Skip to main content
eScholarship
Open Access Publications from the University of California

Two-photon laser scanning microscopy of epithelial cell-modulated collagen density in engineered human lung tissue

  • Author(s): Agarwal, A
  • Coleno, ML
  • Wallace, VP
  • Wu, WY
  • Sun, CH
  • Tromberg, BJ
  • George, SC
  • et al.
Abstract

Tissue remodeling is a complex process that can occur in response to a wound or injury. In lung tissue, abnormal remodeling can lead to permanent structural changes that are characteristic of important lung diseases such as interstitial pulmonary fibrosis and bronchial asthma. Fibroblast-mediated contraction of three-dimensional collagen gels is considered an in vitro model of tissue contraction and remodeling, and the epithelium is one factor thought to modulate this process. We studied the effects of epithelium on collagen density and contraction using two-photon laser scanning microscopy (TPLSM). TPLSM was used to image autofluorescence of collagen fibers in an engineered tissue model of the human respiratory mucosa-a three-dimensional co-culture of human lung fibroblasts (CCD-18 lu), denatured type I collagen, and a monolayer of human alveolar epithelial cell line (A549) or human bronchial epithelial cell line (16HBE14o-). Tissues were imaged at days 1, 8, and 15 at 10 depths within the tissue. Gel contraction was measured concurrently with TPLSM imaging. Image analysis shows that gels without an epithelium had the fastest rate of decay of fluorescent signal, corresponding to highest collagen density. Results of the gel contraction assay show that gels without an epithelium also had the highest degree of contraction (19.8% ± 4.0 %). We conclude that epithelial cells modulate collagen density and contraction of engineered human lung tissue, and TPLSM is an effective tool to investigate this phenomenon.

Many UC-authored scholarly publications are freely available on this site because of the UC Academic Senate's Open Access Policy. Let us know how this access is important for you.

Main Content
Current View