A single cell atlas of the tracheal epithelium reveals the CFTR-rich pulmonary ionocyte
By Lindsey W. Plasschaert, R. Zilionis, R. Choo-wing, Virginia Savova, Judith Knehr, G. Roma, Allon M. Klein, A. Jaffe. Nature, 2018
717 citations recorded by the source corpus.
The functions of epithelial tissues are dictated by the types, abundance and distribution of the differentiated cells they contain. Attempts to restore tissue function after damage require knowledge of how physiological tasks are distributed among cell types, and how cell states vary between homeostasis, injury–repair and disease. In the conducting airway, a heterogeneous basal cell population gives rise to specialized luminal cells that perform mucociliary clearance1. Here we perform single-cell profiling of human bronchial epithelial cells and mouse tracheal epithelial cells to obtain a comprehensive census of cell types in the conducting airway and their behaviour in homeostasis and regeneration. Our analysis reveals cell states that represent known and novel cell populations, delineates their heterogeneity and identifies distinct differentiation trajectories during homeostasis and tissue repair. Finally, we identified a novel, rare cell type that we call the ‘pulmonary ionocyte’, which co-expresses FOXI1, multiple subunits of the vacuolar-type H+-ATPase (V-ATPase) and CFTR, the gene that is mutated in cystic fibrosis. Using immunofluorescence, modulation of signalling pathways and electrophysiology, we show that Notch signalling is necessary and FOXI1 expression is sufficient to drive the production of the pulmonary ionocyte, and that the pulmonary ionocyte is a major source of CFTR activity in the conducting airway epithelium. Single-cell RNA sequencing analysis is used to identify cell types in the tracheal epithelium, including previously unidentified ionocytes, which express high levels of the cystic fibrosis transmembrane conductance regulator, CFTR.
Subfields: Epidemiology, Pulmonary and Respiratory Medicine
View Paper: https://www.semanticscholar.org/paper/28798a37e88bcb416e519dcf182ba56c303c55cb
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Citations in the corpus
- Brief update on endocytosis of nanomedicines (2019, background): “Recent advances in cell sorting and single-cell RNA seq technologies also provide powerful tools that can be harnessed to study cell specificity of nanoparticle-based nucleic acid delivery as well as monitor off target effects by analyzing changes in endogenous mRNA levels [292,297].”
- Current best practices in single‐cell RNA‐seq analysis: a tutorial (2019, background): “(cid:129) If the distribution of QC covariates differ between samples, QC thresholds should be determined separately for each sample to account for sample quality differences as in Plasschaert et al ( 2018 ).”
- Dictionary learning for integrative, multimodal, and massively scalable single-cell analysis (2022, neutral): “…to the previously described datasets, both methods were broadly successful in integrating scRNA-seq and scATAC-seq data but did not identify matches at the same level of resolution (for example, neither method successfully matched ASDCs in scATAC-seq data to the ASDCs in the Azimuth reference; Fig.”
- Single-cell RNA sequencing reveals profibrotic roles of distinct epithelial and mesenchymal lineages in pulmonary fibrosis (2019, background): “Initial proof-of-concept studies have demonstrated feasibility of these approaches in the airway epithelium (19, 20) and peripheral lung in PF (21–24), with a focus on macrophages phenotypes (21, 24) and type II alveolar epithelial cells (22, 23) in a small number of samples.”