Go to The Journal of Clinical Investigation
  • About
  • Editors
  • Consulting Editors
  • For authors
  • Publication ethics
  • Publication alerts by email
  • Transfers
  • Advertising
  • Job board
  • Contact
  • Physician-Scientist Development
  • Current issue
  • Past issues
  • By specialty
    • COVID-19
    • Cardiology
    • Immunology
    • Metabolism
    • Nephrology
    • Oncology
    • Pulmonology
    • All ...
  • Videos
  • Collections
    • In-Press Preview
    • Resource and Technical Advances
    • Clinical Research and Public Health
    • Research Letters
    • Editorials
    • Perspectives
    • Physician-Scientist Development
    • Reviews
    • Top read articles

  • Current issue
  • Past issues
  • Specialties
  • In-Press Preview
  • Resource and Technical Advances
  • Clinical Research and Public Health
  • Research Letters
  • Editorials
  • Perspectives
  • Physician-Scientist Development
  • Reviews
  • Top read articles
  • About
  • Editors
  • Consulting Editors
  • For authors
  • Publication ethics
  • Publication alerts by email
  • Transfers
  • Advertising
  • Job board
  • Contact

Usage Information

Single-cell RNA sequencing identifies diverse roles of epithelial cells in idiopathic pulmonary fibrosis
Yan Xu, Takako Mizuno, Anusha Sridharan, Yina Du, Minzhe Guo, Jie Tang, Kathryn A. Wikenheiser-Brokamp, Anne-Karina T. Perl, Vincent A. Funari, Jason J. Gokey, Barry R. Stripp, Jeffrey A. Whitsett
Yan Xu, Takako Mizuno, Anusha Sridharan, Yina Du, Minzhe Guo, Jie Tang, Kathryn A. Wikenheiser-Brokamp, Anne-Karina T. Perl, Vincent A. Funari, Jason J. Gokey, Barry R. Stripp, Jeffrey A. Whitsett
View: Text | PDF
Research Article Inflammation

Single-cell RNA sequencing identifies diverse roles of epithelial cells in idiopathic pulmonary fibrosis

  • Text
  • PDF
Abstract

Idiopathic pulmonary fibrosis (IPF) is a lethal interstitial lung disease characterized by airway remodeling, inflammation, alveolar destruction, and fibrosis. We utilized single-cell RNA sequencing (scRNA-seq) to identify epithelial cell types and associated biological processes involved in the pathogenesis of IPF. Transcriptomic analysis of normal human lung epithelial cells defined gene expression patterns associated with highly differentiated alveolar type 2 (AT2) cells, indicated by enrichment of RNAs critical for surfactant homeostasis. In contrast, scRNA-seq of IPF cells identified 3 distinct subsets of epithelial cell types with characteristics of conducting airway basal and goblet cells and an additional atypical transitional cell that contributes to pathological processes in IPF. Individual IPF cells frequently coexpressed alveolar type 1 (AT1), AT2, and conducting airway selective markers, demonstrating “indeterminate” states of differentiation not seen in normal lung development. Pathway analysis predicted aberrant activation of canonical signaling via TGF-β, HIPPO/YAP, P53, WNT, and AKT/PI3K. Immunofluorescence confocal microscopy identified the disruption of alveolar structure and loss of the normal proximal-peripheral differentiation of pulmonary epithelial cells. scRNA-seq analyses identified loss of normal epithelial cell identities and unique contributions of epithelial cells to the pathogenesis of IPF. The present study provides a rich data source to further explore lung health and disease.

Authors

Yan Xu, Takako Mizuno, Anusha Sridharan, Yina Du, Minzhe Guo, Jie Tang, Kathryn A. Wikenheiser-Brokamp, Anne-Karina T. Perl, Vincent A. Funari, Jason J. Gokey, Barry R. Stripp, Jeffrey A. Whitsett

×

Usage data is cumulative from December 2024 through December 2025.

Usage JCI PMC
Text version 3,920 1,854
PDF 427 313
Figure 1,873 16
Table 85 0
Supplemental data 109 70
Citation downloads 715 0
Totals 7,129 2,253
Total Views 9,382
(Click and drag on plot area to zoom in. Click legend items above to toggle)

Usage information is collected from two different sources: this site (JCI) and Pubmed Central (PMC). JCI information (compiled daily) shows human readership based on methods we employ to screen out robotic usage. PMC information (aggregated monthly) is also similarly screened of robotic usage.

Various methods are used to distinguish robotic usage. For example, Google automatically scans articles to add to its search index and identifies itself as robotic; other services might not clearly identify themselves as robotic, or they are new or unknown as robotic. Because this activity can be misinterpreted as human readership, data may be re-processed periodically to reflect an improved understanding of robotic activity. Because of these factors, readers should consider usage information illustrative but subject to change.

Advertisement

Copyright © 2025 American Society for Clinical Investigation
ISSN 2379-3708

Sign up for email alerts