Image Informatics for Studying Signal Transduction in Cells Interacting with 3D Matrices

摘要

Cells sense and respond to chemical stimuli on their environment via signal transduction pathways, complex networks of proteins whose interactions transmit chemical information. This work describes an implementation of image informatics, imaging-based methodologies for studying signal transduction networks. The methodology developed focuses on studying signal transduction networks in cells that interact with 3D matrices. It utilizes shRNA-based knock down of network components, 3D high-content imaging of cells inside the matrix by spectral multi-photon microscopy, and single-cell quantification using features that describe both cell morphology and cell-matrix adhesion pattern. The methodology is applied in a pilot study of TGFβ signaling via the SMAD pathway in fibroblasts cultured inside porous collagen-GAG scaffolds, biomaterials similar to the ones used clinically to induce skin regeneration. Preliminary results suggest that knocking down all rSMAD components affects fibroblast response to TGFβ1 and TGFβ3 isoforms in different ways, and suggest a potential role for SMAD1 and SMAD5 in regulating TGFβ isoform response. These preliminary results need to be verified with proteomic results that can provide solid evidence about the particular role of individual components of the SMAD pathway.