Int J Stem Cells.  2021 Feb;14(1):1-8. 10.15283/ijsc20093.

Organoid Model in Idiopathic Pulmonary Fibrosis

Affiliations
  • 1Department of Thoracic and Cardiovascular Surgery, School of Medicine, Kangwon National University, Chuncheon, Korea
  • 2Department of Internal Medicine, School of Medicine, Kangwon National University, Chuncheon, Korea

Abstract

Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive- fibrosing disease characterized by extensive deposition of extracellular matrix (ECM), scarring of the lung parenchyma. Despite increased awareness of IPF, etiology and physiological mechanism of IPF are unclear. Therefore, preclinical model will require relevant and recapitulative features of IPF. Recently, pluripotent stem cells (PSC)-based organoid studies are emerging as an alternative approach able to recapitulate tissue architecture with remarkable fidelity. Moreover, these biomimetic tissue models can be served to investigate the mechanisms of diverse disease progression. In this review, we will overview the current organoids technology for human disease modeling including lung organoids for IPF.

Keyword

Idiopathic pulmonary fibrosis (IPF); Organoid; Disease modeling; Lung; Pluripotent stem cells

Figure

  • Fig. 1 Generation of AOs from hPSCs and fibrosis induction. AOs exhibi-ted an alveolar sac-like structure with multiple alveoli and layers of epithelial cells. For in vitro model for studying IPF, AOs were treated with TGF-β1 (25 ng/ml) for 48 hrs. Histochemical and immunofluorescence staining displayed the fibrotic area (Sirius red stai-ning) and expression of collagen in AOs. Scale bars, 100 μm.

  • Fig. 2 Applications of organoids. Organoids have proven to be particular information for modeling human diseases. The ability to generate 3D organoids in large quantities with high consistency is expected the organoid-based platforms for drug screening and toxicity validation. Using organoid as a platform, drug response associated with genetic profiles derived from patients can be employed to predict treatment response and guide personalized therapies for individual patient.


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