expressed gene detection algorithm
Spatial mapping of RNA turnover kinetics in the mouse brain
Immune cells offer insights on billion-dollar virus
SPIDER improves spatial transcriptomics data using single-cell RNA sequencing
Ultrafast and reference-free sequence discovery in single-cell data
ARCADIA combines RNA sequencing and spatial proteomics to reveal how tissue location shapes cell behavior
An end-to-end computational framework for “Record-seq” transcriptional recording data
A functionally integrated cross-tissue alternative splicing program during short-term calorie restriction
ExoShorkie – predicting RNA-seq coverage of exogenous genomes in yeast by transfer learning
Dietary oxidized plant sterol shifts macrophage state to fuel aortic inflammation
Bonsai reconstructs tree representations for distortion-free visualization and exploration of high-dimensional data
MiRQuery – a user-friendly web app for the interactive analysis and visualization of microRNA sequencing data
RNA sequencing resolves cryptic pathogenic variants in mitochondrial disease
Unlocking the past – new method helps gain insights into old tissue
New RNA sequencing model improves sequencing depth planning for UMI transcriptomics
Combining RNA sequencing and pathology images identifies glioblastoma subgroups linked to survival
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