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Dissecting the cellular specificity of smoking effects and reconstructing lineages in the human airway epithelium
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Dissecting the cellular specificity of smoking effects and reconstructing lineages in the human airway epithelium
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Journal Article
Dissecting the cellular specificity of smoking effects and reconstructing lineages in the human airway epithelium
2020
Overview
Cigarette smoke first interacts with the lung through the cellularly diverse airway epithelium and goes on to drive development of most chronic lung diseases. Here, through single cell RNA-sequencing analysis of the tracheal epithelium from smokers and non-smokers, we generate a comprehensive atlas of epithelial cell types and states, connect these into lineages, and define cell-specific responses to smoking. Our analysis infers multi-state lineages that develop into surface mucus secretory and ciliated cells and then contrasts these to the unique specification of submucosal gland (SMG) cells. Accompanying knockout studies reveal that tuft-like cells are the likely progenitor of both pulmonary neuroendocrine cells and CFTR-rich ionocytes. Our smoking analysis finds that all cell types, including protected stem and SMG populations, are affected by smoking through both pan-epithelial smoking response networks and hundreds of cell-specific response genes, redefining the penetrance and cellular specificity of smoking effects on the human airway epithelium.
Chronic lung diseases are characterized by molecular and cellular composition changes. Here the authors use single-cell RNA sequencing to map cell type-specific changes in human tracheal epithelium related to smoking, and to provide evidence for a tuft-like progenitor for pulmonary neuroendocrine cells and ionocytes.
Publisher
Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
Subject
/ 14/32
/ 38
/ 38/91
/ 82/51
/ Animals
/ Epithelial Cells - metabolism
/ Gene Expression Profiling - methods
/ Humanities and Social Sciences
/ Humans
/ Mice
/ Mucus
/ Non-Smokers - statistics & numerical data
/ Respiratory Mucosa - cytology
/ Respiratory Mucosa - metabolism
/ RNA
/ Science
/ Sequence Analysis, RNA - methods
/ Single-Cell Analysis - methods
/ Smokers - statistics & numerical data
/ Smoking
